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Fast beam training on extremely large-scale multiple-input multiple-output system
Huahua WANG, Changjiang XIE, Jiening FANG
Journal of Computer Applications    2025, 45 (5): 1625-1631.   DOI: 10.11772/j.issn.1001-9081.2024050583
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The eXtremely Large-scale Multiple-Input-Multiple-Output (XL-MIMO) system can significantly improve channel capacity. However, traditional Uniform Linear Arrays (ULAs) experience a drastic reduction in the near-field region at large incident and emitted angles, leading to limited signal coverage. The use of Uniform Circular Arrays (UCAs) can effectively expand the near-field regions, but renders low-overhead beam training schemes based on ULA impractical. To reduce the overhead of near-field beam training with UCA, a new fast beam training scheme was proposed. In the first stage, UCA was approximated as ULA, and a joint method was used to construct a far-field hierarchical codebook for angle domain user search; in the second stage, based on the angles obtained from the first stage, UCA was used for exhaustive search in both angle and distance domains. Simulation results on a UCA system with 512 antennas indicate that the proposed scheme requires only 28 training overheads, while maintaining good robustness across different Signal-to-Noise Ratio (SNR) conditions, and its rate performance achieves 99.16% of the benchmark.

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