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conference paper

Deep Unfolding for Communications Systems: A Survey and Some New Directions

Balatsoukas-Stimming, Alexios  
•
Studer, Christoph
January 1, 2019
Proceedings Of The 2019 Ieee International Workshop On Signal Processing Systems (Sips 2019)
33rd IEEE International Workshop on Signal Processing Systems (IEEE SiPS)

Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey summarizes the principle of deep unfolding and discusses its recent use for communication systems with focus on detection and precoding in multi-antenna (MIMO) wireless systems and belief propagation decoding of error-correcting codes. To showcase the efficacy and generality of deep unfolding, we describe a range of other tasks relevant to communication systems that can be solved using this emerging paradigm. We conclude the survey by outlining a list of open research problems and future research directions.

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Type
conference paper
DOI
10.1109/SiPS47522.2019.9020494
Web of Science ID

WOS:000554872400046

Author(s)
Balatsoukas-Stimming, Alexios  
•
Studer, Christoph
Date Issued

2019-01-01

Publisher

IEEE

Publisher place

New York

Published in
Proceedings Of The 2019 Ieee International Workshop On Signal Processing Systems (Sips 2019)
ISBN of the book

978-1-7281-1927-4

Start page

266

End page

271

Subjects

machine learning

•

channel coding

•

massive mimo

•

deep unfolding

•

networks

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
IEL  
Event nameEvent placeEvent date
33rd IEEE International Workshop on Signal Processing Systems (IEEE SiPS)

Nanjing, PEOPLES R CHINA

Oct 20-23, 2019

Available on Infoscience
August 20, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/170959
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