南方电网技术2026，Vol.20Issue(7)：58-66,121,10.DOI:10.13648/j.cnki.issn1674-0629.2026.07.006
基于深度强化学习的双模通信超帧时隙分配算法
Super Frame Time Slot Allocation Algorithm for Dual-Mode Communication Based on Deep-Reinforcement Learning
摘要
Abstract
The hybrid networking of power line and wireless dual-mode communication can achieve complementary advantages,demonstrating significant application prospects in fields such as the Internet of Things for power systems and smart homes.To enhance the access flexibility and network resource utilization of dual-mode communication MAC algorithms,a superframe time slot resource allocation algorithm for dual-mode gateways based on deep reinforcement learning is proposed.Firstly,an interaction model between the dual-mode communication gateway and terminals is established,detailing the superframe structure and the specific execu-tion steps of each phase.Secondly,key reward functions,state spaces,and action spaces for machine learning applications are defined,with the gateway monitoring and collecting parameters such as superframe access throughput.Through iterative learning and training,optimized parameters including the number and proportion of superframe time slots are obtained.Finally,simulations are conducted to verify the effectiveness and reliability of the proposed algorithm.Comparisons are made with fixed superframe structure and non-superframe structure algorithms,analyzing network throughput,average delay,packet loss rate under different algorithms,as well as the influence patterns of key parameters on system performance.Simulation results indicate that the proposed algorithm can effectively improve system performance in terms of throughput and delay,enabling flexible and efficient resource allocation.关键词
双模通信/双深度Q网络/MAC层接入/超帧结构/时隙分配Key words
dual-mode communication/double deep Q-network/MAC layer access/super frame structure/time slot allocation分类
信息技术与安全科学引用本文复制引用
冯俊豪,詹学滋,颜宇帆,王朋姣,陈智雄..基于深度强化学习的双模通信超帧时隙分配算法[J].南方电网技术,2026,20(7):58-66,121,10.基金项目
国家自然科学基金青年基金项目(61601182) （61601182）
南方电网科学研究院科技项目(SEPRI-K23B060) （SEPRI-K23B060）
广东省科技计划项目(2021B1212050014). Supported by the Youth Funds of National Natural Science Foundation of China(61601182) （2021B1212050014）
the Science and Technology Projects of Electric Power Research Institute of China Southern Power Grid Co.,Ltd.(SEPRI-K23B060) （SEPRI-K23B060）
the Science and Technology Projects of Guangdong Province(2021B1212050014). （2021B1212050014）
