Massive MIMO Technology Based Analysis of Allocation of Energy-Efficient Resources and Selecting a Transmit Antenna

Transmission reliability and channel capacity are both improved by Massive MIMO. Hardware expenses and deployment complexities may be minimized with the assistance of “Transmit Antenna Selection (TAS) and Resource Allocation (RA)”. “The RA and TAS of millimeter-wave massive MIMO wireless communication are the primary foci of this study”. MIMO refers to systems that take in data and send it out in different ways. This research not only presents a technique for properly distributing resources that takes use of Deep Learning, but it also increases the efficiency of the process referred to as Transmit Antenna Selection (TAS). To ensure fair and equal distribution of radio resources (such as power, space, time, and frequency) among all users, “Attention Based Capsule Auto-Encoder (ACAE) architecture” is used here. The BS additionally uses the Battle Royale Optimization (BRO) technique to choose the optimal antenna from a pool of options. Selecting the best antenna for each of the UE is an important part of this optimization method, which aims to reduce the network's power consumption. The performance of MIMO systems may be enhanced by using antennas that optimize for spectral efficiency (SE), energy efficiency (EE), return on investment (ROI), and diversity gain (DG). As a result, both the RA and optimum antenna selection strategies are used to enhance of the efficiency of the wireless communication to using the massive “MIMO technology” for wireless 5G communication process. The effectiveness of the proposed method has been shown using MATLAB simulations. Capacity, energy efficiency, and system effectiveness are all improved by the new approach.