Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

Abstract In mm-wave massive MIMO systems, the inefficiency of the digital or analogue precoders necessitates that each antenna has its own radio frequency chain. From this standpoint, a method of hybrid precoding that is economically viable has been devised. Digital beamformers are used for controlling antenna elements using short-dimensional precoding. From this standpoint, a method of hybrid precoding that is economically viable has been devised. During the channel estimation phase, we propose using deep recurrent convolutional neural networks (Deep RCNNs). Salp Swarm Algorithm (SSA) and Tuna Swarm Optimization (TSO) algorithms are combined in a hybrid meta-heuristic algorithm to improve Deep RCNN's channel estimation efficiency. O-BiLSTM, or optimised bidirectional long short-term memory, is utilised by hybrid precoding in order to execute the optimal precode across the anticipated channel. In relation to spectral efficiency and normalised mean square error (NMSE), the enhanced channel estimation approach surpasses the existing schemes through the implementation of Deep RCNN. The spectrum efficacy of the O-BiLSTM hybrid precoder design method is observed to be superior when compared to alternative approaches.