Paper Info
Title
Position-Aided Beam Learning for Initial Access in mmWave MIMO Cellular Networks
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.
Year
DOI
Venue
2022
10.1109/JSYST.2020.3027757
IEEE SYSTEMS JOURNAL
Keywords
DocType
Volume
Delays, MIMO communication, Cellular networks, Array signal processing, Channel estimation, Long Term Evolution, Signal to noise ratio, Beam, context information, millimeter wave (mmWave), multiple-input-multiple-output (MIMO) systems
Journal
16
Issue
ISSN
Citations 
1
1932-8184
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Anzhong Hu100.68
Jiguang He221.70