Intelligent Antenna Selection For Energy-Efficient MIMO Communications Using Distribution-Based Chicken Swarm Optimization

Abstract

In recent years, the integration of energy harvesting techniques with large-scale multiple antenna systems has emerged as an effective approach to improve energy efficiency by utilizing renewable energy sources and reducing transmission power per user and antenna. Multiple Input Multiple Output (MIMO) systems employ multiple antenna elements at both the transmitter and receiver to enhance communication performance. However, the effectiveness of frequency-selective systems largely depends on how power is distributed across different frequency bands. Existing approaches often fall short in achieving optimal antenna selection while maintaining high energy efficiency in MIMO systems. To address this limitation, this work proposes a Distribution based Chicken Swarm Optimization (DCSO) algorithm for antenna selection. The proposed method optimizes transmit power, selection of active antennas, and antenna configurations at both the transmitter and receiver to improve overall system efficiency. By performing an extensive search process, the algorithm identifies the most suitable solution. Additionally, the proposed approach supports higher data rates and ensures better Quality of Service (QoS) for wireless communication. Considering challenges such as limited resources, fading channels, and user interference, the system focuses on improving spectral efficiency and reliability. The optimization framework incorporates sub-channel allocation, MIMO configuration, and bandwidth distribution to meet real-time application requirements. Experimental results demonstrate that the proposed DCSO-based model achieves superior performance compared to existing methods, including reduced Bit Error Rate (BER), lower energy consumption, and improved sum rate and spectral efficiency. This confirms that the approach provides an effective power allocation strategy by selecting optimal antenna elements based on the best fitness values.