This paper investigates the problem of position estimation and tracking in indoor visible light communication (VLC) systems under a multiple-input single-output (MISO) configuration. The study specifically addresses the impact of signal-dependent shot noise (SDSN) and relative intensity noise (RIN), which significantly degrade localization accuracy in practical VLC scenarios. To establish a theoretical performance benchmark, a novel closed-form Bayesian Cramér-Rao lower bound (BCRLB) is derived for two-dimensional (2D) position estimation. The proposed extended Kalman filter (EKF) demonstrates superior localization accuracy by effectively accounting for motion dynamics through its recursive updates, unlike estimation methods that rely solely on noisy distance measurements. Our findings show that increasing the number of light sources improves spatial diversity, thereby enhancing positioning precision. Furthermore, analysis indicates that RIN has a more pronounced negative effect on tracking accuracy than SDSN. The EKF’s performance is validated against the proposed BCRLB, confirming its suitability for real-time and reliable indoor localization. Overall, this work provides meaningful insights into the design of robust and efficient VLC-based positioning systems.

Laser diode (LD) powered luminaires are already gaining traction in automotive applications and are expected to be increasingly deployed in homes and offices in the near future. This gives a unique opportunity to build visible light communication (VLC) systems with ultra-high-bandwidths offered by the LDs. However, LDs are subject to some inherent noise sources, notably signal-dependent shot noise (SDSN) and relative intensity noise (RIN). RIN basically describes the instability in the power level of a laser and quantifies the output power fluctuations. SDSN arises from variations in the photodetector’s response to optical signals. Our research explores the interplay between SDSN and RIN, revealing their combined adverse effect on channel estimation accuracy in a VLC system. Towards this direction, we first derive the Cramér-Rao lower bound (CLRB) in the presence of the SDSN and the RIN, which gives a lower estimate for the variance of an unbiased estimator. Then, we present the derivation of least square (LS) and maximum likelihood (ML) channel estimators. Furthermore, we present the optimal receiver in ML sense and compare it with a simple threshold detector as a sub-optimal solution, quantifying the impact of channel estimation accuracy on both receivers. Our study results reveal the adverse impact of the joint presence of SDSN and RIN on the channel estimation error; consequently, it degrades the BER of the two proposed receivers. Moreover, RIN emerges as the dominant noise source, especially at higher levels of transmitted power.

This article presents a novel decision fusion algorithm for Internet-of-Things-based wireless sensor networks, where multiple sensors transmit their decisions about a certain phenomenon to a remote fusion center (FC) over a wide area network. The proposed algorithm denoted as the individual likelihood approximation (ILA) can significantly reduce the decision fusion error probability performance while maintaining the low computational complexity of other state-of-the-art fusion algorithms. The performance of the ILA rule is evaluated in terms of the global fusion probability of error, and an efficient analytical expression is derived in terms of a single integral. The analytical results corroborated by Monte Carlo simulation show that the ILA significantly outperforms all other considered rules, such as the Chair-Varshney (CV) and MaxLog rules. Moreover, the impact of the link from the cluster head to the FC, which is modeled as a binary symmetric channel with unknown transition probabilities, has been investigated. It is shown that the probability of error over such links should not exceed 10-3to avoid severe performance degradation. Furthermore, we derive a closed-form expression for the system fusion error probability of the CV rule for the most general system parameters.