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 followed by a single reception radio-frequency chain. The front layer facing the MIMO channel consists of identical unit cells of a fixed NonLinear (NL) response, while 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 communication 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工具对无线传输的数据进行推理。一个新兴研究方向涉及在多输入多输出系统的物理层直接实现推理机器学习模型，但这面临若干重大挑战。本文利用可编程超表面技术，提出了一种超大规模多输入多输出系统，该系统作为完全通过空中执行二元分类任务的极限学习机，并可通过闭式解进行训练。所提系统包含由密集并行放置的超大规模超表面衍射层及单接收射频链构成的接收机架构。面向多输入多输出信道的首层由具有固定非线性响应的相同单元构成，而其余各层则采用可调线性响应元件来近似实现训练后的极限学习机权重。数值研究表明，在超表面单元的超大规模体系下，所提出的超大规模多输入多输出-极限学习机系统在不同数据集和无线场景中均能达到与数字化理想机器学习模型相当的性能，从而证明了在未来通信系统中嵌入空中学习能力的可行性。