Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Ziqing Wang, Hongzheng Liu, Jianan Zhang, Rujing Xiong, Kai Wan, Xuewen Qian, Marco Di Renzo, Robert Caiming Qiu

TL;DR

This work studies a ${\text{SIM}}$-aided ISAC system with an extended target, aiming to minimize the ${\mathbf{CRB}}({\mathbf{H}_s})$ by jointly optimizing the BS beamforming ${\{\mathbf{w}_k\}}$, sensing covariance ${\mathbf{R}_0}$, and the SIM end-to-end matrix ${\mathbf{P}}$ under SINR ${\Gamma_k}$ and power ${P_0}$ constraints. It introduces a Multi-Layer Alternating Optimization (MAO) algorithm that leverages SVD-based relaxations and Semidefinite Relaxation (SDR) to transform non-convex subproblems into convex ones, solved in a layer-by-layer fashion for the SIM via alternating updates of ${\mathbf{w}_k}, {\mathbf{R}_0}, {\mathbf{P}}$ and ${\boldsymbol{\Phi}_\ell}$. The authors validate the approach with hardware experiments using three 1-bit, $16\times16$-unit-cell SIM layers operating at 5.8 GHz, confirming that additional SIM layers yield meaningful improvements in CRB and DoA estimation accuracy, consistent with numerical results. Overall, the work demonstrates that SIM-enabled ISAC can achieve higher spatial resolution and more efficient resource use, motivating practical deployment in future 6G ISAC networks.

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 Cramér-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.

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

TL;DR

This work studies a -aided ISAC system with an extended target, aiming to minimize the by jointly optimizing the BS beamforming , sensing covariance , and the SIM end-to-end matrix under SINR and power constraints. It introduces a Multi-Layer Alternating Optimization (MAO) algorithm that leverages SVD-based relaxations and Semidefinite Relaxation (SDR) to transform non-convex subproblems into convex ones, solved in a layer-by-layer fashion for the SIM via alternating updates of and . The authors validate the approach with hardware experiments using three 1-bit, -unit-cell SIM layers operating at 5.8 GHz, confirming that additional SIM layers yield meaningful improvements in CRB and DoA estimation accuracy, consistent with numerical results. Overall, the work demonstrates that SIM-enabled ISAC can achieve higher spatial resolution and more efficient resource use, motivating practical deployment in future 6G ISAC networks.

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 Cramér-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.
Paper Structure (13 sections, 18 equations, 7 figures, 2 tables)

This paper contains 13 sections, 18 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. System model
  4. SIM-aided Integrated Sensing and Communications
  5. Transmission Beamforming Optimization $\left\{\mathbf{w}_k \right\}$ and $\mathbf{R}_0$
  6. Transmission Coefficient Matrix $\mathbf{\Phi}_L$ Optimization
  7. Transmission Coefficient Matrices $\mathbf{\Phi}_{L-1}, \ldots,\mathbf{\Phi}_{1}$ Optimization
  8. NUMERICAL RESULTS
  9. Experimental Results
  10. The Experimental Platform
  11. Communication Experiment
  12. Sensing Experiment
  13. Conclusion

Figures (7)

  • Figure 1: System model of the considered SIM-aided multi-user ISAC system.
  • Figure 2: Multi-layer Alternating Optimization algorithm (MAO). $I_{\rm max}$ represents the maximum number of iterations.
  • Figure 3: The CRB for $\mathbf{H}_s$ estimation versus the SINR threshold.
  • Figure 4: Stacked Intelligent Metasurface (SIM). (a) The structure of SIM. (b) The structure of a transmissive layer. (c) The structure of a unit cell. (d) 1-bit SIM. (e) 1-bit transmissive layer. (f) Schematic diagram of the logic circuit board.
  • Figure 5: The SIM-aided communication and sensing prototype system. (The RX serves as the object of sensing or the receiver for communication.)
  • ...and 2 more figures