Over-The-Air Computation in Correlated Channels

Abstract

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.

Extended version can be found at arXiv:2007.02648

References in corpus (4)

• Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
• On Analog Gradient Descent Learning over Multiple Access Fading Channels
• Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
• A Scalable Max-Consensus Protocol For Noisy Ultra-Dense Networks

Cited by in corpus (2)

• Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
• Simultaneous Computation and Communication over MAC