Joint beamforming and mode optimization for multi-functional STAR-RIS-aided integrated sensing and communication networks

TL;DR

This paper presents a joint beamforming and mode optimization framework for multi-functional STAR-RIS-assisted integrated sensing and communication networks, balancing sensing accuracy and communication throughput.

Contribution

It introduces a novel two-stage protocol and an optimization approach that jointly designs beamforming, STAR-RIS coefficients, and metasurface modes considering estimation uncertainty.

Findings

Achieves a better trade-off between sensing and communication performance.

Outperforms conventional STAR-RIS-aided ISAC schemes in simulations.

Effectively balances sensing accuracy with communication throughput.

Abstract

This paper investigates the design of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), which act as multi-functional programmable metasurfaces capable of supporting concurrent communication and sensing within a unified architecture. We propose a two-stage ISAC protocol, in which the preparation phase performs direction estimation for outdoor users located in the reflection space, while maintaining communication with both outdoor and indoor users in the transmission space. The subsequent communication phase exploits the estimated directions to enhance information transfer. The directions of outdoor users are modeled as Gaussian random variables to capture estimation uncertainty, and the corresponding average communication performance is incorporated into the design. Building on this framework, we formulate a performance-balanced optimization problem that maximizes the communication sum-rate while guaranteeing the required sensing accuracy, jointly determining the beamforming vectors at the base station (BS), the STAR-RIS transmission and reflection coefficients, and the metasurface partition between energy-splitting and transmit-only modes. The physical constraints of STAR-RIS elements and the required sensing performance are explicitly enforced. To address the non-convex nature of the problem, we combine fractional programming, Lagrangian dual reformulation, and successive convex approximation. The binary metasurface partition is ultimately recovered via continuous relaxation followed by projection-based binarization. Numerical results demonstrate that the proposed design achieves an effective trade-off between sensing accuracy and communication throughput, by significantly outperforming conventional STAR-RIS-aided ISAC schemes.