TY - JOUR
T1 - Deep Learning for Wireless Communications
T2 - An Emerging Interdisciplinary Paradigm
AU - Dai, Linglong
AU - Jiao, Ruicheng
AU - Adachi, Fumiyuki
AU - Poor, H. Vincent
AU - Hanzo, Lajos
N1 - 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:
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.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
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U2 - 10.1109/MWC.001.1900491
DO - 10.1109/MWC.001.1900491
M3 - Article
AN - SCOPUS:85089456421
SN - 1536-1284
VL - 27
SP - 133
EP - 139
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 4
M1 - 9165550
ER -