A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization

TL;DR

This paper introduces a novel deep learning framework for robust downlink beamforming in MISO systems, outperforming traditional convex optimization methods in efficiency and performance by incorporating channel uncertainty sampling and graph neural networks.

Contribution

It presents a model-driven, unsupervised deep learning approach with a new beamforming structure and graph neural network inference for robust beamforming optimization under channel uncertainties.

Findings

Achieves non-conservative robust performance.

Provides higher data rates and power efficiency.

Faster execution than existing methods.

Abstract

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. Furthermore, we propose a bisection search method to address the more challenging power minimization problem with probabilistic rate constraints by leveraging the aforementioned approach. Numerical results confirm that our approach achieves non-conservative robust performance, higher data rates, greater power efficiency, and faster execution compared to state-of-the-art optimization methods.