Abstract
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.








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SG: This work is a part of the PhD thesis. Literature survey, research problem findings, simulation using MATLAB, analyzing the simulation results are conducted by this author. The author has prepared the original draft. Checking the English grammar of the original draft, Editing and reviewing are conducted by him. PM: Conceptualization and supervision of the study is done by this author. Methodology of the controller, validation, helping in writing the original draft, Editing and reviewing are conducted by him / her.
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Gaikwad, S., Malathi, P. Performance Enhancement of Massive MIMO Using Optimal Antenna Selection Technique Towards Antenna Sub Array. Wireless Pers Commun 132, 1273–1291 (2023). https://doi.org/10.1007/s11277-023-10660-5
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DOI: https://doi.org/10.1007/s11277-023-10660-5