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

TL;DR

This paper introduces a learnable SIM architecture inspired by neural networks, enabling advanced wireless signal processing for 6G systems with improved spectrum efficiency and anti-jamming capabilities.

Contribution

It proposes a novel learnable SIM-based ML paradigm and develops two wireless signal processing schemes leveraging SIMs for enhanced 6G wireless communication.

Findings

Enhanced spectrum utilization efficiency

Improved anti-jamming capability

Lightweight implementation of SIM-enabled processing

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.