CRLB Optimization for O-RIS-Assisted VLP Systems

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

doi:10.11999/JEIT260120

Volume 48 Issue 8

Aug. 2026

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Article Navigation > Journal of Electronics & Information Technology > 2026 > 48(8): 3296-3305

ZHANG Zengjie, WU Qi, ZHANG Jian, DUAN Ruijie, FENG Yunhan. CRLB Optimization for O-RIS-Assisted VLP Systems[J]. Journal of Electronics & Information Technology, 2026, 48(8): 3296-3305. 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, 2026, 48(8): 3296-3305. 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: The National Natural Science Foundation of China (62571549, 62331024, 62271505)

Received Date: 2026-01-30
Accepted Date: 2026-04-23
Rev Recd Date: 2026-04-23

Available Online: 2026-05-16

Publish Date: 2026-08-01

Abstract

Abstract

Objective Visible Light Positioning (VLP) has become a promising high-accuracy technology for indoor location-based services. Optical Reconfigurable Intelligent Surface (O-RIS) technology can improve signal coverage and positioning performance in VLP systems. However, positioning accuracy and spatial fairness remain difficult to optimize in O-RIS-assisted VLP systems. This study optimizes the Cramer-Rao Lower Bound (CRLB) under both near-field and far-field channel models. The aim is to improve overall positioning accuracy and regional fairness through O-RIS configuration. Methods Under the far-field channel model, O-RIS orientation optimization is formulated as a received-power maximization problem. A positioning algorithm that combines Particle Swarm Optimization (PSO) with the N-step positioning algorithm is proposed. This method dynamically adjusts O-RIS orientation without prior knowledge of the receiver position. Under the near-field channel model, the allocation problem between O-RIS elements and Light-Emitting Diodes (LEDs) is formulated as a Markov Decision Process (MDP). A reinforcement learning method based on experience replay and knowledge use is designed to solve this problem. The method minimizes the CRLB while maintaining positioning fairness for users in different regions. Results and Discussions Simulation results show that the proposed algorithms improve positioning performance under both channel models. In the far-field model, the PSO-based iterative algorithm dynamically optimizes O-RIS orientation and improves positioning accuracy. The convergence behavior of the N-step positioning algorithm further shows that the method approaches the theoretical CRLB under high signal-to-noise ratios (Fig. 4, Fig. 5). In the near-field model, the reinforcement learning method reduces the CRLB and improves positioning fairness across the whole area. It also reduces performance differences among users in different regions (Fig. 6, Fig. 7, Fig. 8). The MERAC-based reinforcement learning procedure for intelligent resource allocation is summarized in Algorithm 1. Conclusions CRLB optimization methods for O-RIS-assisted VLP systems are investigated under near-field and far-field channel models. In the far-field scenario, a PSO-based iterative algorithm is proposed to optimize O-RIS orientation and improve positioning accuracy without prior receiver-location information. In the near-field scenario, a reinforcement learning-based method is designed to optimize O-RIS element-LED allocation. This method minimizes the CRLB and improves regional positioning fairness. Simulation results verify the effectiveness of the proposed algorithms under both models. Future work may consider more practical channel impairments and multi-user scenarios to improve system robustness and scalability.

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References(30)

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