让智能超表面具备学习能力，实现6G无线信号的高效分离与抗干扰。
A Learnable SIM Paradigm: Fundamentals, Training Techniques, and Applications
- 将超表面结构类比神经网络，设计可训练的智能超表面架构。
- 在真实场景中实现多用户信号分离和通信/干扰信号区分，性能显著提升。
- 适合6G及未来智能无线系统研发人员，推动轻量化智能基础设施发展。
堆叠式智能超表面（SIM）通过多层可编程超表面，在电磁波域实现模拟计算，是无线硬件的重要突破。本文通过分析其结构类比，揭示了SIM与人工神经网络（ANN）之间的深层关联。基于此，本文提出一种可学习的SIM架构，并构建面向6G及未来系统的可学习SIM机器学习范式。进一步开发了两种基于SIM的无线信号处理方案，有效实现多用户信号分离与通信信号与干扰信号的区分。实际应用场景表明，该方案能显著提升频谱利用效率和抗干扰能力，且以轻量化方式实现，为超高效、智能化无线基础设施的发展铺平道路。
原文摘要 · Abstract (English)
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.
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