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.

翻译：最近提出的面向目标的通信范式要求通过机器学习（ML）工具对无线传输数据进行推理。一个新兴的研究方向涉及在多输入多输出（MIMO）系统的物理层直接实现推理ML模型，但这带来了若干重大挑战。本文利用可编程超表面（MS）技术，提出了一种超大（XL）MIMO系统，该系统可作为极速学习机（ELM），完全通过空口（OTA）执行二分类任务，且能以闭式形式进行训练。所提系统包含一种接收架构，由密集并行排列的XL MS衍射层（也称为堆叠智能超表面SIM）组成，其后跟随单个接收射频链。面向XL MIMO信道的前层由具有固定非线性（NL）响应的相同单元构成，而其余层的元件具有可调线性响应，用于在空口近似训练后的ELM权重。数值研究表明，在MS元件处于XL状态时，所提XL-MIMO-ELM系统在不同数据集和无线场景下的性能可与数字化及理想化ML模型相媲美，从而证明了将空口学习能力嵌入未来无线系统的可行性。