[2504.12758] Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining

Electrical Engineering and Systems Science > Signal Processing arXiv:2504.12758 (eess) [Submitted on 17 Apr 2025 (v1), last revised 10 Apr 2026 (this version, v3)] Title:Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining Authors:Kyriakos Stylianopoulos, George C. Alexandropoulos View a PDF of the paper titled Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining, by Kyriakos Stylianopoulos and 1 other authors View PDF HTML (experimental) Abstract:In this paper, we show that an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) wireless system with appropriate analog combining components exhibits the properties of a universal function approximator, similar to a feedforward neural network. By treating the channel coefficients as the random nodes of a hidden layer and the receiver's analog combiner as a trainable output layer, we cast the XL MIMO system to the Extreme Learning Machine (ELM) framework, leading to a novel formulation for Over-The-Air (OTA) edge inference without requiring traditional digital processing nor pre-processing at the transmitter. Through theoretical analysis and numerical evaluation, we showcase that XL-MIMO-ELM enables near-instantaneous training and efficient classification, even in varying fading conditions, suggesting the paradigm shift of beyond massive MIMO systems as OTA artificial neural networks alongside their profound communications role. Compare...