用智能超表面实现无线神经网络的非线性计算，提升性能
Relay-Assisted Activation-Integrated SIM for Wireless Physical Neural Networks
- 将线性超表面与非线性激活层级联，实现模拟域非线性处理
- 通过多跳传播和可训练相位矩阵，分类准确率显著提升
- 适合做低延迟、高能效无线神经计算的硬件研发者
无线物理神经网络（WPNN）作为在无线系统物理层直接进行神经计算的有前景范式，具备低延迟和高能效优势。然而，现有大部分WPNN实现主要依赖线性物理变换，从根本上限制了其表达能力。本文提出一种基于激活集成堆叠智能超表面（AI-SIMs）的中继辅助WPNN架构，其中每个实现线性波操控的被动超表面层与实现模拟域非线性处理的激活超表面层级联。通过精心设计的多跳无线传播，中继放大矩阵与超表面相移矩阵共同充当可训练网络权重，而硬件实现的激活函数提供关键非线性。仿真结果表明，所提架构实现了高分类准确率，且引入硬件级激活函数相比纯线性物理实现显著提升了表征能力和性能。
原文摘要 · Abstract (English)
Wireless physical neural networks (WPNNs) have emerged as a promising paradigm for performing neural computation directly in the physical layer of wireless systems, offering low latency and high energy efficiency. However, most existing WPNN implementations primarily rely on linear physical transformations, which fundamentally limits their expressiveness. In this work, we propose a relay-assisted WPNN architecture based on activation-integrated stacked intelligent metasurfaces (AI-SIMs), where each passive metasurface layer enabling linear wave manipulation is cascaded with an activation metasurface layer that realizes nonlinear processing in the analog domain. By deliberately structuring multi-hop wireless propagation, the relay amplification matrix and the metasurface phase-shift matrices jointly act as trainable network weights, while hardware-implemented activation functions provide essential nonlinearity. Simulation results demonstrate that the proposed architecture achieves high classification accuracy, and that incorporating hardware-based activation functions significantly improves representational capability and performance compared with purely linear physical implementations.
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