用智能超表面在空中实现无线端到端分类，低硬件成本下媲美数字模型。
Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces
- 用非线性超表面做激活函数，多层可调线性超表面模拟网络权重。
- 在多个数据集上达到与理想数字模型相当的分类准确率。
- 适合需要极简硬件、实时无线学习的场景，如物联网边缘计算。
近期提出的面向目标的通信范式要求直接在无线传输的数据上执行机器学习推理。本文提出一种超大规模（XL）多输入多输出（MIMO）系统，作为极限学习机（ELM）实现空中（OTA）二分类。为降低硬件复杂度，接收端采用级联超表面并终止于单一射频链路。前层超表面对输入信号施加固定非线性响应，充当ELM的激活函数；后续可调线性超表面层在波域中物理逼近训练好的网络权重。在多个数据集上的数值评估表明，该XL MIMO架构的分类准确率可媲美理想化的数字模型，从而证明了低复杂度波域无线学习的可行性。
原文摘要 · Abstract (English)
The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary classification. To reduce hardware complexity, the receiver is equipped with cascaded metasurfaces terminating in a single radio-frequency chain. A front metasurface layer applies a fixed nonlinear response to the incoming signal, acting as the ELM's activation function. Subsequent tunable linear metasurface layers physically approximate the trained network weights directly in the wave domain. Numerical evaluations across diverse datasets showcase that our XL MIMO architecture achieves classification accuracy comparable to idealized digital models, thereby proving the viability of low-complexity, wave-domain OTA learning.
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