Channel estimation of 5G OFDM system based on ConvLSTM network

Abstract

In view of the requirement of high speed and low delay in 5G system, traditional channel estimation algorithms are difficult to meet the requirements. This paper regards the channel estimation problem in communication systems as an image processing problem in deep learning, and proposes a channel estimation network based on ConvLSTM network. Convolutional neural network is used in channel estimation, and LSTM structure is introduced to capture the correlation of the channel. The parameters are set to generate the channel data information set of the physical downlink shared channel (PDSCH) based on the 5G new radio (NR) standard, which is used to evaluate the performance of the proposed and existing algorithms. Experimental simulations show that the proposed algorithm has obvious performance improvement and strong robustness compared with least squares algorithm, practical channel estimation and T-CNN network based on image processing.