Machine Learning in the Air

Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld d...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 37; no. 10; pp. 2184 - 2199
Main Authors: Gunduz, Deniz, de Kerret, Paul, Sidiropoulos, Nicholas D., Gesbert, David, Murthy, Chandra R., van der Schaar, Mihaela
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial intelligence
Autoencoders
Automobiles
channel coding
channel estimation
Computers
Data models
data-driven methods
deep learning
distributed learning
distributed resource allocation
federated edge learning
joint source-channel coding
Machine learning
Physical layer
Smart buildings
stochastic approximation
Wireless communication
Wireless communication systems
Wireless communications
Wireless networks
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary:Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2019.2933969