Model-driven beamforming neural networks
journal contributionposted on 19.11.2020, 12:27 by Wenchao Xia, Gan Zheng, Kai-Kit Wong, Hongbo Zhu
Beamforming 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.
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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 CouncilFind out more...
Leverhulme Trust Research Project Grant (Grant No. RPG-2017-129)
- Mechanical, Electrical and Manufacturing Engineering