Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Abstract

AbstractMillimeter‐Wave (mmWave) massive Multi‐User Multiple Input Multiple Output (MU‐MIMO) utilizes the hybrid analog precoding design helps to minimize the amount of Radio‐Frequency (RF) chains without causing loss of sum‐rate performance. The Channel Estimation (CE) is designed to demand the conditions of channels with high time‐varying features. The existing CE frameworks are extremely complex. The accurate CE is still challenging in mmWave massive MIMO system based on the hardware constraints, a maximum number of antennas, and so on. Various proficient algorithms were combined to yield reliable channel estimates. The mmWave massive MIMO system utilizes hybrid precoding to minimize energy consumption loss and reduce hardware complexity. The fully connected layer in hybrid precoding results in high energy efficiency loss. This proposed work aims to develop two ensemble deep learning models for CE and hybrid precoding of the “mmWave massive MIMO system.” Here, the estimation of the channel is performed by a “deep Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN).” With the reconstructed channel, the hybrid precoding phase is performed by deep CNN with AdaBoost. The performance of CE and hybrid precoding is improved with Self Adaptive Searched Galactic Swarm Optimization (SAS‐GSO) by attaining the maximum spectral efficiency and also minimizing the Normalized Mean‐Squared Error (NMSE). Hence, the simulation result is shown that the proposed model achieves enhanced performance. Throughout the analysis, at the 20th iteration, the SAS‐GSO‐ensemble approach gives a lower NMSE rate, which is 31%, 25.9%, 20%, 19.6%, 19.3%, 18.6%, and 18.3% superior to OMP‐Ensemble, AMP‐Ensemble, DLCS‐Ensemble, DGMP‐Ensemble, QALS‐Ensemble, DLQP‐Ensemble, and CSO‐Ensemble, respectively, for hybrid precoding. Therefore, the offered mmWave massive MIMO is achieved better performance regarding diverse error metrics.