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Channel Estimation for Optical IRS-Assisted VLC System via Spatial Coherence

Shiyuan Sun, Fang Yang, Weidong Mei, Jian Song, Zhu Han, Rui Zhang

TL;DR

This work tackles the challenging problem of acquiring channel state information for optical IRSs in VLC systems. By revealing and quantifying the OIRS spatial coherence through a closed-form coherence distance $d_c$, it enables a spatial sampling strategy that partitions the OIRS into subarrays and estimates CSI locally, followed by an interpolation to reconstruct the full channel $oldsymbol{H}_c$. The proposed three-phase algorithm—designing a coherence-based association pattern, performing MMSE local estimations, and recovering the full CSI via 2D spline interpolation—substantially reduces pilot overhead and computational complexity with minimal performance loss. Numerical results corroborate the coherence property and demonstrate effective overhead-accuracy trade-offs in realistic indoor VLC scenarios. Overall, the approach offers a practical, scalable pathway for OIRS-enabled VLC performance gains without requiring full CSI in every pilot block.

Abstract

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm.

Channel Estimation for Optical IRS-Assisted VLC System via Spatial Coherence

TL;DR

This work tackles the challenging problem of acquiring channel state information for optical IRSs in VLC systems. By revealing and quantifying the OIRS spatial coherence through a closed-form coherence distance , it enables a spatial sampling strategy that partitions the OIRS into subarrays and estimates CSI locally, followed by an interpolation to reconstruct the full channel . The proposed three-phase algorithm—designing a coherence-based association pattern, performing MMSE local estimations, and recovering the full CSI via 2D spline interpolation—substantially reduces pilot overhead and computational complexity with minimal performance loss. Numerical results corroborate the coherence property and demonstrate effective overhead-accuracy trade-offs in realistic indoor VLC scenarios. Overall, the approach offers a practical, scalable pathway for OIRS-enabled VLC performance gains without requiring full CSI in every pilot block.

Abstract

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm.
Paper Structure (8 sections, 2 theorems, 23 equations, 7 figures, 1 algorithm)

This paper contains 8 sections, 2 theorems, 23 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. OIRS Spatial Coherence Analysis
  4. Proposed OIRS Channel Estimation Algorithm
  5. Numerical Results
  6. Conclusions
  7. Proof of Lemma 1
  8. Proof of Lemma 2

Key Result

Lemma 1

The growth rate of $\xi(\Delta\textbf{R})$ is given by where $\phi$ denotes the angle of incidence at the receiver with $\cos(\phi) = \widehat{\boldsymbol{N}}_2^T\widehat{\textbf{UR}}$.

Figures (7)

  • Figure 1: OIRS-reflected channel model.
  • Figure 2: The effect of space shift for the IRS-reflected channel gain: (a) Channel gain changes significantly due to the half-wavelength element spacing; (b) Channel gain shows spatial coherence due to the IM scheme adopted in VLC.
  • Figure 3: The proposed OIRS channel estimation based on spatial sampling.
  • Figure 4: Normalized reflected channel gain versus the space shift over the OIRS.
  • Figure 5: NMSE of the proposed OIRS channel estimation algorithm versus $\sigma$.
  • ...and 2 more figures

Theorems & Definitions (5)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • proof