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

You, Minglei; Zheng, Gan; Sun, Hongjian

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


Minglei You

Gan Zheng


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.


You, M., Zheng, G., & Sun, H. (2021). A Data Augmentation based DNN Approach for Outage-Constrained Robust Beamforming. .

Conference Name ICC 2021 - IEEE International Conference on Communications
Conference Location Montreal, Quebec
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


Accepted Conference Proceeding (704 Kb)

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