基于SWIPT无线协作网络的中继选择算法研究

doi:10.16182/j.issn1004731x.joss.20-0518

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (4): 801-808.doi: 10.16182/j.issn1004731x.joss.20-0518

基于SWIPT无线协作网络的中继选择算法研究

方群^1,2, 陈绪凯^1,2, 何昕^1,2, 祝玉军^1,2, 刘毅杨^1,2, 方阳阳^1,2, 李贺举^1,2

1.安徽师范大学计算机与信息学院,安徽芜湖 241002;
2.网络与信息安全安徽省重点实验室,安徽芜湖 241002

收稿日期:2020-07-23 修回日期:2020-09-07 出版日期:2021-04-18 发布日期:2021-04-14
作者简介:方群(1972-),男,博士,教授,研究方向为物联网安全。E-mail：fq0520@ahnu.edu.cn
基金资助:
国家自然科学基金(61702011); 安徽省教育厅重大教研项目(2018jyxm1211)

Study on Relay Selection Algorithm Based on SWIPT Wireless Cooperative Network

Fang Qun^1,2, Chen Xukai^1,2, He Xin^1,2, Zhu Yujun^1,2, Liu Yiyang^1,2, Fang Yangyang^1,2, Li Heju^1,2

1. School of Computer & Information, Anhui Normal University, Wuhu 241002, China;
2. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu 241002, China

Received:2020-07-23 Revised:2020-09-07 Online:2021-04-18 Published:2021-04-14

摘要/Abstract

摘要： 尽管无线协作网络(Wireless Cooperative Network,WCN)显著改善了通信质量和网络吞吐量,但由于中继节点能量限制,造成WCN仍存在网络生命续航和能量补充问题。提出基于携能通信技术的WCN,分析在Nakagami-m信道假设下的多中继节点模型,并研究最优中继节点的选取问题。推导出不同最优中继选择策略下的系统中断概率,并通过蒙特卡洛仿真实验验证其正确性;进一步分析影响系统中断概率的诸多因素。利用前馈神经网络提出基于机器学习的中继选择算法,仿真实验表明：最优中继选择正确率约为93%。

关键词: 无线协作网络, 无线携能通信技术, 前馈神经网络, 最大值合并技术

Abstract: Although the wireless cooperative networks (WCN) significantly improve the communication quality and network throughput of wireless communications. However, it still faces the challenge of the network lifetime and the energy replenishment due to the energy limitation of relay nodes. In order to tackle this challenge, A multi-relay wireless cooperative network combined with the simultaneous wireless information and power transfer (SWIPT) is proposed and the theoretical performance under the Nakagami-m assumption is analyzed. The selection of the optimal relay node for wireless cooperative networks in the framework of the proposed system is studied. The outage probability of the system under different optimal relay selection strategies is obtained, and its correctness is verified by Monte Carlo simulations. The factors that affect the system outage probability are analyzed through a series of simulations. Furthermore, the feedforward neural network (FNN) machine learning algorithm is used to optimize the selection of relay nodes. The experimental results show that: the accuracy of selecting the optimal relay selection is around 93%.

Key words: wireless cooperative network, simultaneous wireless information and power transfer (SWIPT), feedforward neural network, maximum ratio combining

中图分类号:

方群, 陈绪凯, 何昕, 祝玉军, 刘毅杨, 方阳阳, 李贺举. 基于SWIPT无线协作网络的中继选择算法研究[J]. 系统仿真学报, 2021, 33(4): 801-808.

Fang Qun, Chen Xukai, He Xin, Zhu Yujun, Liu Yiyang, Fang Yangyang, Li Heju. Study on Relay Selection Algorithm Based on SWIPT Wireless Cooperative Network[J]. Journal of System Simulation, 2021, 33(4): 801-808.

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基于SWIPT无线协作网络的中继选择算法研究

Study on Relay Selection Algorithm Based on SWIPT Wireless Cooperative Network

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