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Joint Precoding and Phase-Shift Optimization for Beyond-Diagonal RIS-Aided ISAC System

Xuejun Cheng, Qian Zhang, Yuhui Jiao, Shiyao Guo, Xiaotong Xu, Guanghui Luo, Ju Liu

TL;DR

Simulation results demonstrate that the proposed BD-RIS-aided multiuser ISAC system can achieve significant improvement in the trade-offs between communication and sensing performance than the traditional diagonal RIS, verifying the effectiveness of the proposed optimization framework.

Abstract

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) can realize the interconnection between reflecting elements through the impedance network, thereby providing a new approach for the performance improvement of integrated sensing and communication (ISAC) systems. This paper investigates the optimization problem of BD-RIS-aided multiuser ISAC system, aiming to achieve the flexible design of trade-offs between communication and sensing performance. Specifically, we propose an optimization framework jointly combining the multiuser interference management and sensing beam gain approximation method. By jointly optimizing the precoding vector and RIS phase-shift matrix, improving the multiuser communication sum rate through the proposed interference management method, and enhancing the system sensing performance through the beam gain approximation method. For the resulting non-convex weighted optimization problem, we employ the alternating optimization (AO) algorithm to decouple it into two subproblems of precoding vector and phase-shift matrix optimization, with each step admitting closed-form solutions.Simulation results demonstrate that the proposed BD-RIS-aided ISAC system can achieve significant improvement in the trade-offs between communication and sensing performance than the traditional diagonal RIS, verifying the effectiveness of the proposed optimization framework.

Joint Precoding and Phase-Shift Optimization for Beyond-Diagonal RIS-Aided ISAC System

TL;DR

Simulation results demonstrate that the proposed BD-RIS-aided multiuser ISAC system can achieve significant improvement in the trade-offs between communication and sensing performance than the traditional diagonal RIS, verifying the effectiveness of the proposed optimization framework.

Abstract

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) can realize the interconnection between reflecting elements through the impedance network, thereby providing a new approach for the performance improvement of integrated sensing and communication (ISAC) systems. This paper investigates the optimization problem of BD-RIS-aided multiuser ISAC system, aiming to achieve the flexible design of trade-offs between communication and sensing performance. Specifically, we propose an optimization framework jointly combining the multiuser interference management and sensing beam gain approximation method. By jointly optimizing the precoding vector and RIS phase-shift matrix, improving the multiuser communication sum rate through the proposed interference management method, and enhancing the system sensing performance through the beam gain approximation method. For the resulting non-convex weighted optimization problem, we employ the alternating optimization (AO) algorithm to decouple it into two subproblems of precoding vector and phase-shift matrix optimization, with each step admitting closed-form solutions.Simulation results demonstrate that the proposed BD-RIS-aided ISAC system can achieve significant improvement in the trade-offs between communication and sensing performance than the traditional diagonal RIS, verifying the effectiveness of the proposed optimization framework.
Paper Structure (10 sections, 1 theorem, 29 equations, 4 figures)

This paper contains 10 sections, 1 theorem, 29 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Communication and Sensing Model
  5. Beamforming Optimization
  6. Beamforming Optimization
  7. Phase-Shift Matrix Optimization
  8. Desired Beam Phase Optimization
  9. simulation results
  10. conclusion

Key Result

Proposition 1

Consider the following optimization problem where $\zeta$ is a given value. The globally optimal solution of problem eq:phase_opt_problem can be derived as

Figures (4)

  • Figure 1: The model of a BD-RIS-aided ISAC system.
  • Figure 2: User channel gain matrix $\mathbf{F}$ generated for different weighted values $\eta$,
  • Figure 3: Beam pattern generated for different weighted values $\eta$.
  • Figure 4: The trade-offs between the achievable rate and sensing beam gain.

Theorems & Definitions (2)

  • Proposition 1
  • proof : Proof