Research on Signal Detection of OFDM Systems Based on the LSTM Network Optimized by the Improved Chameleon Swarm Algorithm

Abstract

In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement.

Keywords

• OFDM systems
• signal detection
• channel estimation
• LSTM network
• chameleon swarm algorithm