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Full-Space Wireless Sensing Enabled by Multi-Sector Intelligent Surfaces

Yumeng Zhang, Xiaodan Shao, Hongyu Li, Bruno Clerckx, Rui Zhang

TL;DR

The paper addresses full-space sensing with multi-sector intelligent surfaces by extending IS self-sensing to target localization across the entire space. It develops two implementations, derives a joint maximum-likelihood estimator for the target angle and its Cramér-Rao bound, and reveals that the bound factors into probing power $e(\theta)$ and angular-rate $r^2(\theta)$, both governed by geometry and antenna gains. Through analysis of $L=2,3,4$ geometries and both isotropic and directive antenna patterns, it shows that the 4-sector configuration provides the most uniform and strongest sensing performance, outperforming the baseline STARS architecture. Simulations validate the theory, demonstrating that directive patterns improve sensing power and angular sensitivity, with practical implications for IS-based sensing and integrated sensing and communications (ISAC).

Abstract

The multi-sector intelligent surface (IS), benefiting from a smarter wave manipulation capability, has been shown to enhance channel gain and offer full-space coverage in communications. However, the benefits of multi-sector IS in wireless sensing remain unexplored. This paper introduces the application of multi-sector IS for wireless sensing/localization. Specifically, we propose a new self-sensing system, where an active source controller uses the multi-sector IS geometry to reflect/scatter the emitted signals towards the entire space, thereby achieving full-space coverage for wireless sensing. Additionally, dedicated sensors are installed aligned with the IS elements at each sector, which collect echo signals from the target and cooperate to sense the target angle. In this context, we develop a maximum likelihood estimator of the target angle for the proposed multi-sector IS self-sensing system, along with the corresponding theoretical limits defined by the Cramér-Rao Bound. The analysis reveals that the advantages of the multi-sector IS self-sensing system stem from two aspects: enhancing the probing power on targets (thereby improving power efficiency) and increasing the rate of target angle (thereby enhancing the transceiver's sensitivity to target angles). Finally, our analysis and simulations confirm that the multi-sector IS self-sensing system, particularly the 4-sector architecture, achieves full-space sensing capability beyond the single-sector IS configuration. Furthermore, similarly to communications, employing directive antenna patterns on each sector's IS elements and sensors significantly enhances sensing capabilities. This enhancement originates from both aspects of improved power efficiency and target angle sensitivity, with the former also being observed in communications while the latter being unique in sensing.

Full-Space Wireless Sensing Enabled by Multi-Sector Intelligent Surfaces

TL;DR

The paper addresses full-space sensing with multi-sector intelligent surfaces by extending IS self-sensing to target localization across the entire space. It develops two implementations, derives a joint maximum-likelihood estimator for the target angle and its Cramér-Rao bound, and reveals that the bound factors into probing power and angular-rate , both governed by geometry and antenna gains. Through analysis of geometries and both isotropic and directive antenna patterns, it shows that the 4-sector configuration provides the most uniform and strongest sensing performance, outperforming the baseline STARS architecture. Simulations validate the theory, demonstrating that directive patterns improve sensing power and angular sensitivity, with practical implications for IS-based sensing and integrated sensing and communications (ISAC).

Abstract

The multi-sector intelligent surface (IS), benefiting from a smarter wave manipulation capability, has been shown to enhance channel gain and offer full-space coverage in communications. However, the benefits of multi-sector IS in wireless sensing remain unexplored. This paper introduces the application of multi-sector IS for wireless sensing/localization. Specifically, we propose a new self-sensing system, where an active source controller uses the multi-sector IS geometry to reflect/scatter the emitted signals towards the entire space, thereby achieving full-space coverage for wireless sensing. Additionally, dedicated sensors are installed aligned with the IS elements at each sector, which collect echo signals from the target and cooperate to sense the target angle. In this context, we develop a maximum likelihood estimator of the target angle for the proposed multi-sector IS self-sensing system, along with the corresponding theoretical limits defined by the Cramér-Rao Bound. The analysis reveals that the advantages of the multi-sector IS self-sensing system stem from two aspects: enhancing the probing power on targets (thereby improving power efficiency) and increasing the rate of target angle (thereby enhancing the transceiver's sensitivity to target angles). Finally, our analysis and simulations confirm that the multi-sector IS self-sensing system, particularly the 4-sector architecture, achieves full-space sensing capability beyond the single-sector IS configuration. Furthermore, similarly to communications, employing directive antenna patterns on each sector's IS elements and sensors significantly enhances sensing capabilities. This enhancement originates from both aspects of improved power efficiency and target angle sensitivity, with the former also being observed in communications while the latter being unique in sensing.
Paper Structure (29 sections, 1 theorem, 48 equations, 9 figures, 1 table)

This paper contains 29 sections, 1 theorem, 48 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Two implementations of the proposed multi-sector IS self-sensing system
  4. Geometry constellation
  5. Signal model
  6. Performance Analysis
  7. ML estimator and the corresponding CRB
  8. General insights into $e\left( \theta\right)$ and $r^2\left( \theta\right)$ as fundamental CRB components
  9. For $L=2$
  10. For $L=3$
  11. For $L=4$
  12. Numerical scaling laws of $e\left( \theta\right)$ and $r^2\left( \theta\right)$ with specific antenna patterns
  13. Gain of different antenna patterns
  14. Summary and analysis of the numerical scaling laws
  15. Simulation Results
  16. ...and 14 more sections

Key Result

Proposition 1

When the IS elements and the sensors share the same architecture (e.g., $N_\mathrm{I}=N_\mathrm{S}\triangleq N$ and $F_\mathrm{I}\left(\theta \right)=F_\mathrm{S}\left(\theta \right)\triangleq F\left(\theta \right)$, hence $M_\mathrm{I}=M_\mathrm{S}\triangleq M$, $\mathbf{F}_{\mathrm{I}}\left(\theta where $e\left(\theta \right)$ is the probing power on target and $r\left(\theta \right)$ is the rat

Figures (9)

  • Figure 1: Examples of multi-sector IS configurations, for $L=2,~3,~4,~6$ with $L$ being the number of ISs.
  • Figure 2: Two implementations of the proposed multi-sector IS self-sensing system, namely, multi-sector beyond-diagonal IS (left), and multi-sector conventional IS (right).
  • Figure 3: The model of the multi-sector IS self-sensing system under global CCS for $L=2,~3,~4$ from left to right, assuming $N_\mathrm{I}=24$ and $N_\mathrm{S}=12$.
  • Figure 4: The model of the multi-sector IS self-sensing system under local CCS for $L=2,~3,~4$ from left to right, assuming $N_\mathrm{I}=24$ and $N_\mathrm{S}=12$. Herein, the target angle to be estimated in the G-CCS, $\theta$, becomes $\theta_l$ adaptive to each local CCS.
  • Figure 5: The instantaneous $\mathrm{MSE}\left(\theta\right)$ and $\mathrm{CRB}\left(\theta\right)$ w.r.t. $\theta$ for $N=24$, $P^{\mathrm{tr}}=45$ dBm and both half-space isotropic and half-space directive antenna patterns. Herein, "Iso" refers to employing the half-space isotropic antenna pattern and "Dir" refers to employing the half-space directive antenna pattern.
  • ...and 4 more figures

Theorems & Definitions (5)

  • Proposition 1
  • proof
  • Remark 1
  • Remark 2
  • Remark 3