Position-aided beam learning for initial access in mmWave MIMO cellular networks
Hu, Anzhong; He, Jiguang (2020-10-14)
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
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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.
- Avoin saatavuus