Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

Abstract

Generally, Multiple-Input Multiple-Output (MIMO) improves radio communication with enhanced reliability and capacity. The transmitter and receiver side, since there is an occurrence of multiple antennas, the appropriate Transmit Antenna Selection (TAS) for obtaining effectual performance is still a difficult point. In this work, a TAS method in the LTE system by exploiting Self-Adaptive Particle Swarm Optimization (SAPSO) is proposed for enhancing the system performance. Moreover, it develops self adaptiveness in the PSO by deciding the ability enhancement attained by each candidate solution for the TAS issue goes after by updating the particle solution on the basis of the enhancement. The experimentation model considers both Rician and Rayleigh channel, for four antenna configurations such as 2×2, 3×2, 4×2 and 4×4. To the next of the experimentation, it evaluates the performance of SAPSO-TAS with conventional Genetic Algorithm (GA)-TAS, Artificial Bee Colony (ABC)-TAS, Firefly (FF)-TAS, PSO-TAS, and Grey Wolf Optimization (GWO)-TAS models.