电子科技大学学报2025，Vol.54Issue(6)：866-874,9.DOI:10.12178/1001-0548.2024207
基于多智体学习的多小区NOMA协作波束训练
Multi-cell NOMA cooperative beam training based on multi-agent learning
摘要
Abstract
This paper mainly focuses on the beamforming training problem in cooperative non-orthogonal multiple access(NOMA)scenarios in millimeter-wave communication,extending the work from single-cell NOMA to multi-cell NOMA scenarios.To maximize system throughput while considering user locations and channel information,the beam configuration problem at the base station is modeled as a Markov cooperative-competitive game problem.And then the problem is solved by exploiting multi-agent deep deterministic policy gradient(MADDPG)based reinforcement learning algorithm.A multi-agent reinforcement learning-based beamforming training algorithm for cooperative NOMA in multi-cell scenarios is designed to effectively allocate resources such as beams and power in multi-base station systems,thereby enhancing system throughput.Numerical simulations demonstrate that the proposed MADDPG algorithm achieves better system throughput and user coverage.关键词
波束管理/波束训练/多小区NOMA/深度强化学习/多智能体学习Key words
beam management/beam training/multi-cell NOMA/deep reinforcement learning/multi-agent learning分类
电子信息工程引用本文复制引用
王越,刘如意,杨蓓,王建秀,冯钢..基于多智体学习的多小区NOMA协作波束训练[J].电子科技大学学报,2025,54(6):866-874,9.基金项目
国家自然科学基金青年基金项目(62201121) （62201121）
中国电信研究院合作项目(231382) （231382）
