Channel estimation using hybrid optimizer based recurrent neural network long short term memory for MIMO communications in 5G network

Abstract

In the fifth-generation (5G) networks, multiple input multiple-output (MIMO) systems are further developed to enhance transmission reliability. However, channel estimation is one of the major challenges which needs focus for improved data transmission in MIMO. Although efficient estimation techniques have been recently proposed, estimation accuracy needs to be upgraded further. Hence, an optimized Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) network is presented in this paper for channel estimation. At first, history of channel responses of pilot block is collected or estimated using Least Square (LS) channel estimation method. Using these collected channel responses, the proposed RNN-LSTM is trained where weight parameters are chosen optimally using hybrid Particle Swarm Optimization (PSO)-Adam optimizer. Using the trained PSO-Adam optimizer based RNN-LSTM, the current channel response is predicted or estimated. The performance of the proposed channel estimation scheme is analysed by varying pilot sequence length and number of antennas to evaluate the metrics Bit Error Rate (BER) and Mean Square Error (MSE). Complexity analysis of the proposed scheme is compared with standard estimators like LS and Minimum Mean-Square Error (MMSE).