Implementation of Hybrid Wild Geese Migration-Bird Swarm Algorithm-Based Optimal Power Allocation Strategy for Spectral Efficiency Analysis in Massive MIMO System

Abstract

Spectral Efficiency (SE) plays a crucial role in designing and transmitting the amount of data in wireless communication systems that is an important measure for validating the effectiveness of cellular systems. It determines the usage of a limited frequency spectrum, but it fails to measure consumed power. Numerous methods have been developed to analyze the Multiple-Input Multiple-Output (MIMO) system with diverse channel methods. This MIMO system effectively enhances the data throughput, and it can serve diverse users simultaneously within the same frequency band. It significantly enhances the system framework and minimizes the impact of signal fading. However, the MIMO system faces challenges like lower transmit power, maximized coverage space, higher spectral efficiency, and multiplexing gain. To solve these issues, a novel optimization algorithm is developed in massive Multi-User MIMO (MU-MIMO) for maximizing SE in a Downlink (DL) system. This DL communication system is utilized to determine the complex channel conditions among the Base Station (BS) and user equipment. Also, it provides better data transmission control, and it carefully manages the signal strength in the MIMO system. Here, many antennas are provided in BS to distribute individual antenna consumers at the same time in a similar frequency band, and the duplex mode time division uses the beamforming training scheme. An optimal resource allocation together determines the DL transmission signal power training, Uplink (UL) transmission signal power training, UL transmission training duration, and DL transmission to increase the SE, data signal power initiated from the complete given energy budget. Since SE is the main concern of this work, this paper implements an efficient heuristic mechanism for increasing SE in a Massive MIMO (M-MIMO) method. The M-MIMO system accurately provides the best cell edge coverage and maximizes network capacity. It achieves better spectral efficiency to enhance overall throughput performance. It has the ability to focus signals by developing a new model for better signal quality and neglects interferences in the network environment. Here, the hybrid optimization algorithm Integrating the Position of Wild Geese Migration Optimization Algorithm (WGMO) and Bird Swarm Algorithm (BSA) named as IPWGBS is implemented to perform the ideal Power Allocation (PA) for the end users in BSs. The IPWGBS algorithm effectively handles and solves large-scale optimization issues to reduce the risk of getting stuck in local optima. The computational complexity is analyzed through valid simulations for the developed optimal PA mechanism. The experiments demonstrate that the developed model provides enhanced SE with optimal resource allocation compared to previous works. From the statistical analysis, the overall performance of the designed model shows 3.6% ROA, 3.8% GTO, 3.0% GMO, and 4.0% BSA in terms of the best measure.