Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces

TL;DR

This paper proposes an over-the-air Extreme Learning Machine system using nonlinear cascaded metasurfaces in XL MIMO setups, enabling physical-layer inference with training in closed form.

Contribution

It introduces a novel XL MIMO system with metasurfaces that performs OTA binary classification, integrating physical-layer inference with closed-form training.

Findings

Achieves performance comparable to digital ML models across datasets.

Demonstrates feasibility of OTA learning in future wireless systems.

Uses nonlinear metasurfaces for efficient inference in XL MIMO.

Abstract

The recently envisioned goal-oriented communications paradigm calls for the application of inference on wirelessly transferred data via Machine Learning (ML) tools. An emerging research direction deals with the realization of inference ML models directly in the physical layer of Multiple-Input Multiple-Output (MIMO) systems, which, however, entails certain significant challenges. In this paper, leveraging the technology of programmable MetaSurfaces (MSs), we present an eXtremely Large (XL) MIMO system that acts as an Extreme Learning Machine (ELM) performing binary classification tasks completely Over-The-Air (OTA), which can be trained in closed form. The proposed system comprises a receiver architecture consisting of densely parallel placed diffractive layers of XL MSs, also known as Stacked Intelligent Metasurfaces (SIM), followed by a single reception radio-frequency chain. The front layer facing the XL MIMO channel consists of identical unit cells of a fixed NonLinear (NL) response, whereas the remaining layers of elements of tunable linear responses are utilized to approximate OTA the trained ELM weights. Our numerical investigations showcase that, in the XL regime of MS elements, the proposed XL-MIMO-ELM system achieves performance comparable to that of digital and idealized ML models across diverse datasets and wireless scenarios, thereby demonstrating the feasibility of embedding OTA learning capabilities into future wireless systems.