Multi-Element VLC Networks: LED Assignment, Power Control, and Optimum Combining

TL;DR

This paper explores LED grouping, assignment, power control, and optimal combining in multi-element VLC networks to enhance throughput, fairness, and interference suppression, supported by efficient channel response simulation methods.

Contribution

It introduces novel LED assignment, power optimization, and an improved combining method for multi-element VLC networks, enhancing performance and reducing computational complexity.

Findings

Interference suppression improves SINR by 2-5 dB.

Proposed methods achieve higher throughput and fairness.

Efficient channel response calculation reduces simulation complexity.

Abstract

Visible light communications (VLC) is a promising technology to address the spectrum crunch problem in radio frequency (RF) networks. A major advantage of VLC networks is that they can use the existing lighting infrastructure in indoor environments, which may have large number of LEDs for illumination. While LEDs used for lighting typically have limited bandwidth, presence of many LEDs can be exploited for indoor VLC networks, to serve each user by multiple LEDs for improving link quality and throughput. In this paper, LEDs are grouped and assigned to the users based on received signal strength from each LED, for which different solutions are proposed to achieve maximum throughput, proportional fairness and quality of service (QoS). Additionally, power optimization of LEDs for a given assignment is investigated, and Jacobian and Hessian matrices of the corresponding optimization problem are derived. Moreover, for multi-element receivers with LED grouping at the transmitter, an improved optimal combining method is proposed. This method suppresses interference caused by simultaneous data transfer of LEDs and improves the overall signal-to-interference-plus-noise-ratio (SINR) by 2 dB to 5 dB. Lastly, an efficient calculation of channel response is presented to simulate multipath VLC channel with low computational complexity.