用可调超表面让无线信道直接算推理，省电又快。
Integrating Stacked Intelligent Metasurfaces and Power Control for Dynamic Edge Inference via Over-The-Air Neural Networks
- 把超表面当神经元，让无线信道直接做计算
- 实测在多种场景下平衡准确率与能耗，省电显著
- 适合低功耗边缘计算、智能反射系统应用
本文提出一种新型边缘推理框架，突破传统将无线信道视为噪声的做法。利用堆叠式智能超表面（SIMs）调控无线传播，使信道本身实现空中计算，无需接收端进行符号估计，大幅降低计算与通信开销。将发射机-信道-接收机系统建模为端到端深度神经网络（DNN），其中超表面单元响应为可训练参数。为应对信道变化，引入专用DNN模块，基于用户位置动态调节发射功率。性能评估表明，所提融合超表面与深度神经网络的框架，在不同场景下均能有效平衡分类准确率与功耗，实现显著能效提升。
原文摘要 · Abstract (English)
This paper introduces a novel framework for Edge Inference (EI) that bypasses the conventional practice of treating the wireless channel as noise. We utilize Stacked Intelligent Metasurfaces (SIMs) to control wireless propagation, enabling the channel itself to perform over-the-air computation. This eliminates the need for symbol estimation at the receiver, significantly reducing computational and communication overhead. Our approach models the transmitter-channel-receiver system as an end-to-end Deep Neural Network (DNN) where the response of the SIM elements are trainable parameters. To address channel variability, we incorporate a dedicated DNN module responsible for dynamically adjusting transmission power leveraging user location information. Our performance evaluations showcase that the proposed metasurfaces-integrated DNN framework with deep SIM architectures are capable of balancing classification accuracy and power consumption under diverse scenarios, offering significant energy efficiency improvements.
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