Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

Zhang, Juping; You, Minglei; Zheng, Gan; Krikidis, Ioannis; Zhao, Liqiang

Abstract

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

Citation

Zhang, J., You, M., Zheng, G., Krikidis, I., & Zhao, L. (2022). Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information. IEEE Transactions on Wireless Communications, 21(4), 2368-2382. https://doi.org/10.1109/twc.2021.3111843

• Journal Article Type: Article
• Acceptance Date: Sep 2, 2021
• Online Publication Date: Sep 17, 2021
• Publication Date: Apr 1, 2022
• Deposit Date: Oct 25, 2021
• Publicly Available Date: Oct 27, 2021
• Journal: IEEE Transactions on Wireless Communications
• Print ISSN: 1536-1276
• Electronic ISSN: 1558-2248
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 4
• Pages: 2368-2382
• DOI: https://doi.org/10.1109/twc.2021.3111843
• Keywords: Applied Mathematics; Electrical and Electronic Engineering; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/6537255
• Publisher URL: https://ieeexplore.ieee.org/document/9540784
• Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works