  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.
