Optimal and Robust Power Allocation for Visible Light Positioning Systems under Illumination Constraints

TL;DR

This paper proposes optimal and robust power allocation strategies for LED transmitters in visible light positioning systems, aiming to enhance localization accuracy while respecting illumination and power constraints, and accounting for system uncertainties.

Contribution

It introduces convex optimization-based methods for optimal and robust power allocation in VLP systems, considering practical constraints and parameter uncertainties.

Findings

Improved localization accuracy with proposed strategies.

Robust algorithms handle parameter uncertainties effectively.

Energy-efficient power allocation under accuracy constraints.

Abstract

The problem of optimal power allocation among light emitting diode (LED) transmitters in a visible light positioning (VLP) system is considered for the purpose of improving localization performance of visible light communication (VLC) receivers. Specifically, the aim is to minimize the Cram\'{e}r-Rao lower bound (CRLB) on the localization error of a VLC receiver by optimizing LED transmission powers in the presence of practical constraints such as individual and total power limitations and illuminance constraints. The formulated optimization problem is shown to be convex and thus can efficiently be solved via standard tools. We also investigate the case of imperfect knowledge of localization parameters and develop robust power allocation algorithms by taking into account both overall system uncertainty and individual parameter uncertainties related to the location and orientation of the VLC receiver. In addition, we address the total power minimization problem under predefined accuracy requirements to obtain the most energy-efficient power allocation vector for a given CRLB level. Numerical results illustrate the improvements in localization performance achieved by employing the proposed optimal and robust power allocation strategies over the conventional uniform and non-robust approaches.