A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications
arXiv cs.AI / 3/27/2026
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Key Points
- The paper explains how stacked intelligent metasurfaces (SIMs)—multilayer programmable hardware—enable analog computing directly in the electromagnetic (EM) domain for future 6G-and-beyond wireless systems.
- It argues that SIM architectures have a strong structural analogy to artificial neural networks (ANNs) and uses this connection to propose a learnable SIM architecture and an associated ML paradigm.
- The authors develop two SIM-empowered wireless signal processing methods focused on multi-user signal separation and distinguishing communication signals from jamming signals.
- The proposed approach is positioned as lightweight while improving spectrum utilization efficiency and anti-jamming performance, aiming to support more intelligent and resource-efficient wireless infrastructure.
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