OverTheAir Computation in Correlated Channels
Abstract
OvertheAir (OTA) computation is the problem of computing functions of distributed data without transmitting the entirety of the data to a central point. By avoiding such costly transmissions, OTA computation schemes can achieve a betterthanlinear (depending on the function, often logarithmic or even constant) scaling of the communication cost as the number of transmitters grows. Among the most common functions computed OTA are linear functions such as weighted sums. In this work, we propose and analyze an analog OTA computation scheme for a class of functions that contains linear functions as well as some nonlinear functions such as $p$norms of vectors. We prove error bound guarantees that are valid for fastfading channels and all distributions of fading and noise contained in the class of subGaussian distributions. This class includes Gaussian distributions, but also many other practically relevant cases such as Class A Middleton noise and fading with dominant lineofsight components. In addition, there can be correlations in the fading and noise so that the presented results also apply to, for example, block fading channels and channels with bursty interference. We do not rely on any stochastic characterization of the distributed arguments of the OTA computed function; in particular, there is no assumption that these arguments are drawn from identical or independent probability distributions. Our analysis is nonasymptotic and therefore provides error bounds that are valid for a finite number of channel uses. OTA computation has a huge potential for reducing communication cost in applications such as Machine Learning (ML)based distributed anomaly detection in large wireless sensor networks. We illustrate this potential through extensive numerical simulations.
 Publication:

arXiv eprints
 Pub Date:
 July 2020
 arXiv:
 arXiv:2007.02648
 Bibcode:
 2020arXiv200702648F
 Keywords:

 Computer Science  Information Theory;
 Electrical Engineering and Systems Science  Signal Processing
 EPrint:
 IEEE Transactions on Signal Processing, vol. 69, pp. 57395755, 2021