A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming

You, Minglei; Zheng, Gan; Sun, Hongjian

Abstract

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in the multiuser multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major challenge to meet the latency requirement in the future communications systems, e.g., the targeted 1 ms end-to-end latency in 5G. By transforming the robust beamforming problem into a deep learning problem, we propose a new unsupervised data augmentation based deep neural network (DNN) method to address the outage-constrained robust beamforming problem with uncertain channel state information at the transmitter. Simulation results demonstrate that our proposed data augmentation based DNN method for the robust beamforming problem is capable to satisfy the required outage probability, and most importantly, compared to the benchmark BernsteinType Inequality (BTI) method, it is less conservative, more power efficient and several orders of magnitude faster.

Citation

You, M., Zheng, G., & Sun, H. (2021, June). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. Presented at ICC 2021 - IEEE International Conference on Communications, Montreal, Quebec

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICC 2021 - IEEE International Conference on Communications
• Start Date: Jun 14, 2021
• End Date: Jun 23, 2021
• Acceptance Date: Apr 13, 2021
• Online Publication Date: Aug 6, 2021
• Publication Date: 2021
• Deposit Date: Apr 28, 2021
• Publicly Available Date: Apr 28, 2021
• DOI: https://doi.org/10.1109/icc42927.2021.9500736
• Public URL: https://durham-repository.worktribe.com/output/1140905