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Switched-Beam Optimization for an Indoor Visible Light Communication Using Genetic Algorithm

Ladathunya Pumkaew, Monthippa Uthansakul*, Peerapong Uthansakul
School of Telecommunication Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
* Corresponding Author: Monthippa Uthansakul. Email:

Computers, Materials & Continua 2022, 71(1), 1547-1566.

Received 11 August 2021; Accepted 15 September 2021; Issue published 03 November 2021


Nowadays, Visible Light Communication (VLC) is an attractive alternative technology for wireless communication because it can use some simple Light Emitting Diodes (LEDs) instead of antennas. Typically, indoor VLC is designed to transmit only one dataset through multiple LED beams at a time. As a result, the number of users per unit of time (throughput) is relatively low. Therefore, this paper proposes the design of an indoor VLC system using switched-beam technique through computer simulation. The LED lamps are designed to be arranged in a circular array and the signal can be transmitted through the beam of each LED lamp with the method of separating the dataset to increase the number of simultaneous users for enhancing the indoor VLC. The coverage area is determined from the area where the communication can be performed at a location on the receiving plane with a Bit Error Rate less than or equal to the specified value based on coverage illuminance according to International Commission on Illumination (CIE) standards. In this paper, Genetic Algorithm is used to find the suitable solution for designing parameters to achieve maximum coverage area. The results show that a Genetic Algorithm can be used to find a suitable solution and reduce the computational time approximately 382 min in proposed scenarios.


Visible light communication; genetic algorithm; optimization; light emitting diode

Cite This Article

L. Pumkaew, M. Uthansakul and P. Uthansakul, "Switched-beam optimization for an indoor visible light communication using genetic algorithm," Computers, Materials & Continua, vol. 71, no.1, pp. 1547–1566, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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