基于深度学习网络的光纤通信网络信道均衡方法

Channel equalization method of optical fiber communication network based on deep learning network

为克服由传输信道的非线性以及多径效应引起的符号间干扰,利用深度学习网络优化设计光纤通信网络信道均衡方法。在光纤通信原理的支持下,构建光纤通信网络信道模型。利用深度学习网络估计通信网络信道负载与运行状态,在信道容量的约束下,利用装设的通信网络信道均衡器实现信道均衡处理功能。通过性能测试得出结论:综合考虑四种不同的干扰环境,通过优化设计信道均衡方法的应用,光纤通信网络信道的最大传输误码率和最大信道间传输能耗偏度分别为1.2%和0.61,均低于预设值。

In order to overcome the inter-symbol interference caused by the nonlinearity of the transmission channel and the multipath effect,the channel equalization method of optical fiber communication network is optimized by using the deep learning network.Based on the principle of optical fiber communication,the channel model of optical fiber communication network is constructed.The deep learning network is used to estimate the channel load and operation state of the communication network.Under the constraint of channel capacity,the channel equalization processing function is realized by the installed communication network channel equalizer.Through the performance test,it is concluded that the maximum transmission error rate and the maximum inter channel transmission energy consumption skewness of the optical fiber communication network channel are 1.2%and 0.61 respectively,which are lower than the preset value.