Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (10): 2706-2718.doi: 10.13229/j.cnki.jdxbgxb.20250376

Previous Articles    

A decentralized energy allocation algorithm for renewable energy base stations based on non-cooperativegame theory in wireless communication networks

Jin-peng WANG(),Xin-yu YOU,Shun-yao DOU,Xin ZHAO   

  1. School of Information Science and Engineering，Dalian Polytechnic University，Dalian 116034，China
  • Received:2025-04-28 Online:2026-10-01 Published:2026-10-09

RICH HTML

 0   

PDF (PC)

7

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

CLC Number: 

  • TG115.28

Fig.1

Proposed system"

Fig.2

Eenergy allocation strategy"

Fig.3

Eenergy storage strategy based on make-to-stock"

Fig.4

Illustration of renewable energy reservation cost and the backlogged cost"

Fig.5

Illustration of the operation of the system"

Fig.6

Convex of the objective function"

Table 1

System Simulation Parameter Settings"

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si7.29
负载系数ρ0.39， 0.7， 0.8， 0.93
可再生能源RPS的供应成本cs0， 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μ02
可再生能源价格 P11
基站BS端归一储能率νi0.33
积压份额α0.2， 0.5， 0.6， 0.8
积压成本b1， 2， 5， 10
j个连接概率pj0，0.05，0.1，0.15， 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］

Table 2

Mean queue under various traffic condition"

负载系数ρ
类别0390.70.80.93
理论值1.12.44.414.5
实验值0.82.54.314.1

Fig.7

PDF in different traffic conditions"

Fig.8

Central system cost"

Fig.9

Minimum cost， optimal energy rate and reservation"

Fig.10

Optimal response with the NE"

Fig.11

NE Equilibrium costs analysis"

Fig.12

Response of RPS at different ρ with s = s?"

Fig.13

System performance at the NE"

Fig.14

Analysis for the energy supply allocation"

[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.
[1] Cui-ran LI,Yong-sheng YU,Jian-li XIE. Dynamic game algorithm for spectrum sharing based on priority of secondary users [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 315-323.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd