Deep learning for wireless communications: an emerging interdisciplinary paradigm
Deep learning for wireless communications: an emerging interdisciplinary paradigm
Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality, Internet of Things, and so on, becoming a reality. However, these compelling applications have imposed many new challenges, including unknown channel models, low-latency requirement in large-scale super-dense networks, and so on. The amazing success of deep learning in various fields, particularly in computer science, has recently stimulated increasing interest in applying it to address those challenges. Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated. The first one is DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades. The second one is DL-based algorithm design, which will be illustrated by several examples in a series of typical techniques conceived for 5G and beyond. Their principles, key features, and performance gains will be discussed. Open problems and future research opportunities will also be pointed out, highlighting the interplay between DL and wireless communications. We expect that this review can stimulate more novel ideas and exciting contributions for intelligent wireless communications.
133-139
Dai, Linglong
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Jiao, Ruicheng
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Adachi, Fumiyuki
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Poor, H. Vincent
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Hanzo, Lajos
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August 2020
Dai, Linglong
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Jiao, Ruicheng
cce9d579-8813-4f92-9ec7-16afc3478573
Adachi, Fumiyuki
8cde61e9-30b0-41c8-8f3a-025e1edf25ef
Poor, H. Vincent
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Hanzo, Lajos
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Dai, Linglong, Jiao, Ruicheng, Adachi, Fumiyuki, Poor, H. Vincent and Hanzo, Lajos
(2020)
Deep learning for wireless communications: an emerging interdisciplinary paradigm.
IEEE Communications Magazine, 27 (4), , [9165550].
(doi:10.1109/MWC.001.1900491).
Abstract
Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality, Internet of Things, and so on, becoming a reality. However, these compelling applications have imposed many new challenges, including unknown channel models, low-latency requirement in large-scale super-dense networks, and so on. The amazing success of deep learning in various fields, particularly in computer science, has recently stimulated increasing interest in applying it to address those challenges. Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated. The first one is DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades. The second one is DL-based algorithm design, which will be illustrated by several examples in a series of typical techniques conceived for 5G and beyond. Their principles, key features, and performance gains will be discussed. Open problems and future research opportunities will also be pointed out, highlighting the interplay between DL and wireless communications. We expect that this review can stimulate more novel ideas and exciting contributions for intelligent wireless communications.
Text
Final-version of WCM-19-00491
- Accepted Manuscript
More information
Accepted/In Press date: 26 May 2020
e-pub ahead of print date: 12 August 2020
Published date: August 2020
Additional Information:
Funding Information:
AcknoWledgment This work was supported by the National Natural Science Foundation of China for Outstanding Young Scholars (Grant No. 61722109); the Royal Academy of Engineering through the UK-China Industry Academia Partnership Programme Scheme (Grant No. UK-CIAPP\49); and the U.S. National Science Foundation under Grant ECCS-1647198. L. Hanzo would also like to acknowledge the financial support of the European Research Council’s Advanced Fellow Grant.
Funding Information:
Tsinghua University, Beijing, China, in 2011. He is currently an associate professor at Tsinghua University. His current research interests include massive MIMO, millimeter-wave/THz communications, reconfigurable intelligent surface, multiple access, and sparse signal processing. He has received five IEEE conference best paper awards, the Electronics Letters Best Paper Award in 2016, the National Natural Science Foundation of China for Outstanding Young Scholars in 2017, the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2017, the 7th IEEE ComSoc Asia-Pacific Outstanding Paper Award in 2018, the China Communications Best Paper Award in 2019, and the IEEE Communications Society Leonard G. Abraham Prize in 2020.
Publisher Copyright:
© 2002-2012 IEEE.
Identifiers
Local EPrints ID: 441058
URI: http://eprints.soton.ac.uk/id/eprint/441058
ISSN: 0163-6804
PURE UUID: 10fd5722-b203-4a69-ac48-58580f9289d6
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Date deposited: 28 May 2020 16:58
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Linglong Dai
Author:
Ruicheng Jiao
Author:
Fumiyuki Adachi
Author:
H. Vincent Poor
Author:
Lajos Hanzo
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