Year: 2020
Type: article
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.
Source: IEEE Wireless Communications
Authors: Wenchao Xia, Gan Zheng, Kai-Kit Wong, Hongbo Zhu
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3009540182