IEEE/CIC International Conference on Communications in China

Session Opening: Opening Ceremony Session Keynote-1: Keynote 1
Session CIS-01: Data Privacy Session CIS-02: Network Security 1 Session CIS-03: Network Security 2
Session IT-01: Invited Talk 1 Session IT-02: Invited Talk 2
Session IoT-01: Space-air-ground IoT Session IoT-02: Green IoT Session IoT-03: Signal and Information Processing
Session MWN-01: Localization Session MWN-02: Edge Computing Session MWN-03: Internet of Vehicles
Session NGNI-01: Future Wireless Communications Session NGNI-02: Advanced Algorithms for Next Generation Networking Session NGNI-03: Security in Future Generation Networking
Session SAC-01: Offloading and Caching Session SAC-02: Learning-based Schemes Session SAC-03: Resource Allocation
Session SPC-01: NOMA Session SPC-02: MIMO Session SPC-03: Machine Learning
Session WCS-01: Intelligent Reflecting Surface Session WCS-04: NOMA Session WCS-02: Intelligence Communications I Session WCS-05: UAV Aided Wireless Communications Session WCS-03: Intelligence Communications II Session WCS-06: Channel Modeling
Session CT-01: Vehicular Communications

Session Opening

Opening Ceremony

Conference
8:30 AM — 9:00 AM CST
Local
Aug 9 Sun, 5:30 PM — 6:00 PM PDT

Opening Ceremony

Yanchuan Zhang (Secretary General, CIC); Xinbo Gao (President, CQUPT); Vicent Chan (President, IEEE ComSoc); Nei Kato (General Chair lead); Sherman Shen (Awarder)

3
Welcome messages from CIC, CQUPT, IEEE ComSoc; Conference overview; Best paper awards for China Communications.
Session Chair

Qianbin Chen

Session Keynote-1

Keynote 1

Conference
9:00 AM — 12:00 PM CST
Local
Aug 9 Sun, 6:00 PM — 9:00 PM PDT

Empowering 5G Cellular Connectivity Through Intelligent Edge Computing and Aerial Support

Abbas Jamalipour (Univesity of Sydney, Australia)

5
Fifth Generation Cellular Networks (5G) is gradually being implemented around the world, while at the same time research on the next generation or 6G has already been started. Although it would take some time before we get the full picture of credibility of 5G, researchers already know that it would suffer from a range of shortcoming with the introduction of new applications and thus; the need for work on its successor 6G. What we know and expect at this time is that we still need to make users closer to the network edges, thus the inclusion of edge computing; and that we need more autonomous and intelligent techniques in the form of advanced machine learning and artificial intelligence. At the same time, we know that terrestrial network components will have fundamentally physical limitations in providing coverage and accessibility needed in future networks. That would bring the involvement of more of aerial support including drones and low earth orbit satellites. This talk will provide some visionary concepts of the future mobile networks that contemplate edge computing and aerial support using results from some existing intelligent techniques.

Match-Making for Massive MIMO and Deep Learning

Zhi Ding (University of California at Davis, US)

3
The proliferation of advanced wireless services, such as virtual reality, autonomous driving and internet of things has generated increasingly intense pressure to develop intelligent wireless communication systems to meet networking needs posed by extremely high data rates, massive number of connected devices, and ultra low latency. Deep learning (DL) has been recently emerged as an exciting design tool to advance the development of wireless communication system with some demonstrated successes. In this talk, we introduce the principles of applying DL for improving wireless network performance by integrating the underlying characteristics of channels in practical massive MIMO deployment. We develop important insights derived from the physical RF channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) of forward channels with low feedback overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.

From Shannon Theory to Future 6G’s Technique Potentials

Xiaohu You (Southeast University, China)

6
From the perspective of Shannon theory and its extensions, this talk is devoted to evaluating the technique potentials of future 6G mobile communication system. Firstly, the classic Shannon theory framework, including performance tradeoff between block-length, data rate and reliability, is briefly summarized, and the limitations of its application in the contemporary mobile communication system are addressed. Secondly, the multiple-input-multiple-output (MIMO) extension of the classic Shannon theory is described, which has been playing the fundamental roles in the development of contemporary mobile communication systems. Furthermore, aiming at higher spectrum efficiency and power efficiency, higher reliability and lower latency, and higher frequency band, which are essential indicators of future 6G, the technique potentials are discussed theoretically from the perspective of Shannon theory framework. It reveals that by introducing more antennas together with the innovation of cell free network architecture, and by making effective balance between block length, error probability, data rate, and minimum number of antennas, future 6G technology still has great potential to be improved, but it needs both to make a compromise between system performance and deployment cost, and to carefully make use of the special features of MIMO channels in higher frequency band. Finally, several fundamental issues related to future 6G development are summarized.
Session Chair

Song Guo

Session CIS-01

Data Privacy

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Invited Paper: Privacy-Preserving Multilayer In-Band Network Telemetry and Data Analytics

Xiaoqin Pan, Shaofei Tang and Zuqing Zhu (University of Science and Technology of China, China)

0
As a new paradigm for the monitoring and troubleshooting of backbone networks, the multilayer in-band network telemetry (ML-INT) with deep learning (DL) based data analytics (DA) has recently been proven to be effective on real-time visualization and fine-grained monitoring. However, the existing studies on ML-INT\&DA systems have overlooked the privacy and security issues, i.e., a malicious party can apply tapping in the data reporting channels between the data and control planes to illegally obtain plaintext ML-INT data in them. In this paper, we discuss a privacy-preserving DL-based ML-INT&DA system for realizing AI-assisted network automation in backbone networks in the form of IP-over-Optical. We first show a lightweight encryption scheme based on integer vector homomorphic encryption (IVHE), which is used to encrypt plaintext ML-INT data. Then, we architect a DL model for anomaly detection, which can directly analyze the ciphertext ML-INT data. Finally, we present the implementation and experimental demonstrations of the proposed system. The privacy-preserving DL-based ML-INT&DA system is realized in a real IP over elastic optical network (IP-over-EON) testbed, and the experimental results verify the feasibility and effectiveness of our proposal.

Enhanced p-Sensitive k-Anonymity Models for Achieving Better Privacy

Nan Wang, Haina Song, Tao Luo, Jinkao Sun and Jianfeng Li (Beijing University of Posts and Telecommunications, China)

0
To our best knowledge, the p-sensitive k-anonymity model is a sophisticated model to resist link attacks and homogeneous attacks in data publishing. However, if the distribution of sensitive values is skew, the model is difficult to defend against skew attacks and even faces sensitive attacks. In practice, the privacy requirements of different sensitive values are not always identical. The "one size fits all" unified privacy protection level may cause unnecessary information loss. To address these problems, the paper quantifies privacy requirements with sensitivity and concerns more about sensitive groups. Two enhanced anonymous models with personalized protection characteristic, that is, (p,¦Áisg)-sensitive k-anonymity model and (pi,¦Áisg)-sensitive k-anonymity model, are then proposed to resist skew attacks and sensitive attacks. Furthermore, two clustering algorithms with global search and local search are designed to implement our models. Experiment results show that the two enhanced models have outstanding advantages in better privacy at the expense of a little data utility.

Efficient privacy-preserving searchable encryption scheme based on reversible sorting policy in cloud storage

Cao Laicheng, Qirui Wu, Yafei Wang and Guo Xian (School of Computer and Communication, Lanzhou University of Technology, China)

0
Searchable encryption technology gives an effective mechanism for securely searching encrypted data in cloud storage. Recently, the searchable encryption application based on the ciphertext-policy attribute-based encryption (CP-ABE) has become the research focus. However, the computation time of the current CP-ABE schemes are large and the time cost of the users is high. In this paper, we propose a CP-ABE searchable encryption (CP-ABE-SE) scheme based on the reversible sorting algorithm in cloud storage. The data owner only needs to do the data sorting computation on his own data, while the encryption operation that costs more system resources is handed over to the cloud server with powerful computing resources. Similarly, when the data user has searched for the ciphertext, the cloud server first performs the pre-decryption operation, and then the data user can get the searched data plaintext by the reversible sorting operation, which costs less computing resources of the data user. Based on CP-ABE scheme, the trusted authority uses the attributes of the data user and cloud server provider (CSP) to generate their access control private key, which is used to compute the search trapdoor. Only when the search trapdoor matches the keyword ciphertext, the CSP can return the searched data ciphertext. Security proof results show that our scheme has privacy preserving. And the theory and experiments analysis results demonstrate that our scheme can reduce the computation time cost of the data user and the data owner effectively.

Dual Privacy-Preserving Health Data Aggregation Scheme Assisted by Medical Edge Computing

Chengzhe Lai (Xi'an University of Posts and Telecommunications, China); Jinke Wan (Xi'an University of Posts and Telecommunications, China); Dong Zheng (Xi'an University of Posts & Telecommunications, China)

0
In the healthcare system, patients equipped with resource-limited medical devices generate a huge amount of health data. Collected from distributed medical devices, these health data has significant value for public health management. The data aggregation technique can be used to collect health data and effectively reduce communication bandwidth. However, the exiting health data aggregation schemes only retrieve the aggregated result, which constrains the usage of the aggregate function. Besides, there are some security and privacy issues, such as the disclosure of patients' identities, the attacks from malicious users, etc. In this paper, we propose a medical edge computing aided health data aggregation scheme with dual privacy preservation. Specially, the health data can be encoded to achieve recoverable health data aggregation by using Mykletun's algorithm. In addition, dynamic and traceable pseudonym technique is used to prevent the disclosure of patient identity after multiple data submissions. Furthermore, we use the reputation score to prevent attacks from malicious users. Meanwhile, fair incentive mechanism is adopted to stimulate patients to contribute their health data. To eliminate key escrow problem and improve the efficiency of authentication, the certificateless aggregate signature without pairing is utilized. Security analysis shows that our scheme can not only guarantee the privacy, confidentiality and integrity of health data, but also resist to the attacks from external and internal malicious users. Performance evaluation demonstrates that the computational cost of our scheme on the edge server and cloud server is superior to other schemes.

Private, Fair, and Verifiable Aggregate Statistics for Mobile Crowdsensing in Blockchain Era

Miao He and Jianbing Ni (Queen's University); Dongxiao Liu (University of Waterloo, Canada); Haomiao Yang (University of Electronic Science and Technology of China, China); Sherman Shen (University of Waterloo, Canada)

1
In this paper, we propose FairCrowd, a private, fair, and verifiable framework for aggregate statistics in mobile crowdsensing based on the public blockchain. In specific, mobile users are incentivized to collect and share private data values (e.g., current locations) to fulfill a commonly interested task released by a customer, and the crowdsensing server computes aggregate statistics over the values of mobile users (e.g., the most popular location) for the customer. By utilizing the ElGamal encryption, the server learns nearly nothing about the private data or the statistical result. The correctness of aggregate statistics can be publicly verified by using a new efficient and verifiable computation approach. Moreover, the fairness of incentive is guaranteed based on the public blockchain in the presence of greedy service provider, customers, and mobile users, who may launch payment-escaping, payment-reduction, free-riding, double-reporting, and Sybil attacks to corrupt reward distribution. Finally, FairCrowd is proved to achieve verifiable aggregate statistics with privacy preservation for mobile users. Extensive experiments are conducted to demonstrate the high efficiency of FairCrowd for aggregate statistics in mobile crowdsensing.
Session Chair

Chengzhe Lai, Dongfeng Fang

Session IoT-01

Space-air-ground IoT

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Reverse Auction for Cloud-Based Traffic Offloading in Hybrid Satellite-Terrestrial Networks

Cui-Qin Dai, Lan Jin and Qianbin Chen (Chongqing University of Posts and Telecommunications, China)

0
Hybrid Satellite-Terrestrial Networks (HSTNs) are expected to support extremely high traffic demands. In HSTN, cellular cells and satellite network are operated by the mobile network operator (MNO) and satellite network operator respectively, traffic offloading strategies need to be designed joint considering the throughput of HSTN and economic profits of the MNO. In this paper, a reverse auction with cell grouping algorithm (RA-CG) is proposed for cloud-based traffic offloading in HSTN. Firstly, a HSTN model is constructed and the problem of co-channel interference is analyzed. Then, the auction model is adopted to analyze the economic profits of traffic offloading in HSTN, and the optimization is formulated to maximum the MNO's profits. Following that, RA-CG is proposed. In RA-CG, cell grouping is proposed to solve the co-channel interference problem during the offloading process. A Hungarian-based reverse auction algorithm is performed to find the optimal cell-satellite association solution corresponds with the economic profits within each group. Simulation results show that RA-CG can maximize the MNO's profits on the basis of improving the network offloading rate, and simultaneously achieve higher spectrum efficiency.

Towards Ubiquitous Coverage of High Altitude Platforms Aided 5G+ for Massive Internet of Things: A Cell Perspective

Wei Wu (Peng Cheng Laboratory & Beijing Institute of Technology, China); Qinyu Zhang (Peng Cheng Laboratory & Harbin Institute of Technology (Shenzhen), China); Kai Wang and Weizhi Wang (Peng Cheng Laboratory, China)

0
High altitude platforms (HAPs) is promising for providing low-delay and high-throughput wireless communications for massive wireless internet of things networks in non-terrestrial scenarios. In this paper, we propose a heterogeneous layering cell configuration scheme for seamless uniform coverage of HAPs-aided 5G+. To overcome the drawbacks of the existing schemes, we design the two-layer architecture with three and six circular cells considering Preamble Format 2 in 5G NR, and the beam parameters vary across layers. We provide a parameter configuration method to combat the geometrical enlargement of the distance from the projection point of a HAP to its users on the normal direction. The simulation results confirm the superiority of our scheme to the existing schemes in terms of the received signal level in the service area. We finally discuss the open challenges to deploy HAPs-aided 5G+ and the opportunities.

Sum Rate Maximization via Reconfigurable Intelligent Surface in UAV Communication: Phase Shift and Trajectory Optimization

Jingyi Li (Xidian University, China); Jiajia Liu (Northwestern Polytechnical University, China)

0
Facing the fast-growing application demands in future wireless communication networks, unmanned aerial vehicles (UAVs) have emerged to provide users with highly cost-effective deployment and flexible access services. Due to the complex communication environment, the line-of-sight transmissions of aerial-ground networks are probably blocked, which seriously affects the communication quality. Reconfigurable intelligent surface (RIS) is known as a promising solution to improve the wireless environment through reconfigurable passive units. Motivated by this, we explore a novel RIS-assisted aerial-ground communication scenario, investigating the UAV trajectory design and RIS's phase shift optimization problem aiming at maximizing the sum rate. Considering that the problem is non-convex, we develop an alternating optimization method that decomposes our formulated problem into two blocks when another variable is fixed. With the given optimal phase shifts, the trajectory design subproblem is well solved by resorting to the successive convex approximation method. The experimental results show the remarkable performance of our proposed scheme compared with other schemes.

Drift compensation algorithm based on Time-Wasserstein dynamic distribution alignment

Tao Yang, Ke wei Zeng and Zhi fang Liang (Chongqing University of Posts and Telecommunications, China)

1
The electronic nose (E-nose) is mainly used to detect different types and concentrations of gases. At present, the average life of the electronic nose is relatively short, mainly due to the drift of the sensor resulting in a decrease in the effect. Therefore, it is the focus of research in this field to propose an effective solution to sensor drift. This paper proposes Time-Wasserstein dynamic distribution alignment (TWDDA) to solve drift compensation according to the domain adaptive thought. This method simultaneously adapts the dynamic distribution according to the data distribution and the collection time span. Finally, this paper uses the public data set provided by the University of California San Diego (UCSD) to conduct experiments. The experimental results show that TWDDA is superior to other comparison algorithms and achieves better results.

A Robust Timing Synchronization Method for OFDM systems Over Multipath Fading Channels

Han Yang (School of Information and Electronics Beijing Institute of Technology, China); Lintao Li (School of Computer&Communication Engineering, University of Science and Technology Beijing, China); Jiaxuan Li (School of Information and Electronics Beijing Institute of Technology, China)

0
Timing synchronization is premise and foundation of reliable receiving for OFDM signals, especially when multipath fading and Doppler shifting exists. In this work, a robust timing synchronization method based on fast timing search window and double threshold for decision is proposed. The fast timing search window can effectively reduce the false alarm probability and the double threshold decision can reduce the acquisition lost probability. Also, simulations to testify the effectiveness of the proposed strategy are carried out.
Session Chair

Xiulong Liu, Jihong Yu

Session IT-01

Invited Talk 1

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

A Comprehensive Optical Mobile Fronthaul Access Network

Weisheng Hu (Shanghai Jiao Tong University, China)

1
Both cloud radio access network (C-RAN) and next generation fronthaul interface (NGFI) are the key solutions for the 5G deployment. In both architectures, the baseband units (BBUs) are centralized, and remote radio units (RRUs) are separately allocated, where the BBUs and RRUs are connected through a fronthaul network with Common Public Radio Interface (CPRI) and evolved eCPRI. In this work, we proposed a comprehensive optical mobile fronthaul access network (COMFAN) to meet the various fronthaul requirements. To support both the CPRI and eCPRI interfaces, several low-cost high bitrate optical transmission techniques are comparatively studied.

Toward a Trustworthy and Evolvable Future Internet

Hongbin Luo (Beihang University, China)

0
Although the Internet has made great success since its inception, it faces many serious issues such as the lack of trustworthiness, the rigidity in deploying novel technologies at layer 3, as evidenced by the proliferation of various cyberattacks and the difficulty in deploying IPv6. These issues makes it extremely difficult to further expand the Internet to satellite networks, industrial networks and vehicular networks because, as widely recognized, IP does not perform well in these network environments. In this talk, we present the core ideas of an architecture for a Trustworthy and Evolvable Future Internet.

Resource Orchestration of Optical Networks with Multi-Access Edge Computing

Shanguo Huang (Beijing University of Posts and Telecommunications, China)

0
With the advent of the 5G, the traffic pressure on the bearer network is increasing. Meanwhile, the rapid development and large-scale application of IoT devices have brought about low-latency, high-reliability information processing and transmission requirements. Multi-access Edge Computing (MEC) introduced by sinking cloud resources from the Remote Cloud to the edge of the network is one of the solutions to support 5G low-latency applications. Optical networks with MEC is considered a promising candidate to meet the demanding bandwidth and latency requirements of future communications. At present, for optical networks with MEC, a key issue is how to provide services with lower latency and higher efficiency for end-users. Based on this, we investigate the resource orchestration and benefits of optical networks with MEC. This presentation first introduces the basic principle and characteristics of the optical network and Multi-access Edge Computing, then several resource orchestration schemes are explained in detail, and the simulation results are discussed at the end. The results show that the proposed schemes can effectively improve the resource utilization of the system while reducing user latency.
Session Chair

Xiaoxue Gong

Session MWN-01

Localization

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Multi-Source Data Fusion Method for Indoor Localization System

Jishi Cui, Bin Li, Lyuxiao Yang and Nan Wu (Beijing Institute of Technology, China)

1
In this paper, a multi-source data fusion method for indoor localization system is designed to realize the high-accuracy locations. The indoor localization system consists of WiFi nodes, ultra-wideband (UWB) nodes and inertial measurement unit (IMU), where the IMU is integrated in a android smartphone. For our indoor localization system, there include two stages: offline stage and online stage. In the offline stage, we use crowdsourcing method to train a fingerprint database, which can be constructed at a low labor cost. In the online stage, particle filter to estimate the locations based on WiFi received signal strengths, UWB rangings, and IMU data. Experimental results show that our indoor localization system based on the multi-source data fusion method expands the coverage and improves the localization accuracy.

Carrier Phase-based Wi-Fi Indoor Localization Method

Wei He, Ziying Yue, Zengshan Tian and Zhenya Zhang (Chongqing University of Posts and Telecommunications, China)

1
Channel State Information (CSI)-based indoor Angle of Arrival (AoA) localization has gradually become a hot research direction. However, due to the lack of the method toward improving angle resolution of AOA, AOA-based indoor localization method is difficult to achieve sub-meter accuracy. In this paper, a novel method for carrier phase-based Wi-Fi indoor localization using finer-grained and more diverse carrier phase information of CSI is proposed to improve the localization accuracy. According to the center frequency carrier phase of CSI and Time of Flight (ToF) in Line of Sight (LoS) path, the distance from the target to multiple receivers can be estimated. Secondly, a carrier phase localization model is constructed to get the initial target location. Then, combining the Least-squares Ambiguity Decorrelated Adjustment (LAMBDA) algorithm, we eliminate the integer ambiguity of carrier phase localization model. Finally, the initial location is updated to obtain the precise location of target. The experimental results show that the localization error reaches 0.25 m when there are 7 receivers in the environment.

Three-dimensional DV-Hop Localization Based on Improved Lion Swarm Optimization Algorithm

Falei Ji and Mingyan Jiang (Shandong University, China)

1
With the development of wireless sensor networks, research on three-dimensional(3D) node localization algorithms is becoming more and more important. 3D Distance Vector Hop(DV-Hop) is a non-ranging-based 3D positioning algorithm with low positioning accuracy and large errors. Aiming at above problems, 3D DV-Hop localization based on improved lion swarm optimization(ILSO) algorithm is proposed. The wolf swarm hunting idea of gray wolf optimization algorithm and the herd interaction idea of sheep optimization algorithm are used to improve the lion swarm optimization algorithm. The ILSO algorithm is compared with several algorithms and performs well. Then it is applied to the optimization of unknown node coordinates. Simulation results show that the proposed algorithm has higher positioning accuracy than classic 3D DV-Hop algorithm and the 3D DV-Hop algorithm based on the original lion swarm optimization algorithm.

A Fingerprint Database Construction Method Based on Universal Kriging Interpolation for Outdoor Localization

Qing Wu, Gang Chuai and Weidong Gao (Beijing University of Posts and Telecommunications, China)

1
In fingerprint positioning, the construction of a fingerprint database has a crucial impact on positioning. Because of the wide outdoor environment, the accurate construction of a traditional fingerprint database requires technologies such as large-scale drive testing, which consumes a lot of manpower and material resources. In this paper, Universal Kriging interpolation method is proposed to quickly construct outdoor fingerprint database for solving the problem of time-consuming and laborious. In order to detect the performance of the algorithm, the Reference Signal Receiving Power at the sampling point is calculated by using the Standard Propagation Model. The experiment is done using Universal Kriging algorithm and Inverse Distance Weighted algorithm to interpolate the database with 30% sampling points. The results show that the fingerprint database constructed based on the Universal Kriging interpolation algorithm is higher accuracy.

D2D Cooperative Localization Approach Based on Euclidean Distance Matrix Completion

Yaohua Li, Liang Bo Xie, Mu Zhou and Qing Jiang (Chongqing University of Posts and Telecommunications, China)

1
As one of the key technologies of the 5G, Device-to-device (D2D) can realize communication between terminals without a base station, thus making the cooperative positioning more convenient. In this paper, we propose a D2D cooperative localization approach based on matrix completion, which can tackle the problem of localization with an incomplete Euclidean Distance Matrix (EDM). In concrete terms, first of all, an incomplete EDM is constructed based on the known inadequate distance values between nodes, and then the Singular Value Threshold (SVT) algorithm is used to complete the EDM to obtain a recovered EDM. Secondly, Multidimensional Scaling (MDS) is used to reduce the recovered EDM dimension to obtain the relative position of nodes while maintaining the distance value between nodes. Finally, according to the relative position and global position of the anchor node, Procrustes Analysis (PA) is applied to obtain the transformation relationship, and the global positions of all nodes are further obtained. From extensive experimental results, it is evident that the proposed approach still has high localization performance even when a large proportion of elements are missing in the EDM.
Session Chair

Bin Li, Weidong Gao

Session NGNI-01

Future Wireless Communications

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

An Oblivious Game-Theoretic Perspective of RRM in Vehicular Communications

Xianfu Chen (VTT Technical Research Centre of Finland, Finland); Celimuge Wu (The University of Electro-Communications, Japan)

0
In this paper, we study the problem of radio resource management in a vehicle-to-vehicle communication network, which takes into account the dynamic characteristics originated from the vehicle mobility and traffic variations. Due to the limited frequency resource, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a roadside unit (RSU) across the discrete scheduling slots with aim of optimizing its own expected long-term performance. Such non-cooperative interactions among VUE-pairs are modelled as a stochastic game. The semi-continuous state space motivates us to transform the stochastic game into an equivalent stochastic game with the definition of a partitioned control policy, for which an oblivious equilibrium (OE) is adopted to approximate the Markov perfect equilibrium. Moreover, we put forward an online reinforcement learning scheme to approach the OE solution due to the lack of a priori statistical knowledge of dynamics. Simulations validate the proposed theoretical studies.

A Hybrid Routing Algorithm in Terrestrial-Satellite Integrated Network

Huihui Xu (University of Wuhan, China); Deshi Li and Mingliu Liu (Wuhan University, China); Guangjie Han (Hohai University, China); Wei Huang (Electronic Information School, Wuhan University, China); Chan Xu (Wuhan University, China)

0
Due to the wide coverage of satellite networks and high bandwidth of terrestrial networks, the terrestrial-satellite integrated networks have been proposed and received much attention in both industry and academia. To this end, we propose a hybrid routing algorithm to provide seamless integration of satellite network with terrestrial networks, in which transmission path can be selected adaptively according to the traffic demands of user terminals (UTs). To minimize the end-to-end delay, we first formulate the routing problem as terrestrial-satellite routing equipments (TSEs) selection problem. Then, according to the periodical movement characteristic, a satellite routing algorithm based on two-hop Inter-Satellite Links (ISLs) is proposed, in which the transmission delay of satellite network can be predicted. Finally, to derive an optimal TSE pair, all possible delay combinations of the total link are enumerated. Simulation results show that the proposed hybrid routing scheme offers good performance in terms of end-to-end delay and throughput.

A Synergic Architecture for Content Distribution in Integrated Satellite and Terrestrial Networks

Siyu Yang, Hewu Li, Zeqi Lai and Jun Liu (Tsinghua University, China)

0
Satellites are attracting increasing attention as novel broadband Internet access. It is a trend for satellites to enhance the content distribution efficiency in cooperation with the terrestrial network. While current studies mainly treat Low Earth Orbit (LEO) satellites as stable overlays by a series of snapshots, ignoring satellites' capability of cache, movement and networking. To further improve the global world content distribution efficiency in the integrated satellite and terrestrial networks, this paper proposes a synergic distribution architecture with satellites and the Content Delivery Network (CDN). The architecture takes advantage of LEOs' mobility characteristic to cache and deliver multiple times along the trajectory. The content distribution process not only involves content transfer among static nodes in the terrestrial CDN, but also mobile satellites serving as couriers. To optimize the overall bandwidth saving and reduce the distribution time, the distribution process is modeled as a maximum matching problem between target receivers and satellites' trajectory. The problem is solved with an integer linear programming. The distribution architecture is analyzed with the edge servers distribution of a CDN provider, Akamai, and an emerging satellite constellation, Starlink. The simulation results show that the distribution improves the distribution efficiency of existing CDNs with significant savings of bandwidth and delivery time. A single satellite in a cache-and-multiple-deliver manner saves tens of TBs bandwidth consumption in one revolution. And the satellite network saves 25% (40%) of bandwidth consumption in 30 (60) minutes when satellites multicast at a bottleneck rate on the ground. The distribution time is reduced by 25% in half an hour at the same time.

A Novel Resource Allocation Scheme With Unmanned Aerial Vehicles in Disaster Relief Networks

Zhou Su, Minghui Dai and Qichao Xu (Shanghai University, China); Ruidong Li (National Institute of Information and Communications Technology (NICT), Japan)

0
The growing number of natural disasters results in the infrastructure communication facing critical challenges. However, existing networks might be destroyed or overloaded in disasters, the performance of real-time response and low latency is the crucial issue in disaster relief services. Therefore, in this paper, a resource allocation scheme with unmanned aerial vehicles (UAVs) in disaster relief networks is proposed to improve the quality of experience (QoE) for user equipments (UEs). First, the network model is developed for UAVs to provide communication services in disaster area. Second, the channel allocation problem for UEs is presented to improve the throughput of UEs, and the channel allocation algorithm based on potential game for UEs is provided. Third, to incentivize UAVs and improve the efficiency of resource allocation, the incentive scheme for channel allocation is established. Finally, simulation results demonstrate that the proposed scheme can significantly improve the communication efficiency.

A Learnable Gauss-Seidel Detector for MIMO Detection

Qi Wang and Han Hai (Donghua University, China); KaiZhi Peng and Binbin Xu (Wuhan Maritime Communication Research Institute, China); Xueqin Jiang (Donghua University, China)

1
Multiple-Input Multiple-Output (MIMO) is a key technology due to its high spectral efficiency and data rate in communication systems. Due to the high complexity of linear Minimum Mean Square Error (MMSE) detection, Gauss-Seidel iterative method is applied to MIMO detection as an approximate method of MMSE and achieves the effect of MMSE detection. In this paper, we propose a learnable Gauss-Seidel detector based on model-driven Deep Learning (DL) for MIMO systems. The proposed detector is designed by unfolding the Gauss-Seidel detection method. In the proposed detector, we add some parameters that can be learned to improve the detection performance. Simulation results show that the proposed detector has better detection performance than traditional Gauss-Seidel detector.
Session Chair

Jianguo Ma, Qingwen Han

Session SAC-01

Offloading and Caching

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Design and Implementation of a 5G NR-based Link-adaptive System

Jichao Wang, Yu Han, Xiao Li and Shi Jin (Southeast University, China)

0
Time-varying channel is an important characteristic of a mobile communication system, which will cause random fluctuations of the quality of received signal at the user equipment (UE). Link adaptation is one of the key technologies to deal with the variant channel condition. It maximizes the data transmission rate over a limited bandwidth. In this paper, we design and implement an end-to-end link-adaptive system based on the 5G New Radio (NR) standard, adopting software defined radio (SDR) equipment as the baseband processing module. In the designed system, the selection of channel quality information (CQI) and the link-adaptive module are implemented at the base station (BS). The design and implementation of the physical layer according to 5G NR standard, including system parameters, synchronization scheme and duplex mode, are provided. Further, the over-the-air (OTA) results validate the feasibility of the system design, and show that link adaptation module can significantly improve the quality of the received signal.

Semantic Fusion Infrastructure for Unmanned Vehicle System Based on Cooperative 5G MEC

Yongxing Lian, Liang Qian and Lianghui Ding (Shanghai Jiao Tong University, China); Feng Yang (Shanghai Jiaotong University, China)

0
Since local sensing system is inherently limited, it is a trend to combine Cooperative Vehicle Infrastructure System (CVIS) and autonomous driving technologies to address the limitations of the vehicle- centric perception. However, problems such as high vehicle cost and perception limitations still exist. In this paper, from the perspective of distributed cooperative processing, a scalable 5G multi-access edge computing (MEC) driven vehicle infrastructure cooperative system is proposed. Based on the edge offload capability of 5G MEC, this system supports mapping sensor observations into a semantic description of the vehicle' s environment. Through interactive perception fusion, it provides environment awareness of high-precision maps for autonomous driving. The experiment confirms that the cooperative sensing network can achieve 33 fps and improve the detection precision by around 10% as opposed to the typical detection method. In addition, compared with single viewpoint perception, the accuracy of the fusion scheme is further improved. In particular, over the 5G telecommunication network, the cooperative system can be more scalable to connect distributed sensors and is expected to lead to efficient autonomous driving.

Cooperative Computation Offloading in NOMA-Based Edge Computing

Fusheng Zhu (GuangDong Communications & Networks Institute, China); Yuwen Huang (The Chinese University of Hong Kong, Hong Kong); Yuan Liu and Xiuyin Zhang (South China University of Technology, China)

0
This paper studies a cooperative mobile edge computing (MEC) system with user cooperation, which consisting of a user, a helper, and an access point (AP). The mobile user can offload the computation data to the helper and the AP simultaneously on the same resource block using non-orthogonal multiple-access (NOMA). The helper can locally compute the data offloaded by the user, in addition to processing its own data. An offloading data maximization problem is formulated by joint design of radio and-computation resources. We find the optimal solution by exploring some properties of the problem. Simulation results show that the proposed scheme effectively improves the system offloading data and benefits both the user and helper.

Edge Intelligence-Based Joint Caching and Transmission for QoE-Aware Video Streaming

Peng Lin (Northeastern University, China); Qingyang Song (Chongqing University of Posts and Telecommunications, China); Jing Song (Northeastern University, China); Lei Guo (Chongqing University of Posts and Telecommunications, China); Abbas Jamalipour (University of Sydney, Australia)

0
The integration of mobile edge caching and coordinated multipoint (CoMP) joint transmission (JT) is regarded as a promising method to support high-throughput wireless video streaming in mobile networks. In this paper, we propose a quality of experience (QoE)-aware joint caching and transmission scheme to realize autonomous content caching and spectrum allocating for video streaming. We jointly optimize content placement and spectrum allocation to minimize content delivery delay, taking into account time-varying content popularity, transmission method selection, and different QoE requirements of users. The optimization problem is transformed into a Markov decision process (MDP) in which a reward characterizing content delivery delay and QoE on video streaming is defined. Then, we propose an edge intelligence (EI)-based learning algorithm, named quantum-inspired reinforcement learning (QRL), which exploits quantum parallelism to overcome the "curse of dimensionality". The optimal policy is obtained in an online fashion with a high learning efficiency. The convergence rate, content delivery delay, and stalling rate are evaluated in the simulations, and the results show the effectiveness of our method.

Privacy-Aware Task Offloading via Two-Timescale Reinforcement Learning

JiYu Dong (Wuhan University, China); Dongqing Geng (WuHan University, China); Xiaofan He (Wuhan University, China)

0
Driven by the ever-increasing computing demands due to various emerging computing-intensive and delay-sensitive mobile applications, mobile-edge computing (MEC) emerges as a new promising computing paradigm. In MEC, the computing resources are deployed at the logical edge of the network, and this architecture allows the mobile users to enjoy better computing services with low-latency and high energy-efficiency by wirelessly offloading some their computation tasks to the edge servers in the vicinity. Meanwhile, as user privacy is receiving increasing attention in the modern society, mitigating the privacy leakage caused by task offloading in MEC becomes imperative. In this paper, we develop a reinforcement learning (RL) based privacy-aware task offloading scheme that can synthetically take into account the data privacy, the usage pattern privacy, and the location privacy of the mobile users. To find the optimal offloading strategy, a novel two-timescale RL algorithm, dubbed as statistic prediction-post decision state-virtual experience (SP-PDS- VE), is proposed. The proposed algorithm can construct the state transition model of the underlying problem via the fast timescale learning and, in the meantime, uses the learned model to create a set of virtual experience for the slow timescale learning, so as to speed up the convergence and allow the mobile device to learn the optimal privacy-aware offloading strategy much faster. In addition to the analysis, simulations results are presented to corroborate the effectiveness of the proposed scheme.
Session Chair

Chao Xu, Xijun Wang

Session SPC-01

NOMA

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Low Density Superposition Modulation using DCT for 5G NOMA scheme

Kun Lu and Sheng Wu (Beijing University of Posts and Telecommunications, China); Lihong Lv (Beijing Space Information Relay and Transmission Technology Research Center, China); Hongwen Yang (Beijing University of Posts and Telecommunications, China)

1
Non-orthogonal multiple access (NOMA) scheme is a promising multiple access technique for the fifth generation (5G) New Radio (NR) due to its high spectral efficiency, massive connectivity and low latency. In this paper, a Low Density Superposition Modulation (LDSM) using discrete-cosine transform (DCT) scheme with 5G-NR low-density parity-check (LDPC) channel code is proposed for the 5G scenario. Moreover, we adopt a low-complexity elementary signal estimator (ESE) detection algorithm for the multi-user detection. Simulation results show that our proposed scheme has the 3-5 dB peak-to-average power ratio (PAPR) reduction as compared with the conventional sparse code multiple access (SCMA) and pattern division multiple access (PDMA). Besides, our scheme brings about 0.6 dB-2.0 dB performance gains. Therefore, the DCT-LDSM scheme is efficient and suitable for 5G scenario.

Non-orthogonal Multiple Access in SWIPT Enabled Cooperative D2D Network

Rui Cheng, Xiaotian Zhou and Haixia Zhang (Shandong University, China); Fang Fang (The University of Manchester, United Kingdom (Great Britain)); Dongfeng Yuan (Shandong University, China)

1
In this paper, we investigate resource allocation in a downlink cooperative communication system. In the scenario where cellular users (CUs) and the base station (BS) cannot communicate directly, D2D user pairs participate in the cooperative communication to complete forwarding information required by CU and themselves through non-orthogonal multiple access (NOMA). The application of simultaneous wireless information and power transfer (SWIPT) ensures that D2D users save their own energy during the forwarding process. The ultimate optimization goal is to maximize the achievable rate of D2D user pairs under the condition of guaranteeing CU's quality of service (QoS). A stepwise iterative algorithm is proposed to obtain the suboptimal solution to the problem. By comparing with the ergodic optimal solution, numerical simulations show that the proposed algorithm can approximate the optimal solution with low computational cost in a certain error range.

Block Error Rate Analysis of Short-Packet NOMA Communications with Imperfect SIC

Ruiqiang Fu (Zhejiang University, China); Qiao Qi (ZheJiang University, China); Caijun Zhong, Xiaoming Chen and Zhaoyang Zhang (Zhejiang University, China)

0
In this paper, we study the cellular internet of things (IoT) with multi-user short-packet communications. To achieve low-latency as well as a high spectral efficiency, non-orthogonal multiple access (NOMA) technique is adopted. Considering the practical scenario with imperfect successive interference cancellation, this paper provides a detailed analysis of the average block error rate (BLER) of NOMA systems. Exact approximated expressions are derived for the BLER of an arbitrary user. In addition, to gain further insights, simplified BLER expressions of the worst and best users are obtained in the high signal to noise ratio regime. It was shown that the best user can achieve full diversity order while the worst user can only achieve unit diversity order. Extensive simulation results are provided to validate the analytical results.

Angle-Delay-Doppler Domain NOMA over Massive MIMO-OTFS Networks

Weidong Shao and Shun Zhang (Xidian University, China); Caijun Zhong (Zhejiang University, China); Xianfu Lei and Pingzhi Fan (Southwest Jiaotong University, China)

1
In this paper, we propose an uplink angle-delay-Doppler domain non-orthogonal multiple access (NOMA) scheme over massive multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) networks, which is inspired to address the situation that user connectivity is dramatically restricted if users have overlapped angle signature with limited delay-Doppler domain resources. To be specific, with aid of NOMA along the uplink transmission, we propose to schedule multiple users with the overlapped angle signature to employ the same delay-Doppler domain resources. Correspondingly, a user clustering algorithm and optimal transmission strategy with respect to power allocation for the proposed scheme are then designed. Afterwards, we propose a path following-based iteration algorithm to solve the original nonconvex optimization problem and finally obtain a suboptimal solution. Simulation results are provided to demonstrate the validity of the proposed scheme.

NDA-EVM based Co-channel Interference Analysis in Co-frequency Network

Xiaoping Zeng (Chongqing Communication Institute, China); Shiqi Li and Xin Jian (Chongqing University, China); Yang Fan (Chong Qing University & CCEE, China)

2
Transmission in co-frequency network always affected by co-channel interferences. In order to achieve higher communication performance, interference elimination schemes should be applied. Thus, analysis of co-channel interference in co-frequency network becomes the core issue, which offer threshold references to the designs of interference elimination and transmission mechanism. A novel method to quantify the co-channel interference based on nondata-aided error vector magnitude (NDA-EVM) was proposed in this paper. NDA-EVM was considered as a new metric to evaluate the change of the channels. Specifically, the NDA-EVM upper bound of multiple quadrature amplitude modulation (MQAM) signal under co-channel interferences is analytically derived. Theoretical analysis and simulation experiments indicate that, the derived upper bound closely matches with the theoretical one, especially at low SNR. Moreover, the time complexity of upper bound is linear order while that of theory is square order, which means it has a quicker react when channel estimating.
Session Chair

Shun Zhang, Feifei Gao

Session WCS-01

Intelligent Reflecting Surface

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Energy-efficient Resource Allocation for Secure IRS Networks with Active Eavesdropper

Jianhong Yang (Chongqing University of posts and Telecommunications, China); Yongjun Xu, Qilie Liu and Guoquan Li (Chongqing University of Posts and Telecommunications, China); Haijian Sun (University of Wisconsin-Whitewater, USA)

0
In this paper, in order to solve the poor communication problems under the shadowing effect, long-distance transmission, and secure communication, an energy-efficient maximization based resource allocation (RA) algorithm is proposed to improve system energy efficiency (EE) and information security for secure intelligent reflecting surface (IRS) networks under the active eavesdropper. Firstly, a nonlinear RA model with the coupled variables (i.e., beamforming vector and phase shift) is built for a multiple-input single-output (MISO) cellular communication system with the help of IRS, where the minimum secure rate constraint of user, the maximum transmit power constraint of base station (BS), and continue phase shift constraint are considered simultaneously. Then, the fractional objective function is transformed into a parameter subtractive form by using Dinkelbach's approach meanwhile the coupled beamforming vector of BS and the phase shift of IRS are decoupled by using an alternative iteration method. Moreover, the non-convex problem is converted into a semidefinite programming (SDP) problem which is solved by using convex optimization tools. Finally, simulation results demonstrate the proposed algorithm has good EE and security.

Artificial Noise-Aided Secure SWIPT Communication Systems Using Intelligent Reflecting Surface

Yue Xiu (University of Electronic Science and Technology of China, China); Jiao Wu (Seoul National University, South Korea); Guan Gui (Nanjing University of Posts and Telecommunications, China); Ning Wei and Zhongpei Zhang (University of Electronic Science and Technology of China, China)

1
Intelligent reflecting surface (IRS) enhanced transmission has been considered as a promising technology to address the physical layer security issue. In this paper, we study the security issues of an IRS-aided simultaneous wireless information and power transfer (SWIPT) system. When the full channel state information (CSI) of the eavesdropper is unknown, our goal is to maximize the power of artificial noise (AN) to jam the eavesdropper under transmit power and energy-harvesting (EH) constraints by optimizing the beamforming matrix at the access point (AP) and IRS. To solve the non-convex optimization problem, we propose an alternating optimization algorithm to effectively improve the secrecy rate. In particular, based on the manifold optimization the phase shifts at the IRS is obtained, and then propose a sequential convex approximation (SCA)-based algorithm to optimize the transmit beamforming at the AP. Simulation results show that, the secrecy rate of system increase with the increase of the transmit power, and is much higher than that of the SWIPT system without IRS.

Beamforming Design for Intelligent Reflecting Surface Aided Multi-Antenna MU-MIMO Communications with Imperfect CSI

Piao Zeng, Deli Qiao and Haifeng Qian (East China Normal University, China)

2
In this paper, the joint design of the beamforming scheme in intelligent reflecting surface (IRS) assisted multiuser (MU) multiple-input multiple-output (MIMO) downlink transmissions is investigated. It is assumed that the channel state information (CSI) of each link is imperfect and the weighted sum rate (WSR) is adopted as the performance metric. An optimization problem to maximize the WSR is formulated. An algorithm based on the recursive successive convex approximation (SCA) technique is proposed to obtain the optimal beamforming matrices at the base station (BS) and IRS. Due to the limitations of hardware and cost, low-resolution phase shifters (PSs) are taken into account to facilitate practical implementations as well. Simulation results demonstrate the effectiveness and superiority of the proposed scheme. Overall, a viable solution to the joint design of the beamforming in IRS-aided MU-MIMO downlink communication systems is provided.

Performance of Massive MIMO System with Cross-layer Design over Composite Rayleigh Fading Channel

Hui Wang, Xiangbin Yu, Yuheng Du and Xiaoyu Dang (Nanjing University of Aeronautics and Astronautics, China)

1
In this paper, the cross-layer design (CLD) performance of uplink massive MIMO system is studied by combining the discrete rate adaptive modulation with truncated automatic repeat request over composite Rayleigh fading channel. As shown in the performance analysis, each user's effective signal-to-noise ratio (SNR) and the corresponding conditional probability density function are, respectively, derived. Furthermore, we deduce the switching thresholds for CLD by means of the approximation of the PER with the constraint of the target packet error rate (PER). Based on switching thresholds, using the appropriate numerical integration method, average PER (APER) and overall average spectral efficiency (ASE) of massive MIMO with CLD are derived, and resultant closed-form expressions of APER and ASE can be achieved. The theoretical results above can agree with the corresponding simulations, and thus the system performance can be feasibly assessed. Simulation results illustrate that the CLD system is able to increase the ASE while maintaining the target PER, and thus the theoretical analysis is valid. Besides, with the increase of the maximum retransmission number, the ASE increment will decrease while the PER will increase. Moreover, the system performance will become better with the number of receive antennas increases, as expected.

Multi-objective Conflict Coordination in Radio Access Networks

Falu Xiao and Shi Yan (Beijing University of Posts and Telecommunications, China); Mugen Peng (Beijing University of posts & Telecommunications, China); Xueyan Cao (Beijing University of Post and Telecommunication, China); Yajuan Qiao (Beijing University of Posts and Telecommunications, China)

0
The conflicting demands of intelligent applications and services with the limited resource constitute key challenges to the radio access networks (RANs). To overcome these obstacles, a multi-objective conflict coordination scheme in RANs is proposed in this paper. In particular, the resource competition among different services is formulated as a multi-objective optimization problem with the constraints of performance thresholds, service fairness and available resources. To solve this complicated problem efficiently, a semi-distributed algorithm based on deep reinforcement learning is proposed, which aims at maximizing the sum satisfaction of conflicting objectives. Numerical results of the case study show that the proposed scheme has better convergence and performance enhancement with high feasibility in terms of multi-objective conflict coordination.
Session Chair

Yongjun Xu, Jiao Wu

Session WCS-04

NOMA

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Buffer-Aided Cooperative Non-Orthogonal Multiple Access for Downlink Transmission

Yunwu Wang, Peng Xu and Jianping Quan (Chongqing University of Posts and Telecommunications, China); Gaojie Chen (University of Leicester, United Kingdom (Great Britain))

0
This paper investigates a downlink buffer-aided cooperative non-orthogonal multiple access (C-NOMA) system for downlink transmission. The direct transmission from the base station to the users and the buffer-aided cooperative transmission between two users are coordinated. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the system outage probability of the proposed scheme is theoretically derived with a closed-form expression. Furthermore, the full diversity order of three is demonstrated to be achieved if the buffer size is not less than three, which is larger than conventional non-buffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system.

Four-dimensional Modulation Superposition NOMA Scheme with Non-ideal Channel Estimation

Jiyuan Sun, Jun Zou, Jing Qu, Meng Li and Chen Xu (Nanjing University of Science and Technology, China)

0
With the increasing demand for higher communication capacity, the scarcity of spectrum resources consequently led to research on the improvement of spectrum efficiency. In this paper, thus, a non-orthogonal multiple access (NOMA) scheme with four-dimensional modulation superposition and non-ideal channel estimation is proposed. The four-dimensional modulation is based on the spherical code which is different from the traditional QAM modulation with two dimensions. We analyze the upper bound of the two users's word error probabilities in a NOMA group, with non-ideal channel estimation. Then, the power allocation factor optimizing the transmit power with a given word error probability is derived. Simulation results show that, our proposed power allocation factor can work well with different channel estimation errors.

A Serially Concatenated NOMA scheme for Cluster-Based Vehicular Communications

Zhe Yan and Zhongwei Si (Beijing University of Posts and Telecommunications, China)

0
In this paper we apply non-orthogonal multiple access (NOMA) in vehicular networks and propose a serially concatenated NOMA scheme for cluster-based V2X communications. The vehicles transmit to the cluster head in a non-orthogonal manner, then the cluster head communicates also non-orthogonally with the base station. We employ the code-domain features of NOMA and investigate the mappings from the vehicles to the cluster head and from the cluster head to the base station. The overall structure results in a serially-concatenated factor graph, which is beneficial for eliminating the constellation overlap due to the use of binary superposition matrices. Optimizations are carried out to minimize the average pair-wise error probability, where the optimized superposition matrices are obtained by using the genetic algorithm. Numerical results in terms of the upper bound for symbol error rate are provided, which shows the feasibility of the proposed scheme for vehicular communications.

Hardware-Efficient Hybrid Precoding and Power Allocation in Multi-User mmWave-NOMA Systems

Xiaolei Qi (Beijing University of Posts and Telecommunications, China); Gang Xie (Beijing University of Posts and Telecommunicaitions, China); Yuanan Liu (Beijing University of Posts and Telecom, China)

0
In this paper, we propose a hardware-efficient hybrid precoding (HP) scheme and resource allocation strategy for millimeter-wave non-orthogonal multiple access (NOMA) communication system. Specifically, we utilize a switch and inverter (SI) network to realize the phase shift operation of HP structure (referred to as SIHP structure). A user clustering scheme is first formulated according to the channel correlation and gain difference of the users. Based on this, we design a two-step HP scheme for the proposed HP structure. With these results, a power allocation problem is formulated to maximize the overall achievable rate under per-cluster' power constraint and per-user' quality of service requirement. Then, we propose a power allocation algorithm with intra-cluster power allocation and inter-cluster power allocation strategies to obtain the sub-optimal power allocation. Simulation results verify that the proposed mmWave-NOMA system with SIHP obviously outperforms the mmWave-NOMA system with traditional HP in terms of energy efficiency.

Fairness Resource Allocation Scheme for GBR Services in Downlink SCMA System

Chenju Chen (Beijing University of Posts and Telecommunications, China); Hui Tian (Beijng university of posts and telecommunications, China); Gaofeng Nie (Beijing University of Posts and Telecommunications, China)

0
In this paper, a resource allocation scheme, SCMA-Hungarian (SH), for downlink SCMA system is proposed. By jointly considering the fairness and the guaranteed bit rate (GBR) constraint, Hungarian method is used to achieve a global best assignment, aiming to make more users meeting GBR requirement with high fairness. To further reduce the computation complexity, the Block-based SCMA-Hungarian (BSH) scheme is proposed for practical scenario. Simulation results demonstrate that BSH and SH have similar performance. The two schemes outperform in both throughput by nearly 8.5% and the number of users meeting GBR requirement by maximum 15.6% compared to an existing fairness scheme, SCMA-PF.
Session Chair

Miao Liu, Chuan Huang

Session CIS-02

Network Security 1

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Coordinated Multi-Point Secure Transmissions in mmWave Cellular Networks

Hao-Wen Liu (Xi'an JiaoTong University, China); Tong-Xing Zheng (Xi'an Jiaotong Unviersity, China); Yating Wen, ShaoDi Wang, Zhaowei Wang and Hui-Ming Wang (Xi'an Jiaotong University, China)

0
In this paper, we study the coordinated multi-point secure transmissions in mmWave cellular networks against randomly distributed eavesdroppers. We analyse and optimize security performance in terms of secrecy throughput under a stochastic geometry framework. We first introduce coordinated multi-point (CoMP) transmission schemes with and without exploiting artificial noise (AN), and then derive analytical expressions for connection outage probability (COP) and secrecy outage probability (SOP) which are used to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we maximize the secrecy throughput subject to an SOP constraint by jointly designing transmission rates and the power allocation of the AN scheme. Numerical results are also presented to validate the theoretical analyses and to demonstrate the security performance of our proposed transmission schemes. Interestingly, our results show that the CoMP with AN can bring a remarkable improvement for secrecy throughput particularly when facing a rigorous secrecy constraint.

Hybrid Precoding Design for Security MU-MISO SWIPT Millimeter Wave Systems

Chi Zhang (ZhengZhou University, China); Zhengyu Zhu and Ning Wang (Zhengzhou University, China)

0
In this paper, we investigate the hybrid precoding design for security multi-user multi-input single-output simultaneous wireless information and power transfer (SWIPT) millimeter Wave (mmWave) system. Digital precoding and analog precoding are designed by minimizing the transmit power subject to the target security rate, the target harvested power, the total transmit power and analog precoding unit amplitude constraints. Obviously, the formulated problem is non-convex and the two sets of design variables are coupled. To deal with the complicated problem, we employ an alternating optimization method to divide the primary problem into two subproblems. Specifically, for the problem of digital precoding design, we first propose a two-layer optimization approach to solve it. To reduce the computational complexity, we further introduce a successive convex approximation approach. Then, for the problem of analog precoding design, we put forward a novel phase matching algorithm to tackle it. Analog precoding and digital precoding are iteratively calculated until convergence. Simulation results present the effectiveness of proposed hybrid precoding algorithms.

A Stigmergy Learning Approach at the Edge: Securely Cooperative Caching for Fog Radio Access Networks

Yajuan Qiao and Yang Liu (Beijing University of Posts and Telecommunications, China); Mugen Peng (Beijing University of posts & Telecommunications, China); Wenyun Chen and Falu Xiao (Beijing University of Posts and Telecommunications, China)

0
To support the rapid development of multimedia services under the Internet of Things (IoT), fog radio access network (F-RAN) has become an emerging architecture in the 5G era. In this paper, content caching in a cloud and fog heterogeneous cooperative manner for F-RAN is investigated. More specifically, we jointly consider cache placement and file transmission in F-RAN, where fog access points (F-APs) serving as collaborative caching agents to provide caching for popular files, thus reducing the traffic from cloud and improving content delivery efficiency. A file download latency minimization problem subject to the storage capacities of F-APs is formulated. A distributed learning algorithm based on a swarm collaboration framework, i.e., stigmergy which enables an F-AP to expand its influence to other F-APs is proposed to improve caching resource utilization. In addition, a double-masking protocol is proposed to guarantee the security of F-APs' locations during stigmergy learning. Extensive simulations are conducted to show the effectiveness and reliability of our proposed scheme.

Towards High-efficient QoT Prediction of Wide-area Optical Backbone Network: A Reservoir Computing View

Yingqi Li, Jialin Wang, Cai Di, Li Zhigang, Duomin Fu and Linlin Qin (North China University of Science and Technology, China)

0
Wide-area optical backbone network provides crucial support for the fifth-generation (5G) development. Precise quality of transmission (QoT) prediction is conducive to assisting in the automatic low-margin network configurations to meet the demands of the 5G network, such as high reliability, high efficiency, and low latency. At present, machine learning has been developed to capture the valuable information hidden in the QoT data, whereas it is still a challenging problem for these existing models to simultaneously ensure prediction accuracy and training efficiency. To address this issue, a novel QoT prediction method based on reservoir computing is proposed, considering echo state network and its variants. The basic idea is that nonlinearity can be dealt by means of linear regression in the high-dimension "reservoir" state space. On real-world QoT time series benchmark, the experimental results show that our proposals significantly outperform the state-of-the-art models for single-channel QoT prediction. Besides, their superiorities are reconfirmed by statistical analysis.

GATAE: Graph Attention-based Anomaly Detection on Attributed Networks

Ziquan You (Shanghai Jiao Tong Universuty, China); Xiaoying Gan and Luoyi Fu (Shanghai Jiao Tong University, China); Zhen Wang (The Third Research Institute Of The Ministry Of Public Security, China)

0
Anomaly detection on attributed network has broad applications in many practical scenarios. Most of existing methods figure out the anomaly detection task by using graph convolution networks to embed the attributed networks. However, these methods will inevitably suffer over-smoothing problems. To approach this problem, in this paper, we propose a graph attention-based autoencoder model. Firstly, we encode the attributed network with a graph attention network. The attention mechanism not only alleviate the over-smoothing problem, but also help encoder learn nodes' representation better. Secondly, we use two decoders to reconstruct the original network and obtain reconstruction errors subsequently. Thus, we are able to detect anomalies by measuring the reconstruction errors. Experiments on real-word datasets show that our proposed model has better performance than other baseline methods in AUC score.
Session Chair

Dongfeng Fang, Qichao Xu

Session IoT-02

Green IoT

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Protocol-Aware Backscatter Communication Using Commodity Radios

Longzhi Yuan (University of Science and Technology of China, China); Rongrong Zhang (Capital Normal University, China); Kai Yang and Jianping An (Beijing Institute of Technology, China); Si Chen (Simon Fraser University, Canada); Wei Gong (University of Science and Technology of China, China)

0
Backscatter is one of the important techniques of IoTs as it can offer low-cost and low-energy wireless communication. With the help of widely available ambient signals, backscatter communication can even work without specialized carrier generation. The current solutions, however, are unable to identify various ambient signals. So, we introduce a backscatter system to do this. In particular, we realize the identification of OFDM WiFi (802.11a/g/n), 802.11b WiFi, Bluetooth Low Energy, and ZigBee. Further, we implement our backscatter in the FPGA hardware to evaluate the design. Comprehensive field studies show that our backscatter can identify four protocols at an average identification accuracy of about 90%. We also demonstrate that our identification is compatible with different backscatter modulation for all four signals in 2.4GHz band.

Joint Power Control and Time Allocation for WBANs with RF Energy Harvesting

Rongrong Zhang and Xinglong Li (Capital Normal University, China)

0
Energy harvesting Wireless Body Area Network (WBANs) where sensor nodes can harvest energy from their ambient environment have attracted considerable attention recently. However, how to control transmission power of energy source and charging duration to maximizing the sum of transmission rate has not been addressed systematically. In order to bridge this gap, we devote this paper to developing an optimal joint power control and time allocation scheme with the objective to maximize the sum of data transmission rate for WBANs with RF energy harvesting. To this end, we first formulate an optimization problem maximizing the sum of data transmission rate with constraint of the transmission power and interference power. The formulated problem is non-convex optimization, so we transform it into three solvable convex subproblems. We then design an optimal scheme by jointing power control and time allocation and compare it with two reference schemes. The simulation results demonstrate that our proposed optimal scheme performs best in terms of the sum of data transmission rate.

Joint Beamforming and Phase-Shifting Optimization in MISO with RIS-Assisted Communication

Kehao Wang (WHUT, China); Zhenhua Xiong, Zhixin Hu and Xueyan Chen (Wuhan University of Technology, China); Lin Chen (Sun Yat-Sen University, China)

1
In this paper, we jointly investigate beamforming and phase shift of reconfigurable intelligent surface (RIS) for energy efficiency (EE) maximization in an MISO downlink system with multiple users. Specially, we assume that we only know the channel statistics information between RIS to users. The formulated problem is formulated to a non-convex one such that it is difficult to obtain the optimal solution. To tackle this non- convexity, we decouple the original problem to two subproblems and further propose two iterative suboptimal algorithms based on the successive convex approximation (SCA) to solve the two subproblems, respectively. Then we propose an iterative algorithm to obtain a suboptimal solution of original problem in polynomial time. The numerical results verifies the effectiveness of our proposed algorithm.

A Novel Link-Selection Strategy for DCSK-SWIPT Relay System with Buffer

Mi Qian, Guofa Cai, Yi Fang and Guojun Han (Guangdong University of Technology, China)

1
Adaptive link selection for buffer-aided relaying can achieve significant performance gain compared with the conventional relaying with fixed transmission criterion. However, most of the existing link-selection strategies are designed based on perfect channel state information (CSI), which are very complex by requiring channel estimator. To solve this issue, in this paper, we investigate a buffer-aided differential chaos-shift-keying based simultaneous wireless information and power transfer (DCSK-SWIPT) relay system, where a decode-and-forward (DF) protocol is considered and the relay is equipped with a data buffer and an energy buffer. In particular, we propose a novel link-selection protocol for the proposed system based on harvested energy, data-buffer status and energy-shortage status, where the CSI is replaced by the harvested energy to avoid the channel estimation and the practical problem of the decoding cost at the relay is considered. Furthermore, the bit-error-rate (BER) and average-delay closed-form expressions of the proposed protocol are derived over multipath Rayleigh fading channels, which are validated via simulations.

Performance Analysis of A SLIPT-Based Hybrid VLC/RF System

Huijie Peng and Qiang Li (Huazhong University of Science and Technology, China); Ashish Pandharipande (Signify, The Netherlands); Xiaohu Ge (Huazhong University of Science & Technology, China); Jiliang Zhang (The University of Sheffield, United Kingdom (Great Britain))

0
To alleviate the energy constraint and coverage limitations in Internet of Things (IoT) applications, in this paper we investigate a dual-hop hybrid visible light communication (VLC)/radio frequency (RF) cooperative communication system based on simultaneous lightwave information and power transfer (SLIPT). The proposed system consists of a light-emitting diode (LED) source (S), an off-the-grid relay (R) that moves randomly within the coverage of S, and a destination (D) that is located outside the coverage of S. To facilitate communications between S and D, the received optical signal at R in the first phase can be separated into alternating current (AC) and direct current (DC) components for information decoding and energy harvesting, respectively. Then the energy harvested by R is used to forward the decoded information to D using RF in the second phase. In view of the distinct channel conditions across the two hops, the decoding status and the energy available at R, the end-to-end system outage probability is analytically derived. Simulation results are used to evaluate the impact of different system parameters and demonstrate the efficiency of the proposed system.
Session Chair

Rongrong Zhang, Ye Yu

Session IT-02

Invited Talk 2

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Optimal Scheduling of Mobile Edge Computing for Space Information Networks

Qinyu Zhang (Harbin Institute of Technology, Shenzhen China.)

1
Mobile Edge Computing (MEC) is a promising solution to tackle the upcoming computing tsunami in space information network (SIN), by effectively utilizing the idle resource at the edge. In this work, we study such a multi-hop D2D-enabled MEC scenario for SIN, where mobile devices at network edge connect and share resources with each other via multi-hop D2D. We focus on the micro-task scheduling problem in the multi-hop D2D-enabled MEC system, where each task is divided into multiple sequential micro-tasks, such as data downloading micro-task, data processing micro-task, and data uploading micro-task, according to their functionalities as well as resource requirements. We propose a joint Task Failure Probability and Energy Consumption Minimization problem (called TFP-ECM), which aims to minimize the task failure probability and the energy consumption jointly. To solve the problem, we propose several linearization methods to relax the constraints, and convert the original problem into an integer linear programming (ILP). Simulation results show that our proposed solution outperforms the existing solutions (with indivisible tasks or without resource sharing) in terms of both the total cost and the task failure probability.

Key Technologies of Full-Band Optical Transmission Systems and Networking

Gangxiang Shen (Soochow University, China)

1
The transmission technology based on the traditional C-band standard single-mode fiber (SSMF) has approached its transmission capacity limit. However, the remaining capacity of an SSMFЎЇs low-loss window is still abundant, up to 400 nm. To explore this potential capacity, this talk will introduce the key technical aspects that enable the full utilization of this full-band. The related technologies on transmission systems and networks are discussed.

Neural Network-based equalizer for intensity modulation and direct detection systems

Lilin Yi (Shanghai Jiao Tong University, China)

1
The neural network (NN) has been widely used as a promising technique in fiber optical communication owing to its powerful learning capabilities. Specifically, the NN-based equalizer is qualified to mitigate mixed linear and nonlinear impairments, providing better performance than traditional algorithms, especially in intensity modulation and direct detection (IMDD) systems. Many demonstrations employ a traditional pseudo-random bit sequence (PRBS) as the test data. However, it has been revealed that the NN can learn the generation rules of the PRBS during training, resulting in abnormally high performance. So it is important to distinguish whether data features are learned by an NN model, what type of dataset can be used to avoid the above problem. After solving the data training issue, optimizing the NN structure to improve the equalization performance without improving the complexity becomes an important objective. In this talk, we analyze the detailed learning process when an NN is trained using a PRBS and determine the effect of the detection of generation rules. We then provide a mutual verification strategy to verify the training effectiveness and propose a combination strategy to construct a strong random sequence that will not be learned by the NN or other advanced algorithms.
Session Chair

Xu Zhang

Session MWN-02

Edge Computing

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Moving Server: Follow-up Computation Offloading Paradigm for Vehicular Users

Xuefei Zhang, Weilong Lin, Yijing Li, Qimei Cui and Xiaofeng Tao (Beijing University of Posts and Telecommunications, China); Xueqing Huang (New York Institute of Technology, USA); Pinyi Ren (Xi'an Jiaotong University, China)

0
With the emergence of ever-growing compute-intensive applications, geo-distributed cloud resources are deployed at the mobile edge and alleviate the resulting surging demand in communication and computation. For a high-velocity vehicular user, however, frequent handovers among wireless base stations and corresponding task migrations among edge servers can lead to throughput degradation, longer latency, and increased energy consumption. To tackle this issue, we design a novel follow-up computation offloading paradigm, where moving servers can provide extra computing resources and the deep Q-learning based computation offloading strategy will improve the quality of service. Our study provides insights into the following questions: Who is capable of being a moving server? How to offload to a moving server?, and Which server is the best choice? Furthermore, to resolve the conflict between the long convergence time of learning algorithm and high mobility of the vehicular user, we enhance the server selection scheme by incorporating the prior probability of the availability of each moving server. The simulation results demonstrate the efficiency and performance superiority of our proposed paradigm over the existing schemes.

Multi-Armed Bandits Scheme for Tasks Offloading in MEC-enabled Maritime Communication Networks

Shan Gao (Dalian Maritime University, China); Tingting Yang (Dongguan University of Technology, China); Hua Ni (Dalian Maritime University, China); Genglin Zhang (Dalian Chinacreative, China)

0
With the advancement of science and technology, the issue of marine ship Internet of Things (IoT) users' assignment of computing tasks offloading has become more and more challenging. When faced with the complex and dynamic marine environment, considering the different quality requirements of maritime applications, we have addressed this issue in this paper. We first propose the space-air-ground-edge (SAGE) maritime communication network architecture. This novel architecture is used to offload computing-intensive applications and services for IoT users in the marine environment. At the same time, based on the Multi-Armed Bandits (MAB) problem, considering budget constraints and other marine environmental conditions such as delay and energy consumption, we propose an algorithm for selecting an edge server strategy. Simulation results of this paper show that the scheme has a better performance under different QoS requirements, which proves that the scheme is effective.

A QoE-based DASH Multicast Grouping Algorithm for Mobile Edge Computing

Lei Xu, Xiaobin Tan, Simin Li and Shunyi Wang (University of Science and Technology of China, China)

1
Dynamic Adaptive Streaming over HTTP (DASH) can adaptively select the appropriate video bitrate for mobile users. Mobile Edge Computing (MEC) scenario is of great benefit to improve the performance of mobile networks by providing computing and storage capabilities. And the utilization of spectrum resources can be improved by multicast transmission, but the performance of the multicast transmission will be directly affected by the selected grouping algorithm. Therefore, we propose a grouping algorithm for DASH multicast in MEC to complete a more reasonable grouping of users, thereby improving the Quality of Experience (QoE) of mobile users. QoE is not only our optimization goal but also the grouping basis of the algorithm proposed in this paper. We dynamically group multiple users in the same Multicast Broadcast Single Frequency Network (MBSFN) area in three dimensions based on the three components of QoE. The simulation results show that the proposed grouping algorithm performs well in QoE and fairness.

Decentralized Computation Offloading and Resource Allocation in MEC by Deep Reinforcement Learning

Yeteng Liang, Yejun He and Xiaoxu Zhong (Shenzhen University, China)

1
Mobile edge computing (MEC) as a promising technology to relieve edge user equipment (UE) computing pressure by offloading part of a task, is able to reduce the execution delay and energy consumption effectively, and improve the quality of computation experience for mobile users. Nevertheless, we are facing a challenge of design of computation offloading and resource allocation strategy on a part of a task offloaded to MEC server. A task is divided into two sub-tasks firstly. Then one of the two sub-tasks is executed locally, and the other will be offloaded to MEC server that is located near the base station (BS). Based on dynamic offloading and resource allocation strategy, the best offloading proportion of a task, local calculation power and transmission power are investigated by deep reinforcement learning (DRL). In this paper, we propose two DRL-based approaches, which are named as deep Q network (DQN) and deep deterministic policy gradient (DDPG), to minimize the weighted sum cost including execution delay and energy consumption of UE. DQN and DDPG can deal with large scale state spaces and learn efficient offloading proportion of task and power allocation independently at each UE. Simulation results demonstrate that each UE can learn the effective execution policies, and the proposed schemes achieve a significant reduction in the sum cost of task compared with other traditional baselines.

Traffic Optimization for In-flight Internet Access via Air-to-Ground Communications

Kai Wan, Zhen Wang, Yuanyuan Wang and Chi Zhang (University of Science and Technology of China, China); Jianqing Liu (University of Alabama in Huntsville, USA)

0
Passengers traveling by air demand for Internet connectivity to effectively utilize their valuable in-flight time. Recently, direct air-to-ground (A2G) communications for in-flight Internet access have attracted extensive attention and research due to the low propagation delay and deployment cost. However, in the current A2G network, the commercial aircraft selects a ground base station with the highest received signal strength for data transmissions, which may cause congestion at busy ground base stations and increase the traffic delay when Internet traffic demands of passengers are high. In this paper, a software-defined A2G framework is proposed to efficiently and flexibly select multiple ground base stations for data transmissions from a global perspective. We use a bipartite graph to model in-flight Internet access network and develop a flow deviation method for matching to optimize the allocation of Internet traffic flows to aircraft through multiple ground base stations. The proposed scheme can achieve load balance among ground base stations and minimize the traffic delay in the network. Besides, we consider the optimality gap introduced by unpredictable network changes and delayed network reconfigurations and propose a re-route policy to keep the gap small while minimizing the network reconfiguration cost. Finally, numerical performance evaluation validates our traffic optimization scheme and re-route policy.
Session Chair

Chi Zhang, Xuefei Zhang

Session NGNI-02

Advanced Algorithms for Next Generation Networking

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

A BPSO-based Controller Placement Algorithm for Hierarchical Service Function Chaining

Guanwen Li (Huawei Technologies Co., Ltd., China); Bohao Feng (Beijing Jiaotong Unviersity, China); Fan Wu and Huachun Zhou (Beijing Jiaotong University, China)

0
With the emergence of hierarchical service function chaining, network providers are able to offer more flexible and scalable network functions for various requirements. However, considering scalability, how to cope with the cross-domain controller placement is still challenging, as the signaling interactions among controllers are complicated. To this end, we propose a BPSO-based algorithm in the paper, to minimize signalling costs of hierarchical controller placement. Particularly, we first formulate the controller placement of hierarchical service function chaining as an integer programming model. Then, we propose a heuristic algorithm (BPSO-HPA) to find the optimal solution. Additionally, we propose a greedy-based algorithm (G-HPA) as a benchmark for comparisons. Finally, we evaluate performance and effectiveness of the proposed algorithms with extensive simulations. The results prove that the proposed BPSO-based algorithm is able to approximate the optimal results and outperform the greedy-based algorithm with respect to the running time.

Community Detection for Information Propagation Relying on Particle Competition

Wenzheng Li (People's Public Security University of China, China); Jingjing Wang and Yong Ren (Tsinghua University, Beijing, China); Dechun Yin and Yijun Gu (People's Public Security University of China, China)

0
In the process of information propagation, different communities may be formed due to different opinions, interests or hobbies. However, for the application for information propagation, targeted dynamic community detection methods have not been proposed previously. In this paper, we propose a particle competition aided community detection scheme for the sake of solving the dynamic community detection for information propagation. In comparison to traditional particle competition models, the particles in our proposed model are capable of performing the operations of walking, splitting and jumping and the domination matrix of the network changes continuously. Moreover, with the aid of combining the previous particle competition experiences as well as the defined particle's walking rules, our propose community detection scheme can automatically select and update the core nodes based on the results of previous evolution. Finally, simulation results show both the effectiveness and superiority of our proposed particle competition aided community detection model for information propagation, which may have compelling applications in the context of the spread of opinions and computer viruses, etc.

AODC: Automatic Offline Database Construction for Indoor Localization in a Hybrid UWB/Wi-Fi Environment

Huilin Jie (University of Chongqing, China); Kai Liu (Chongqing University, China); Hao Zhang (University of Chongqing, China); Ruitao Xie (University of Shenzhen, China); Weiwei Wu (Southeast University, China); Songtao Guo (Chongqing University, China)

0
With the rapid development of mobile terminals and the ever-expanding deployment of wireless infrastructures, the requirement of location-based services (LBS) has permeated majority industries. However, fingerprint-based localization method has a bottleneck, namely, the offline database construction is time-consuming and labor-intensive, which hampers its implementation and adaption. Moreover, the mismatch of fingerprint due to various environmental interferences during the online localization phase is another critical issue to be addressed. In view of this, we consider the fingerprint-based indoor localization in a hybrid UWB/WiFi environment, which integrates UWB and Wi-Fi to speed up the construction of offline database, while maintaining a meter-level localization accuracy.Specifically, the system does not require manual labeling efforts for reference points (RP). Instead, we propose a heterogeneous data synchronization scheme (HDSS) to integrate the RSSI data obtained by the Wi-Fi device and the corresponding coordinates obtained by UWB device. Then, an automatic radio map generation scheme (ARMGS) is proposed, which can automatically generate the fingerprints profiles of RPs. Furthermore, for the online localization, unlike traditional approaches, which estimate locations only based on features of signal space, we proposed a dual-domain constraints localization algorithm (DCLA), which takes the physical space into consideration at the same time based on mean shift clustering algorithm. Finally, we implement the prototype of AODC system and carry out extensive real-world experiments. The experimental results validate the effectiveness of the proposed solutions.

Cache Pollution Prevention Mechanism based on Cache Partition in V-NDN

Jie Zhou (Chongqing University of Posts and Telecommunication, China); Jiangtao Luo and LiangLang Deng (Chongqing University of Posts and Telecommunications, China); Junxia Wang (Chongqing University of Posts and Telecommunication, China)

0
The information-centric networking, which aims to solve the demand for distributing a large amount of content on the Internet, has proved to be a promising example for various network solutions, such as the Vehicular ad-hoc network (VANET). However, some problems are introduced when the named data networking is combined with V-NDN, such as the cache pollution. In order to solve the cache pollution attack, we propose a mechanism based on cache partition, which divides the cache of nodes into two parts and stores the content of different popularity respectively. We monitor the interest packets received by each node and get the corresponding popularity of each content. According to the popularity of the content, the content is stored in the corresponding cache. In addition, when the popularity of the content changes, we add the name of the content to the monitoring list to determine whether it is an attack content.

This paper simulates the cache partition mechanism under different request frequencies and different forwarding strategies. The experimental results show that the average hit rate of node cache can be increased by 14% and the user request delay can be reduced by 30% when the node is attacked. At the same time, the number of Interest packets requested by normal users in the whole network has also been greatly reduced, which greatly reduces the traffic within the network. Experiments show that the cache partition mechanism can effectively resist the attack of cache pollution.
Session Chair

Jiawen Kang, Siyuan Zhou

Session SAC-02

Learning-based Schemes

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Invited Paper: The Design-for-Cost of millimeter-wave Front-End for 5G and Beyond

Jianguo Ma (Guangdong University of Technology, China)

0
Millimeter-wave techniques and MIMO techniques are the key techniques for 5G and beyond. Fully integrated millimeter-wave front-ends are one of the key solutions for reducing the overall system costs and the sizes. It is not so challenging to realize working integrated millimeter-wave front-ends technically, the key of the success for 5G and beyond is the overall low cost for potential commercial implementations. Therefore, Design-for-Cost (DfC) becomes the key challenging. This paper compares the implementations basing on both CMOS 65nm and SiGe 0.18um technologies and the results show that the cost using CMOS 65nm is more than 3.3 times higher than that of using SiGe 0.18um, meanwhile, integrated millimeter-wave front-ends by using SiGe 0.18um technology have much better reliability than that of using CMOS 65nm.

Deep Learning based Millimeter Wave Beam Tracking at Mobile User: Design and Experiment

Pengbo Si, Yu Han and Shi Jin (Southeast University, China)

0
Beam tracking is of great interest in millimeter wave (mmWave) communication systems, because it can significantly improve the user's received signal power for high-speed communications. However, the existing algorithms have high beam training overhead, and it is difficult to achieve real-time tracking of the beam. This paper proposes a novel beam tracking for mmWave systems based on deep learning (DL) network. Specifically, considering the attribute of the user's mobile behavior, beam training is performed at several consecutive moments. Then, the designed long short-term memory (LSTM) network utilizes historical beam measurements to predict the best future communication beam. In addition, in order to make the network applicable in different scenarios, we also add a switching module to adjust the output according to the characteristics of the current environment. The over-the-air (OTA) results demonstrate that the network performs well and is robust to various scenarios.

A Low Complexity Dispersion Matrix Optimization Scheme for Space-Time Shift Keying

Yun Wu, Wenming Han, Xueqin Jiang and Bai Enjian (Donghua University, China); Miaowen Wen (South China University of Technology, China); Jian Wang (Fudan University, China)

0
Space-Time Shift Keying (STSK) is a multi-antenna technique, which transmits additional data bits by selecting indices of a pre-designed dispersion matrix set. However, conventional STSK scheme employs a random search scheme to optimize the dispersion matrix set, which requires high computational complexity. In this paper, a low complexity STSK dispersion matrix optimization scheme is proposed. Firstly, the dispersion matrix entries are discretized to reduce the search space of the candidate dispersion matrix sets and the saturation level is optimized. Then, an alternating optimization algorithm is proposed to optimize the dispersion matrix set. The complexity analysis and simulation results show that, compared with the conventional random search scheme, the proposed scheme can significantly reduce complexity and achieve excellent bit error rate (BER) performance.

Deep Learning Based Active User Detection for Uplink Grant-Free Access

Jiaqi Fang, Yining Li, Changrong Yang, Wenjin Wang and Xiqi Gao (Southeast University, China)

0
In massive machine-type communication (mMTC) systems, a large number of devices transmit small packets sporadically. Grant-free (GF) nonorthogonal multiple access turns to be a competitive candidate, since it avoids the granting access and reduces the signaling overheads. Exploiting the active users' inherent sparsity nature, we formulate the active user detection (AUD) problem as a single measurement vector (SMV) problem, and prove that our SMV model could support more active users than conventional multiple measurement vector (MMV) model. Based on the iterative soft thresholding (IST) algorithm, we propose a learning IST network (LISTnet), which is easy to be trained and performs better than conventional methods when the users' active rate is high. Besides, we add connections between the layers of LISTnet and develop a residual LISTnet (ResLISTnet), which can adaptively adjust the number of layers to reduce the computational complexity. Numerical simulation results show the superiority of our methods.

An Enhanced Handover Scheme for Cellular-Connected UAVs

Wenbin Dong (Xidian University, China); Xinhong Mao (Institute of Telecommunication Satellite, China); Ronghui Hou, Xixiang Lv and Hui Li (Xidian University, China)

0
In this paper, we propose an enhanced handover scheme for cellular-connected UAVs. Specifically, our handover scheme considers the following characteristics: 1) UAV can detect multiple cells with the comparable RSRP levels which may cause many unnecessary handovers. The handover event trigger parameters in our scheme are dynamically adjusted to avoid a UAV to handover from a cell to another cell with the comparable RSRP level; 2)In the process of taking off, the UAV would fly through the null space of antenna lobes many times, while the time duration is normally very short. The RSRP during the UAV taking off varies quickly, so that the measurement reports may not provide an accurate channel information for the UAV. In this case, when the link quality between the UAV and the BS is below a threshold, the BS allows the link being maintained for a while with the hope that the link quality would get better again. We implement our proposed handover scheme on the NS3 platform, and compare with the current LTE handover scheme and the sojourn time estimation-based handover algorithm. Our simulation results demonstrate that our proposed scheme can significantly reduce the number of unnecessary handovers. Moreover, the network throughput of our scheme is improved, since the the communication resources taken by the unnecessary handovers is utilized by the UAV for transmitting data.
Session Chair

Hongguang Sun

Session SPC-02

MIMO

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Data-enhanced Bayesian MIMO-OFDM Channel Estimation Strategy with Universal Noise Model

Jia-Cheng Jiang and Hui-Ming Wang (Xi'an Jiaotong University, China)

0
Model-based methods are dominant in current systems for their optimal designs under given models, but may suffer from inaccurate modeling assumptions. Recently, data-based deep learning methods have achieved remarkable performances by training a large amount of data but encounter some challenges such as, lack of available training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-driven methods, named model based method enhanced by data, which is training affordable, theoretically interpretable and model flexible. To show the idea more concretely, we consider a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel state information (CSI) acquisition approach. Specifically, we utilize a universal mixture of Gaussian (MoG) model to deal with the nongaussianity of the noise and interference in complex communication environments, which can adaptively adjust involved parameters to fit the true distribution by observed data. We propose a variational Bayesian framework to derive the specific form of minimum mean square error (MMSE) estimator. Simulations are performed to verify the efficiency of our proposed method and the accuracy of our analysis.

A New Real-Time Acoustic Echo Cancellation Algorithm Using Blind Source Separation and Multi-delay Filter

Xiuxiang Yang (Chongqing University of Posts and Telecommunications, China)

0
Adaptive algorithm is a traditional method for solving acoustic echo cancellation (AEC) problem, which need to perform well in different scenes, especially in the double talk (DT) scenarios. It is found that some algorithms designed for blind source separation (BSS) performs well in the DT scenario, which requires little prior information. However, in order to be used in real-time processing, the frame length of the input is required to be shorter, the performance will be worse accordingly. In this paper, we propose an algorithm framework based on BSS and multi-delay filter (MDF) for AEC, which the coefficients are updated by auxiliary independent vector analysis (AuxIVA) algorithm. The numerical studies including utterances corrupted by echo under different reverberation times that the improved algorithm outperforms the Speex AEC algorithm.

Low Complexity Activity Detection for Massive Access with Massive MIMO

Yongxin Liu (Tsinghua University, China); Shidong Zhou (Tsinghua University, Canada)

0
We propose a low complexity activity detection scheme for massive access scenarios with massive multiple-input multiple-output (MIMO) communication systems. Numerous devices with sporadic access behavior characterize these scenarios; therefore, only a subset is active. Limited by massive potential devices in the network and coherence time, which contains L signal dimensions, it is infeasible to assign a unique orthogonal pilot to each device in advance. In this case, device detection is the first critical problem to be solved. A compressed sensing-based (CS) activity detection algorithm has an excellent performance in the case of sparse active devices. However, due to the massive number of potential devices and non-orthogonal pilots, the algorithm's complexity is very high, and the base station (BS) needs much cache to store the pilot codebook for all potential devices is unacceptable in this scenario. In order to solve this problem, this paper proposes a low complexity activity detection scheme. The scheme uses a linear combination of orthogonal pilots to construct non-orthogonal pilots, which does not need to store the pilot codebook at BS. Also, we propose a device by device activity detection algorithm for this scheme. When the number of potential devices is less than L 2 - L, and the number of antennas goes to infinity, the error probability of the proposed algorithm approaches 0, and the complexity of the algorithm is 50-100 times lower than the compressed sensing-based algorithm.

Max-Min Energy-Efficient Multi-Cell Massive MIMO Transmission Exploiting Statistical CSI

Yufei Huang, Li You, Jiayuan Xiong, Wenjin Wang and Xiqi Gao (Southeast University, China)

0
With the dramatic increment of data traffic, energy efficiency has become a critical concern in the beyond 5G mobile system. In this paper, we study the max-min fairness-based energy efficient transmission strategy design for multi-cell massive multiple-input multiple-output (MIMO) downlink transmission, exploiting the statistical channel state information (CSI). We first derive the closed-form eigenvectors of the optimal downlink transmit covariance matrices, which reduces the original precoding design into a power allocation problem. Then, by exploiting the minorization-maximization procedure and Dinkelbach's transform, we propose an iterative energy-efficient power allocation algorithm with low complexity and guaranteed convergence. The performance gain of the proposed algorithm over other baselines is demonstrated in the numerical results.

Analysis on Functions and Characteristics of the Rician Phase Distribution

Zhongtao Luo and Yanmei Zhan (Chongqing University of Posts and Telecommunications, China); Edmond Jonckheere (USC, USA)

0
For a complex variable consisting of the deterministic signal and the zero-mean complex Gaussian noise, the module and phase follow the Rice distribution and the Rician phase distribution, respectively. This paper discusses the distribution functions and numerical characteristics of the Rician phase distribution. For the unwrapped noise and the wrapped noise, we present the noise distributions and then develop the functions of probability density and cumulative distribution. The formulas of the mean and the variance are derived. Besides, the variance of the unwrapped noise is approximated in closed-form. The relationship between the characteristics and the parameters are analyzed. This paper provides fundamental analysis and preparation for signal processing in the phase domain.
Session Chair

Hui-Ming Wang, Li You

Session WCS-02

Intelligence Communications I

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Deep Learning Assisted Hybrid Precoding with Dynamic Subarrays in mmWave MU-MIMO System

Jing Jiang (Xi'an University of Posts and Telecommunications, China); Yun Yang (Xi'an University of Posts and Telecommunication, China)

0
In millimeter wave communication, analog-digital hybrid precoding is used to decrease hardware complexity and energy consumption. The performance and hardware complexity of hybrid precoding can be compromised by using sub-connected architecture further, but it also brings about the problem of high computational complexity. To tackle this issue, a multiuser hybrid precoding framework based on deep learning is proposed in this paper. Specifically, two deep neural networks (DNN), which can be connected by the transformation matrix, are constructed to maximize the effective channel gain, thus maximizing the sum rate of multiuser. Simulation results exhibit that the DNN-based framework achieves better performance while maintaining low computational complexity compared with the traditional method in hybrid precoding.

BLDnet: Robust Learning-Based Detection for High-Order QAM With Nonlinear Distortion

Longhao Zou and Ming Jiang (Southeast University, China); Chunming Zhao (National Mobile Communications Research Laboratory, Southeast University, China); Yuan He and Desen Zhu (Southeast University, China); Qisheng Huang (National Mobile Communications Research Lab., Southeast University, China)

1
The performances of wireless communication systems are strongly limited by the nonlinearities that exist in the transceiver. We first study the effect of nonlinearities of power amplifiers on high-order quadrature amplitude modulation (QAM) signals and then propose a bit-level demodulator network (BLDnet) to reduce the nonlinear interference. More specifically, the BLDnet can not only perform hard decisions but also provide the soft outputs for further processing in channel decoder. From the simulations, the BLDnet is observed to have a better performance than the conventional scheme in the Rapp model and the Saleh model. Compared to other detection schemes, the BLDnet has a comparatively low computation complexity without performance loss in the case of high-order modulation, such as 1024QAM.

DNN Based Iterative Detection for High Order QAM OFDM Systems with Insufficient Cyclic Prefix

Huan Cai (Southeast University, China); Chunming Zhao (National Mobile Communications Research Laboratory, Southeast University, China); Wei Shi (Southeast University, China)

1
In this paper, we consider high order QAM orthogonal frequency-division multiplexing (OFDM) systems with insufficient cyclic prefix (CP) which will lead to intersymbol interference (ISI) and intercarrier interference (ICI) in the receiver. To cope with the error floor induced by ICI and ISI in one-tap equalization and iterative serial interference cancellation (ISIC), we develop a maximum likelihood (ML) based iterative grouping detection algorithm (ML-IG), which utilizes the correlation among the received signals on adjacent subcarriers to improve the detection accuracy. Since ML-IG for OFDM systems with high order QAM modulation is of significant complexity, detection network (DetNet) based iterative grouping detection algorithm (DetNet-IG) is designed to imitate ML-IG. Simulations show that both ML-IG and DetNet-IG can provide better BER performance than ISIC, and DetNet-IG exhibits distinguished robustness against channel model incongruity.

A Novel Neural Network Denoiser for BCH codes

Hongfei Zhu (Peking University & School of Electronics Engineering and Computer Science, China); Zhiwei Cao, Yuping Zhao and Do Li (Peking University, China)

0
Traditional filters are dedicated to reducing the out-of-band noise while the in-band noise is beyond their capability. With the development of deep learning, the deep neural network (DNN) provides a more powerful and effective approach to denoising. In this paper, we propose a novel neural network denoiser for BCH codes. The denoiser directly learns an end-to-end mapping from a noisy codeword to its corresponding denoised codeword. Simulation results show that the signal to noise ratio (SNR) improvement and the symbol error rate (SER) reduction of the denoiser is significant. Consequently, the denoiser assists the traditional decoder in achieving far better bit error rate (BER) and frame error rate (FER) performance.

A Deep Learning based Resource Allocation Algorithm for Variable Dimensions in D2D-Enabled Cellular Networks

Errong Pei and GuangCai Yang (Chongqing University of Posts and Telecommunications, China)

0
Optimization algorithms play an important role in resource allocation problems. However, the algorithms are difficult to be applied in practice due to the high complexity. Some deep neural networks (DNNs) are thus proposed to approach the traditional algorithms, which can realize realtime resource allocation. However, the DNNs is designed for invariable dimensions. Therefore, it remains unclear whether the neural network under variable dimensions can still approach the traditional algorithm. Furthermore, it still remains unclear how to train the neural network for variable dimensions. In this work, we propose a deep learning based power control scheme for variable D2D pairs, where low-dimensional inputs are preprocessed by zero-padding, and several hybrid training methods are proposed. Through a large number of experimental simulations, it is proved that the preprocessing method can better deal with the variable dimensions problem without introducing new interference. The fully connected DNN trained by different-dimensional data is proved to be the closest to the traditional algorithms.
Session Chair

Jing Jiang, Guan Gui

Session WCS-05

UAV Aided Wireless Communications

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

UAV Deployment Design for Maximizing Effective Data with Delay Constraint in a Smart Farm

Junwei Zhao, Ying Wang, Zixuan Fei and Xue Wang (Beijing University of Posts and Telecommunications, China)

0
Data transmission and real-time processing of Internet of Things (IoT) devices in remote unmanned area are becoming a challenging issue. In this paper, an Unmanned Aerial Vehicle (UAV) enabled computing system is investigated in a smart farm, where multiple UAVs are deployed to make intelligent decisions based on data collected by sensors. First, considering the delay constraint of data processing, the concept of effective data is introduced. Next, the effective data of farm monitoring devices (FMDs) is maximized by jointly optimizing computing and communication resources allocation and the deployment of UAVs. The formulated problem is a combinatorial optimization problem that is hard to tackle. Furthermore, this problem is transformed into two sub-problems and a two-layer iterative optimization algorithm is proposed to get an approximate optimal solution. Finally, simulation results demonstrate that the proposed algorithm has a significant increase in terms of effective data.

Joint Task and Resource Allocation in SDN-based UAV-assisted Cellular Networks

Yujiao Zhu, Sihua Wang, Xuanlin Liu, Haonan Tong and Changchuan Yin (Beijing University of Posts and Telecommunications, China)

0
In this paper, we study the problem of minimizing the weighted sum of the delay and energy consumption for task computation and transmission in an unmanned aerial vehicle (UAV)-assisted cellular network, where the UAV collaborates with base stations (BSs) under the control of software defined network (SDN) controller. In particular, the UAV acts as a computing server to compute users' tasks or as a relay node to forward tasks to BSs equipped with mobile edge computing (MEC) capacities. With the assistance of the UAV, users' tasks can be computed in three modes, including local computing mode, UAV computing mode, and edge computing mode. SDN controller dynamically adjusts the task computing mode and resource allocation scheme to meet the users' needs. The proposed problem is formulated as an optimization problem whose goal is to minimize the weighted sum of the delay and energy consumption of the UAV and all users by adjusting the task computing mode and resource allocation scheme. The proposed problem is a mixed-integer combined non-convex problem and it is hard to solve. We propose a joint mode selection and resource allocation optimization algorithm to solve it, where the original problem is decoupled into two subproblems, i.e., task computing mode selection subproblem and resource allocation subproblem. These two subproblems are solved alternatively by the branch and bound (BB) method and the convex optimization method, respectively. Simulation results show that the proposed algorithm can reduce the weighted sum of the delay and energy consumption of the UAV and all users by up to 33.2% and 55.7% compared to cases that computed with random mode selection and fully computed locally, respectively.

Multi-UAV Deployment for MEC Enhanced IoT Networks

Lei Yang (Beijing University Of Technology, China); Haipeng Yao (Beijing University of Posts and Telecommunications, China); Xing Zhang (BUPT, China); Jingjing Wang (Tsinghua University, Beijing, China); Yunjie Liu (Beijing University of Posts and Telecommunications, China)

0
Unmanned aerial vehicles (UAVs) are already widely used to provide both relay services and enhanced information coverage to the terrestrial Internet of Things (IoT) networks. IoT devices may not be able to handle heavy computing tasks due to their severely limited processing capability. In this paper, a multi-UAV deployment for mobile edge computing (MEC) enhanced IoT architecture is designed, where multiple UAVs are endowed with computing offloading services for ground IoT devices with limited local processing capabilities. In order to balance the load of UAV, this paper proposes a multi-UAV deployment mechanism which is based on the difference evolution (DE) algorithm. Meanwhile, the node access problem is formulated as a generalized assignment problem (GAP), and then an approximate optimal solution scheme is used to solve the problem. Based on this, we realize the load balance of multiple UAVs, guarantee the constraint of coverage range and meet the quality of service (QoS) of MEC network. Finally, sufficient simulations prove the effectiveness of our proposed multi-UAV deployment algorithm.

D2D-enabled Multicast Optimal Scheduling in mmWave Cellular Networks

Songling Zhang, Danpu Liu, Jie Lv and Zhilong Zhang (Beijing University of Posts and Telecommunications, China)

0
MmWave communications have been considered as the key enablers for future mobile cellular networks. The narrow beam communication of the mmWave large-scale antenna system suppresses co-channel interference and has greater potential for parallel communication and spatial multiplexing. Meanwhile, the introduction of multicast via point-to-multipoint communications further improves the spectrum efficiency for mmWave small cells. In order to reduce energy consumption, concurrent transmission and device-to-device (D2D)-enabled multicast has attracted great interests of many researchers, and some heuristic solutions have been provided. In this paper, we propose an optimal D2D-enabled multicast scheduling policy (OSP) aiming to minimize energy consumption in mmWave cellular networks. The optimal scheduling strategy is obtained by formulating the joint optimization of D2D pairing, parallel link selection and time slot allocation into an integer linear program (ILP) problem. Specifically, the concurrent transmissions which globally maximizes the spatial sharing gain while consuming the least energy at each time slot are identified by solving the ILP problem. Extensive simulations results demonstrate that our proposed algorithm reduces energy consumption by more than 18% compared with the baselines in all cases.
Session Chair

Haipeng Yao, Jinlong Sun

Session CIS-03

Network Security 2

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Secure Transmission Based on Non-Overlapping AOA in Cell-Free Massive MIMO Networks

Jiahua Qiu (Army Engineering University of PLA); Kui Xu and Xiaochen Xia (Army Engineering University of PLA, China)

1
In this paper, we mainly study the secure transmission method of cell-free massive multiple-input multiple-output (MIMO) system under active eavesdropping. In cell-free massive MIMO system, active pilot attacks will seriously affect the uplink channel estimation and secure transmission. In order to effectively reduce the impact of active pilot attacks on downlink transmission, this paper proposes a channel estimation algorithm based on the non-overlapping angle of arrival (AOA) to improve channel estimation accuracy. First of all, we propose an access point (AP) selection strategy based on AOA information to select APs that provides services for users. Then according to non-overlapping AOA between legitimate user and eavesdropper, we use discrete Fourier transform (DFT) to distinguish the uplink channels of legitimate user and eavesdropper from the angle domain, thereby eliminating the pilot contamination caused by active pilot attacks. Finally, we obtain the downlink transmission secrecy rate of cell-free massive MIMO system under the multipath channel model. The results show that the proposed channel estimation algorithm can reduce the estimation error by 2-10 dB compared with least-square (LS) estimation, and can increase the secrecy rate of 4 bit/s/Hz at most, which effectively enhances the secure transmission performance of cell-free massive MIMO in strong interference environment.

Secure Cognitive Communication via Cooperative Jamming

Keting Wu, Dawei Wang, Ruonan Zhang and Daosen Zhai (Northwestern Polytechnical University, China)

0
Nowadays, unmanned aerial vehicles (UAVs) are widely used to ensure the security of wireless communications. In this paper, we propose a cooperative jamming scheme to secure cognitive radio (CR) networks. In the proposed scheme, the source UAV transmits confidential information to ground users. Because of the presence of an eavesdropper (Eve), the UAV jammer sends friendly interference signals to protect private information. It should be noted that the primary transmission of CR networks cannot be disturbed. Therefore, we formulate an optimization problem to maximize the average secrecy rate under the constraint of primary interference. Since trajectory planning and power control are coupled, we propose the alternative optimization (AO) algorithm. Furthermore, as the sub-problems are non-convex, sequential convex approximation (SCA) algorithm is a good choice, which makes our scheme converge to the Karush-Kuhn-Tucker point. Simulation results show that the joint optimization of power and trajectory of our proposed scheme is effective for improving the systematic secure performance.

A Secure Transmission Scheme Based on Efficient Transmission Fountain Code

Le Chai, Pinyi Ren and Qinghe Du (Xi'an Jiaotong University, China)

0
Improving the security of data transmission in wireless channels is a key and challenging problem in wireless communication. This paper presents a data security transmission scheme based on high efficiency fountain code. If the legitimate receiver can decode all the original files before the eavesdropper, it can guarantee the safe transmission of the data, so we use the efficient coding scheme of the fountain code to ensure the efficient transmission of the data, and add the feedback mechanism to the transmission of the fountain code so that the coding scheme can be updated dynamically according to the decoding situation of the legitimate receiver. Simulation results show that the scheme has high security and transmitter transmission efficiency in the presence of eavesdropping scenarios.

Precoding and Destination-aided Cooperative Jamming in MIMO Untrusted Relay Systems

Luyuan Zhang (Beijing University of Posts and Telecommunications, China); Hang Long (Beijing University of Posts & Telecommunications, China); Li Huang (Beijing University of Posts and Telecommunications, China)

1
In this paper, secrecy communications with cooperative jamming in a two-hop one-way untrusted relay system is studied. Assuming that all nodes are equipped with multiple antennas, new precoding designs are proposed to increase the secrecy capacity. Two situations are considered, namely only designing precoding vector for jamming signal when user signal has been determined, and designing precoding vectors for both user and jamming signals by jointly considering the source node. For each situation, two alignment algorithms are proposed, namely zero-forcing-based (ZF) alignment algorithm and minimum-mean-squared-error-based (MMSE) alignment algorithm. Both algorithms are designed to ensure that the equivalent wiretap channel matrix forms two aligned vectors. The channel vector from the destination node is designed to be aligned with that from the source node, so that the eavesdropping node cannot decode user signals. Analytical and simulation results show that all proposed scheme can achieve secure communication, and the MMSE algorithm performs better than the ZF. In addition, the destination-relay link has greater impact on the secrecy performance than the relay-destination link.

Impact of Cooperative Attack on User Scheduling in Massive MIMO Systems

ShaoDi Wang and Hui-Ming Wang (Xi'an Jiaotong University, China)

0
In this paper, the downlink user scheduling design of a massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active attackers. A large-scale antenna array base station (BS) with zero-forcing (ZF) precoders transmits confidential information to multiple scheduled users via random user selection. Meanwhile, multiple active attackers collaboratively contaminate the uplink channel estimates by sending pilot sequences identical to those of the legitimate users, aiming at minimizing the number of scheduled users of the legitimate system. First, we evaluate the impact of this cooperative attack on the uplink channel training results and derive an analytical expression for the achievable downlink sum-rate of ZF precoders aided massive MIMO system. Then, relying on the random matrix theory (RMT)-based large-system analysis, we derive a deterministic approximation of the achievable downlink sum- rate. Furthermore, we formulated an optimization problem from the standpoint of the attackers to minimize the number of the scheduled users. Numerical results verify the correctness of the theoretical analyses, and reveal that cooperative attack has a great impact on the downlink user scheduling design.
Session Chair

Qinghe Du, Ning Zhang

Session CT-01

Vehicular Communications

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Invited Paper: Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning

Jidong Zhang (Guangdong Communictions and Networks Institute, China); Yu Yu (South China University of Technology, China); Zhigang Wang (Guangdong Communictions and Networks Institute, China); Shaopeng Ao, Jie Tang and Xiuyin Zhang (South China University of Technology, China); Kai Kit Wong (University College London, United Kingdom (Great Britain))

0
In this paper, a UAV-assisted wireless powered communication system for IoT network is studied. Specifically, the UAV performs as base station (BS) to collect the sensory information of the IoT devices as well as to broadcast energy signals to charge them. Considering the devices' limited data storage capacity and battery life, we propose a multi-objective optimization problem that aims to minimize the average data buffer length, maximize the residual battery level of the system and avoid data overflow and running out of battery of devices. Since the services requirements of IoT devices are dynamic and uncertain and the system can not be full observed by the UAV, it is challenging for UAV to achieve trajectory planning. In this regard, a deep Q network (DQN) is applied for UAV's flight control. Simulation results indicate that the DQN-based algorithm provides an efficient UAV's flight control policy for the proposed optimization problem.

Task Offloading for Vehicular Edge Computing: A Learning-Based Intent-Aware Approach

Wenxuan Kong (North China Electric Power University, China); Lurui Jia (School of Electric and Electronic Engineering, North China Electric Power University, China); Zhenyu Zhou (North China Electric Power University & Waseda University, China); Haijun Liao (North China Electric Power University, China)

0
Air-ground integrated vehicular edge computing (AGI-VEC) has emerged as an effective solution for task processing in vehicular networks. However, due to vehicle mobility, the network topology and available computing resources vary rapidly and are difficult to predict. In this paper, we develop a novel task offloading framework for AGI-VEC, which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. Simulation results confirm that the proposed algorithm can approach the optimal performance.

Graph-based Resource Allocation For V2X Communications In Typical Road Scenarios

Yang Jiang, Shangjun Hao and Qingwen Han (Chongqing University, China)

0
As one of the ad hoc networks, VANET (Vehicular Ad-hoc Network) also faces the conflict between user numbers and resource limits. Moreover, dynamic topology features and limited frequency resources bring a representative problem, which deadly influence VANET performance. In general, two factors influence the efficiency of resource allocation. The one is system structure, while the other is a resource allocation algorithm. In this paper, a hierarchical structure, which includes fixed control layer and mobile control layer, is designed to realize allocation control, while the graph coloring method is used to allocate frequency points. Simulation results show that the proposed method could improve network performance and able to allocate resources reasonably.

Joint 3D Placement and Power Allocation for UAV-aided MIMO-NOMA Networks

Fusheng Zhu (GuangDong Communications & Networks Institute, China); Zhigang Wang (Guangdong Communictions and Networks Institute, China); Wanmei Feng, Jie Tang, Yuan Liu and Xiuyin Zhang (South China University of Technology, China)

0
This paper studies an unmanned aerial vehicle (UAV)-aided Multiple-input-Multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV acts as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices. To improve the transmission efficiency, the sum rate of all IoT devices can be maximized by jointly optimizing the three-dimensional (3D) position of the UAV, beam pattern and transmit power. To tackle this problem, we first transform the non-convex problem into a total path loss minimization problem, and then the standard convex optimization techniques is applied to obtain the optimal 3D placement of the UAV. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is proposed for achieving high steering performance of multi-beams. Finally, the closed-form expression of transmit power is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show the significant performance gains in terms of sum rate of all IoT devices can be achieved by the proposed algorithm.

Reliability Performance of Transmitter Selection in Wireless Vehicular Networks

Zhifeng Tang (The Australian National University & Australian National University, Australia); Zhuo Sun, Chunhui Li and Nan Yang (The Australian National University, Australia)

0
In this paper, we propose a novel and simple transmitter selection criterion to enhance the reliability performance of downlink transmission in a wireless vehicular network. In this network, the Manhattan-type urban street model is adopted such that the location of horizontal and vertical streets is generated by two independent and identical Poisson Point Processes (PPPs). Moreover, the location of vehicles on each street is modeled by a one-dimensional PPP and base stations (BSs) are located at the intersection of streets. According to the proposed criterion, the vehicle receives signals from either the nearest front vehicle via dedicated short range communications or the nearest front BS via cellular communications. Considering the generalized Nakagami-m fading, we derive a new easy-to-compute expression for the coverage probability of the signal-interference-plus-noise ratio of the vehicle at the origin. Aided by numerical results, we demonstrate the accuracy of the derived expression and explicitly show the performance advantage of the proposed criterion. In addition, we find that there is an optimal vehicle intensity to maximize the coverage probability.
Session Chair

Chunhui Li, Zhuo Sun

Session IoT-03

Signal and Information Processing

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

An Intelligent Prediction Method for Device Status Based on IoT Temporal Knowledge Graph

Shujuan You and Xiaotao Li (China Mobile Research Institute, China); Wai Chen (CMRI, USA)

0
With the development of Internet of Things (IoT) technology, unattended operation of devices has become an important feature of IoT, which requires devices to perform proper actions to provide services without human intervention. To achieve the unattended operation of IoT, a major challenge is how to accurately predict the actions that the device will perform and meet the personalized requirements of users. To address this challenge, we propose a novel method to predict device status based on the IoT temporal knowledge graph (TKG) and the long short term memory (LSTM) model. We firstly build a TKG for the IoT to provide rich semantic information for the objects and the continuously changing time series data in the IoT. Then, leveraging the advantages of LSTM in sequence learning, the timing characteristics of semantic information in TKG are learned to realize intelligent prediction of the equipment status. To verify the effectiveness of our method, we conducted an experimental verification of device status prediction in a smart home use case, and the experimental results show that our method achieves the state-of-the-art performance.

Access Authentication Architecture Design of Industrial Internet Identification and Resolution System

Baoluo Ma (CAICT, China)

0
Identification and resolution system is an important part of the industrial Internet, is the key hub to realize the information exchange of all elements and links of industry, and is one of the core infrastructure of the safe operation of the industrial internet. As an important content of industrial internet security, Identification and resolution security is an important guarantee for the healthy development of industrial Internet. It has become the most important issue in the deployment of identification and resolution system. This article is oriented to identity resolution node access authentication, an industrial internet identification and resolution node access authentication architecture is proposed, and analyzes the authentication business process of identity application, identity registration and identity resolution, to provide ideas and references for the construction of industrial internet identification and resolution security certification capacity.

Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis

Ruyan Wang and Zhongyuan Tao (Chongqing University of Posts and Telecommunications, China)

0
The sentiment analysis of the data collected by Internet of Things devices has been widely concerned by researchers. Aspect sentiment analysis is a fine-grained task of sentiment analysis, which aims to identify the sentiment polarity of specific aspects in a given context. Most of the previous studies have modelled context and aspect terms using RNN and attention mechanisms. However, the RNN model is difficult to process in parallel, taking into account only the global context features, not the correlation between the sentiment polarity and the local context. To address this issue, this paper proposes an Interactive Attention Encoder Network Model with Local Context Features (IAEN-LCF) for identifying aspect-level sentiment polarity. First, the word embedding and aspect term embedding are pre-trained by Bidirectional Encoder Representations from Transformers (BERT). Secondly, the attention-over-attention (AOA) module in the machine reading comprehension task is applied to the attentional encoder network, and a network model named Interactive Attention Encoder (IAEN) is proposed to extract global context features. By setting a fixed text window, the local context features are captured in a dynamic weighted manner. Finally, the performance of the proposed model is verified in three public data sets. Experimental results show that the proposed model can outperform state-of-the-art methods in aspect sentiment analysis tasks.

Fast Recovery of Low-Rank and Joint-Sparse Signals in Wireless Body Area Networks

Yanbin Zhang (Beijing University of Posts and Telecommunications, China); Longting Huang (Wuhan University of Technology, China); Yangqing Li, Kai Zhang and Changchuan Yin (Beijing University of Posts and Telecommunications, China)

1
E-health monitoring signals collected from wireless body area networks (WBANs) usually have some highly correlated structures in a certain transform domain (e.g., discrete cosine transform (DCT)). We exploit these structures and propose a fast recovery algorithm for low-rank and joint-sparse (L&S) structured WBAN signal in the framework of compressed sensing (CS). By using a simultaneously L&S signal model, we employ the number of the bigger singular values and Bayesian learning which incorporates an L&S-inducing prior over the signal and the appropriate hyperpriors over all hyperparameters to recover the signal. Experiments show that the proposed algorithm has a superior performance to state-of-the-art algorithms.

Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network

Xuexin Zhao, Junhua Wu, Maoli Wang, Guangshun Li, Haili Yu and Wenzhen Feng (Qufu Normal University, China)

0
Multi-sensor data fusion technique plays a key role in the agricultural services such as data collection and processing. However, the collected data usually is featured by redundancies and errors, which deteriorate the reliability of network. In this paper, based on adaptive trust estimation, we propose a multi-sensor data fusion algorithm (T-NN) in neural network, aiming to solve the problem of low accuracy and poor stability of multi-sensor data fusion. In particular, the original data collected by the sensors first are pre-processed by exponential smoothing. Then, trust estimation model is applied to calculate the value of trust among the sensing nodes and optimize the data, and the performance of redundancy and reliability are enhanced. Furthermore, the data optimized is introduced into BP neural network for training and fusion. Extensive simulations show that the algorithm proposed in this paper greatly outperforms adaptive weighted average model and traditional BPNN model, in terms of the accuracy of data fusion.
Session Chair

Kehao Wang, Hui Cao

Session MWN-03

Internet of Vehicles

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Convergence Estimation of Ergodic Capacity for Vehicle-Mounted APs:Large Deviation Theory

Jun Dai (Huazhong University of Science and Technology, China); Lijun Wang (Wuhan University & Wenhua College, China); Wei He and Tao Han (Huazhong University of Science and Technology, China)

1
In the practical scene of Vehicular Ad-Hoc Network, due to diverse geographical topologies, complex landform forms and the powerful dynamics vehicles, mobile access points (APs) are more powerful than roadside units in processing and communication abilities, especially the ability to traverse the network, and also have lower installation and operating costs. In this paper, we propose a method for estimating the ergodic capacity of vehicle-mounted APs, and the convergence reflected by the rate function can be used to know the ceiling of the ergodic capacity, which is of great importance for the performance analysis. We match the actual constraints of mobile APs better with tracking data of the real road networks, and present a fundamental study on the convergence rate of ergodic capacity based on large deviation theory. Through analysis, the connectivity of vehicle-mounted APs selection varies from the theoretical model, due to changes in regional functions and actual geographic relationships at different locations (such as elevated road and circular road). Numerical results validate the theoretical analysis that ergodic capacity converges with the approximate exponential rate function asymptotically and demonstrate that the proposed method has near-actual performance in terms of system SINR and trajectory heterogeneity.

Machine Learning based Resource Allocation Strategy for Network Slicing in Vehicular Networks

Yaping Cui, Xinyun Huang and Dapeng Wu (Chongqing University of Posts and Telecommunications, China); Hao Zheng (CQUPT, China)

0
To deal with the lack of prediction and management for vehicular network slice in existing research, this paper designs a machine learning based resource allocation strategy for vehicular network slicing. Firstly, a traffic prediction mechanism based on Convolutional Long Short Term Memory (ConvLSTM) is proposed, which will capture the spatial-temporal dependencies of the traffic to predict traffic of complex slice services in the vehicular networks. Secondly, considering the imbalance of wireless resource utilization caused by the space-time difference between application scenarios, a shared proportional fairness scheme is proposed to achieve efficient and differentiated utilization of wireless resources. Finally, on the basis of ensuring the demand of each slice, the resource allocation algorithm based on the primal-dual interior-point method is used to solve the optimal slice weight allocation to minimize the system delay. Simulation results show that the service traffic prediction mechanism can be used to predict service traffic in the future. The average error rates of SMS, phone, and web traffic will be reduced, so that the user load distribution can be obtained a priori. Based on the predicted load distribution, slice weight distribution is performed in advance so that arranging delay is saved. The resource allocation algorithm based on the primal-dual interior-point method can well calculate the optimal slice weight distribution at this time.

Q-Learning Based Task Offloading and Resource Allocation Scheme for Internet of Vehicles

Fan Jiang and Wei Liu (Xian University of Posts and Telecommunications, China); Junxuan Wang (Xi'an University of Posts and Telecommunications, China); Xinying Liu (Keysight Technologies (China) CO., LTD, China)

1
In this paper, the task offloading and resource allocation problem for the Internet of Vehicles (IoV) is investigated. In our considered offloading scheme, a Bayesian classifier is first adopted to classify the task according to its different requirements in latency and energy consumption. Based on the classification results, each vehicle user equipment (VUE) then selects the corresponding offloading mode. More specifically, if the VUE has higher requirements for energy consumption, the task will be carried out at other vehicles through the vehicle to vehicle (V2V) offloading mode. Otherwise, it will choose to offload the task through mobile edge computing (MEC) offloading mode. To achieve a trade-off between latency requirement and energy consumption in the task executing process through offloading decision, we formulate the offloading and resource allocation scheme as a mixed integer non-linear problem. To obtain an approximate solution, a Q-learning based solution is proposed. Simulation results demonstrate that the proposed scheme has better performance in terms of higher system throughput, lower latency, and lower energy consumption compared with the existing schemes.

Design and Optimization of Edge Computing for Data Fusion in V2I Cooperative Systems

Qun Zhang, Zhiyong Chen and Bin Xia (Shanghai Jiao Tong University, China); Xin Jiang and Chengfeng Xiong (China Mobile (Shanghai) Industry Research Institute, China)

0
Real-time data fusion combining with information from vehicles and roadside unit (RSU) is a promising solution to promote traffic safety and efficiency. In this paper, we design a multi-source data fusion scheme in edge computing-enabled vehicle-to-infrastructure (V2I) cooperative systems, where data fusion can be processed at RSU or vehicle. In order to balance the tradeoff between the vehicle speed and the fusion range, we define a new performance metric, namely fusion gain. We formulate the jointly data offloading decision, fusion range and computing resource allocation problem for maximizing the system fusion gain while minimizing local and edge computational resource consumption. We reformulate the stated problem and design a substitution-knapsack algorithm to reach a sub-optimal solution. Numerical results show that the proposed scheme has a significant performance gain and effectively promotes system utility in varying traffic environments.

A Novel 3D Non-stationary Channel Model with UPA for Massive MIMO V2V Communication in Crossroads Scattering Environments

Fan Liu, Nan Ma, Jianqiao Chen and Lulu Gu (Beijing University of Posts and Telecommunications, China)

0
In this paper, we propose a novel three-dimensional (3D) non-stationary geometry-based channel model with a uniform planar antenna array (UPA) for massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication in crossroads scattering environments. Considering the near-field effect, we first calculate the received phases and Doppler frequency variations caused by movements under the spherical wavefront assumption. In this case, we adopt a two-ring and four-quarter-cylinder model to characterize the moving vehicles and static scatterers on the building, respectively. Then, we derive the closed-form expressions of channel impulse responses (CIRs) of the proposed model and discuss statistical properties in detail, e.g., temporal auto-correlation function (ACF) and spatial cross-correlation function (CCF). Furthermore, the impacts of the movements, distribution of scatterers and setting of UPA on channel statistical properties are studied. Finally, our numerical and simulation results show that our proposed model with limited scatterers can capture the characteristics of massive MIMO V2V communication in crossroads scattering environments while agreeing well with the theoretical model, thereby demonstrating the efficiency of our model.
Session Chair

Yaping Cui, Fan Jiang

Session NGNI-03

Security in Future Generation Networking

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Identity-based Secret Sharing Access Control Framework for Information-Centric Networking

LiangLang Deng and Jiangtao Luo (Chongqing University of Posts and Telecommunications, China); Jie Zhou and Junxia Wang (Chongqing University of Posts and Telecommunication, China)

0
Information-centric networking (ICN) has played an increasingly important role in the next generation network design. However, to make better use of request-response communication mode in the ICN network, revoke user privileges more efficiently and protect user privacy more safely, an effective access control mechanism is needed. In this paper, we propose IBSS (identity-based secret sharing), which achieves efficient content distribution by using improved Shamir's secret sharing method. At the same time, collusion attacks are avoided by associating polynomials' degree with the number of users. When authenticating user identity and transmitting content, IBE and IBS are introduced to achieve more efficient and secure identity encryption. From the experimental results, the scheme only introduces an acceptable delay in file retrieval, and it can request follow-up content very efficiently.

A Security Trust Mechanism for Data Collection with Mobile Vehicles in Smart City

Qingyong Deng (XiangTan University, China); Shaobo Huang, Shujuan Tian, Haolin Liu and Jianglian Cao (Xiangtan University, China); Shuwen Jia (University of Sanya, China)

0
Smart city includes all kinds of advanced technologies and solutions, whose construction is based on the collection of data. In Delay Tolerant Network (DTN), sensors lacking connectivity can store data temporarily and wait for the mobile vehicles in the city to forward, which greatly improves the efficiency of data collection in the city. However, mobile vehicles are not always considered credible, the attack of malicious vehicles may lead to the failure of city management. Therefore, there is an urgent need for a security data collection strategy with mobile vehicles in DTN. In this paper, a Consistency Trust Verification strategy for Mobile Vehicles (CTV-MV) is proposed, including three stages: opportunistic routing stage, recruitment stage, and trust verification stage. Specifically, an Average Distance based Outlier Detection (ADOD) algorithm is designed as a consistency mechanism of heterogeneous data. Then a Baseline mechanism is used to trust reasoning for evaluating the trust vaule of mobile vehicles. Furthermore, a recruitment strategy that considers trust and data coverage ratio is proposed to maximize data quality as much as possible. Finally, the performance of CTV-MV is analyzed through experiments in terms of excellent ratio of data and recruit cost, respectively.

Multi-dimensional Security Risk Assessment Model Based on Three Elements in the IoT System

WenJie Kang (National University of Defense Technology, China); JiaLe Deng and PeiDong Zhu (Changsha University, China); Xuchong Liu (Hunan Police Academy, China); Wei Zhao (National University of Defense Technology & Hunan Police Academy, China); Zhi Hang (Key Laboratory of Hunan Province for Mobile Business Intelligence, China)

0
In order to manage and control the risk of the Internet of things (IoT) system, we first propose a multi-dimensional security risk assessment model based on three elements, which evaluates the security risk from different dimensions of assets, threats and vulnerabilities. Secondly, we design the mathematical assessment model for computing risk value of IoT system and establish the mapping relationship table that the risk value is transformed into risk level. Thirdly, according to the risk level of a certain dimension of the IoT system, defenders can decide to implement the risk plan and execute the risk management until the risk level was reduced. Finally,the real data of IoT company is used to evaluate the risk level of IoT system. The research results show that the method can obtain the risk values of all measurement dimensions, which further verifies the effectiveness and practicability of the method.

HABEm: Hierarchical Attribute Based Encryption with Multi-Authority for the Mobile Cloud Service

Qian He (Guilin University of Electronic Technology, China); Jing Song (Guilin University Of Electronic Technology, China); Hong Xu and Yong Wang (Guilin University of Electronic Technology, China)

1
For the mobile cloud service, the computation and power resources of the mobile terminal are limited. The normal attribute-based encryption has the uncertainty of the attribute expiration time, and it may result in user privacy leakage and the waste of computing and broadband resources. To deal with these problems, a hierarchical attribute-based encryption with multi-authority for the mobile cloud service (HABEm) is proposed in this paper. A hierarchical multi-level authorization mechanism, referring to different levels of authorities, manages different attributes of mobile terminal. A proxy is introduced to delegate the high complexity decryption algorithm to improve the decryption efficiency of the mobile terminal. The mobile terminal performs attribute revocation through the authorization authority when the rank of the mobile terminal is changed. Based on the deterministic assumption of the standard model, HABEm is proved to be CPA-safe in theory. The experiment results show that HABEm has higher decryption performance and is very suitable for the mobile cloud service environment.

User Authentication Leveraging Behavioral Information using Commodity WiFi devices

Shulin Yang, Yantong Wang, Xiaoxiao Yu and Yu Gu (Hefei University of Technology, China); Fuji Ren (The University of Tokushima, Japan)

0
User authentication is a major area of interest within the field of Human Computer Interaction (HCI). Meanwhile, it prevents unauthorized accesses to certain the security of data. Personal Identification Number (PIN) and biometrics are the main approaches for identifying the user on the basis of his/her identity. However, PIN can be easily leaked to others, and biometrics usually require specialized devices. In this paper, we prototype our system, a new method for user authentication by leveraging commodity WiFi. The basic methodology is to explore the typing habit of users from Channel State Information (CSI). The design and implementation of our system face two challenges, i.e. extracting keystroke features from wireless channel data and authenticating the user via typing habit from the corresponding keystroke features. For the former, we capture signal fluctuations caused by the micro movements like typing and extract the keystroke features on channel response obtained from commodity WiFi devices. For the latter, we design a computational intelligence driven mechanism to authenticate users from the corresponding keystroke feature. We prototype our system on the low-cost off-the-shelf WiFi devices and evaluate its performance in real-world experiments. We have explored four classifiers including K Nearest Neighbor(KNN), Support Vector Machine (SVM), Random Forest, and Decision Tree for recognizing users. Empirical results show that KNN provides the best performance, i.e., 85.2% authentication accuracy, 12.8% false accept rate, and 11.2% false reject rate on average over 9 participants.
Session Chair

Wenzheng Li, Licheng Wu

Session SAC-03

Resource Allocation

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Computation Resource Allocation in Mobile Blockchain-enabled Edge Computing Networks

Yiping Zuo (Southeast University, China); Shengli Zhang (Shenzhen University, China); Yu Han and Shi Jin (Southeast University, China)

1
In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

Collaborative Anomaly Detection for Internet of Things based on Federated Learning

Seongwoo Kim, He Cai, Cunqing Hua and Pengwenlong Gu (Shanghai Jiao Tong University, China); Wenchao Xu (Caritas Institute of Higher Education, Hong Kong); Jeonghyeok Park (Shanghai Jiao Tong University, China)

1
In this paper, we propose a federated learning(FL)-based collaborative anomaly detection system. This system consists of multiple edge nodes and a server node. The edge nodes are in charge of not only monitoring and collecting data, but also to train an anomaly detection neural network classification model based on the local data. On the other hand, the server aggregates the parameters from the edges and generates a new model for the next round. This system structure achieves light weight transmission between the server and the edge nodes, and user privacy can be well protected since raw data are not communicated directly. We implement the proposed scheme in the practical system and present experimental results that demonstrate results competitive with those of state-of-the-art models.

Super-resolution Electromagnetic Vortex SAR Imaging Based on Compressed Sensing

Yanzhi Zeng, Yang Wang, Chenhong Zhou, Jian Cui and Jinghan Yi (Chongqing University of Posts and Telecommunications, China); Jie Zhang (University of Sheffield, Dept. of Electronic and Electrical Engineering, United Kingdom (Great Britain))

0
Electromagnetic (EM) vortex wave carrying orbital angular momentum (OAM) can potentially be utilized to achieve azimuthal super-resolution in synthetic aperture radar (SAR) imaging realms. This contribution proposes an imaging algorithm based on the Compressed Sensing (CS) theory for the side-view EM vortex strip-map SAR. Firstly, the observation geometry and the echo signal model are described and established. Subsequently, the imaging algorithm, including Bessel function compensation, range compression and azimuth process, is carried out to realize the two dimensional (2D) joint detection of the point target in the range and azimuth domain. Simulation results validate the effectiveness of the presented algorithm, and the different CS reconstruction algorithms for multi-target imaging are analyzed as well. Compared with the existing traditional Range Doppler (RD) algorithm, this proposed method can achieve superior azimuth resolution for target identification, which not only solve the problem of target's azimuth profile at high side-lobe levels, but also reduce the cost of radar hardware system. The work and results provide suggestions to the development of forthcoming and new-generation EM vortex SAR imaging technology.

Performance and Cost of Upstream Resource Allocation for Inter-Edge-Datacenter Bulk Transfers

Xiao Lin (Fuzhou University, China); Junyi Shao and Ruiyun Liu (Shanghai Jiao Tong University, China); Weiqiang Sun (Shanghai Jiaotong University, China); Weisheng Hu (Shanghai Jiao Tong University, China)

1
Emerging edging computing services and applications bring an unprecedented demand for bulk transfers at the network edges. However, the expensive access cost makes it difficult to deliver bulk data between geo-distributed-edge-datacenter. In this paper, storage in edge-datacenter is introduced into the transmission scheme for temporarily storing delay-tolerant bulky flows. We formulate the upstream resource allocation as a stratified multi-objective optimization model, which can adjust the spectrum and storage allocation between latency-critical flows and delay-tolerant flows. Our studies reveal that 60% of the system cost can be saved by trading cost-effective storage for expensive spectrum resources.

A Modification of UCT Algorithm for WTN-EinStein w¨¹rfelt nicht! Game

Xiali Li, Yingying Cai, Luyao Yu, Licheng Wu, Xiaojun Bi, Yue Zhao and Bo Liu (Minzu University of China, China)

0
WTN-EinStein w¨¹rfelt nicht! (abbreviated as EWN) chess game has been attracting much attention owing to its characteristics of randomness and incompleteness. In this study, a modified upper confidence bounds applied to trees (UCT) algorithm is proposed by optimizing selection strategy, simulation of tree nodes and establishing the game tree based on probabilistic rules and natural characteristics of the chess game. Experimental results verify that the program applying the modified UCT algorithm can greatly improve winning rate compared with others with plain UCT or Monte Carlo algorithms. The program won the first prize in 2019 Chinese University Student Computer Games Competition and 13th National Computer Games Tournament.
Session Chair

Yanpeng Dai

Session SPC-03

Machine Learning

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Deep Learning based Intelligent Recognition Method in Heterogeneous Communication Networks

Hao Gu (`Nanjing University of Posts and Telecommunications, China); Yu Wang and Sheng Hong (Nanjing University of Posts and Telecommunications, China); Yongjun Xu (Chongqing University of Posts and Telecommunications, China); Guan Gui (Nanjing University of Posts and Telecommunications, China)

0
Friendly signal coexistence problem over unlicensed bands has been received strongly attention in design next-generation wireless communication systems. Typically, it is very challenge to recognize wireless fidelity (WiFi) signal and long-term evolution (LTE) signal over the unlicensed bands (LTE-U) in heterogeneous communication networks. The main reason is that LTE-U may occupy the spectrum resources of WiFi. Hence it is necessary to solve this problem and then to lay the foundation for the friendly coexistence of LTE-U and WiFi technology. In this paper, we proposed a deep learning based intelligent recognition method for identifying LTE-U and WiFi signals in heterogeneous communication networks. First, we collect LTE-U and WiFi signal samples and introduce random phase offset and two data forms to them. Second, we use deep learning algorithms to train these samples to get the best preprocessing method and neural network algorithm parameters. Finally, experiments are conducted to show that our proposed method can efficiently recognize LTE and WiFi signals with excellent recognition accuracy and robustness.

Massive MIMO Data Detection Using 1-dimensional Convolutional Neural Network

Isayiyas Nigatu Tiba and Ben Baraka Kulimushi (Xidian University, China); Chrianus Kajuna (St. Joseph University in Tanzania, Tanzania)

0
In this work, we explore the use of an adaptive one-dimensional convolutional neural network (1d-CNN) for the massive multiple-input multiple-output (MIMO) data detection. To be able to detect under the randomly varying channel scenario, we employ a data augmentation approach along with the convolutional networks. Our method is simple, and a non-iterative which works by unfolding the potential of deep networks. We construct datasets corresponding to 100s of base station antennas serving 10s of transmit antennas to show that increasing the number of base station antennas will also increase the learning ability of the network by proving the relevant information. We will show through simulation that the proposed method can significantly improve the learning and ability to achieve competitive performances compared to the traditional detectors.

Location Aided Intelligent Deep Learning Channel Estimation for Millimeter Wave Communications

Xintong Lin, Lin Zhang and Yuan Jiang (Sun Yat-sen University, China)

0
Millimeter wave (MMW) communication provides a promising solution for high data rate services thanks to the wide MMW bandwidth. However, the channel conditions may vary more dramatically due to MMW transmissions, thus MMW receivers require the intelligent channel estimation with the low complexity to attain reliable and high data rate performances. In this paper, considering the characteristic property of the line of sight transmission over MMW bands, we propose to utilize the location information to evaluate the channel frequency response (CFR) together with the deep learning method based on the propagation model. In our design, we consider the scenario of the 60 GHz wireless local area network (WLAN) systems. At the receiver, the deep neural network (DNN) used for the channel estimation (CE) is trained offline using the pilots and location coordinates as inputs and the known CFRs as outputs. Then at the online deployment stage, with the trained neural network architecture, MMW receiver can retrieve the CFR intelligently. Simulation results demonstrate the proposed location aided DNN channel estimation can achieve lower normalized mean square error (NMSE), while providing intelligent CE with lower complexity.

Message Structure Aided Attentional Convolution Network for RF Device Fingerprinting

Lintianran Weng and Jianhua Peng (PLA Strategic Support Force Information Engineering University, China); Jinsong Li (People's Liberation Army Strategic Support Force Information Engineering University & National Digital Switching System Engineering and Technological Center, China); and Yuhang Zhu (PLA Strategic Support Force Information Engineering University, China)

0
RF device fingerprinting has become an emerging technology which identifies the device-specific fingerprint based on inherent defects in the hardware. However, existing methods pay little attention to the potential improvement of rough priori information such as message structure on the identification performance. In this paper, we propose a message structure aided attentional convolution network (MSACN) for RF device fingerprinting. Portions with different pulse waveform distribution are separated and fed into the identification network. The network extracts and merges the feature map contained in multiple data blocks, which is helpful to explore the internal relation of data. Furthermore, we design a spatial attention mechanism for low-dimensional discrete signals to pursue more efficient feature fusion. Experimental results on the dataset of real-world ADS-B transmissions show that MSACN can achieve 98.20% identification accuracy outperforming previous works.

Research on Human Activity Recognition Technology under the Condition of Through-the-wall

Ruoyu Cao, Xiaolong Yang, Zhenhua Yang, Mu Zhou and Xie Liangbo (Chongqing University of Posts and Telecommunications, China)

0
Wi-Fi-based human activity recognition is playing a critical role in wireless sensing. However, the existing through-wall human activity recognition method does not fully analyze the influence of the wall on the signal, which results in poor robustness of the Wi-Fi indoor human activity recognition system. In order to solve this problem, this paper proposes a Wi-Fi based activity recognition algorithm under through-the-wall scenarios. First, the distribution of Wi-Fi signals in the presence of wall barriers is analyzed according to the Wi-Fi signal model. Then, according to the distribution characteristics of different Wi-Fi signals, the principal component analysis (PCA) algorithm is used to reconstruct the signal to complete the de-nosing processing of the Wi-Fi signal. Finally, feature extraction and feature classification in the time-frequency domain is performed to complete the human activity recognition. It is worth mentioning that in terms of feature extraction, we innovatively use the empirical mode decomposition (EMD) algorithm to extract the difference in time series of similar actions. Experimental results show that the system achieves an average accuracy of 95.82 percent in through-the-wall scenarios.
Session Chair

Jiang Xue, Feifei Gao

Session WCS-03

Intelligence Communications II

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Dynamic Spectrum Access Scheme of Joint Power Control in Underlay Mode Based on Deep Reinforcement Learning

Xiping Chen, Xie Xian-Zhong, Zhaoyuan Shi and Zishen Fan (Chongqing University of Posts and Telecommunications, China)

0
With the increasing complexity of wireless networks and the increasing shortage of spectrum resources, a novel dynamic spectrum access (DSA) solution is urgently needed. For complex and dynamic cognitive radio networks (CRN), this paper proposes a joint DSA and power control scheme based on deep reinforcement learning (DRL). In order to improve the convergence speed of the algorithm, the DRL is improved to a hierarchical DRL, centralized DSA is implemented through CBS, and distributed power control is implemented at each secondary user (SU). Sufficient simulation experiments show that the proposed algorithm has faster convergence speed and lower packet loss.

A Stacking Ensemble Learning Model for Mobile Traffic Prediction

Zhigang Li, Di Cai, Jialin Wang, Jingchang Fu, Linlin Qin and Duomin Fu (North China University of Science and Technology, China)

0
Mobile traffic prediction has been the foundation to enable effective network design and intelligent management. Machine learning methods has drawn extensive concern in this field. However, Many existing methods fail to reach a satisfactory outcome, due to the fact that but the performance of a single ML model is not always good. In this paper we develop a novel mobile traffic prediction model based on the stacking ensemble learning method. This model consists of two parts, i.e., a base learner with distributed multilayer perceptrons (MLPs) and a meta learner called the self-adaptive support vector regression model (SSVR). On real-world mobile traffic flows of various mobile applications at different base stations, we demonstrate that the proposed model is significantly superior to linear regression model in prediction performance. Besides, the statistical analysis methods verify this effectiveness.

Channel Estimation Based on Improved Compressive Sampling Matching Tracking for Millimeter-wave Massive MIMO

Yong Liao, Lei Zhao, Haowen Li, Fan Wang and Guodong Sun (Chongqing University, China)

0
In the millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems, the channel has a certain degree of sparsity, and the sparseness needs to be used as prior information which will bring that the selection of atoms during iteration is not flexible and large in number when the compressive sampling matching pursuit (CoSaMP) algorithm is used for channel estimation. Therefore, we propose an improved CoSaMP channel estimation algorithm called iCoSaMP. iCoSaMP uses a fuzzy threshold selection strategy to perform a second screening of the preselected atom index set after the preselection stage to ensure that the more relevant atoms constitute a new preselected atom index set. It can avoid the blind adjustment of the preselected atom set caused by the excessive adjustment of the sparseness, which leads to the increase of the algorithm calculation complexity, thereby improving the algorithm's reconstruction ability and reducing the algorithm calculation complexity, effectively reducing the redundancy of the preselected atom set. Simulation results show that the proposed algorithm has high reconstruction accuracy and low computational complexity, and can accurately recover mmWave massive MIMO channel information.

3D Deployment with Machine Learning and System Performance Analysis of UAV-Enabled Networks

Xuan Li and Qiang Wang (Beijing University of Posts and Telecommunications, China); Jie Liu (Beijing University of Post and Telecommunications, China); Wenqi Zhang (Beijing University of Posts and Telecommunications, China)

0
Exploring the base station (BS) placement in both horizontal and vertical directions is beneficial but challenging for unmanned aerial vehicle (UAV)-enabled wireless network. In this paper, we propose a three dimensional (3D) deployment approach for UAVs and analyze the system performance of finite UAV-enabled networks in which UAVs are equipped with BS. By modeling UAVs as a deep reinforcement learning (DRL) agent, we propose a novel framework to deploy UAVs in 3D space to maximize the network utility. Then utilizing tools from stochastic geometry, we model the locations of UAVs as binomial point process (BPP) and derive exact expressions of coverage probability for directional antennas and omnidirectional antennas equipped UAVs. The expressions are functions of UAVs' altitudes and sector angles. The analysis is meaningful for setting UAVs' altitude and sector angle of directional antennas. Simulation results show that 3D deployment of UAVs achieves a remarkable system performance and the analysis provides useful performance trends.
Session Chair

Xiaochuan Sun, Ning Zhang

Session WCS-06

Channel Modeling

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Experimental Performance of the Tri-Polarized MIMO Channel in UMi Scenario at 4.9 GHz

Zuolong Ying (Beijing University of Posts and Telecommunications, China); Yuxiang Zhang (Beijing University Of Posts And Telecommunications, China); Pan Tang, Zhen Zhang and Jianhua Zhang (Beijing University of Posts and Telecommunications, China); Lei Tian (Beijing University of Posts and Telecommunications & Wireless Technology Innovation Institute, China); Guangyi Liu (Research Institute of China Mobile, China); Yi Zheng (China Mobile, China)

0
Tri-polarized MIMO system can provide higher capacity, which has been proved theoretically and verified in some real simple indoor channel measurements. In this paper, we did the channel measurements using dipole to form tri-polarized MIMO antennas with 100 MHz bandwidth at 4.9 GHz carrier frequency in Urban Microcell (UMi) scenario. Typical channel propagation characteristics are analysed based on the channel measurement, including cross-polarization discrimination (XPD), correlation coefficient (CC), eigenvalue distributions (ED) of channel matrix, and capacity gain (CG). It can be observed that the tri-polarized MIMO channel has three non-zero eigenvalues which support three independent subchannels. It is worthy noted that in UMi scenario, there is a nearly threefold CG in both line-of-sight (LOS) and non-line-of-sight (NLOS) routes by analyzing CC and ED. This can be well explained by density buildings and low antenna heights in UMi scenario, which leads to rich scattering environment. Therefore, the tri-polarized MIMO is promising to improve the performance in rich- scattering scenarios, e.g., UMi scenario. The results can provide insights for the application of tri-polarized MIMO systems.

Ray-Tracing Based Millimeter-Wave Channel Characteristics in Subway Carriage

Zhiyi Yao, Xiong Lei and Haiyang Miao (Beijing Jiaotong University, China)

0
The rapid deployment of the fifth generation (5G) systems and the ever growing demand for high-density services have promoted our urgent research on the channel characteristics. Based on the ray-tracing (RT) simulation technology, this paper discusses the channel characteristics in subway carriage scenarios at 26 GHz and 38 GHz millimeter-wave (mm-wave) bands. Key parameters such as path loss, root mean square (RMS) delay spread, power angle spectrum, angular spread, and spatial correlation are investigated. In addition, the parameter of lateral distance from the longitudinal centerline of the carriage is introduced into the modified path loss model, to improve the prediction accuracy.

Channel Characteristics of Subway Station Based on Ray-Tracing at 5G mmWave Band

Haiyang Miao and Xiong Lei (Beijing Jiaotong University, China)

0
In order to satisfy the increasing demand for higher transmission capacity of ``smart subway", millimeter wave (mmWave) plays a significant role to provide high-data rate communication. In this paper, a three-dimensional (3D) model of subway station scenario based on the ray-tracing (RT) technology is exployed to study the channel characteristics at 5G mmWave band. Key channel parameters such as path loss, root mean square (RMS) delay spread, Ricean K-factor, angular spread, etc., are investigated. Then, the main factors affecting channel characteristics, including antenna position, antenna array, array element spacing, etc., were analyzed to provide some recommendations for the design of 5G communication network in urban rail traffic station.

A Novel 3D Non-stationary Single-twin cluster Model for Mobile-mobile MIMO Channels

Wangyong Ji, Zhi-Zhong Zhang and Haonan Hu (Chongqing University of Posts and Telecommunications, China)

0
In the traditional mobile-mobile (M2M) scenario, the dual non-stationary characteristics of the array domain and the time domain did not considered, where single or twin cluster link models are too simple to simulate actual scenarios. In this paper, a novel three dimensional (3D) single-twin cluster non-stationary multiple-input multiple-output (MIMO) channel model is proposed. The model simulates the non-stationary characteristics of the channel during the switching of the antenna array and the movement of the transceiver, meanwhile, an array-time domain channel parameter evolution algorithm is applied to this new model. In addition, based on the distribution of small-scale angle parameter spectrum obeying Von-Mise Fisher (VMF), the temporal and spatial correlation functions of the model are derived. The simulated results show the theoretical results closely match the simulation results and the novel model can simulate the single or twin cluster model well, which indicate that the novel channel model can be used as a design scheme for modeling single-twin cluster non-stationary MIMO channel.

A Low Complexity Joint Iterative Multi-User Detection Decoding Receiver Based on Verified Message

Jiansong Miao, Xuejia Hu, Weijie Li and Hairui Li (Beijing University of Posts and Telecommunications, China)

0
The sparse code multiple access technology and polar code technology can meet the functional requirements of the three major scenarios of 5G. A SCMA system receiver can combine a multi-user detector with a polar code decoder, but it requires high computational complexity to obtain the ideal bit error rate (BER) performance. This paper proposes a joint iterative detection and decoding receiver (VJDD) scheme for transmitting verified messages based on a serial structure. This solution improves the iterative convergence speed of the receiver by transmitting verified messages to each node in the factor graph in time. Simulation results show that the VJDD scheme can reduce the computational complexity of the receiver on the premise of ensuring the performance of the receiver.
Session Chair

Hao Jiang, Jianhua Zhang

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