LSTM-Based Channel Estimation Method in Time-Varying Channels

doi:10.12068/j.issn.1005-3026.2023.11.001

Abstract

Abstract: Aiming to address the limitations of traditional channel estimation methods in time-varying channel environments， as well as the low estimation accuracy or high complexity of deep learning-based channel estimation methods， a channel estimation network based on long short-term memory structure is proposed， which consists of a bidirectional long short-term memory(BiLSTM)network and a multilayer perceptron(MLP)network， namely BiLSTM-MLP. First， the BiLSTM network is used to learn the time-varying characteristics of the channel. Then， a MLP network is used to denoise and reconstruct the channel estimation. Simulation results show that the proposed channel estimation method has better performance than traditional methods， and has lower complexity and better performance compared with the same type of deep learning-based estimation methods. Furthermore， the proposed method is also robust to different pilot densities.

Key words: time-varying channel; channel estimation; deep learning; long short-term memory (LSTM); multilayer perceptron (MLP)

CLC Number:

TN911.7

JI Ce， WANG Xin， GENG Rong， LIANG Min-jun. LSTM-Based Channel Estimation Method in Time-Varying Channels[J]. Journal of Northeastern University(Natural Science), 2023, 44(11): 1521-1528.

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