The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Fast Beamforming Design via Deep Learning
Abstract
Beamforming is considered as one of the most important techniques for designing advanced multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum rate maximization (SRM) under a total power constraint is a challenge due to its nonconvexity. Existing techniques for the SRM problem only obtain local optimal solutions but require huge amount of computation due to their complex matrix operations and iterations. Unlike these conventional methods, we propose a deep learning based fast beamforming design method without complex operations and iterations. Specifically, we first derive a heuristic solution structure of the downlink beamforming through the virtual equivalent uplink channel based on optimum MMSE receiver which separates the problem into power allocation and virtual uplink beamforming (VUB) design. Next, beamforming prediction network (BPNet) is designed to perform the joint optimization of power allocation and VUB design. Moreover, the BPNet is trained offline using two-step training strategy. Simulation results demonstrate that our proposed method is fast while obtains the comparable performance to the state-of-the-art method.
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Cites
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Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
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Self-citationFadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
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Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios
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Metadata
- Authors
- Huang, H., Peng, Y., Yang, J., Xia, W., & Gui, G.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2020-01
- Volume
- 69
- Issue
- 1
- Pages
- 1065-1069
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2981598692
- Cited by
- 281
- References
- 23
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- closed
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