TY  - INPR
UR  - http://dx.doi.org/10.1109/jsac.2024.3431583
SP  - 1
KW  - Robust beamforming
KW  -  deep learning
KW  -  outage
probability constraint
KW  -  multiuser MISO
TI  - A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SN  - 0733-8716
ID  - discovery10195807
AV  - public
EP  - 1
JF  - IEEE Journal on Selected Areas in Communications
N2  - This paper investigates the optimization of the 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.
PB  - Institute of Electrical and Electronics Engineers (IEEE)
Y1  - 2024/07/31/
A1  - Liang, Kai
A1  - Zheng, Gan
A1  - Li, Zan
A1  - Wong, Kai-Kit
A1  - Chae, Chan-Byoung
ER  -