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This paper introduces an eXtremely Large (XL) MIMO system that integrates Extreme Learning Machine (ELM) principles for Over-The-Air (OTA) binary classification, utilizing cascaded nonlinear and linear metasurfaces to simplify hardware requirements. The front metasurface layer applies a fixed nonlinear response as the ELM's activation function, while subsequent tunable layers approximate trained network weights in the wave domain. Numerical evaluations demonstrate that this architecture achieves classification accuracy on par with traditional digital models, highlighting its potential for efficient, low-complexity machine learning in wireless communications.
Achieving classification accuracy comparable to digital models, this novel XL MIMO system leverages wave-domain learning to drastically reduce hardware complexity.
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.
