Compressed Beam Selection for Single/multi-cell Beam Management

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Compressed Beam Selection for Single/multi-cell Beam Management

Li, Xia / Gao, Bo / Wang, Yongcheng / Luo, Qingkai / Shao, Shijia / Yang, Xikun / Yan, Wenjun / Wu, Hao / Han, Bingtao

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.

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Document information

Title :

Compressed Beam Selection for Single/multi-cell Beam Management

Contributors:

Li, Xia (author) / Gao, Bo (author) / Wang, Yongcheng (author) / Luo, Qingkai (author) / Shao, Shijia (author) / Yang, Xikun (author) / Yan, Wenjun (author) / Wu, Hao (author) / Han, Bingtao (author)

Published in:

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) ; 1-5

Publication date :

2022-06-01

Size :

1096870 byte

ISBN :

978-1-6654-8243-1

ISSN :

2577-2465

DOI :

https://doi.org/10.1109/VTC2022-Spring54318.2022.9860886