一种基于改进ANN的光纤通信网络链路均衡方法  

A link equalization method for fiber optic communication networks based on improved ANN

作　　者：宇文骊敏[1] YUWEN Limin(State Grid Jibei Langfang Electric Power Supply Co.,Ltd,Langfang Hebei 065000,China)

机构地区：[1]国网冀北廊坊供电公司,河北廊坊065000

出　　处：《太赫兹科学与电子信息学报》2025年第4期410-415,共6页Journal of Terahertz Science and Electronic Information Technology

摘　　要：针对目前强度调制和直接检测(IMDD)短距离光纤通信系统补偿非线性失真时存在较高的实现复杂度的问题,提出一种基于自适应人工神经网络(ANN)均衡器。结合光通信网络特点,确定ANN中输入层、隐藏层节点和训练样本的数量;考虑到光纤系统的噪声以及由于色散引起的失真,对训练样本进行扩展,从而提高ANN均衡器泛化能力;自适应调整ANN均衡器权重,并利用权重值的小变化跟踪信道波动,缓解光纤通道的参数因环境条件的变化发生波动导致权重偏移的问题。试验结果表明,与ANN相比,所提自适应ANN的综合增益、计算复杂性和内存需求具备优势。该模型对噪声环境的光纤通信具备较强的鲁棒性,具有一定实用价值。To address the issue of high implementation complexity in compensating for nonlinear distortion in short-range optical fiber communication systems using Intensity Modulation and Direct Detection(IMDD),a method based on an Adaptive Artificial Neural Network(ANN)equalizer is proposed.By considering the characteristics of optical communication networks,the number of nodes in the input layer and hidden layer of the ANN,as well as the number of training samples,are determined.Taking into account the noise in the optical fiber system and the distortion caused by dispersion,the training samples are expanded to enhance the generalization capability of the ANN equalizer.The weights of the ANN equalizer are adaptively adjusted,and small changes in the weight values are utilized to track channel fluctuations,thereby alleviating the problem of weight offset caused by variations in the fiber channel parameters due to changes in environmental conditions.Experimental results show that compared to a non-adaptive ANN,the proposed adaptive ANN has advantages in terms of overall gain,computational complexity,and memory requirements.The model demonstrates strong robustness in optical fiber communication under noisy conditions and has practical value.

关 键 词：光纤通信 非线性失真 人工神经网络 训练样本 自适应 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]