A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications
This work addresses the need for ultra-efficient and intelligent wireless infrastructures in 6G-and-beyond systems, representing a novel paradigm rather than an incremental improvement.
The paper tackles the challenge of enhancing wireless signal processing by introducing a learnable stacked intelligent metasurface (SIM) architecture that mimics artificial neural networks, enabling multi-user signal separation and jamming signal distinction, which significantly improves spectrum utilization efficiency and anti-jamming capability in a lightweight manner.
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.