Multi-Agent Deep Reinforcement Learning for Cross-Layer Scheduling in Mobile Ad-Hoc Networks  

作　　者：Xinxing Zheng Yu Zhao Joohyun Lee Wei Chen 

机构地区：[1]Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China [2]Department of Electrical and Electronic Engineering,Hanyang University,Ansan 15588,South Korea

出　　处：《China Communications》2023年第8期78-88,共11页中国通信（英文版）

基　　金：supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA));supported by the National Natural Science Foundation of China under Grant No. 61971264;the National Natural Science Foundation of China/Research Grants Council Collaborative Research Scheme under Grant No. 62261160390

摘　　要：Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.

关 键 词：Ad-hoc network cross-layer scheduling multi agent deep reinforcement learning interference elimination power control queue scheduling actorcritic methods markov decision process 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]