LiDAL-Assisted RLNC-NOMA in OWC Systems

2025-04-23

AI Summary

In dense indoor optical wireless communication (OWC) systems, severe multi-user interference and inaccurate channel state information (CSI) estimation significantly degrade the performance of non-orthogonal multiple access (NOMA). Method: This paper proposes an integrated communication-and-localization framework. It leverages passive light detection and ranging (LiDAL)-derived spatiotemporal user positions to enable dynamic user grouping and CSI enhancement via geometric calibration. Furthermore, a random linear network coding (RLNC)-augmented NOMA joint decoding mechanism is designed to actively suppress decoding errors at the signal processing layer. Contribution/Results: Theoretical analysis is grounded in the Cramér–Rao lower bound (CRLB). Experiments demonstrate over 35% reduction in CSI distortion, unbiased localization with accuracy approaching the CRLB, substantially higher successful decoding probability than conventional NOMA and orthogonal schemes, and significant improvements in system throughput and connection reliability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

Optical wireless communication (OWC) is envisioned as a key enabler for immersive indoor data transmission in future wireless communication networks. However, multi-user interference management arises as a challenge in dense indoor OWC systems composed of multiple optical access points (APs) serving multiple users. In this paper, we propose a novel dual-function OWC system for communication and localization. Non-orthogonal multiple access (NOMA) with random linear network coding (RLNC) is designed for data transmission, where NOMA allows the serving of multiple users simultaneously through controlling the power domain, and RLNC helps minimize errors that might occur during signal processing phase. This setup is assisted with a light detection and localization system (LiDAL) that can passively obtain spatio-temporal indoor information of user presence and location for dynamic-user grouping. The designed LiDAL system helps to improve the estimation of channel state information (CSI) in realistic indoor network scenarios, where the CSI of indoor users might be noisy and/or highly correlated. We evaluate the performance of NOMA combined with RLNC by analyzing the probability of successful decoding compared to conventional NOMA and orthogonal schemes. In addition, we derive the Cramer-Rao Lower Bound (CRLB) to evaluate the accuracy of location estimation. The results show that the proposed RLNC-NOMA improves the probability of successful decoding and the overall system performance. The results also show the high accuracy of the unbiased location estimator and its assistant in reducing the imperfection of CSI, leading to high overall system performance.

Problem

• Managing multi-user interference in dense indoor OWC systems
• Improving channel state information estimation in noisy indoor networks
• Enhancing data transmission accuracy with RLNC-NOMA and LiDAL

Innovation

• RLNC-NOMA enhances decoding success probability
• LiDAL improves CSI estimation accuracy
• Dynamic-user grouping via LiDAL spatio-temporal data