# Integrating Stacked Intelligent Metasurfaces and Power Control for Dynamic Edge Inference via Over-The-Air Neural Networks

arXiv: https://arxiv.org/abs/2509.18906 · HTML: https://mlsanity.com/paper/2509.18906/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Expressive Power of Neural Networks: A View from the Width](https://mlsanity.com/paper/1709.02540/) (2017)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G](https://mlsanity.com/paper/2305.08079/) (2023)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques](https://mlsanity.com/paper/2203.03176/) (2022)
- [RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing](https://mlsanity.com/paper/2301.03535/) (2023)
- [Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks](https://mlsanity.com/paper/2504.00233/) (2025)
- [Implementing Neural Networks Over-the-Air via Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2508.01840/) (2025)
- [Towards Distributed and Intelligent Integrated Sensing and Communications for 6G Networks](https://mlsanity.com/paper/2402.11630/) (2024)
- [Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining](https://mlsanity.com/paper/2504.12758/) (2025)
- [Massive Access of Static and Mobile Users via Reconfigurable Intelligent Surfaces: Protocol Design and Performance Analysis](https://mlsanity.com/paper/2309.05964/) (2023)
- [Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis](https://mlsanity.com/paper/2407.00933/) (2024)

## Built on by (most cited first, 2 of 2)

- [RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization](https://mlsanity.com/paper/2512.18788/) (2025)
- [Over-the-Air Goal-Oriented Communications](https://mlsanity.com/paper/2512.20533/) (2025)

## Abstract (arXiv)

This paper introduces a novel framework for Edge Inference (EI) that bypasses the conventional practice of treating the wireless channel as noise. We utilize Stacked Intelligent Metasurfaces (SIMs) to control wireless propagation, enabling the channel itself to perform over-the-air computation. This eliminates the need for symbol estimation at the receiver, significantly reducing computational and communication overhead. Our approach models the transmitter-channel-receiver system as an end-to-end Deep Neural Network (DNN) where the response of the SIM elements are trainable parameters. To address channel variability, we incorporate a dedicated DNN module responsible for dynamically adjusting transmission power leveraging user location information. Our performance evaluations showcase that the proposed metasurfaces-integrated DNN framework with deep SIM architectures are capable of balancing classification accuracy and power consumption under diverse scenarios, offering significant energy efficiency improvements.