A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911545002295296 |
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| author | Wang, Hetong Cao, Yashuai Lv, Tiejun |
| author_facet | Wang, Hetong Cao, Yashuai Lv, Tiejun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24599 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications Wang, Hetong Cao, Yashuai Lv, Tiejun Signal Processing Artificial Intelligence 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. |
| title | A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2603.24599 |