[1]CHEN Zhixiong,ZHAN Xuezi,ZUO Jiashuo.Adaptive MAC layer access algorithm for power line and wireless dual-mode communication based on deep reinforcement learning[J].CAAI Transactions on Intelligent Systems,2025,20(2):344-354.[doi:10.11992/tis.202312023]
Copy

Adaptive MAC layer access algorithm for power line and wireless dual-mode communication based on deep reinforcement learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 344-354 Column: 学术论文—机器学习 Public date: 2025-03-05
Title:
Adaptive MAC layer access algorithm for power line and wireless dual-mode communication based on deep reinforcement learning
Author(s):
CHEN Zhixiong1; 2;  ZHAN Xuezi1;  ZUO Jiashuo1
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Electric Power Internet of Things Technology, Baoding 071003, China
Keywords:
power line communication; wireless communication; dual-mode nodes; deep reinforcement learning; double deep Q-network; MAC layer access; fairness utility function; P-persistent access
CLC:
TM721
DOI:
10.11992/tis.202312023
Abstract:
Aiming to address the issue of channel competition in hybrid networks of PLC and WC, this study proposes a MAC access algorithm based on deep reinforcement learning for dual-mode communication over power lines and wireless channels. Dual-mode nodes adaptively access the dual-medium channel based on data such as network broadcast information and channel usage. First, a dual-mode node data collection model is established based on interactions and statistical information from dual-mode communication networks. Then, the DRL state space, action space, and rewards are defined based on collaborative information, and an adaptive access algorithm is developed using a dual deep Q-network. This algorithm incorporates a node decision-making process that combines the α-fairness utility function with the P-persistent access mechanism. Finally, simulations and comparative analyses of the algorithm’s performance are performed. Simulation results show that the proposed access algorithm effectively improves the access performance of dual-mode communication nodes while ensuring fairness in dual-mode network and channel access.
References:
[1] 谢可, 王剑锋, 金尧, 等. 电力物联网关键技术研究综述[J]. 电力信息与通信技术, 2022, 20(1): 1-12.
XIE Ke, WANG Jianfeng, JIN Yao, et al. Review of key technologies for power Internet of things[J]. Electric power information and communication technology, 2022, 20(1): 1-12.
[2] 张丽, 郝佳恺. 5G网络切片电力通信业务与测试技术研究[J]. 电力信息与通信技术, 2022, 20(5): 74-79.
ZHANG Li, HAO Jiakai. Research on 5G network slicing technology in power communication service and testing[J]. Electric power information and communication technology, 2022, 20(5): 74-79.
[3] 杨璐, 吴清亮. 无线接入网络自适应公平调度算法[J]. 通信学报, 2012, 33(1): 102-106,113.
YANG Lu, WU Qingliang. Self-adaptive fair scheduling algorithm in wireless network[J]. Journal on communications, 2012, 33(1): 102-106,113.
[4] YOON S G, JANG S, KIM Y H, et al. Opportunistic routing for smart grid with power line communication access networks[J]. IEEE transactions on smart grid, 2014, 5(1): 303-311.
[5] PAPADOPOULOS T A, KALOUDAS C G, CHRYSOCHOS A I, et al. Application of narrowband power-line communication in medium-voltage smart distribution grids[J]. IEEE transactions on power delivery, 2013, 28(2): 981-988.
[6] 蒲红红, 刘晓胜, 韩铭, 等. 电力线通信信道下协作非正交多址接入系统的分布式机会中继选择[J]. 电工技术学报, 2020, 35(11): 2306-2318.
PU Honghong, LIU Xiaosheng, HAN Ming, et al. Distributed opportunistic relay selection for cooperative non-orthogonal multiple access power line communication networks[J]. Transactions of China electrotechnical society, 2020, 35(11): 2306-2318.
[7] MATHUR A, BHATNAGAR M R, AI Yun, et al. Performance analysis of a dual-hop wireless-power line mixed cooperative system[J]. IEEE access, 2018, 6: 34380-34392.
[8] 陈智雄, 韩东升, 邱丽君. 室内无线和电力线双媒质协作通信系统性能研究[J]. 中国电机工程学报, 2017, 37(9): 2589-2599.
CHEN Zhixiong, HAN Dongsheng, QIU Lijun. Research on the performance of cooperation communication system based on indoor double media with wireless channel and power line[J]. Proceedings of the CSEE, 2017, 37(9): 2589-2599.
[9] ZHU Yihua, XU Mengying. Enhancing network throughput via the equal interval frame aggregation scheme for IEEE 802.11ax WLANs[J]. Chinese journal of electronics, 2023, 32(4): 747-759.
[10] VLACHOU C, BANCHS A, SALVADOR P, et al. Analysis and enhancement of CSMA/CA with deferral in power-line communications[J]. IEEE journal on selected areas in communications, 2016, 34(7): 1978-1991.
[11] LIU Ran, YUAN Jinsha, CHEN Zhixiong, et al. Research on the MAC layer performance of wireless and power line parallel communication[C]//The 8th International Conference on Computer Engineering and Networks. Shanghai: Springer, 2018: 729-737.
[12] CHEN Zhixiong, ZHI Leixin, ZHAO Xiongwen, et al. Parallel access scheme for wireless and power line dual-connection communication[J]. IEEE transactions on vehicular technology, 2022, 71(7): 7997-8001.
[13] CHEN Zhixiong, ZHI Leixin, CHEN Peiru, et al. An MAC layer algorithm based on power line-wireless dual media channels and multiplexing[J]. China communications, 2022, 19(10): 78-91.
[14] 孙志军, 薛磊, 许阳明, 等. 深度学习研究综述[J]. 计算机应用研究, 2012, 29(8): 2806-2810.
SUN Zhijun, XUE Lei, XU Yangming, et al. Overview of deep learning[J]. Application research of computers, 2012, 29(8): 2806-2810.
[15] 周文吉, 俞扬. 分层强化学习综述[J]. 智能系统学报, 2017, 12(5): 590-594.
ZHOU Wenji, YU Yang. Summarize of hierarchical reinforcement learning[J]. CAAI transactions on intelligent systems, 2017, 12(5): 590-594.
[16] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529-533.
[17] NGUYEN T T, NGUYEN N D, NAHAVANDI S. Deep reinforcement learning for multiagent systems: a review of challenges, solutions, and applications[J]. IEEE transactions on cybernetics, 2020, 50(9): 3826-3839.
[18] 赵晓荣. 基于深度强化学习的异构网络资源分配[D]. 济南: 山东大学, 2023.
ZHAO Xiaorong. Resource allocation of heterogeneous networks based on deep reinforcement learning[D]. Jinan: Shandong University, 2023.
[19] 姜华, 杨家伟, 黄巍, 等. 基于深度强化学习的D2D辅助MEC网络资源分配算法[J]. 电力信息与通信技术, 2023, 21(7): 51-58.
JIANG Hua, YANG Jiawei, HUANG Wei, et al. A D2D-assisted MEC network resource allocation algorithm based on deep reinforcement learning[J]. Electric power information and communication technology, 2023, 21(7): 51-58.
[20] 刘乔寿, 周雄, 刘爽, 等. 基于深度强化学习的OFDM自适应导频设计[J]. 通信学报, 2023, 44(9): 104-114.
LIU Qiaoshou, ZHOU Xiong, LIU Shuang, et al. Adaptive pilot design for OFDM based on deep reinforcement learning[J]. Journal on communications, 2023, 44(9): 104-114.
[21] 赵玉新, 杜登辉, 成小会, 等. 基于强化学习的海洋移动观测网络观测路径规划方法[J]. 智能系统学报, 2022, 17(1): 192-200.
ZHAO Yuxin, DU Denghui, CHENG Xiaohui, et al. Path planning for mobile ocean observation network based on reinforcement learning[J]. CAAI transactions on intelligent systems, 2022, 17(1): 192-200.
[22] 何云, 申敏, 王蕊, 等. 无蜂窝毫米波大规模MIMO系统基于深度强化学习的节能睡眠策略[J]. 电子学报, 2023, 51(10): 2831-2843.
HE Yun, SHEN Min, WANG Rui, et al. Energy-efficient sleep-mode based on deep reinforcement learning for cell-free mmwave massive MIMO systems[J]. Acta electronica sinica, 2023, 51(10): 2831-2843.
[23] PEI Zhonghui, CHEN Wei, DU Luyao, et al. MAC contention protocol based on reinforcement learning for IoV communication environments[C]//2021 IEEE 6th International Conference on Computer and Communication Systems. Chengdu: IEEE, 2021: 546-552.
[24] WU C M, KAO Y C, CHANG Kaifu, et al. A Q-learning-based adaptive MAC protocol for Internet of Things networks[J]. IEEE access, 2021, 9: 128905-128918.
[25] NAPARSTEK O, COHEN K. Deep multi-user reinforcement learning for distributed dynamic spectrum access[J]. IEEE transactions on wireless communications, 2019, 18(1): 310-323.
[26] YU Yiding, LIEW S C, WANG Taotao. Carrier-sense multiple access for heterogeneous wireless networks using deep reinforcement learning[C]//2019 IEEE Wireless Communications and Networking Conference Workshop. Marrakech: IEEE, 2019: 1-7.
[27] YU Yiding, LIEW S C, WANG Taotao. Non-uniform time-step deep Q-network for carrier-sense multiple access in heterogeneous wireless networks[J]. IEEE transactions on mobile computing, 2021, 20(9): 2848-2861.
[28] YE Xiaowen, YU Yiding, FU Liqun. Deep reinforcement learning based MAC protocol for underwater acoustic networks[J]. IEEE transactions on mobile computing, 2022, 21(5): 1625-1638.
[29] TONELLO A M, LETIZIA N A, RIGHINI D, et al. Machine learning tips and tricks for power line communications[J]. IEEE access, 2019, 7: 82434-82452.
[30] BIANCHI G. Performance analysis of the IEEE 802.11 distributed coordination function[J]. IEEE journal on selected areas in communications, 2000, 18(3): 535-547.
Similar References:

Memo

-

Last Update: 2025-03-05

Copyright © CAAI Transactions on Intelligent Systems