Improving the Performance of a Massive Multi-Input Multi-Output System by Developing a Hybrid Beamforming Model

The massive MIMO ‘‘Multiple-Input Multiple-Output (MMIMO)’’ system uses beamforming techniques to transfer power directly. In current scenario, partially connected hybrid beamformers (HBF), rather than fully connected HBF, reduces the cost, power consumption and complexity of the hardware. As a result, an optimization-driven model for hybrid beam forming for massive multiple-input multiple-output is proposed to reduce interference and strengthen robustness. The proposed model optimizes both analog and digital beams. An improved Social SkiDriver Algorithm (ISSDA) is developed to obtain the optimal digital beam, and analog phase alignment-linear searching (APA-LS) is introduced to obtain the optimal analog beam. MATLAB simulation is utilized for evaluating the proposed algorithm. The simulation outcomes show a better performance in terms of signal-to-noise ratio (SNR) along with beam width. The proposed ISDDA-APALS method is compared to these other methods like MU-SIC, PDJ- BPSA, Hy-SBD and I-BAT.