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

Wireless Communications Systems

Session WCS-01

Intelligent Reflecting Surface

Conference
1:30 PM — 3:00 PM CST
Local
Aug 10 Mon, 1:30 AM — 3:00 AM EDT

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 10 Mon, 1:30 AM — 3:00 AM EDT

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 WCS-02

Intelligence Communications I

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 3:10 AM — 4:40 AM EDT

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, 3:10 AM — 4:40 AM EDT

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 WCS-03

Intelligence Communications II

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 4:50 AM — 6:20 AM EDT

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, 4:50 AM — 6:20 AM EDT

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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