无线通信网络中基于非合作博弈论的可再生能源基站的分散式能量分配算法

doi:10.13229/j.cnki.jdxbgxb.20250376

摘要/Abstract

摘要：

为降低下一代无线通信网络二氧化碳排放并提升能源效率，提出了一种绿色无线通信系统，基站由可再生能源（RPS）供电，该系统由容量受限的可再生能源电源和基站组成；基站均须能够满足那些来自移动终端可预测的随机性连接，预留可再生能源，且需RPS策略对基站供电。本文视该问题为基站（BS）和RPS间的非合作博弈，首先，采用M/M/1的排队模型研究非合作方式优化各自成本的分散博弈决策，分析了能源储备策略与供给率设定之间的竞争关系，描述该非合作博弈的纳什均衡策略；然后，引入一组简单的线性契约达到最优性能，采用了一种激励相容机制令BS如实报告需求。仿真结果表明：连接需求不高时使用RPS可节省传统能源；网络流量较大时仍能由组合利用RPS和传统能源的供电方式节省能源成本。

关键词: 能量分配, 可再生能源, 非合作博弈, 绿色通信系统

Abstract:

This paper proposes a green wireless system， base stations are powered by renewable power sources （RPS） to reduce carbon dioxide emissions and improve efficiency in new wireless networks. The proposed method comprises the capacity-limited RPS and the Base Station（BS）. The BS can meet random connection demands of mobile terminals. The BS， powered by a combination of RPS and power grid， can monitor the RPS. Cooperation among RPSs is essential for implementing distribution strategies to supply power. The BS can reserve power from RPS， and the RPS determines the energy allocated to the BS. The paper solves the issue as a non-cooperative game between BSs and RPSs to denote the relationships among individuals. Firstly， the study adopts an M/M/1 queuing model to analyze the transition and investigate the decentralized game； it analyzes the competitive relationship between the reserve strategy and supply rate and mathematically describes a Nash Equilibrium （NE） of a non-cooperative game to find the causes of inefficiencies. A set of easy linear contracts is used to coordinate the system. Secondly， it employs an Incentive Compatible （IC） method to accurately report demands. Finally， simulational results indicate that， in scenarios with low link requirements， the solution can significantly reduce the consumption of traditional energy sources for base stations； and even if traffic is heavy， the proposed scheme can still reduce energy costs by integrating renewable energy alongside conventional energy through renewable energy BSs.

Key words: energy allocation, renewable power source, non-cooperative game, green wireless syste

中图分类号:

TG115.28

王金鹏,尤欣宇,窦顺瑶,赵昕. 无线通信网络中基于非合作博弈论的可再生能源基站的分散式能量分配算法[J]. 吉林大学学报(工学版), 2026, 56(10): 2706-2718.

Jin-peng WANG,Xin-yu YOU,Shun-yao DOU,Xin ZHAO. A decentralized energy allocation algorithm for renewable energy base stations based on non-cooperativegame theory in wireless communication networks[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(10): 2706-2718.

图/表 16

图1

图2

图3

图4

图5

图6

表1

表2

图7

图8

图9

图10

图11

图12

图13

图14

参考文献 18

[1]	Group Ericsson. Ericsson mobility report[DB/OL]. [2024-03-05]..
[2]	Lü L, Yang X R, Xiang Y, et al. Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis[J]. Energy, 2020, 198: 117301.
[3]	Xu D, Hao J, Zhao C L, et al. Joint caching and sleepactive scheduling for energy-harvesting based small cells[C]∥The 9th International Conference on Wireless Communications and Signal Processing, Nanjing,China,2017: 1-6.
[4]	Wang W J, Xu K J, Yan Y G, et al. Relay selection-based cooperative backscatter transmission with energy harvesting: throughput maximization[J]. IEEE Wireless Communications Letters, 2022, 11(7): 1533-1537.
[5]	Karthik R P, Sushma S R, Surenderkumar S. Control strategy for power sharing in a microgrid using wind/fuel cell hybrid system[C]∥International Conference on Smart Systems for applications in Electrical Sciences, Tumakuru, India, 2023:1-5.
[6]	Ko Y, Jung K. Renewable energy-enabled cellular networks[J]. SSRN Electronic Journal, 2021.
[7]	Fang F, Ye G S, Zhang H J, et al. Energy-efficient joint user association and power allocation in a heterogeneous network[J]. IEEE Transactions on Wireless Communications, 2020, 19(11): 7008-7020
[8]	Yang J, Ge X, Li Y. Principle of computation power optimization in millimeter wave massive MIMO systems[J]. IEEE Trans Mobile Comput, 2021, 21(8): 2955-2966.
[9]	Mankar P D, Parida P, Dhillon H S, et al. Distance from the nucleus to a uniformly random point in the 0-cell and the typical cell of the poisson-voronoi tessellation[J]. Journal of Statistical Physics, 2020, 181: 1678-1698.
[10]	李昆, 方家琨, 艾小猛, 等. 考虑通信与配套设备协调优化的大规模5G 宏基站网络能量管理模型[J]. 中国电机工程学报, 2023, 43(14): 5391-5403.
	Li Kun, Fang Jia-kun, Ai Xiao-meng, et al. Energy management model of large-scale 5G macro base station network considering the coordinated optimization of communication equipment and standard equipment [J].Proceedings of the CSEE, 2023, 43(14): 5391-5403.
[11]	Balakrishnan A, De S, Wang L C. Networked energy cooperation in dual powered green cellular networks[J]. IEEE Transactions on Communications, 2022, 70(10): 6977-6991.
[12]	Huang Y. Analysis of 5G network technology and research on wireless network planning [J]. Wireless Int Technol, 2019, 16: 13,14.
[13]	I C L, Han S F, Bian S. Energy-efficient 5G for a greener future[J]. Nature Electronics, 2020, 3: 182-184.
[14]	Belkhir L, Elmeligi A. Assessing ICT global emissions footprint: trends to 2040 & recommendations[J]. Journal of Cleaner Production, 2018, 177(10): 448-463.
[15]	王晓卫, 康乾坤, 梁振锋, 等. 考虑5G 基站储能参与配网供电恢复研究[J]. 电工技术学报, 2024, 39(11): 3538-3555.
	Wang Xiao-wei, Kang Qian-kun, Liang Zhen-feng, et al. Distribution network restoration supply method considers 5G base station energy storage participation [J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3538-3555.
[16]	Fan W H, Liu J, Hua M Y, et al. Joint task offloading and resource allocation for multi-access edge computing assisted by parked and moving vehicles[J]. IEEE Transactions on Vehicular Technology, 2022, 71(5): 5314-5330.
[17]	胡龙, 方八零, 樊飞龙, 等. 用户-基站-充电站能量共享、互动优化方法及在园区中的应用[J]. 上海交通大学学报, 2023,59(7): 8877-8888.
	Hu Long, Fang Ba-ling, Fan Fei-long, et al. Energy interaction and energy storage sharing optimization method for users, base stations, and charging stations[J]. Journal of Shanghai Jiao Tong University, 2023,59(7): 8877-8888.
[18]	Zhang Q, Wu X H, Deng X S, et al. Bidding strategy for wind power and Large-scale electric vehicles participating in day-ahead energy and frequency regulation market[J]. Applied Energy, 2023, 341: 121063.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

类别	参数	数值
可再生能源RPS端	可再生能源发电量μ∈［1.2λ~2.2λ］	［0.48， 3.52］
	归一供能率s_i	7.29
	负载系数ρ	0.39， 0.7， 0.8， 0.93
	可再生能源RPS的供应成本c_s	0， 1， 5
	服务连接到达率λ	［0.4， 1.6］
	平均供电成本X（μ）	［0， 1］
	最大发电量限制μ₀	2
	可再生能源价格 P₁	1
基站BS端	归一储能率ν_i	0.33
	积压份额α	0.2， 0.5， 0.6， 0.8
	积压成本b	1， 2， 5， 10
	j个连接概率p_j	0，0.05，0.1，0.15， 0.2
	排队队列长度	0， 10， 20， 30， 40， 60
	可再生能源需求	1， 2， 3， 4， 5
	电网价格P₂	［5， 10］

	负载系数ρ
类别	039	0.7	0.8	0.93
理论值	1.1	2.4	4.4	14.5
实验值	0.8	2.5	4.3	14.1