康奈尔大学团队推出超大规模MIMO系统，用物理超表面实现机器学习，波域处理精度媲美数字模型。
该研究提出eXtremely Large MIMO系统作为Extreme Learning Machine执行空中二分类。接收端配备级联超表面层，前端固定非线性响应作为激活函数，后续可调线性层在波域近似训练权重。在多个数据集上评估显示，该架构分类精度与理想数字模型相当，证明了低复杂度波域空中学习的可行性。
Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces
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.