Adaptive Deep Learning Strategy with Red Deer Algorithm for Sparse Channel Estimation and Hybrid Precoding in Millimeter Wave Massive MIMO-OFDM systems

Millimeter-wave massive multiple-input multiple-output (MIMO) employs a lens antenna array for minimizing the radio frequency (RF) chains count, yet it remains as a challenge since the RF chain count is lesser when compared to the antennas. The beamspace channel estimation is defined as a sparse signal recovery problem through the exploitation of sparsity of beamspace channels. In the case of multi-user millimeter wave (mmWave) MIMO-OFDM systems, the critical task to lessen the cost as well as the complexity is the hybrid precoding attaining an adequate sum-rate. The conventional approaches related to the hybrid precoding works on the basis of the greedy or optimization techniques. These techniques suffer from higher complexity or contain sub-optimum performance. The main intent of this paper is to plan for the adaptive deep learning strategy for sparse channel estimation and hybrid precoding in millimeter-wave massive MIMO-OFDM communication system. The proposed methodology covers two main phases (a) uplink channel estimation, and (b) hybrid precoding for downlink data transmission. In the first phase, an adaptive deep neural network (ADNN) is used for performing the uplink channel estimation. Here, the benchmark dataset is used for training the information to be predicted at base station regarding the channel. Here, the ADNN-based channel estimation covers both channel amplitude estimation and channel reconstruction. Once the channel reconstruction is done, hybrid pre-coding performs the downlink data transmission that involves both digital and analog pre-coders. Here, the adaptive long short term memory (ALSTM) is used for performing the hybrid pre-coding, in which the estimated channel vectors is used for training. For both sparse channel estimation and hybrid-precoding, the trial-based red deer algorithm (T-RDA) is used for improvising the ADNN and ALSTM, in which it optimizes or tunes the training hidden neurons. The main objective of tuning the hidden neurons in both phases of T-RDA is to maximize the accuracy. When SNR equals 15 dB, the NMSE of the T-RDA-ADNN is 40%, 25%, 37.5%, and 34.78% improved than RDA-ADNN, SFO-ADNN, GWO-ADNN, and PSO-ADNN for channel estimation. Similarly, at SNR 20 dB, the spectral efficiency of T-RDA-ADNN is 37.5%, 43.48%, 22.22%, and 94.12% advanced than RDA-ADNN, SFO-ADNN, GWO-ADNN, and PSO-ADNN for hybrid precoding. Thus, the simulation outcomes show that the developed sparse channel estimation and hybrid pre-coding in mmWave massive MIMO-OFDM communication system has attained lesser error and high spectral efficiency when differentiated over the existing approaches.