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[2602.19312] Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference
Summary
This article presents Metasurfaces-Integrated Neural Networks (MINNs) as a novel framework for efficient edge inference in 6G wireless networks, leveraging programmable metasurfaces to reduce power consumption while maintaining performance.
Why It Matters
As the demand for low-latency and energy-efficient solutions in IoT applications grows, MINNs offer a transformative approach to offload computation to the physical layer of wireless communication, addressing the limitations of traditional digital hardware in machine learning.
Key Takeaways
- MINNs utilize programmable metasurfaces for efficient edge inference.
- The framework offloads computation to the physical layer, reducing power usage.
- Performance of MINNs is comparable to traditional digital neural networks.
- The architecture consists of three modules: Encoder, Channel, and Decoder.
- The study highlights open challenges and future research directions.
Computer Science > Emerging Technologies arXiv:2602.19312 (cs) [Submitted on 22 Feb 2026] Title:Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference Authors:Kyriakos Stylianopoulos, Mario Edoardo Pandolfo, Paolo Di Lorenzo, George C. Alexandropoulos View a PDF of the paper titled Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference, by Kyriakos Stylianopoulos and 3 other authors View PDF HTML (experimental) Abstract:The upcoming sixth Generation (6G) of wireless networks envisions ultra-low latency and energy efficient Edge Inference (EI) for diverse Internet of Things (IoT) applications. However, traditional digital hardware for machine learning is power intensive, motivating the need for alternative computation paradigms. Over-The-Air (OTA) computation is regarded as an emerging transformative approach assigning the wireless channel to actively perform computational tasks. This article introduces the concept of Metasurfaces-Integrated Neural Networks (MINNs), a physical-layer-enabled deep learning framework that leverages programmable multi-layer metasurface structures and Multiple-Input Multiple-Output (MIMO) channels to realize computational layers in the wave propagation domain. The MINN system is conceptualized as three modules: Encoder, Channel (uncontrollable propagation features and metasurfaces), and Decoder. The first and last modules, realized respectively at the multi-antenna trans...
