Machine Learning in the Air

Gunduz, Deniz and de Kerret, Paul and Sidiropoulos, Nicholas D and Gesbert, David and Murthy, Chandra R and van der Schaar, Mihaela (2019) Machine Learning in the Air. In: IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 37 (10, SI). pp. 2184-2199.

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Abstract

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.

Item Type:	Journal Article
Publication:	IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Publisher:	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Additional Information:	copyright for this article belongs to IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords:	Autoencoders; channel coding; channel estimation; data-driven methods; distributed learning; distributed resource allocation; deep learning; federated edge learning; joint source-channel coding; machine learning; stochastic approximation; wireless communications
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	23 Oct 2019 09:55
Last Modified:	23 Oct 2019 09:56
URI:	http://eprints.iisc.ac.in/id/eprint/63758

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