Research on Wireless Propagation Channel Model Based on Recurrent Neural Network in High-speed Railway
In order to solve the problem of signal instability in wireless communication caused by high-speed rail,LSTM cyclic neural network is used to design equalizer and assist channel construction.A basic channel model is established.According to the signal changes brought by high-speed railway environment,an adaptive equalizer is designed by using LSTM cyclic neural network to track the channel.The results show that the minimum signal symbol error rate of LSTM cyclic neural network e-qualizer can be reduced to less than 0.4％.The results also show that in the face of high-speed wireless signals,the design of equalizer based on LSTM cyclic neural network can help channel construction,realize long-distance non differential signal prop-agation,and contribute to the development of high-speed railway network.
万方数据
维普
