Fast Beam Training for FDD Multi-User Massive MIMO Systems With Finite Phase Shifter Resolution



Xu, Ke, Zheng, Fu-Chun, Cao, Pan, Xu, Hongguang and Zhu, Xu ORCID: 0000-0002-7371-4595
(2020) Fast Beam Training for FDD Multi-User Massive MIMO Systems With Finite Phase Shifter Resolution. IEEE Transactions on Vehicular Technology, 70 (1). pp. 459-473.

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Abstract

A fast beam training scheme for the massive antenna arrays is a key to frequency division duplex (FDD) mmWave systems, as channel reciprocity between the down-link (DL) and up-link (UL) channels does not hold in general, requiring feedback mechanism for DL beam selection. Due to the large number of antennas in a massive array, it is not very practical to conduct an exhaustive search, especially when considering the small angular coverage of one directional narrow beam (hence the number of candidate beams). To address such a challenge, we consider the 3D beam matching problem for a multi-user massive multiple-input multiple-output (MIMO) system, and propose a two-stage hierachical codebook along with the corresponding fast beam training scheme. The proposed codebook contains a primary codebook for hierarchical beams and an auxiliary codebook for narrow beams following the limited resolution of the phase shifters (PSs). The fast beam training scheme based on the codebook not only reduces beam training overhead, but also is applicable to the scenario where there exist multiple propagation paths for one mobile station (MS). Numerical results show that the proposed scheme not only enjoys a lower beam matching complexity (i.e. the training overhead), but also achieves a comparable performance to the exhaustive search scheme.

Item Type: Article
Uncontrolled Keywords: Radio frequency, Training, Array signal processing, Uplink, Three-dimensional displays, Spatial resolution, Manganese, Millimeter wave (mm-wave) communications, massive MIMO, beam training
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 08 Apr 2021 09:59
Last Modified: 17 Mar 2024 11:33
DOI: 10.1109/tvt.2020.3045301
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3118209