基于非线性堆叠智能超表面的空中极限学习机
Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces
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中文总结 AI 辅助
本文提出一种基于非线性堆叠智能超表面的超大MIMO系统作为极限学习机，实现空中二分类，其分类精度接近理想数字模型，验证了低复杂度波域OTA学习的可行性。
中文摘要 AI 辅助
近期提出的目标导向通信范式要求机器学习推理直接在无线传输的数据上执行。本文提出一种作为极限学习机（ELM）运行的超大（XL）多输入多输出（MIMO）系统，用于执行空中（OTA）二分类。为降低硬件复杂度，接收机配备级联超表面，末端仅含一条射频链。前端超表面层对入射信号施加固定非线性响应，充当ELM的激活函数；后续可调线性超表面层直接在波域近似训练好的网络权重。在多种数据集上的数值评估表明，本文的XL MIMO架构实现了与理想化数字模型相当的分类精度，从而证明了低复杂度、波域OTA学习的可行性。
英文摘要
The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary classification. To reduce hardware complexity, the receiver is equipped with cascaded metasurfaces terminating in a single radio-frequency chain. A front metasurface layer applies a fixed nonlinear response to the incoming signal, acting as the ELM's activation function. Subsequent tunable linear metasurface layers physically approximate the trained network weights directly in the wave domain. Numerical evaluations across diverse datasets showcase that our XL MIMO architecture achieves classification accuracy comparable to idealized digital models, thereby proving the viability of low-complexity, wave-domain OTA learning.
发表机构
- National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）
- University of Bologna（博洛尼亚大学）
- CNIT（意大利国家电信研究所）
机构由 AI 辅助整理，请以论文原文为准。
