The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
Open accessAbstract
The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We envision that the appealing deep learning- based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.
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Cites
Spatially Sparse Precoding in Millimeter Wave MIMO Systems
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An Introduction to Deep Learning for the Physical Layer
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Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Ye, H., Li, G. Y., & Juang, B.-H. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2017.2757490 · Cited by 2,000
Deep Learning for Massive MIMO CSI Feedback
Wen, C.-K., Shih, W.-T., & Jin, S. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2818160 · Cited by 1,174
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
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Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
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Self-citationDeep learning for wireless physical layer: Opportunities and challenges
Wang, T., Wen, C.-K., Wang, H., Gao, F., Jiang, T., & Jin, S. (2017) · China Communications · doi:10.1109/cc.2017.8233654 · Cited by 606
Intelligent 5G: When Cellular Networks Meet Artificial Intelligence
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Deep Learning for an Effective Nonorthogonal Multiple Access Scheme
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Self-citationAMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Borgerding, M., Schniter, P., & Rangan, S. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2708040 · Cited by 455
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Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
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Metadata
- Authors
- Huang, H., Guo, S., Gui, G., Yang, Z., Zhang, J., Sari, H., & Adachi, F.
- Venue
- IEEE Wireless Communications
- Published
- 2020-02
- Volume
- 27
- Issue
- 1
- Pages
- 214-222
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W2966038277
- Cited by
- 431
- References
- 13
- FWCI
- 19.91
- Type
- journal article
- Language
- en
- Open access
- green
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