Optimal transmit antenna selection for LTE system using self-adaptive grey wolf optimization

Abstract

In general, MIMO upgrades the radio communication with improved capacity and reliability. As there is a presence of multiple antennas at transmitter and receiver side, the proper Transmit Antenna Selection (TAS) for attaining effective performance is still a challenging point. This paper intends to introduce a TAS algorithm in LTE system using Self-Adaptive Grey Wolf Optimization (SAGWO) for improving the system performance. It introduces self-adaptiveness in the Grey Wolf Optimization (GWO) by determining the capacity improvement accomplished by each candidate solution for the TAS problem followed by updating the candidate solution based on the improvement. The simulation model considers both Rayleigh channel and Rician channel, for four antenna configurations like 2 $\times$ 2, 3 $\times$ 2, 4 $\times$ 2 and 4 $\times$ 4. To the next of the simulation, it compares the performance of SAGWO-TAS with EDB-TAS, ECB-TAS, ABC-TAS, GA-TAS, FF-TAS, PSO-TAS and GWO-TAS, i.e., traditional TAS models using Artificial Bee Colony (ABC), Ergodic Capacity (ECB), Euclidean Distance (EDB), Firefly (FF), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GWO, respectively. It observes the BER (bit error ratio) and mean BER at varied SNR (signal-to-noise ratio) in the analysis section. The analysis proves that the BER is highly reduced for proposed optimal TAS model.