The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Model-Driven Deep Learning for Physical Layer Communications
Open accessAbstract
Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, most existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, model-driven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted.
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Cites
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2832128 · Cited by 823
Self-citationDeep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018) · arXiv · doi:10.48550/arxiv.1802.01290 · Cited by 823
Self-citationDeep Learning in Physical Layer Communications
Qin, Z., Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800601 · Cited by 692
Self-citationChannel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN
Ye, H., Li, G. Y., Juang, B.-H. F., & Sivanesan, K. (2018) · 2018 IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644250 · Cited by 244
Self-citationOn a Novel Deep-Learning-Based Intelligent Partially Overlapping Channel Assignment in SDN-IoT
Tang, F., Mao, B., Fadlullah, Z. M., & Kato, N. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701227 · Cited by 123 · cited 1×
“Recently, it has been applied to wireless communications, such as physical layer communications [1–4], resource allocation [5, 6], and intelligent traffic control [7].”
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Cited by
Deep Learning in Physical Layer Communications
Qin, Z., Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800601 · Cited by 692
Self-citationShe, C., Sun, C., Gu, Z., Li, Y., Yang, C., Poor, H. V., & Vucetic, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3053601 · Cited by 405
Terahertz Wireless Communications for 2030 and Beyond: A Cutting-Edge Frontier
Chen, Z., Han, C., Wu, Y., Li, L., Huang, C., Zhang, Z., Wang, G., & Tong, W. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.011.2100195 · Cited by 268
Deep Unfolding for Communications Systems: A Survey and Some New Directions
Balatsoukas-Stimming, A., & Studer, C. (2019) · 2019 IEEE International Workshop on Signal Processing Systems (SiPS) · doi:10.1109/sips47522.2019.9020494 · Cited by 266
Das, S. K., Mudi, R., Rahman, M. S., Rabie, K. M., & Li, X. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3466858 · Cited by 27
Metadata
- Authors
- He, H., Jin, S., Wen, C.-K., Gao, F., Li, G. Y., & Xu, Z.
- Venue
- IEEE Wireless Communications
- Published
- 2019-10
- Volume
- 26
- Issue
- 5
- Pages
- 77-83
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W2964121960
- Cited by
- 481
- References
- 6
- FWCI
- 29.47
- Type
- journal article
- Language
- en
- Open access
- green
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- He, H., Jin, S., Wen, C.-K., Gao, F., Li, G. Y., & Xu, Z. (2019). Model-Driven Deep Learning for Physical Layer Communications. IEEE Wireless Communications. https://doi.org/10.1109/mwc.2019.1800447
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