CRLB Optimization for O-RIS-Assisted VLP Systems

ZHANG Zengjie; WU Qi; ZHANG Jian; DUAN Ruijie; FENG Yunhan

doi:10.11999/JEIT260120

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ZHANG Zengjie, WU Qi, ZHANG Jian, DUAN Ruijie, FENG Yunhan. CRLB Optimization for O-RIS-Assisted VLP Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260120

Citation:

ZHANG Zengjie, WU Qi, ZHANG Jian, DUAN Ruijie, FENG Yunhan. CRLB Optimization for O-RIS-Assisted VLP Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260120

ZHANG Zengjie, WU Qi, ZHANG Jian, DUAN Ruijie, FENG Yunhan. CRLB Optimization for O-RIS-Assisted VLP Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260120

Citation:

ZHANG Zengjie, WU Qi, ZHANG Jian, DUAN Ruijie, FENG Yunhan. CRLB Optimization for O-RIS-Assisted VLP Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260120

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CRLB Optimization for O-RIS-Assisted VLP Systems

doi: 10.11999/JEIT260120 cstr: 32379.14.JEIT260120

Information Engineering University, Zhengzhou 450007, China

Funds: This study was supported by the National Natural Science Foundation of China (NSFC) under grant 62571549, 62331024 and 62271505

Accepted Date: 2026-04-23
Rev Recd Date: 2026-04-23

Available Online: 2026-05-16

Abstract

Abstract

Objective With the rapid development of indoor location-based services, Visible Light Positioning (VLP) has emerged as a promising high-accuracy positioning technology. The integration of Optical Reconfigurable Intelligent Surfaces (O-RIS) into VLP systems can effectively enhance signal coverage and improve positioning performance. However, optimizing the positioning accuracy and fairness across different user areas in RIS-assisted VLP systems remains a challenging issue. This study focuses on optimizing the Cramer-Rao Lower Bound (CRLB) of the system under both near-field and far-field channel models, aiming to enhance overall positioning precision and fairness through RIS configuration. Methods Under the far-field channel model assumption, the RIS orientation optimization problem is formulated as a received power maximization problem. A positioning algorithm combining Particle Swarm Optimization (PSO) and N-step iteration is proposed to dynamically adjust the RIS orientation optimally without prior knowledge of the receiver’s position. Under the near-field channel model assumption, the allocation problem between RIS elements and LEDs is constructed as a Markov Decision Process (MDP). A reinforcement learning method based on experience replay and knowledge utilization is designed to solve this problem, aiming to minimize the CRLB while ensuring positioning fairness for users in different regions. Results and Discussions Simulation results demonstrate that the proposed algorithms effectively enhance system positioning performance under both models. In the far-field model, the PSO-based iterative algorithm achieves dynamic optimization of RIS orientation, significantly improving positioning accuracy (Fig. 3). Under the near-field model, the reinforcement learning approach not only minimizes the CRLB but also considerably improves positioning fairness across the entire area, with a noticeable reduction in performance disparity among users in different zones (Fig. 5, Fig. 6). Comparative experiments show that the proposed methods outperform conventional RIS configuration strategies in terms of both average positioning error and fairness index (Table 1). Conclusions This paper investigates CRLB optimization methods for O-RIS-assisted VLP systems under near-field and far-field channel models. In the far-field scenario, a PSO-based iterative algorithm is proposed to optimize RIS orientation, enhancing positioning accuracy without requiring prior receiver location information. In the near-field scenario, a reinforcement learning-based approach is designed to optimize RIS element–LED allocation, which effectively minimizes the CRLB and improves positioning fairness across the whole area. Simulation results validate the effectiveness of the proposed algorithms in both models. Future work may consider more practical channel impairments and multi-user scenarios to further improve the robustness and scalability of the system.

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References(30)

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