Deep learning for mmWave beam-management: state-of-the-art, opportunities and challenges
Deep learning for mmWave beam-management: state-of-the-art, opportunities and challenges
Benefiting from huge bandwidth resources, millimeter-wave (mmWave) communications provide one of the most promising technologies for next-generation wireless networks. To compensate for the high pathloss of mmWave
signals, large-scale antenna arrays are required both at the base stations and user equipment to establish directional beamforming, where beam-management is adopted to acquire and track the optimal beam pair having the maximum received power. Naturally, narrow beams are required for achieving high beamforming gain, but they impose enormous training overhead and high sensitivity to blockages. As a remedy, deep learning (DL) may be harnessed for beam-management. First, the current state-of-the-art is reviewed, followed by the associated challenges and future research opportunities. We conclude by highlighting the associated DL design insights and novel beam-management mechanisms.
Adaptation models, Array signal processing, Feature extraction, Structural beams, Supervised learning, Training, Wireless sensor networks
1-8
Ma, Ke
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Wang, Zhaocheng
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Tian, Wenqiang
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Chen, Sheng
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Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 August 2022
Ma, Ke
7c09ef9b-873b-472f-9424-7e99a4875f8f
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Tian, Wenqiang
c4c9a96b-95cb-4abf-a47e-435bb0547633
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Ma, Ke, Wang, Zhaocheng, Tian, Wenqiang, Chen, Sheng and Hanzo, Lajos
(2022)
Deep learning for mmWave beam-management: state-of-the-art, opportunities and challenges.
IEEE Wireless Communications, .
(doi:10.1109/MWC.018.2100713).
Abstract
Benefiting from huge bandwidth resources, millimeter-wave (mmWave) communications provide one of the most promising technologies for next-generation wireless networks. To compensate for the high pathloss of mmWave
signals, large-scale antenna arrays are required both at the base stations and user equipment to establish directional beamforming, where beam-management is adopted to acquire and track the optimal beam pair having the maximum received power. Naturally, narrow beams are required for achieving high beamforming gain, but they impose enormous training overhead and high sensitivity to blockages. As a remedy, deep learning (DL) may be harnessed for beam-management. First, the current state-of-the-art is reviewed, followed by the associated challenges and future research opportunities. We conclude by highlighting the associated DL design insights and novel beam-management mechanisms.
Text
Deep Learning for mmWave Beam-Management: State-of-the-Art, Opportunities and Challenges
- Accepted Manuscript
More information
Accepted/In Press date: 25 July 2022
Published date: 1 August 2022
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Adaptation models, Array signal processing, Feature extraction, Structural beams, Supervised learning, Training, Wireless sensor networks
Identifiers
Local EPrints ID: 468707
URI: http://eprints.soton.ac.uk/id/eprint/468707
ISSN: 1536-1284
PURE UUID: 3fd18b3f-7f73-4a9f-b803-702073a5e445
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Date deposited: 23 Aug 2022 16:50
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Ke Ma
Author:
Zhaocheng Wang
Author:
Wenqiang Tian
Author:
Sheng Chen
Author:
Lajos Hanzo
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