# Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces

arXiv: https://arxiv.org/abs/2608.27137 · HTML: https://mlsanity.com/paper/2608.27137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 27 Aug 2026.

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

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications](https://mlsanity.com/paper/2006.07838/) (2020)
- [Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G](https://mlsanity.com/paper/2305.08079/) (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)
- [Wave-based extreme deep learning based on non-linear time-Floquet entanglement](https://mlsanity.com/paper/2107.08564/) (2021)
- [On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels](https://mlsanity.com/paper/2302.04993/) (2023)
- [Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond](https://mlsanity.com/paper/2111.14207/) (2021)
- [Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining](https://mlsanity.com/paper/2504.12758/) (2025)
- [Over-the-Air Semantic Alignment with Stacked Intelligent Metasurfaces](https://mlsanity.com/paper/2512.05657/) (2025)
- [Nonlinear Stacked Intelligent Surfaces for Wireless Systems](https://mlsanity.com/paper/2510.23780/) (2025)
- [Nonlinear EM-based Signal Processing](https://mlsanity.com/paper/2512.04595/) (2025)
- [Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference](https://mlsanity.com/paper/2602.19312/) (2026)

## Abstract (arXiv)

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.