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RIS-Empowered Integrated Location Sensing and Communication with Superimposed Pilots

Wenchao Xia, Ben Zhao, Wankai Tang, Yongxu Zhu, Kai-Kit Wong, Sangarapillai Lambotharan, Hyundong Shin

TL;DR

This work tackles RIS-assisted integrated sensing and communication without prior CSI or UE position information by introducing a location-coherence frame that combines a sensing phase with superimposed pilot transmission. It develops a rigorous location-estimation framework based on FIM and CRB analysis, implements a 2D-FFT/IFFT approach for UE localization to support channel estimation, and derives a closed-form, deterministic bound on ergodic uplink rate under SP that enables RIS phase optimization via a GA. The proposed scheme demonstrates submeter localization accuracy for moderate UE-RIS distances, and substantial sum-rate gains from RIS-aided SP compared to regular pilots, improving spectral efficiency while maintaining robust localization. The results highlight the viability of joint sensing and communication with RIS under mobility, providing a practical pathway for ISAC/DFRC in next-generation networks.

Abstract

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive \text {Cramér-Rao bound} (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

RIS-Empowered Integrated Location Sensing and Communication with Superimposed Pilots

TL;DR

This work tackles RIS-assisted integrated sensing and communication without prior CSI or UE position information by introducing a location-coherence frame that combines a sensing phase with superimposed pilot transmission. It develops a rigorous location-estimation framework based on FIM and CRB analysis, implements a 2D-FFT/IFFT approach for UE localization to support channel estimation, and derives a closed-form, deterministic bound on ergodic uplink rate under SP that enables RIS phase optimization via a GA. The proposed scheme demonstrates submeter localization accuracy for moderate UE-RIS distances, and substantial sum-rate gains from RIS-aided SP compared to regular pilots, improving spectral efficiency while maintaining robust localization. The results highlight the viability of joint sensing and communication with RIS under mobility, providing a practical pathway for ISAC/DFRC in next-generation networks.

Abstract

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive \text {Cramér-Rao bound} (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

Paper Structure

This paper contains 26 sections, 1 theorem, 99 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. System Setup
  4. Protocol Design
  5. Channel Model
  6. Location Sensing
  7. FIM Analysis
  8. Location Estimation
  9. Superimposed Pilot and Data Transmission
  10. Ergodic Achievable Rate
  11. RIS Phase Profile Optimization
  12. Simulation Results
  13. Location Sensing Performance
  14. Uplink SP Transmission Performance
  15. Conclusions
  16. ...and 11 more sections

Key Result

Theorem 1

Considering the uplink SP transmission where the MRC receiver is adopted. The lower bound of the ergodic achievable rate of the $k$-th UE can be given as $R_k=\log_2{(1+\bar{\gamma}_k)}$, where in which $\Pi_0={p_k\tau_C M_{\rm B} \lambda_{k}^{2}(\chi_{k}^{-2} \Delta_{k}-M_{\rm B})}$, $\Pi_1=\sum_{i=1}^4\Pi_{1i}$, and

Figures (9)

  • Figure 1: A snapshot of the considered system model.
  • Figure 2: The frame structure of the considered transmission protocol.
  • Figure 3: The estimation error and CRB of (a) UE position and (b) angles. The RIS has a UPA of 64 $\times 64$.
  • Figure 4: The CRB of position estimation error with respect to the number of RIS elements.
  • Figure 5: Comparison of the IFFT and MLE algorithms: (a) position error and (b) average running time per sample.
  • ...and 4 more figures

Theorems & Definitions (1)

  • Theorem 1