Liu, Wang, Pan, Cunhua, Ren, Hong, Wang, Jiangzhou (2024) Beam Training for Multiuser XL-MIMO Systems: A Graph Neural Network Approach. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC). . IEEE ISBN 979-8-3503-0359-9. E-ISBN 979-8-3503-0358-2. (doi:10.1109/wcnc57260.2024.10571042) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:106555)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
Official URL: https://doi.org/10.1109/wcnc57260.2024.10571042 |
Abstract
Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as one of the key technologies for future 6G networks, which can further improve spectral efficiency by deploying far more antennas than conventional massive MIMO systems. However, beam training in multiuser XL-MIMO systems is challenging. To tackle this issue, we propose a graph neural network (GNN)-based beam training scheme for the multiuser XL-MIMO system, in which only the far-field wide beams need to be tested for each user. Specifically, the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user, where the information of the surrounding users can also be utilized by the GNN to further improve the accuracy of the beam training. Simulation results show that the performance of the proposed scheme can approach that of the exhaustive scheme but has more than a 93 % reduction in the pilot overhead.
Item Type: | Conference or workshop item (Proceeding) |
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DOI/Identification number: | 10.1109/wcnc57260.2024.10571042 |
Uncontrolled keywords: | Training, 6G mobile communication, Accuracy, Array signal processing, Spectral efficiency, Simulation, Massive MIMO |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 17 Jul 2024 13:47 UTC |
Last Modified: | 18 Jul 2024 14:16 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/106555 (The current URI for this page, for reference purposes) |
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