Performance Enhancement of Massive MIMO Using Optimal Antenna Selection Technique Towards Antenna Sub Array

Antenna selection is performed by the use of multi-input multi-output (MIMO) equipment, whereby leverages radio frequency (RF) switching that chooses a preferred subset of antennas. It appeals to high MIMO systems because it reduces the need for a large number of RF transceivers. In large MIMO antenna selection systems, RF switching arrangements should be carefully addressed. When considering circuit power consumption (CPC) in fifth-generation networks, energy efficiency (EE) is a significant design factor. As the number of antennas in huge MIMO systems grows, various challenges develop due to interference for channel state information. To overcome the above existing drawbacks; this study proposed a Performance Enhancement of Massive MIMO using the Optimal Antenna Selection Technique towards Antenna Sub Array. Learning-based Monte Carlo sunflower optimization technique to address the best antenna selection problem for a huge MIMO system (MC-SFO). Antenna selection methods improve system energy efficiency while at the base station, limiting the density of radio frequency (RF) chains. MC-SFO-based optimum antenna selection methods dynamically activate a subset of its antennas to reduce power consumption and improve the system's energy efficiency. Finally, the results of the experiments show that our proposed strategy outperforms previous methods.