Mobile and Wireless Networks

Session MWN-01


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

Multi-Source Data Fusion Method for Indoor Localization System

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

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)

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)

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)

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)

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 MWN-02

Edge Computing

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

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)

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)

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)

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)

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)

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 MWN-03

Internet of Vehicles

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

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)

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)

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)

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)

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)

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

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