A Fast Deep Unfolding Learning Framework for Robust MU-MIMO Downlink Precoding

This paper reformulates a worst-case sum-rate maximization problem for optimizing robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding under realistic per-antenna power constraints. We map the fixed number of iterations in the developed mean-square-error uplink-downlink dual...

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Veröffentlicht in:IEEE transactions on cognitive communications and networking Jg. 9; H. 2; S. 1
Hauptverfasser: Xu, Jing, Kang, Chaohui, Xue, Jiang, Zhang, Yizhai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Beamforming
Computing time
Deep learning
deep unfolding
Downlink
Downlinking
Iterative algorithms
Iterative methods
Machine learning
MIMO communication
Neural networks
Optical wavelength conversion
Optimization
per-antenna power constraint
Precoding
robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding
Robustness
Signal processing algorithms
worst-case robust precoder design
ISSN:2332-7731, 2332-7731
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Zusammenfassung:This paper reformulates a worst-case sum-rate maximization problem for optimizing robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding under realistic per-antenna power constraints. We map the fixed number of iterations in the developed mean-square-error uplink-downlink duality iterative algorithm into a layer-wise trainable network to solve it. In contrast to black-box approximation neural networks, this proposed unfolding network has higher explanatory power due to fusing domain knowledge from existing iterative optimization approaches into deep learning architecture. Moreover, it could provide faster robust beamforming decisions by using several trainable key parameters. We optimize the determination of the channel error's spectral norm constraint to improve the sum rate performance. The experimental results verify that the proposed deep unfolding "RMSED-Net" could combat channel errors in comparison with the non-robust baseline. It is also confirmed by the simulations that the proposed RMSED-Net in a fixed network depth could substantially reduce the computing time of the conventional iterative optimization method at the cost of a mild sum rate performance loss.
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ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2023.3235763