University of Oulu

A. Hu and J. He, "Position-Aided Beam Learning for Initial Access in mmWave MIMO Cellular Networks," in IEEE Systems Journal, vol. 16, no. 1, pp. 1103-1113, March 2022, doi: 10.1109/JSYST.2020.3027757

Position-aided beam learning for initial access in mmWave MIMO cellular networks

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Author: Hu, Anzhong1; He, Jiguang2
Organizations: 1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2Centre for Wireless Communications, University of Oulu, FI-90014 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-30


In this article, beam learning based on position information (PI) about mobile station positions in the initial access (IA) of millimeter wave (mmWave) multiple-input–multiple-output (MIMO) cellular networks is investigated. The existing PI-based IA procedure cannot efficiently tackle the position inaccuracy and blockage or may cause a long IA delay because of the inefficient beam learning. Based on the sparse scattering of mmWave signals, the serving area is partitioned into smaller areas and the beams are learned for each small area. Moreover, the number of learned beams is restricted and fixed after learning. Thus, the impact of position inaccuracy and blockage can be mostly mitigated and the IA delay is short in each successful IA. The analysis shows the lower bound of the probability of miss detection. Additionally, the simulation results show that the proposed approach can achieve a reasonable IA delay and superior IA performance than other PI-based approaches.

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Series: IEEE systems journal
ISSN: 1932-8184
ISSN-E: 1937-9234
ISSN-L: 1932-8184
Volume: 16
Issue: 1
Pages: 1103 - 1113
DOI: 10.1109/jsyst.2020.3027757
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by Zhejiang Provincial Natural Science Foundation of China under Project LY20F010007 and in part by National Natural Science Foundation of China under Project 61601152 and Project U1609216.
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