Improved Butterfly Optimization Algorithm for Energy Efficient Antenna Selection Over Wireless Cellular Networks

The emergence of a new trajectory in wireless networks can be attributed to the assessment of mobile devices and applications in the present decade. A recently developed approach that combines energy harvesting with large-scale multiple antenna technology has emerged as a promising means of enhancing energy efficiency through the utilization of renewable energy sources and the reduction of transmission power per user and per antenna. Multiple Input Multiple Output (MIMO) refers to systems with more than one antenna element in both the transmitting and receiving sections. In the existing system, energy efficiency and optimal antenna selection is not achieved in MIMO system. Hence, in this work, Improved Butterfly Optimization (IBFO) algorithm-based antenna selection is proposed. Using adaptive hybrid analog-digital beamforming, this research evaluates a fifth-generation (5G) MIMO millimeter wave (mmWave) wireless cellular beamforming system. In order to achieve the highest possible level of energy efficiency, finding the best transmit power, number of active antennas, and antenna subsets at both transmitter and receiver is the main focus. In order to maintain a higher data rate for wireless access, it is also employed to provide excellent Quality of Service (QoS). The optimization method uses sub-channel allocation, MIMO systems, and bandwidth allocation to offer the desired data rate for applications in real time. The proposed IBFO model improves wireless power allocation schemes by using the best fitness value and optimal antenna elements to lower Bit Error Rate (BER), energy consumption, sum rate, throughput, and spectral efficiency.