Model-Driven Beamforming Neural Networks.pdf (387.29 kB)
Download fileModel-driven beamforming neural networks
journal contribution
posted on 2020-11-19, 12:27 authored by Wenchao Xia, Gan Zheng, Kai-Kit Wong, Hongbo ZhuBeamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing are simpler but at the expense of performance loss. Alternatively, DL is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data-and model-driven BNNs, presents various possible learning strategies, and also discusses complexity reduction for DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.
Funding
National Natural Science Foundation of China (Grant Nos. 61871446, 61801244, and 61801238)
Natural Science Foundation of Jiangsu Province (Grant No. BK20180754)
Unlocking Potentials of MIMO Full-duplex Radios for Heterogeneous Networks (UPFRONT)
Engineering and Physical Sciences Research Council
Find out more...Leverhulme Trust Research Project Grant (Grant No. RPG-2017-129)
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IEEE Wireless CommunicationsVolume
27Issue
1Pages
68 - 75Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Publication date
2020-03-04Copyright date
2020ISSN
1536-1284eISSN
1558-0687Publisher version
Language
- en