A Deep Learning Framework for Optimization of MISO Downlink Beamforming

By Wenchao Xia, Gan Zheng, Yongxu Zhu, Jun Zhang, Jiangzhou Wang, Athina P. Petropulu. IEEE Transactions on Communications, 2019

313 citations recorded by the source corpus.

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time 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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f4996081ea7ea0995d70f5ad2e9bb121dfe5d384

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Citations in the corpus

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “In this context, a DL based framework for optimization of downlink beamforming is proposed in [92].”
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis (2020, background): “Most of the existing works adopt MLPs [7], [9], [11], [21] or convolutional neural networks (CNNs) [8], [12].”
6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities (2020, background): “Adaptive beamforming in a vehicular communication scenario [139] and [140] is possible, but frequent beam training may be needed.”