Xia, W; Zheng, G; Wong, K-K; Zhu, H; (2020) Model-Driven Beamforming Neural Networks. IEEE Wireless Communications , 27 (1) pp. 68-75. 10.1109/MWC.001.1900239.

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Abstract

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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