Deep Learning-Based Channel Estimation and Beamforming Architecture for Massive MIMO Systems

Channel estimation and beamforming are challenging in massive multiple input multiple output (MIMO) to increase array gain without using many radios frequency (RF) cables. Several state-of-the-art studies use AI algorithms to estimate channel and beamforming however; their computational complexity and power consumption limit their effectiveness. We suggested a hybrid technique to address the problems at hand, in which we construct both a transmit beamformer (precoder) and a receive beamformer (combiner) for each training cycle. Firstly, we use the deep learning method based on hybrid beamforming (DLM-HB). The proposed method includes optimal wireless channel selection using reinforcement learning with deep networks (RL-DQN). A field of view (FOV)-selective receiver focuses on signals from a specific angular range while minimizing interference from other directions. Antenna characteristics should be optimized to guarantee that signals are captured within the designated angular range. Data transmission involves encoding and transmitting data from a sender to a receiver over a communication channel, where the sender modulates the data. We then implement MIMO systems, which by their very nature can boost spectral efficiency since they can broadcast several data streams simultaneously. At this point, we used Alamouti. The goal of user scheduling in MIMO is to allocate spatial resources as efficiently as feasible while accounting for their availability. Alamouti STBC stands for space–time block coding. MIMO user scheduling aims to assign spatial resources as effectively as possible while taking into account their availability. Several characteristics, including processing time, bit error rate, spectral efficiency, and pilot overhead against NMSE, and SNR with NMSE and MSE, are used to verify the suggested technique.