基于卷积循环神经网络的短波时变信道盲均衡算法

Short-Wave Time-Varying Channel Blind Equalization Algorithm Based on Convolutional Recurrent Neural Network

针对短波时变信道码间干扰严重、误符号率高等问题,采用卷积循环神经网络(convolutional recurrent neural network,CRNN),即将卷积神经网络(convolutional neural network,CNN)和循环神经网络(recurrent neural network,RNN)相结合,提出一种基于CRNN的短波时变信道盲均衡算法,设计了针对短波时变信道(瑞利平坦衰落信道和频率选择性衰落信道)的卷积循环神经网络盲均衡器(convolution recurrent neural network blind equalizer,CRNNBE)。该盲均衡器基于CNN收敛速度快和RNN便于处理序列信号的特点,克服码间干扰问题,有效提高了通信质量。仿真实验结果表明:相比基于RNN与CNN的盲均衡器,训练完成后的CRNNBE准确率更高、交叉熵损失值更低,并且收敛速度明显高于RNN盲均衡器,模型在20次左右即可完成收敛;在短波时变信道中,整体而言,相比其他均衡器,在相同信噪比条件下,CRNNBE的误符号率最低,通信可靠性最高。

Aiming at the problems of severe inter-symbol interference and high symbol error rate in short-wave time-varying channels,convolutional recurrent neural network(CRNN),which combines convolutional neural network(CNN)and recurrent neural network(RNN),is used as blind equalization algorithm for the short-wave time-varying channel.A CRNN blind equalizer(CRNNBE)is designed for short-wave time-varying channels(such as Rayleigh flat fading channels and frequency selective fading channels).The blind equalizer is based on the fast convergence speed of CNN and the ease of processing sequence signals by RNN,which overcomes the problem of inter-symbol interference and effectively improves the communication quality.The simulation experiment results show that compared with the blind equalizer based on RNN and CNN,the CRNNBE after training has higher accuracy,lower cross-entropy loss,and the convergence speed is significantly higher than that of the RNN blind equalizer.The model can complete convergence in about 20 times.In the short-wave time-varying channel,as a whole,compared with other equalizers,under the same signal-to-noise ratio,CRNNBE has the lowest symbol error rate and the highest communication reliability.