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.
@article{arxiv.2603.24599,
title = {A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications},
author = {Hetong Wang and Yashuai Cao and Tiejun Lv},
journal= {arXiv preprint arXiv:2603.24599},
year = {2026}
}
Comments
9 pages, 5 figures, accepted by IEEE Wireless Communications Magazine