Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference

• URL: http://arxiv.org/abs/2602.19312v1
• Date: Sun, 22 Feb 2026 19:23:49 GMT
• 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,
• Abstract summary: 6G wireless networks envision ultra-low latency and energy efficient Edge Inference (EI) for diverse Internet of Things (IoT) applications.<n>Traditional digital hardware for machine learning is power intensive, motivating the need for alternative computation paradigms.<n>Over-The-Air (OTA) computation is regarded as an emerging transformative approach assigning the wireless channel to actively perform computational tasks.<n>This article introduces the concept of Metasurfaces-Integrated Networks (MINNs), a physical-layer-enabled deep learning framework.
• Score: 34.75476728721598
• License: http://creativecommons.org/licenses/by/4.0/
• 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 transmitter and receiver, consist of conventional digital or purposely designed analog Deep Neural Network (DNN) layers, and the metasurfaces responses of the Channel module are optimized alongside all modules as trainable weights. This architecture enables computation offloading into the end-to-end physical layer, flexibly among its constituent modules, achieving performance comparable to fully digital DNNs while significantly reducing power consumption. The training of the MINN framework, two representative variations, and performance results for indicative applications are presented, highlighting the potential of MINNs as a lightweight and sustainable solution for future EI-enabled wireless systems. The article is concluded with a list of open challenges and promising research directions.

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