To Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design

Release Date:2020-03-20  Author:XUE Songyan, LI Ang, WANG Jinfei, YI Na, MA Yi, Rahim Tafazolli, and Terence Dodgson  Click:

To Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design

XUE Songyan1, LI Ang1, WANG Jinfei1, YI Na1, MA Yi1, Rahim Tafazolli 1, and Terence Dodgson2
(1. Institute for Communication Systems, University of Surrey, Guildford, GU2 7XH, the United Kingdom;
2. Airbus Defense and Space, Portsmouth, PO3 5PU, the United Kingdom)

Abstract: Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing debate as to what additional values artificial intelligence (or machine learning) could bring to us, particularly on the physical layer design; and what penalties there may have? These questions motivate a fundamental rethinking of the wireless modem design in the artificial intelligence era. Through several physical-layer case studies, we argue for a significant role that machine learning could play, for instance in parallel error-control coding and decoding, channel equalization, interference cancellation, as well as multiuser and multiantenna detection. In addition, we discuss the fundamental bottlenecks of machine learning as well as their potential solutions in this paper.
Keywords: deep learning; neural networks; machine learning; modulation and coding

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