通信学报2023，Vol.44Issue(12)：86-98,13.DOI:10.11959/j.issn.1000-436x.2023235
基于多智能体强化学习的异构网络CRE偏置动态优化算法
Multi-agent reinforcement learning based dynamic optimization algorithm of CRE offset for heterogeneous networks
摘要
Abstract
To cope with the high throughput demand caused by the proliferation of wireless network users,a multi-agent reinforcement learning based dynamic optimization algorithm of cell range expansion(CRE)offset was proposed for in-terference scenarios in macro-pico heterogeneous networks.Based on the value decomposition network framework of collaborative multi-agent reinforcement learning,a personalized online local decision of CRE offset for all pico-base sta-tions was achieved by reasonably utilizing and interacting the intra-system user distribution and their interference levels among pico-base stations.Simulation results show that the proposed algorithm has significant advantages in increasing system throughput,balancing the throughput of each base station and improving edge-user throughput,compared to CRE=5 dB and distributed Q-learning algorithms.关键词
异构网络/小区范围扩展/多智能体强化学习/值分解网络算法Key words
heterogeneous network/cell range expansion/multi-agent reinforcement learning/value decomposition net-work algorithm分类
信息技术与安全科学引用本文复制引用
张铖,朱家烨,刘泽宁,黄永明..基于多智能体强化学习的异构网络CRE偏置动态优化算法[J].通信学报,2023,44(12):86-98,13.基金项目
国家自然科学基金资助项目(No.62225107,No.62271140) （No.62225107,No.62271140）
江苏省前沿引领技术基础研究重大基金资助项目(No.BK20222001) （No.BK20222001）
江苏省创新创业人才计划基金资助项目(No.JSSCBS20211332)The National Natural Science Foundation of China(No.62225107,No.62271140),The Natural Science Founda-tion on Frontier Leading Technology Basic Research Project of Jiangsu(No.BK20222001),The Jiangsu Innovative and Entrepre-neurial Talent Program(No.JSSCBS20211332) （No.JSSCBS20211332）
