Optimization for Multi User-MIMO using BAT Algorithm Under Perfect Channel State Information

Abstract

Data rates are being requested as the speed of wireless transmission increases. This, in turn, emphasises how spectral and EE may be improved under poor channel conditions. The main purpose of this study is to increase channel capacity by lowering the bit rate by minimizing channel error estimates. The bit error rate may be decreased in order to boost capacity. The capacity of the MU-MIMO system is assessed using an appropriate compressed sensing model channel estimator. The MU-MIMO model training sequence design and optimization is determined by block and auto coherence matrices. The BAT algorithm is used to optimize a block coherence matrix to yield less coherence for varying degrees of sparsity. The signal to noise ratio performance of SU-MIMO utilizing Genetic algorithm is increased by 1dB, whereas MU-MIMO performance is stated to be 0.93dB utilizing the BAT algorithm.