Near-Field Multiuser Beam-Training for Extremely Large-Scale MIMO Systems
Liu W, Pan C, Ren H, Wang J, Schober R (2024)
Publication Language: English
Publication Type: Journal article
Publication year: 2024
Journal
DOI: 10.1109/TCOMM.2024.3468208
Abstract
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. Firstly, new near-field channel models and near-field XL-MIMO transmit beamforming (TBF) codebooks have to be adopted due to the dramatic increase in the number of antennas, which results in an excessive pilot overhead for beam training. Secondly, when the user density is high, the wireless propagation environments of the adjacent users are similar and hence the pilot signals received by the BS from different users appear to be interrelated, which is potentially beneficial but difficult to exploit. Thirdly, different users might share the same beam-direction, which causes excessive inter-user interference. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the best available near-field codeword for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme significantly improves beam training accuracy and reduces pilot overhead compared to traditional neural network-based benchmarks. Hence it is more suitable for multiuser XL-MIMO systems. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.
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APA:
Liu, W., Pan, C., Ren, H., Wang, J., & Schober, R. (2024). Near-Field Multiuser Beam-Training for Extremely Large-Scale MIMO Systems. IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2024.3468208
MLA:
Liu, Wang, et al. "Near-Field Multiuser Beam-Training for Extremely Large-Scale MIMO Systems." IEEE Transactions on Communications (2024).
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