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A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization by Liang, Kai; Zheng, Gan; Li, Zan; Wong, Kai-Kit; Chae, Chan-Byoung is a scholarly article available to read on EtoBox.

What is A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization about?

This paper investigates the optimization of the long-standing probabilistically robust transmit beamforming problem with channel uncertainties in the multiuser multiple-input single-output (MISO) downlink transmission. This problem poses significant analytical and computational challenges. Currently, the state-of-the-art optimization method relies on convex restrictions as tractable approximations to ensure robustness against Gaussian channel uncertainties. However, this method not only exhibits high computational complexity and suffers from the rank relaxation issue but also yields conservative solutions. In this paper, we propose an unsupervised deep learning-based approach that incorporates the sampling of channel uncertainties in the training process to optimize the probabilistic system performance. We introduce a model-driven learning approach that defines a new beamforming structure with trainable parameters to account for channel uncertainties. Additionally, we employ a graph neural network to efficiently infer the key beamforming parameters. We successfully apply this approach to the minimum rate quantile maximization problem subject to outage and total power constraints. F

Author

Liang, Kai; Zheng, Gan; Li, Zan; Wong, Kai-Kit; Chae, Chan-Byoung

Published

2024

Language

EN