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RIS-aided ISAC with $K$-Rydberg Atomic Receivers

Hong-Bae Jeon, Chan-Byoung Chae

TL;DR

The paper addresses joint downlink RIS-ISAC design with multiple RAR users, formulating a CRB-constrained, sum-rate optimization over a shared transmit beamformer and RIS phases. It develops a novel AO framework that combines fractional programming, MM, and ADMM to transform the SINR-based communication objective and to enforce a tractable CRB constraint via LMIs. The main contributions are (i) a unified RIS-ISAC model with RAR readout, (ii) a CRB-aware, FP-MM-ADMM optimization framework, and (iii) extensive simulations showing significant gains over benchmarks and highlighting the benefits of multi-RAR reception for 6G ISAC systems. The approach demonstrates how RIS field shaping enhances sensing-communication tradeoffs and reduces beamforming effort needed to meet sensing accuracy, enabling practical RIS-RAR ISAC deployments.

Abstract

In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communications (ISAC) framework equipped with multiple Rydberg atomic receiver (RAR)-aided users. By leveraging the reference-assisted reception mechanism of RARs, we develop a unified signal model that jointly captures downlink multi-user communication with RARs and monostatic radar sensing. To explicitly balance communication performance and sensing accuracy, we formulate a Cramer-Rao bound (CRB)-constrained utility maximization problem. To address these challenges, we propose a joint optimization framework that combines fractional programming (FP), majorization-minimization (MM), and the alternating direction method of multipliers (ADMM). Simulation results demonstrate that the proposed framework consistently outperforms the conventional approach over a wide range of system environments, thereby highlighting the importance of the proposed framework in unlocking the potential of RARs for 6G.

RIS-aided ISAC with $K$-Rydberg Atomic Receivers

TL;DR

The paper addresses joint downlink RIS-ISAC design with multiple RAR users, formulating a CRB-constrained, sum-rate optimization over a shared transmit beamformer and RIS phases. It develops a novel AO framework that combines fractional programming, MM, and ADMM to transform the SINR-based communication objective and to enforce a tractable CRB constraint via LMIs. The main contributions are (i) a unified RIS-ISAC model with RAR readout, (ii) a CRB-aware, FP-MM-ADMM optimization framework, and (iii) extensive simulations showing significant gains over benchmarks and highlighting the benefits of multi-RAR reception for 6G ISAC systems. The approach demonstrates how RIS field shaping enhances sensing-communication tradeoffs and reduces beamforming effort needed to meet sensing accuracy, enabling practical RIS-RAR ISAC deployments.

Abstract

In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communications (ISAC) framework equipped with multiple Rydberg atomic receiver (RAR)-aided users. By leveraging the reference-assisted reception mechanism of RARs, we develop a unified signal model that jointly captures downlink multi-user communication with RARs and monostatic radar sensing. To explicitly balance communication performance and sensing accuracy, we formulate a Cramer-Rao bound (CRB)-constrained utility maximization problem. To address these challenges, we propose a joint optimization framework that combines fractional programming (FP), majorization-minimization (MM), and the alternating direction method of multipliers (ADMM). Simulation results demonstrate that the proposed framework consistently outperforms the conventional approach over a wide range of system environments, thereby highlighting the importance of the proposed framework in unlocking the potential of RARs for 6G.
Paper Structure (30 sections, 110 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 30 sections, 110 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Overall ISAC Architecture
  4. Optical Readout at $K$ RAR-Aided Users
  5. RIS-Aided Monostatic Sensing at BS
  6. ISAC Problem Formulation
  7. Communication Utility for $K$ RAR-aided Users
  8. Cramér-Rao Bound of Sensing Performance
  9. Joint Precoder and RIS Phase Design Problem
  10. Joint Optimization Framework
  11. FP-Based Transformation of $\mathcal{U}_{\mathrm{com}}$
  12. Reformulation of CRB-Constraint
  13. Equivalent CRB-Constrained Optimization Problem
  14. Updating Blocks
  15. Update $\mathbf r$ and $\{\mathbf c_k\}$
  16. ...and 15 more sections

Figures (5)

  • Figure 1: An RIS-ISAC system with $K$ RAR-aided users.
  • Figure 2: Illustration of signal processing in RAR. Specifically, the incident EM wave couples two highly excited Rydberg states (e.g., $53D_{3/2}$ and $54P_{3/2}$), giving rise to the AT-splitting phenomenon. The resulting spectral separation $\Delta f$ is subsequently mapped to the corresponding Rabi frequency $\Omega$ via \ref{['rabidef']}.
  • Figure 3: (a) Illustration of the position of BS, RIS, RARs, and target and (b) $\mathcal{U}_{\mathrm{com}}$ versus the number of iterations.
  • Figure 4: $\mathcal{U}_{\mathrm{com}}$ versus (a) received SNR (b) the number of RIS elements and (c) the CRB constraint.
  • Figure 5: $\mathcal{U}_{\mathrm{com}}$ versus (a) the number of RAR elements (b) RSR and (c) number of RAR-aided users.

Theorems & Definitions (1)

  • Remark 1