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Rate-Splitting Multiple Access for Transmissive Reconfigurable Intelligent Surface Transceiver Empowered ISAC System

Ziwei Liu, Wen Chen, Qingqing Wu, Jinhong Yuan, Shanshan Zhang, Zhendong Li, Jun Li

TL;DR

The work tackles joint sensing and communication optimization in a TRIS-empowered ISAC system by leveraging RSMA to manage interference between sensing and communications. It introduces sensing QoS criteria and casts the design as a nonconvex optimization problem, which is solved via alternating optimization with convexification (SCA) and DC programming, complemented by an EKF-based tracking loop. The proposed framework yields higher sensing accuracy and robust communication performance, with numerical results showing that increasing TRIS elements tightens beams, lowers localization CRB, improves detection probability, and outperforms SDMA/NOMA in RSMA-enabled scenarios. The approach offers a low-energy, scalable pathway for integrated sensing and communications in future wireless networks, enabling efficient resource allocation and reliable multi-terminal operation.

Abstract

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing QoS criteria as the objective function to optimize the sensing stream precoding matrix and the communication stream precoding matrix. Due to the coupling of optimization variables, the formulated problem is a non-convex optimization problem that cannot be solved directly. To tackle the above-mentioned challenging problem, alternating optimization (AO) is utilized to decouple the optimization variables. Specifically, the problem is decoupled into three subproblems about the sensing stream precoding matrix, the communication stream precoding matrix, and the auxiliary variables, which is solved alternatively through AO until the convergence is reached. For solving the problem, successive convex approximation (SCA) is applied to deal with the sum-rate threshold constraints on communications, and difference-of-convex (DC) programming is utilized to solve rank-one non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in terms of improving the communication and sensing QoS.

Rate-Splitting Multiple Access for Transmissive Reconfigurable Intelligent Surface Transceiver Empowered ISAC System

TL;DR

The work tackles joint sensing and communication optimization in a TRIS-empowered ISAC system by leveraging RSMA to manage interference between sensing and communications. It introduces sensing QoS criteria and casts the design as a nonconvex optimization problem, which is solved via alternating optimization with convexification (SCA) and DC programming, complemented by an EKF-based tracking loop. The proposed framework yields higher sensing accuracy and robust communication performance, with numerical results showing that increasing TRIS elements tightens beams, lowers localization CRB, improves detection probability, and outperforms SDMA/NOMA in RSMA-enabled scenarios. The approach offers a low-energy, scalable pathway for integrated sensing and communications in future wireless networks, enabling efficient resource allocation and reliable multi-terminal operation.

Abstract

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing QoS criteria as the objective function to optimize the sensing stream precoding matrix and the communication stream precoding matrix. Due to the coupling of optimization variables, the formulated problem is a non-convex optimization problem that cannot be solved directly. To tackle the above-mentioned challenging problem, alternating optimization (AO) is utilized to decouple the optimization variables. Specifically, the problem is decoupled into three subproblems about the sensing stream precoding matrix, the communication stream precoding matrix, and the auxiliary variables, which is solved alternatively through AO until the convergence is reached. For solving the problem, successive convex approximation (SCA) is applied to deal with the sum-rate threshold constraints on communications, and difference-of-convex (DC) programming is utilized to solve rank-one non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in terms of improving the communication and sensing QoS.
Paper Structure (19 sections, 2 theorems, 72 equations, 8 figures, 3 tables, 3 algorithms)

This paper contains 19 sections, 2 theorems, 72 equations, 8 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System model
  3. TRIS Transmitter Characteristics
  4. Channel Model
  5. Signal Model
  6. Quality of Service Evaluation Metrics And Problem Formulation
  7. Communication Quality of Service
  8. Target Detection
  9. Target Localization
  10. Target Tracking
  11. Problem Formulation
  12. Joint Design of Communication and Sensing
  13. Convex Transformation of the Formulated Problem
  14. Problem Solving
  15. Convergence and Computational Complexity Analysis
  16. ...and 4 more sections

Key Result

Lemma 1

Let ${\hat{x}_1} = \int {r\left( t \right)s_c^*\left( {t - {\tau _{d,1}}} \right)} {e^{ - j2\pi {f_{d,1}}t}}dt$ denote the output of the receiver. The optimal detector under the Neyman-Pearson sense is likelihood ratio test (LRT), which is given by where $\delta$ is a threshold.

Figures (8)

  • Figure 1: TRIS transmitter empowered ISAC systems.
  • Figure 2: Encoding and decoding processes.
  • Figure 3: The convergence of the proposed joint communication and sensing optimization algorithm $({R_{th}}=1Mbits)$.
  • Figure 4: An example of angle and Doppler frequency estimation. ($N=16, R_{th}=1Mbits, L=1024, f_s=2f_c$)
  • Figure 5: Detection Performance: The detection probability $P_D$ versus communication threshold under different TRIS elements and $P_{FA}$
  • ...and 3 more figures

Theorems & Definitions (3)

  • Lemma 1
  • Remark 1
  • Proposition 1