Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

作     者：Zhao Di Zheng Zhong Qin Pengfei Qin Hao Song Bin Zhao Di;Zheng Zhong;Qin Pengfei;Qin Hao;Song Bin

作者机构：State Key Laboratory of Integrated Services NetworksXidian University710071China China Academy of Space TechnologyBeijing 100081China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2024年第21卷第5期

页      面：77-96页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.62071354) the Key Research and Development Program of Shaanxi(No.2022ZDLGY05-08) supported by the ISN State Key Laboratory 

主　　题：dynamic resource allocation multi-user cellular network spectrum efficiency user fairness 

摘      要：To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing *** this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user *** first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)***,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource *** results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.