Enabling Over-the-Air AI for Edge Computing via Metasurface-Driven Physical Neural Networks
Chao Feng, Shuo Peng Liang, Chenghui Li, Gaoteng Zhao, Beier Jing, Yaxiong Xie, Xiaojiang Chen · 2025
We present MetaAI, a novel wireless computing paradigm that integrates neural network computation directly into wireless signal propagation. Unlike traditional approaches that treat wireless channels as mere data conduits, MetaAI transforms them into active computing elements through programmable metasurfaces, enabling concurrent data transmission and neural network processing. By leveraging the inherent linearity of both wireless propagation and neural networks, our design resolves the fundamental mismatch between sequential wireless transmission and parallel neural computation, while supporting efficient multi-sensor late-stage data fusion. We implemented MetaAI using metasurfaces at both dual-band (2.4/5 GHz) and single-band (3.5 GHz) frequencies. Extensive experiments demonstrate robust performance across diverse classification tasks, achieving 82.8% average accuracy (up to 89.8%) even with a simple linear architecture. Multi-sensor fusion further improves accuracy by up to 27.06%. MetaAI represents a fundamental shift in Edge AI architecture, where wireless infrastructure becomes an integral part of the computing pipeline.