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

Hu, Anzhong; He, Jiguang (2020-10-14)

 
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URL:
https://doi.org/10.1109/jsyst.2020.3027757

Hu, Anzhong
He, Jiguang
Institute of Electrical and Electronics Engineers
14.10.2020

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

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doi:https://doi.org/10.1109/jsyst.2020.3027757
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https://urn.fi/URN:NBN:fi-fe2022083056772
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Abstract

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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