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| 1 | Joint Allocation of Wireless Resource and Computing Capability in MEC-Enabled Vehicular Network显示文摘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. | Yanzhao Hou Chengrui Wang Min Zhu Xiaodong Xu Xiaofeng Tao Xunchao Wu | 2021 | China Communications2021，18，6： | 4 |
| 2 | Space-air-ground integrated vehicular network for connected and automated vehicles:Challenges and solutions显示文摘Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles,in particular for new use cases like autonomous driving and vehicle platooning.In this paper,we propose a novel Space-Air-Ground integrated vehicular network(SAGiven)architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites,High-Altitude Platform stations(HAPs),low-altitude Unmanned Aerial Vehicles(UAVs),and terrestrial cellular communication systems.One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources.We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges.Specifically,the solutions will cover the following topics:(1)space-air-ground integrated network reconfiguration under dynamic space resources constraints;(2)multi-dimensional sensing and efficient integration of multi-dimensional context information;(3)real-time,reliable,and secure communications among vehicles and between vehicles and the SAGiven platform;and(4)a holistic integration and demonstration of the SAGiven.Finally,it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications. | Zhisheng Niu Xuemin S.Shen Qinyu Zhang Yuliang Tang | 2020 | Intelligent and Converged Networks2020，1，2： | 4 |
| 3 | Learning-Based Joint Resource Slicing and Scheduling in Space-Terrestrial Integrated Vehicular Networks显示文摘In this paper,we investigate the resource slicing and scheduling problem in the space-terrestrial integrated vehicular networks to support both delay-sensitive services(DSSs)and delay-tolerant services(DTSs).Resource slicing and scheduling are to allocate spectrum resources to different slices and determine user association and bandwidth allocation for individual vehicles.To accommodate the dynamic network conditions,we first formulate a joint resource slicing and scheduling(JRSS)problem to minimize the long-term system cost,including the DSS requirement violation cost,DTS delay cost,and slice reconfiguration cost.Since resource slicing and scheduling decisions are interdependent with different timescales,we decompose the JRSS problem into a large-timescale resource slicing subproblem and a small-timescale resource scheduling subproblem.We propose a two-layered reinforcement learning(RL)-based JRSS scheme to find the solutions to the subproblems.In the resource slicing layer,spectrum resources are pre-allocated to different slices via a proximal policy optimization-based RL algorithm.In the resource scheduling layer,spectrum resources in each slice are scheduled to individual vehicles based on dynamic network conditions and service requirements via matching-based algorithms.We conduct extensive trace-driven experiments to demonstrate that the proposed scheme can effectively reduce the system cost while satisfying service quality requirements. | Huaqing Wu Jiayin Chen Conghao Zhou Junling Li Xuemin(Sherman)Shen | 2021 | Journal of Communications and Information Networks2021，6，3： | 3 |
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