App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Low-overhead beam training method in hybrid field for controllable beam split-based XL-MIMO

Zheng Tianhao
Xie Zhili
Zhang Guangchi
Cui Miao
Fu Hao
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0027

Abstract

To address the problem of excessive beam training overhead caused by the user classification scheme based on the Rayleigh distance in hybrid-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an efficient two-stage hybrid-field beam training method using controllable beam splitting. In the first stage, the method performs coarse scanning for users to obtain their beam angle and half-power beamwidth, then calculates a critical threshold distance (a modified Rayleigh distance) . Leveraging angular domain sparsity, the method quickly distinguishes far-field users from near-field users. In the second stage, the method generates a near-field codebook of appropriate size according to system requirements and performs refined beam focusing for near-field users, thereby ensuring that their beam gain is comparable to that of far-field users. Simulation results show that, in hybrid-field scenarios, the proposed scheme achieves over 90% of the average rate performance of a pure near-field scheme with less than 50% of its training overhead. In summary, the proposed two-stage hybrid-field beam training method efficiently classifies far-field and near-field users via controllable beam splitting, performs differentiated beam training for different user types, significantly reduces the beam training overhead in hybrid-field XL-MIMO systems, while maintaining favorable rate performance.

Foundation Support

广东省基础与应用基础研究基金资助项目(2023A1515011980)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0027
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-07-01] Accepted Paper

Cite This Article

郑天昊, 谢志立, 张广驰, 等. 基于可控波束分裂的超大规模MIMO混合场低开销波束训练方法 [J]. 计算机应用研究, 2026, 43 (9). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0027. (Zheng Tianhao, Xie Zhili, Zhang Guangchi, et al. Low-overhead beam training method in hybrid field for controllable beam split-based XL-MIMO [J]. Application Research of Computers, 2026, 43 (9). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0027. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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