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    FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Chengyue Lu, Zihan Wang, Wenbo Ding, Gang Li, Sicong Liu, Ling Cheng
    2021, 18(6): 1-11.
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    In urban Vehicular Ad hoc Networks (VANETs), high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm. In this paper, we propose a Multi-Agent Reinforcement Learning (MARL) based decentralized routing scheme, where the inherent similarity between the routing problem in VANET and the MARL problem is exploited. The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information. Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05 % averaging failure rate with varying vehicle capacities.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Yu Zhao, Joohyun Lee, Wei Chen
    2021, 18(6): 12-23.
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    This paper proposes a Reinforcement learning (RL) algorithm to find an optimal scheduling policy to minimize the delay for a given energy constraint in communication system where the environments such as traffic arrival rates are not known in advance and can change over time. For this purpose, this problem is formulated as an infinite-horizon Constrained Markov Decision Process (CMDP). To handle the constrained optimization problem, we first adopt the Lagrangian relaxation technique to solve it. Then, we propose a variant of Q-learning, Q-greedyUCB that combines $\varepsilon$-greedy and Upper Confidence Bound (UCB) algorithms to solve this constrained MDP problem. We mathematically prove that the Q-greedyUCB algorithm converges to an optimal solution. Simulation results also show that Q-greedyUCB finds an optimal scheduling strategy, and is more efficient than Q-learning with $\varepsilon$-greedy, R-learning and the Average-payoff RL (ARL) algorithm in terms of the cumulative regret. We also show that our algorithm can learn and adapt to the changes of the environment, so as to obtain an optimal scheduling strategy under a given power constraint for the new environment.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Tiantian Zhu, Congduan Li, Yanqun Tang, Zhiyong Luo
    2021, 18(6): 24-38.
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    In the Internet of vehicles (IoV), direct communication between vehicles, i.e., vehicle-to-vehicle (V2V) may have lower latency, compared to the schemes with help of Road Side Unit (RSU) or base station. In this paper, the scenario where the demands of a vehicle are satisfied by cooperative transmissions from those one in front is considered. Since the topology of the vehicle network is dynamic, random linear network coding is applied in such a multi-source single-sink vehicle-to-vehicle network, where each vehicle is assumed to broadcast messages to others so that the intermediate vehicles between sources and sink can reduce the latency collaboratively. It is shown that the coding scheme can significantly reduce the time delay compared with the non-coding scheme even in the channels with high packet loss rate. In order to further optimize the coding scheme, one can increase the generation size, where the generation size means the number of raw data packets sent by the source node to the sink node in each round of communication. Under the premise of satisfying the coding validity, we can dynamically select the Galois field size according to the number of intermediate nodes. It is not surprised that the reduction in the Galois field size can further reduce the transmission latency.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Wugedele Bao, Celimuge Wu, Siri Guleng, Jiefang Zhang, Kok-Lim Alvin Yau, Yusheng Ji
    2021, 18(6): 39-52.
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    In order to support advanced vehicular Internet-of-Things (IoT) applications, information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments. Federated learning (FL), which is a type of distributed learning technology, has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy. However, client selection and networking scheme for enabling FL in dynamic vehicular environments, which determines the communication delay between FL clients and the central server that aggregates the models received from the clients, is still under-explored. In this paper, we propose an edge computing-based joint client selection and networking scheme for vehicular IoT. The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach, and uses the edge vehicles as FL clients to conduct the training of local models, which learns optimal behaviors based on the interaction with environments. The clients also work as forwarder nodes in information sharing among network entities. The client selection takes into account the vehicle velocity, vehicle distribution, and the wireless link connectivity between vehicles using a fuzzy logic algorithm, resulting in an efficient learning and networking architecture. We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Bin Yang, Rui Chen, Bin Li, Changle Li
    2021, 18(6): 53-63.
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    As the basis of location-based services (LBS), positioning is one of the most essential parts in intelligent transportation systems (ITS). Although global positioning system (GPS) has been widely used in vehicle positioning, it can not achieve lane level positioning accuracy. Motivated by the mature ranging technologies such as radar and ultra-wideband (UWB), several cooperative positioning (CP) methods have been proposed to enhance the accuracy and robustness of GPS. In this paper, we proposed a two-stage CP algorithm that combines multidimensional scaling (MDS) and Procrustes analysis for vehicles with GPS information. Specifically, the optimized MDS based on the scaling by majorizing a complicated function (SMACOF) algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions, then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information. All the computations are performed at the mobile edge computing node (MECN) for the request of ultra-reliable and low latency communications (URLLC). Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Yanzhao Hou, Chengrui Wang, Min Zhu, Xiaodong Xu, Xiaofeng Tao, Xunchao Wu
    2021, 18(6): 64-76.
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    In MEC-enabled vehicular network with limited wireless resource and computation resource, stringent delay and high reliability requirements are challenging issues. In order to reduce the total delay in the network as well as ensure the reliability of Vehicular UE (VUE), a Joint Allocation of Wireless resource and MEC Computing resource (JAWC) algorithm is proposed. The JAWC algorithm includes two steps: V2X links clustering and MEC computation resource scheduling. In the V2X links clustering, a Spectral Radius based Interference Cancellation scheme (SR-IC) is proposed to obtain the optimal resource allocation matrix. By converting the calculation of SINR into the calculation of matrix maximum row sum, the accumulated interference of VUE can be constrained and the the SINR calculation complexity can be effectively reduced. In the MEC computation resource scheduling, by transforming the original optimization problem into a convex problem, the optimal task offloading proportion of VUE and MEC computation resource allocation can be obtained. The simulation further demonstrates that the JAWC algorithm can significantly reduce the total delay as well as ensure the communication reliability of VUE in the MEC-enabled vehicular network.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Siyu Fu, Wei Zhang, Zhiyuan Jiang
    2021, 18(6): 77-88.
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    With the emerging connected autonomous driving paradigm, more advanced applications leveraging vehicular communications are drawing tremendous attentions. In order to analyze the feasibility and performance of these applications, it is necessary to build an evaluation platform that jointly considers vehicular communication, road traffic and vehicle dynamics. This article describes our recent progress on network-level autonomous driving simulator based on the Cellular-Vehicle-to-Everything (C-V2X) protocol, and a joint platform combined with SUMO and CARLA simulators for evaluating road traffic and vehicle dynamics. To demonstrate its effectiveness, this article implements a hybrid multi-intersection scheduling scheme on the platform, and shows the advantages of the scheme in terms of traffic efficiency and fault tolerance. A remote driving application based on CARLA, wherein the interplay between communication and computation is also investigated.
  • FEATURE TOPIC: TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS
    Huiyuan Fu, Jun Guan, Feng Jing, Chuanming Wang, Huadong Ma
    2021, 18(6): 89-99.
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    In this paper, we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks, which aims at collecting the vehicles’ locations, trajectories and other key driving parameters for the time-critical autonomous driving’s requirement. The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario. . Our proposed framework is composed of three modules: multi-vehicle detection, multi-vehicle association and miss-detected vehicle tracking. For the first module, we integrate self-attention mechanism into detector of using key point estimation for better detection effect. For the second module, we apply the multi-dimensional information for robustness promotion, including vehicle re-identification (Re-ID) features, historical trajectory information, and spatial position information For the third module, we re-track the miss-detected vehicles with occlusions in the first detection module. Besides, we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up. Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.
  • COVER PAPER
  • COVER PAPER
    Dujia Yang, Xiaowei Qin, Xiaodong Xu, Chensheng Li, Guo Wei
    2021, 18(6): 100-113.
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    Reinforcement learning can be modeled as markov decision process mathematically. In consequence, the interaction samples as well as the connection relation between them are two main types of information for learning. However, most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes. In this paper, in order to utilize more sample information, we propose another learning system based on directed associative graph (DAG). The DAG is built on all trajectories in real time, which includes the whole connection relation of all samples among all episodes. Through planning with directed edges on DAG, we offer another perspective to estimate state-action pair, especially for the unknowns to deep neural network (DNN) as well as episodic memory (EM). Mixed loss function is generated by the three learning systems (DNN, EM and DAG) to improve the efficiency of the parameter update in the proposed algorithm. We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments. Furthermore, the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.
  • REVIEW PAPER
  • REVIEW PAPER
    Shengchen Wu, Hao Yin, Haotong Cao, Longxiang Yang, Hongbo Zhu
    2021, 18(6): 114-136.
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    Network virtualization (NV) is a high-profile way to solve the ossification problem of the nowadays Internet, and be able to support the diversified network naturally. In NV, Virtual Network Embedding (VNE) problem has been widely considered as a crucial issue, which is aimed to embed Virtual Networks (VNs) onto the shared substrate networks (SNs) efficiently. Recently, some VNE approaches have developed Node Ranking strategies to drive and enhance the embedding efficiency. Node Ranking Strategy rank/sort the nodes according to the attributes of the node, including both residual local attributes (CPU, Bandwidth, storage, Etc.) and the global topology attributes (Number of neighborhood Nodes, Delay to other nodes, Etc.). This paper presents an overview of Node Ranking Strategies in Virtual Network Embedding, and possible directions of VNE Node Ranking Strategy.
  • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Dazhi Piao, Meng Wang, Jie Zuo, Hao Zhou
    2021, 18(6): 137-145.
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    The dual-port compact multiple-input multiple-output (MIMO) dipole antennas with close spacing d of 0.5$\lambda$ and 0.3$\lambda$ are designed, and some electromagnetic band gap (EBG) structures are inserted between them to reduce mutual coupling. Those MIMO antennas with d= 0.5$\lambda$ and 0.3 $\lambda$, and with different mutual couplings are fabricated and measured, the channel capacity and correlation coefficient (CC) are analyzed and compared in a rich multipath reverberation chamber (RC), an office and a conference room. Results show that if d is reduced from 0.5$\lambda$ to 0.3 $\lambda$, in the RCs, channel capacities of all the antennas are very close to that of the i.i.d. Rayleigh channel, although the average CCs are increased from 0.168 in the nonlossy RC to 0.269 in the lossy RC. In the office and conference rooms, compared with the RC, the average capacities of those antennas get a slight reduction, however, in most cases, the capacity of d= 0.5$\lambda$ is larger than that of d= 0.3 $\lambda$, and the antennas with EBG have a larger capacity compared with the antennas without EBG, with a corresponding reduction of CC. A non-line-of-sight (NLOS) scenario of through-the-wall is also investigated.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Yao Zhang, Meng Zhou, Haitao Zhao
    2021, 18(6): 146-161.
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    In this paper, the spectral efficiency (SE) of an uplink hardware-constrained cell-free massive multi-input multi-output (MIMO) system with maximal ratio combining (MRC) receiver filters in the context of superimposed pilots (SPs) is investigated. Tractable closed-form SE expressions for the considered system are derived, which share us with opportunities to explore the impacts of the hardware quality coefficient, the length of coherence interval, and the power balance factor between pilot and data signals. Numerical results indicate that the achievable SE deteriorates as the hardware quality decreases and is more susceptible to the hardware impairments at the user equipments (UEs). Besides, we observe that SPs outperform regular pilots (RPs) in terms of SE and this performance gain is heavily dependent on the values of power balance factor and coherence interval. However, the superiorities of SPs over RPs have vanished when severe hardware imperfections are considered.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Sicong Liu, Xiao Huang
    2021, 18(6): 162-171.
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    The deep convolutional neural network (CNN) is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output (MIMO) systems. The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training. Then accurate channel inference can be efficiently implemented using the trained network. The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas. It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.
  • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Zihan Li, Zhaofeng Ma
    2021, 18(6): 172-183.
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    With the in-depth application of new technologies such as big data in education fields, the storage and sharing model of student education records data still faces many challenges in terms of privacy protection and efficient transmission. In this paper, we propose a data security storage and sharing scheme based on consortium blockchain, which is a credible search scheme without verification. In our scheme, the implementation of data security storage is using the blockchain and storage server together. In detail, the smart contract provides protection for data keywords, the storage server stores data after data masking, and the blockchain ensures the traceability of query transactions. The need for precise privacy data is achieved by constructing a dictionary. Cryptographic techniques such as AES and RSA are used for encrypted storage of data, keywords, and digital signatures. Security analysis and performance evaluation shows that the availability, high efficiency, and privacy-preserving can be achieved. Meanwhile, this scheme has better robustness compared to other educational records data sharing models.
  • NETWORKS & SECURITY
    Zhen Wang, Fuhui Zhou, Yuhao Wang, Qihui Wu
    2021, 18(6): 184-200.
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    In this paper, we consider a new spectrum sharing scenario for a cognitive relay network, where a secondary unmanned aerial vehicle (UAV) relay receives information from the ground secondary base station (SBS) and transmits information to the ground secondary user (SU), coexisting with the primary users (PUs) at the same wireless frequency band. We investigate the optimization of the UAV relay's three-dimensional (3D) trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs. The information throughput maximization problem is studied by jointly optimizing the UAV relay's 3D trajectory and the transmit power of the SBS and the UAV, subject to the constraints on the velocity and elevation of the UAV relay, the maximum and average transmit power, and the information causality, as well as a set of interference temperature (IT) constraints. An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique, the successive convex approximation technique and the alternating optimization method. Finally, simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput.
  • NETWORKS & SECURITY
    Jiangtao Li, Xu Bao, Wence Zhang
    2021, 18(6): 201-213.
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    Driven by the continuous penetration of high data rate services and applications, a large amount of unregulated visible light spectrum is used for communication to fully meet the needs of 6th generation (6G) mobile technologies. Visible light communication (VLC) faces many challenges as a solution that complements existing radio frequency (RF) networks. This paper studies the optimal configuration of LEDs in indoor environments under the constraints of illumination and quality of experience (QoE). Based on the Voronoi tessellation(VT) and centroidal Voronoi tessellation (CVT) theory, combined with the Lloyd's algorithm, we propose two approaches for optimizing LED deployments to meet the illumination and QoE requirements of all users. Focusing on (i) the minimization of the number of LEDs to be installed in order to meet illumination and average QoE constraints, and (ii) the maximization of the average QoE of users to be served with a fixed number of LEDs. Monte Carlo simulations are carried out for different user distribution compared with hexagonal, square and VT deployment. The simulation results illustrate that under the same conditions, the proposed deployment approach can provide less LEDs and achieve better QoE performance.
  • SIGNAL PROCESSING
  • SIGNAL PROCESSING
    Weigang Chen, Yalong He, Changcai Han, Jinsheng Yang, Zhan Xu
    2021, 18(6): 214-227.
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    As the complexity of space exploration missions augments, how to enhance the overall performance of communication, ranging or other functions has become a challengeable problem. Considering the integration of communication and ranging, we present a bit-level composite signal for simultaneous ranging and communication. In this composite method, through a specially designed mapping scheme using low-weight codewords, the information sequence is converted to a sparse sequence which is then superimposed on the ranging code. For ranging, the correlation characteristics of the ranging code component can be maintained to calculate the transmitter-receiver distance. For communications, the sparse sequence can be extracted without interference by eliminating the ranging code component. Simulation results show that the proposed composite signal can support communication and ranging simultaneously with limited sacrifice of ranging performance, and the performance loss of ranging can be controlled and minimized by lowering the density of information sequences using different sparsification encoding methods.
  • SIGNAL PROCESSING
    Hui Ren, Nan Gao, Jia Li
    2021, 18(6): 228-243.
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    Learning-based multi-task models have been widely used in various scene understanding tasks, and complement each other, i.e., they allow us to consider prior semantic information to better infer depth. We boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary task. To address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training, we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset, which not only provides a new way to conduct multi-task training, but also helps us to evaluate results compared with those of other algorithms. In addition, in order to comprehensively use the extracted features of the two tasks in the early perception stage, we use a strategy of sharing weights in the network to fuse cross-domain features, and introduce a novel multi-task loss function to further smooth the depth values. Extensive experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task, as well improved semantic segmentation.
  • SIGNAL PROCESSING
    Minghui Min, Weihang Wang, Liang Xiao, Yilin Xiao, Zhu Han
    2021, 18(6): 244-260.
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    Location-based services (LBS) in vehicular ad hoc networks (VANETs) must protect users' privacy and address the threat of the exposure of sensitive locations during LBS requests. Users release not only their geographical but also semantic information of the visited places (e.g., hospital). This sensitive information enables the inference attacker to exploit the users' preferences and life patterns. In this paper we propose a reinforcement learning (RL) based sensitive semantic location privacy protection scheme. This scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack history. This scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service (QoS) loss without being aware of the current inference attack model in a dynamic privacy protection process. To solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables, a deep deterministic policy gradient-based semantic location perturbation scheme (DSLP) is developed. The actor part is used to generate continuous privacy budget and perturbation angle, and the critic part is used to estimate the performance of the policy. Simulations demonstrate the DSLP-based scheme outperforms the benchmark schemes, which increases the privacy, reduces the QoS loss, and increases the utility of the vehicle.