Over-the-Air Computation in Correlated Channels

Over-the-Air (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 better-than-linear (depending on the function, often logarithmic or...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 69; S. 5739 - 5755
Hauptverfasser: Frey, Matthias, Bjelakovic, Igor, Stanczak, Slawomir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Anomalies
boosting
Channels
Combined source-channel coding
Computation
Correlation
distributed computing
Distributed databases
Fading
Fading channels
Linear functions
Machine learning
Noise
Norms
Random variables
robustness
Transmitters
Wireless communication
Wireless sensor networks
ISSN:1053-587X, 1941-0476
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Zusammenfassung:Over-the-Air (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 better-than-linear (depending on the function, often logarithmic or even constant) scaling of the communication cost as the number of transmitters grows. 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 <inline-formula><tex-math notation="LaTeX">p</tex-math></inline-formula>-norms of vectors. We prove error bounds that are valid for fast-fading channels and all distributions of fading and noise in the class of sub-Gaussian distributions. This class includes Gaussian distributions, but also many other practically relevant cases such as Class A Middleton noise and fading with dominant line-of-sight components. Moreover, 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. There is no assumption that the distributed function arguments follow a particular probability law; in particular, they do not need to be independent or identically distributed. 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.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2021.3106115