Task Offloading and Resource Allocation in NOMA-VEC:A Multi-Agent Deep Graph Reinforcement Learning Algorithm  

作　　者：Hu Yonghui Jin Zuodong Qi Peng Tao Dan 

机构地区：[1]School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China

出　　处：《China Communications》2024年第8期79-88,共10页中国通信（英文版）

基　　金：supported by the Talent Fund of Beijing Jiaotong University(No.2023XKRC028);CCFLenovo Blue Ocean Research Fund and Beijing Natural Science Foundation under Grant(No.L221003).

摘　　要：Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.

关 键 词：edge computing graph convolutional network reinforcement learning task offloading 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]