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A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Xia, Wenchao, Zheng, Gan, Zhu, Yongxu, Zhang, Jun, Wang, Jiangzhou, Petropulu, Athina P. (2019) A Deep Learning Framework for Optimization of MISO Downlink Beamforming. IEEE Transactions on Communications, 68 (3). pp. 1866-1880. ISSN 0090-6778. E-ISSN 1558-0857. (doi:10.1109/TCOMM.2019.2960361​) (KAR id:79640)

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Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

Item Type: Article
DOI/Identification number: 10.1109/TCOMM.2019.2960361​
Uncontrolled keywords: Deep learning, beamforming, MISO, beamforming neural network
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Jiangzhou Wang
Date Deposited: 20 Jan 2020 12:08 UTC
Last Modified: 16 Feb 2021 14:10 UTC
Resource URI: (The current URI for this page, for reference purposes)
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