Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks  

作　　者：Junhui Zhao Fajin Hu Jiahang Li Yiwen Nie 

机构地区：[1]School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China [2]School of Information Engineering,East China Jiaotong University,Nanchang 330013,China [3]Research Institute of China United Network Communications Group Co.,Ltd.,Beijing,100048,China

出　　处：《Digital Communications and Networks》2025年第1期182-190,共9页数字通信与网络(英文版)

基　　金：funded in part by the National Key Research and Development of China Project (2020YFB1807204);in part by National Natural Science Foundation of China (U2001213 and 61971191);in part by the Beijing Natural Science Foundation under Grant L201011;in part by the key project of Natural Science Foundation of Jiangxi Province (20202ACBL202006)。

摘　　要：In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.

关 键 词：DATA-DRIVEN Deep reinforcement learning Resource allocation V2X communications 

分 类 号：TN9[电子电信—信息与通信工程]