Multi-user ISAC through Stacked Intelligent Metasurfaces: New Algorithms and Experiments

TL;DR

This paper presents new algorithms and experimental validation for a SIM-assisted ISAC system that jointly optimizes beamforming and metasurface transmission to enhance target estimation and communication performance.

Contribution

It introduces a joint optimization framework for SIM-assisted ISAC, employing AO and SDR algorithms, and demonstrates experimental validation of the system.

Findings

Optimized beamforming and SIM transmission improve target estimation accuracy.

Proposed algorithms effectively balance communication and sensing requirements.

Experimental platform validates the practical feasibility of the approach.

Abstract

This paper investigates a Stacked Intelligent Metasurfaces (SIM)-assisted Integrated Sensing and Communications (ISAC) system. An extended target model is considered, where the BS aims to estimate the complete target response matrix relative to the SIM. Under the constraints of minimum Signal-to-Interference-plus-Noise Ratio (SINR) for the communication users (CUs) and maximum transmit power, we jointly optimize the transmit beamforming at the base station (BS) and the end-to-end transmission matrix of the SIM, to minimize the Cram\'er-Rao Bound (CRB) for target estimation. Effective algorithms such as the alternating optimization (AO) and semidefinite relaxation (SDR) are employed to solve the non-convex SINR-constrained CRB minimization problem. Finally, we design and build an experimental platform for SIM, and evaluate the performance of the proposed algorithms for communication and sensing tasks.