Sunflower Optimization with Elite Learning Strategy (SFO-ELS) for Antenna Selection in Massive MIMO Subarray Switching Architecture

Massive MIMO is a promising technology used by fifth generation of wireless technology to increase the channel capacity significantly. But the use of RF transceivers for every antenna at the base station increases hardware complexity and implementation cost of the system making it very challenging for deployment. This paper focuses on addressing the hardware complexity and cost challenges associated with Massive Multiple-Input Multiple-Output systems. To mitigate these challenges, an efficient antenna selection algorithm is essential for identifying a subset of antennas that contribute maximum to the channel capacity. By employing advanced antenna selection schemes, this study aims to identify the most effective approach for optimizing antenna selection in Massive MIMO technology. The Sunflower Optimization algorithm, combined with the elite learning strategy, offers a novel approach to antenna selection in Massive MIMO subarray switching architecture. It leverages the benefits of both the SFO algorithm and the elite learning strategy to improve the selection of antennas, thereby optimizing system performance. Sunflower Optimization with elite learning strategy has been proposed and evaluated the effectiveness of the simulated SFO algorithm by comparing it with traditional approaches. The result shows that the proposed method for antenna selection significantly improves upon other methods offering a more efficient and effective approach for enhanced channel capacity in a Massive MIMO system.