Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces

2026-08-27

AI Summary

本文提出了一种利用非线性堆叠智能超表面的极大MIMO系统，直接在无线传输的数据上执行二分类任务，以简化硬件复杂度并实现波域中的低复杂度学习。

Abstract

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.

Problem

• Over-The-Air
• Extreme Learning Machine
• MIMO
• Metasurfaces
• Wireless Data

Innovation

• Over-The-Air
• Extreme Learning Machine
• Nonlinear Stacked Intelligent Metasurfaces