基于神经网络的计算机通信系统干扰信号分离

Interference Signal Separation of Computer Communication System Based on Neural Network

为了解决当前通信系统干扰信号分离方法存在的误码率高、通信质量差等问题,提出一种基于神经网络的计算机通信干扰信号分离方法.首先通过分析当前计算机通信干扰信号的分离研究现状,指出其局限性;然后建立计算机通信系统的信道模型,并采用神经网络对计算机通信系统的稳定性进行优化控制,将干扰信号分离;最后采用仿真实验对其有效性和优越性进行分析.结果表明,该方法可实现对计算机通信干扰信号的准确分离,提高了输出信号的信噪比,降低了通信系统的误码率.

In order to solve the problems of high bit error rate and poor communication quality in the current communication system interference signal separation method,we proposed a method of computer communication interference signal separation based on neural network.Firstly,the limitations were pointed out by analyzing the current computer communication interference signal separation.Secondly,the channel model of computer communication system was built,and neural networks was used to optimize and control the stability of computer communication system,and the interference signals were separated.Finally,the simulation experiment was used to analyze the validity and superiority.The results show that the proposed method can separate computer communication interference signal accurately,improve the signal to noise ratio of output signal,and reduce bit error rate of communioation system.