Graduate School and Research Center in Digital Sciences

Machine learning in the air

Gundüz, Deniz; de Kerret, Paul; Sidiropoulos, Nicholas D; Gesbert, David; Murthy, Chandra; van der Schaar, Mihaela

IEEE Journal on Selected Areas in Communications, Vol.37, N°10, October 2019

Thanks to the recent advances in processing speed and 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.

Doi Arxiv Bibtex

Title:Machine learning in the air
Type:Journal
Language:English
City:
Date:
Department:Communication systems
Eurecom ref:5872
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Bibtex: @article{EURECOM+5872, doi = {http://dx.doi.org/10.1109/JSAC.2019.2933969}, year = {2019}, month = {09}, title = {{M}achine learning in the air}, author = {{G}und{\"u}z, {D}eniz and de {K}erret, {P}aul and {S}idiropoulos, {N}icholas {D} and {G}esbert, {D}avid and {M}urthy, {C}handra and van der {S}chaar, {M}ihaela}, journal = {{IEEE} {J}ournal on {S}elected {A}reas in {C}ommunications, {V}ol.37, {N}°10, {O}ctober 2019}, url = {http://www.eurecom.fr/publication/5872} }
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