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A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications

Hetong Wang, Yashuai Cao, Tiejun Lv

Abstract

Stacked intelligent metasurfaces (SIMs) represent a breakthrough in wireless hardware by comprising multilayer, programmable metasurfaces capable of analog computing in the electromagnetic (EM) wave domain. By examining their architectural analogies, this article reveals a deeper connection between SIMs and artificial neural networks (ANNs). Leveraging this profound structural similarity, this work introduces a learnable SIM architecture and proposes a learnable SIM-based machine learning (ML) paradigm for sixth-generation (6G)-andbeyond systems. Then, we develop two SIM-empowered wireless signal processing schemes to effectively achieve multi-user signal separation and distinguish communication signals from jamming signals. The use cases highlight that the proposed SIM-enabled signal processing system can significantly enhance spectrum utilization efficiency and anti-jamming capability in a lightweight manner and pave the way for ultra-efficient and intelligent wireless infrastructures.

A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications

Abstract

Stacked intelligent metasurfaces (SIMs) represent a breakthrough in wireless hardware by comprising multilayer, programmable metasurfaces capable of analog computing in the electromagnetic (EM) wave domain. By examining their architectural analogies, this article reveals a deeper connection between SIMs and artificial neural networks (ANNs). Leveraging this profound structural similarity, this work introduces a learnable SIM architecture and proposes a learnable SIM-based machine learning (ML) paradigm for sixth-generation (6G)-andbeyond systems. Then, we develop two SIM-empowered wireless signal processing schemes to effectively achieve multi-user signal separation and distinguish communication signals from jamming signals. The use cases highlight that the proposed SIM-enabled signal processing system can significantly enhance spectrum utilization efficiency and anti-jamming capability in a lightweight manner and pave the way for ultra-efficient and intelligent wireless infrastructures.

Paper Structure

This paper contains 32 sections, 5 figures, 1 table.

Figures (5)

  • Figure 1: ML paradigm with the SIM-enabled learnable architecture including: (a) The SIM provides a physical platform for implementing a learnable architecture inspired by ANNs; (b) The SIM is integrated with the BS as the combiner to effectively distinguish multi-user transmission signals in MU-MISO uplink systems; (c) The learnable SIM can be further designed to separate communication and jamming signals, and defend against Mallory's jamming attack.
  • Figure 2: Visualization of end-to-end quasi-orthogonal subchannel formation via SIM-based wavefront processing, with $K = 4$ users, and $N = 100$ meta-atoms, compared with conventional RISs.
  • Figure 3: Average loss functions vs. episodes for different $\eta_0$ and $\beta$; constellation diagrams for different values of $N$, and invariant angles and ranged distance.
  • Figure 4: Constellation diagrams with jamming-agnostic and jamming-aware SIM configurations during the training period.
  • Figure 5: (a) SER and SR comparison with jamming-agnostic and jamming-aware SIM configurations across various SNR, with $K = 4, N = 100, L=6$; (b) SER performance under hardware imperfections.