Over-The-Air Computation in Correlated Channels
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jan 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 2.
5 papers.
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
- 2022A Survey on Over-the-Air Computationcited by 19
- 2024Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clippingcited by 3
- 2023Federated Learning in Wireless Networks via Over-the-Air Computationscited by 2
- 2024Boosting Fairness and Robustness in Over-the-Air Federated Learningcited by 1
- 2024Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping23 Sep 2024
- 2024Boosting Fairness and Robustness in Over-the-Air Federated Learning7 Mar 2024
- 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions3 Jul 2023
- 2023Federated Learning in Wireless Networks via Over-the-Air Computations8 May 2023
- 2022A Survey on Over-the-Air Computation20 Oct 2022