物联网学报2024，Vol.8Issue(2)：26-35,10.DOI:10.11959/j.issn.2096-3750.2024.00388
基于强化学习的多基站协作接收时隙Aloha网络信道接入机制
Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception
摘要
Abstract
With the increasingly dense deployment of base stations in the internet of things(IoT),the importance of inter-ference management becomes ever more pronounced.In IoT environments,devices often employ random access,connect-ing to channels in a distributed manner.In scenarios involving massive numbers of devices,severe interference may arise between nodes,leading to significant degradation in the throughput performance of the network.To address interference control issues in networks with random access,a multi-base station slotted Aloha network based on cooperative reception was considered,the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference,optimized network throughput performance,and enhanced network fairness.Firstly,an adaptive transmission algorithm were devised based on Q-learning,which was verified to maintain high network throughput performance under varying traffic conditions through simulation.Secondly,to improve network fairness,the penalty function method was employed to refine the adaptive transmission algorithm.Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance.关键词
强化学习/物联网/随机接入/多基站网络/时隙AlohaKey words
reinforcement learning/internet of things/random access/multi-base station network/slotted Aloha分类
信息技术与安全科学引用本文复制引用
黄元康,詹文,孙兴华..基于强化学习的多基站协作接收时隙Aloha网络信道接入机制[J].物联网学报,2024,8(2):26-35,10.基金项目
国家重点研发计划(No.2023YFB2904100) （No.2023YFB2904100）
深圳市科技计划资助项目(No.RCBS20210706092408010) The National Key Research and Development Program of China(No.2023YFB2904100),Shenzhen Science and Technology Program(No.RCBS20210706092408010) （No.RCBS20210706092408010）
