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<p>This paper investigates a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system that simultaneously supports multi-user communication and target parameter estimation. Unlike existing studies that rely on the deterministic Cramér–Rao bound (CRB), this work derives the Bayesian Cramér–Rao bound (BCRB) to characterize the theoretical performance limit of direction-of-arrival (DoA) estimation by incorporating prior information on target parameters. Based on the derived BCRB, a joint transmit beamforming and RIS reflection design problem is formulated to maximize the communication sum-rate while satisfying the sensing accuracy requirement, the transmit power constraint, and the unit-modulus constraint of RIS elements. The resulting optimization problem is highly non-convex due to the coupling among design variables. To address this challenge, an efficient alternating optimization framework is developed by integrating fractional programming (FP), the majorization–minimization (MM) method, and the alternating direction method of multipliers (ADMM). In addition, a prior-aware initialization strategy is proposed to mitigate the cold-start issue caused by parameter uncertainty. Simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Furthermore, by exploiting accurate prior information, the required sensing resource can be significantly reduced, thereby improving the achievable communication sum-rate. Numerical comparisons show that the proposed scheme achieves an average sum-rate gain of approximately 2.8 bps/Hz over conventional deterministic CRB-based baseline methods.</p>

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BCRB-constrained joint beamforming and reflection design for RIS-assisted ISAC systems with prior information

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月5日

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  • Xinran Gu

摘要

This paper investigates a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system that simultaneously supports multi-user communication and target parameter estimation. Unlike existing studies that rely on the deterministic Cramér–Rao bound (CRB), this work derives the Bayesian Cramér–Rao bound (BCRB) to characterize the theoretical performance limit of direction-of-arrival (DoA) estimation by incorporating prior information on target parameters. Based on the derived BCRB, a joint transmit beamforming and RIS reflection design problem is formulated to maximize the communication sum-rate while satisfying the sensing accuracy requirement, the transmit power constraint, and the unit-modulus constraint of RIS elements. The resulting optimization problem is highly non-convex due to the coupling among design variables. To address this challenge, an efficient alternating optimization framework is developed by integrating fractional programming (FP), the majorization–minimization (MM) method, and the alternating direction method of multipliers (ADMM). In addition, a prior-aware initialization strategy is proposed to mitigate the cold-start issue caused by parameter uncertainty. Simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Furthermore, by exploiting accurate prior information, the required sensing resource can be significantly reduced, thereby improving the achievable communication sum-rate. Numerical comparisons show that the proposed scheme achieves an average sum-rate gain of approximately 2.8 bps/Hz over conventional deterministic CRB-based baseline methods.