Compressed Beam Selection for Single/multi-cell Beam Management

The applications of large-scale antenna arrays in 5G bring new challenges for beam management in communication. For the sake of reducing time cost on beam management, we study compressed beam selection instead of searching through a whole beam set, and use deep learning methods to predict the best b...

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Bibliographic Details
Published in2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) pp. 1 - 5
Main Authors Li, Xia, Gao, Bo, Wang, Yongcheng, Luo, Qingkai, Shao, Shijia, Yang, Xikun, Yan, Wenjun, Wu, Hao, Han, Bingtao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
5G mobile communication
beam management
Communication systems
compressed beam selection
Costs
Data mining
Deep learning
mmWave
Simulation
Vehicular and wireless technologies
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Summary:The applications of large-scale antenna arrays in 5G bring new challenges for beam management in communication. For the sake of reducing time cost on beam management, we study compressed beam selection instead of searching through a whole beam set, and use deep learning methods to predict the best beam pair of single/multi-cell mmWave beam management for 5G and beyond. Some strategies like data rearrangement are utilized to reduce error. The proposed method has much better performance than the traditional scheme and method in the literature. Comprehensive simulation results provide support for future research.
ISSN:2577-2465
DOI:10.1109/VTC2022-Spring54318.2022.9860886