Optimizing SNR for indoor visible light communication by using TLBO algorithm

Abstract

This article explores the selection of Communication Light-Emitting Diodes (C-LEDs) in a multiuser indoor visible light communication system. It is aimed to obtain uninterrupted and quality communication by selecting appropriate communication LEDs depending on the location of the users. In indoor visible light communication, the data from a group of C-LEDs reach the receiver through multiple transmission paths directly or as a result of reflections. Inter-symbol interference occurs due to these multiple transmission paths. As a result, the signal-to-noise ratio and the communication quality decrease. In this study, to guarantee a low bit error rate and high enough signal-to-noise ratio values for all users, a Teaching-Learning Based Optimization algorithm based on a valid rate threshold was used for selecting communication LEDs for the first time. The simulation results showed that the Teaching-Learning Based Optimization algorithm is suitable to improve the signal-to-noise ratio. Also, the time required to select the communication LEDs was shortened by determining the appropriate valid rate threshold value. When the Teaching-Learning Based Optimization algorithm was compared with other metaheuristic optimization algorithms used in the literature, it was seen that a better signal-to-noise ratio was obtained with lower iteration and population number.