Enabling Deep Learning and Swarm Optimization Algorithm for Channel Estimation for Low Power RIS Assisted Wireless Communications

Abstract

In this study, convolutional neural networks (CNN) and particle swarm optimization are used to offer a channel estimate technique for low power reconfigurable intelligent surface (RIS) assisted wireless communications (PSO). The suggested approach makes use of the RIS channels' sparsity to lower the CNN model's training complexity and uses PSO to optimize the CNN model's hyperparameters. The proposed system has been trained using 70% of dataset, 25% of data was used for testing and remaining 5% was used for cross-validation. In comparison to previous methods, simulation results demonstrate that the proposed method delivers correct channel estimate with much less computing cost. The suggested technique also exceeds current techniques in terms of bit error rate (BER) and mean squared error (MSE) performance. The research found 96.47% and 90.96% of accuracy for CNN and PSO algorithm respectively. Moverover, the network was trained using a dataset mentioned in methodology section for channel realizations, and achieved a mean squared error (MSE) value of 0.012 using CNN algorithm. Also, the study reported the proposed technique outperformed other state-of-the-art techniques. The proposed technique of PSO to optimize the channel estimation, and achieved a mean squared error (MSE) value of 0.0075.