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.
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