Robust channel estimation, localization, and tracking in visible light systems under signal-dependent noise

Abstract

Visible Light Communication (VLC) is an emerging wireless technology that employs light-emitting diodes (LEDs) or lasers to transmit data over the visible light spectrum. Due to its inherent advantages, such as license-free spectrum, high data rates, low power consumption, and enhanced security, VLC has attracted significant attention for a broad range of applications, including indoor wireless networking, vehicular communications, underwater communications, smart lighting, and indoor positioning systems. Additionally, VLC is immune to electromagnetic interference, making it particularly suitable for environments where radio-frequency systems are undesirable or restricted. Despite these advantages, VLC systems face several challenges, including limited coverage range, susceptibility to interference from ambient light sources, and performance degradation under mobility. One of the most critical challenges arises from noise, which may originate from the inherent properties of light and hardware components. In particular, signal-dependent shot noise (SDSN) and relative intensity noise (RIN) significantly degrade the accuracy of channel estimation and localization in practical VLC systems. In the first part of this thesis, we focus on channel estimation in the presence of SDSN, proposing a neural-network-augmented estimation framework that integrates with traditional estimators such as least squares (LS) and maximum likelihood estimation (MLE).We develop a complete mathematical framework enabling analytical mean-square-error (MSE) derivations and fair benchmarking. Simulations show that, under SDSN, the proposed method consistently outperforms LS while remaining competitive when SDSN is absent. In the second part, we extend to visible light positioning (VLP) under SDSN. For a SISO VLC system, we study range estimation using the extended Kalman filter (EKF), MLE, and nonlinear least squares (NLS), and establish Bayesian Cramér–Rao lower bounds (BCRLBs). Monte Carlo results confirm the analysis and demonstrate EKF’s superior accuracy. Finally, we address 2D localization and tracking for dynamic targets in a multipleinput single-output (MISO) VLC configuration. Accounting for both SDSN and RIN, we derive a closed-form 2D-BCRLB and show that EKF’s recursive updates yield superior real-time tracking compared to static measurement-based methods. We further show that increasing the number of light sources improves spatial diversity, and that RIN has a more pronounced adverse effect on tracking accuracy than SDSN. Overall, this thesis provides a noise-aware framework that bridges traditional estimation with machine learning for robust VLC channel estimation and localization.