# Over-The-Air Computation in Correlated Channels

arXiv: https://arxiv.org/abs/2101.04690 · HTML: https://mlsanity.com/paper/2101.04690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping](https://mlsanity.com/paper/2409.15100/) (2024)
- [Federated Learning in Wireless Networks via Over-the-Air Computations](https://mlsanity.com/paper/2305.04630/) (2023)
- [Boosting Fairness and Robustness in Over-the-Air Federated Learning](https://mlsanity.com/paper/2403.04431/) (2024)

## Abstract (arXiv)

This paper addresses the problem of Over-The-Air (OTA) computation in wireless networks which has the potential to realize huge efficiency gains for instance in training of distributed ML models. We provide non-asymptotic, theoretical guarantees for OTA computation in fast-fading wireless channels where the fading and noise may be correlated. The distributions of fading and noise are not restricted to Gaussian distributions, but instead are assumed to follow a distribution in the more general sub-gaussian class. Furthermore, our result does not make any assumptions on the distribution of the sources and therefore, it can, e.g., be applied to arbitrarily correlated sources. We illustrate our analysis with numerical evaluations for OTA computation of two example functions in large wireless networks: the arithmetic mean and the Euclidean norm.