Self-adaptive Differential Evolution based Optimized MIMO Beamforming 5G Networks


The industrial factory is one of the challenging environments for future wirelesscommunication systems, where the goal is to produce products with low cost in shorttime. This high level of network performance is achieved by distributing massiveMIMO that provides indoor networks with joint beamforming that enhances 5Gnetwork capacity and user experience as well. Judging from the importance of thistopic, this study introduces a new optimization problem concerning the investigationof multi-beam antenna (MBA) coverage possibilities in 5G network for indoorenvironments, named Base-station Beams Distribution Problem (BBDP). Thisproblem has an extensive number of parameters and constrains including user’slocation, required data rate and number of antenna elements. Thus, BBDP can beconsidered as NP-hard problem, where complexity increases exponentially as itsdimension increases. Therefore, it requires a special computing method that canhandle it in a reasonable amount of time. In this study, several differential evolution(DE) variants have been suggested to solve the BBDP problem. The results showthat among all DE variants the self-adaptive DE (jDE) can find feasible solutions andoutperform the classical ones in all BBDP scenarios with coverage rate of 85% andbeam diameter of 500 m.