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

[2504.12758] Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search 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. Compared to conventional ELMs and deep learning approaches, whose training takes seconds to minutes, the proposed framework achieves on par performance (above $90\%$ classification accuracy across multiple data sets) with optimization latency of few milliseconds under the same number of trainable parameters, considering rich fading, low noise channels with XL receive antennas, making it highly attractive for inference tasks with ultra-low-power devices. Comments: Extended version of a paper submitted to an IEEE Letters Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG) Cite as: arXiv:2504.12758 [eess.SP] (or arXiv:2504.12758v3 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2504.12758 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kyriakos Stylianopoulos [ view email ] [v1] Thu, 17 Apr 2025 08:53:30 UTC (804 KB) [v2] Mon, 8 Sep 2025 16:10:37 UTC (994 KB) [v3] Fri, 10 Apr 2026 05:12:28 UTC (1,899 KB) Full-text links: Access Paper: 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) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2025-04 Change to browse by: cs cs.LG eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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