基于GA-Elman的无线通信链路可靠性置信区间预测算法

doi:10.13229/j.cnki.jdxbgxb.20240696

摘要/Abstract

摘要：

针对无线通信链路状态随时间变化的情况，为实现链路可靠性预测的实时更新和调整，提出了基于GA-Elman的无线通信链路可靠性置信区间预测算法。基于无线通信链路质量特征分析，构建了对数距离路径损耗模型，将无线通信链路可靠性研究转化为对无线通信链路信噪比的研究。通过小波分解将无线通信链路信噪比信号分解为平稳序列和噪声序列，并分别输入Elman神经网络进行预测。利用遗传算法（GA）迭代优化Elman神经网络权重和偏置参数，以提升预测模型的准确性。结合预测值与置信水平，实现无线通信链路可靠性置信区间预测。通过实验验证发现，当遗传算法迭代60次时，能够获得最优的Elman神经网络参数，有效提升预测准确性。本文算法在无线通信链路领域具有较高的准确性和可靠性，能够提供较为准确的预测信息，可为无线通信链路可靠性评估和优化提供有力支持。

关键词: GA-Elman, 无线通信链路, 置信区间预测, 链路质量, 信噪比, 小波分解

Abstract:

Aiming at the situation where the state of wireless communication links changes over time， in order to achieve real-time updating and adjustment of link reliability prediction，in this paper a wireless communication link reliability confidence interval prediction algorithm based on GA-Elman is proposed. Based on the analysis of the quality characteristics of wireless communication links， a logarithmic distance path loss model is constructed to transform the study of wireless communication link reliability into the study of wireless communication link signal-to-noise ratio. The wireless communication link signal-to-noise ratio signal is decomposed into stationary sequences and noisy sequences through wavelet decomposition， and respectively input into Elman neural networks for prediction. Iteratively optimize the weights and bias parameters of the Elman neural network using genetic algorithm （GA） to improve the accuracy of the prediction model. Combine the predicted values with confidence levels to achieve confidence interval prediction of wireless communication link reliability. Through experimental verification found that the optimal Elman neural network parameters can be obtained when the genetic algorithm iterates 60 times， effectively improving prediction accuracy. This algorithm has higher accuracy and reliability in the field of wireless communication links， and can provide more accurate prediction information， providing strong support for the reliability evaluation and optimization of wireless communication links.

Key words: GA-Elman, wireless communication links, confidence interval prediction, link quality, signal-to-noise ratio, wavelet decomposition

中图分类号:

TN92

王娜娜,韩升. 基于GA-Elman的无线通信链路可靠性置信区间预测算法[J]. 吉林大学学报(工学版), 2025, 55(10): 3394-3400.

Na-na WANG,Sheng HAN. GA-Elman based confidence interval prediction algorithm for wireless communication link reliability[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(10): 3394-3400.

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参考文献 15

[1]	宋芳, 钱罕林, 何薇. 无线通信网络链路资源分布式智能调度算法[J]. 自动化技术与应用, 2022, 41(4): 63-66.
	Song Fang, Qian Han-lin, He Wei. Distributed intelligent scheduling algorithm for wireless communication network link resources[J]. Automation Technology and Applications, 2022, 41(4): 63-66.
[2]	文辉. 基于无线传感器网络弹性路由的链路可靠性预测模型[J]. 单片机与嵌入式系统应用, 2023, 23(7):8-11, 16.
	Wen Hui. Link reliability prediction model based on elastic routing in wireless sensor networks[J]. Application of Microcontrollers and Embedded Systems, 2023, 23(7): 8-11, 16.
[3]	Salehiyan A, Emadi M J.Performance analysis of uplink optical wireless communications in the presence of a simultaneously transmitting and reflecting reconfigurable intelligent surface[J].IET Optoelectronics, 2023,17(4):129-138.
[4]	李新钰. 基于SPN的LTE无线通信可靠性建模研究[J]. 无线互联科技, 2023, 20(1): 5-7.
	Li Xin-yu. Research on reliability modeling of LTE wireless communication based on SPN[J]. Wireless Interconnection Technology, 2023, 20(1): 5-7.
[5]	Wang J, Wang J Q, Chen Y Q, et al.Fractional stochastic configuration networks-based nonstationary time series prediction and confidence interval estimation[J]. Expert Systems with Applications, 2022, 192(3): No. 116357.
[6]	程真英, 江文姝, 方旭, 等. 基于遗传算法和Elman神经网络的接触式探头动态特性补偿[J]. 机械工程学报, 2022, 58(10): 24-30.
	Cheng Zhen-ying, Jiang Wen-shu, Fang Xu, et al. Dynamic compensation of contact probe based on genetic algorithm and Elman neural network[J]. Journal of Mechanical Engineering, 2022, 58(10): 24-30.
[7]	张书毓, 施伟斌, 王文龙, 等. 基于线性回归的无线传感器网络链路质量估计方法研究[J].计算机应用研究, 2022, 39(7): 2132-2136, 2155.
	Zhang Shu-yu, Shi Wei-bin, Wang Wen-long, et al. Research on link quality estimation method for wireless sensor networks based on linear regression[J]. Computer Application Research, 2022, 39(7): 2132-2136, 2155.
[8]	孟超, 周倩, 郭林, 等. 基于相关性传输模型的无线链路质量估计方法及路由优化算法[J]. 电子学报, 2022, 50(10): 2409-2424.
	Meng Chao, Zhou Qian, Guo Lin, et al. Wireless link quality estimation method and routing optimization algorithm based on correlation transmission model[J]. Acta Electronica Sinica, 2022, 50(10): 2409-2424.
[9]	赵夙, 王伟, 朱晓荣, 等. 基于自适应网络编码的异构无线链路并发传输控制方法研究[J]. 电子与信息学报, 2022, 44(8): 2777-2784.
	Zhao Su, Wang Wei, Zhu Xiao-rong, et al. Research on concurrent transmission control method for heterogeneous wireless links based on adaptive network coding[J]. Journal of Electronics and Information Technology, 2022, 44(8): 2777-2784.
[10]	王渭, 马梦轩, 王圣杰, 等. 基于小波分析的电力信息通信过程不良数据辨识研究[J]. 自动化技术与应用, 2022, 41(10): 129-132.
	Wang Wei, Ma Meng-xuan, Wang Sheng-jie, et al. Research on identification of bad data in power information communication process based on wavelet analysis[J]. Automation Technology and Application, 2022, 41(10): 129-132.
[11]	丁嘉伟, 冯乃勤. 云计算平台下基于小波分解的数据同步采集[J]. 计算机仿真, 2024, 41(1): 390-394.
	Ding Jia-wei, Feng Nai-qin. Data synchronization acquisition based on wavelet decomposition on cloud computing platform[J]. Computer Simulation, 2024, 41(1): 390-394.
[12]	皮志勇, 朱益, 廖玄, 等. 智能变电站多信息融合建模的通信链路故障定位方法[J].中国电力, 2023, 56(8): 207-215.
	Pi Zhi-yong, Zhu Yi, Liao Xuan, et al. Communication link fault location method based on multi information fusion modeling for intelligent substations[J]. China Electric Power, 2023, 56(8): 207-215.
[13]	徐武, 汤弘毅, 杨昊东, 等. 基于ISSA优化的Elman神经网络短期电力负荷预测[J]. 扬州大学学报:自然科学版, 2022, 25(1): 37-42.
	Xu Wu, Tang Hong-yi, Yang Hao-dong, et al. Elman neural network short-term power load forecasting based on ISSA optimization[J]. Journal of Yangzhou University(Natural Science Edition), 2022, 25(1): 37-42.
[14]	齐放, 张成, 柯旺松, 等.基于风险均衡度的电力5G通信链路自动选择方法研究[J]. 电子技术应用, 2023,49(4):1-6.
	Qi Fang, Zhang Cheng, Ke Wang-song, et al. Research on automatic selection method of power 5G communication link based on risk balance degree[J]. Electronic Technology Application, 2023, 49(4): 1-6.
[15]	王剑楠, 崔英花. 一种基于种群多样性的新型自适应遗传算法[J]. 太赫兹科学与电子信息学报, 2023, 21(5): 671-676.
	Wang Jian-nan, Cui Ying-hua. A novel adaptive genetic algorithm based on population diversity[J]. Journal of Terahertz Science and Electronic Information, 2023, 21(5): 671-676.

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参数	实际值
变电站电网电压/kV	220
变压器额定容量/（MV·A）	100
无线通信系统频段/GHz	2.4
通信速率/Mbps	100
日常网络通信流量/Mbps	10~50
流量波动率/%	±10
噪声传感器型号	JHM-NS02
测量范围/dB	30~120
测量误差/dB	±1.5
转换精度/dB	0.1
频率响应/Hz	35~20000