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Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data

Xu Gan, Chongwen Huang, Zhaohui Yang, Caijun Zhong, Xiaoming Chen, Zhaoyang Zhang, Qinghua Guo, Chau Yuen, Merouane Debbah

TL;DR

This work tackles pilot overhead in double-RIS aided uplink ISAC by proposing a superimposed symbol scheme that blends sensing pilots with data. It introduces a structure-aware sparse Bayesian learning framework that exploits decoded data as side information to enhance sensing while preserving spectral efficiency, and pairs it with a low-complexity multi-user algorithm based on SCMA-UAMP-SBL and reduced-dimension refinements for fast localization. The key contributions include two-timescale channel modeling, a TR-based LS initialization, EM-based structure-aware SBL, SCMA-enabled data detection, and off-grid grid refinement that maps angular estimates to 3D UE locations. Numerical results show centimeter-level localization accuracy with up to 96% of the SE of sensing-free communications and substantial throughput gains over conventional ISAC approaches, underscoring the practical value of joint sensing and communication with superimposed pilots in double-RIS systems.

Abstract

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, we develop a structure-aware sparse Bayesian learning framework, where decoded data symbols serve as side information to enhance sensing performance and increase SE. To meet the low-latency requirements of emerging ISAC applications, we further propose a low-complexity simultaneous communication and localization algorithm for multiple users. This algorithm employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization while attaining up to $96\%$ of the SE of conventional communications without sensing capabilities. Moreover, compared to other typical ISAC schemes, the proposed superimposed symbol scheme can provide an effective throughput improvement over $133\%$.

Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data

TL;DR

This work tackles pilot overhead in double-RIS aided uplink ISAC by proposing a superimposed symbol scheme that blends sensing pilots with data. It introduces a structure-aware sparse Bayesian learning framework that exploits decoded data as side information to enhance sensing while preserving spectral efficiency, and pairs it with a low-complexity multi-user algorithm based on SCMA-UAMP-SBL and reduced-dimension refinements for fast localization. The key contributions include two-timescale channel modeling, a TR-based LS initialization, EM-based structure-aware SBL, SCMA-enabled data detection, and off-grid grid refinement that maps angular estimates to 3D UE locations. Numerical results show centimeter-level localization accuracy with up to 96% of the SE of sensing-free communications and substantial throughput gains over conventional ISAC approaches, underscoring the practical value of joint sensing and communication with superimposed pilots in double-RIS systems.

Abstract

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, we develop a structure-aware sparse Bayesian learning framework, where decoded data symbols serve as side information to enhance sensing performance and increase SE. To meet the low-latency requirements of emerging ISAC applications, we further propose a low-complexity simultaneous communication and localization algorithm for multiple users. This algorithm employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization while attaining up to of the SE of conventional communications without sensing capabilities. Moreover, compared to other typical ISAC schemes, the proposed superimposed symbol scheme can provide an effective throughput improvement over .
Paper Structure (34 sections, 1 theorem, 56 equations, 11 figures, 1 table, 3 algorithms)

This paper contains 34 sections, 1 theorem, 56 equations, 11 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmission Protocol
  4. Channel Model
  5. Signal Model
  6. Received signals from the fixed site
  7. Received signals from multiple UEs
  8. ISAC Period for the Fixed site
  9. Initial Estimates by the TR-based LS Method
  10. Structure-aware Bayesian learning framework
  11. E-Step
  12. M-Step I
  13. M-Step II
  14. Multi-user location sensing and communication
  15. SCMA-UAMP-SBL for initial estimates
  16. ...and 19 more sections

Key Result

Lemma 1

By maximizing the first term of (li), the estimated data yields where $\boldsymbol{\mu}_{H\text{eff}}^{(j-1)}$ and $\boldsymbol{\Sigma}_{H\text{eff}}^{(j-1)}$ are given in Eq. (mu_H) and Eq. (sigma_H), respectively.

Figures (11)

  • Figure 1: Illustration of the double-RIS aided multi-UE uplink ISAC scenario.
  • Figure 2: Illustration of two-timescale transmission protocol.
  • Figure 3: Flow chart for ISAC signal processing.
  • Figure 4: Flow chart for multi-UE SCMA encoding and decoding process for the case with $K=6$ and $N_\text{s}=4$, where the pilots and data of $K$ UEs will be encoded by SCMA codebook and transmitted simultaneously, then our algorithm is proposed to decode the communication data and obtain the location coordinates of $K$ UEs at the receiver side.
  • Figure 5: Factor graph of (\ref{['facto']}) for deriving UAMP-SBL.
  • ...and 6 more figures

Theorems & Definitions (7)

  • Remark 1
  • Remark 2
  • Lemma 1
  • Remark 3
  • Remark 4
  • Remark 5
  • Remark 6