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Hybrid-Field Channel Tracking for Extremely Large-Scale MIMO Systems with Mobility

  • Yilong Liu
  • Xi Yang
  • Ting Liu
  • Yu Han
  • Shi Jin
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Abstract

In this paper, we propose a hybrid-field channel tracking algorithm for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with mobility, leveraging the historical channel state information. The multiple path scenario is considered, and the line-of-sight path is coarsely estimated through the sparse peak search within a narrow window in the fractional Fourier domain based on the temporal continuity of the user's motion. Moreover, coarse estimations of the non-line-of-sight (NLoS) paths are determined by ensuring the scatterer survival probability, eliminating the necessity of detecting existing NLoS paths. Then, a Newton-based refinement is applied to the estimated paths before seeking possible new paths. Numerical results validate that the proposed algorithm achieves superior performance with low computational complexity.

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Cite

BibTeXRIS
BibTeX
@misc{liu2026hybridfield,
  title = {{Hybrid-Field Channel Tracking for Extremely Large-Scale MIMO Systems with Mobility}},
  author = {Yilong Liu and Xi Yang and Ting Liu and Yu Han and Shi Jin},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.19047},
  eprint = {2609.19047},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.19047},
}
RIS
TY  - UNPB
TI  - Hybrid-Field Channel Tracking for Extremely Large-Scale MIMO Systems with Mobility
AU  - Yilong Liu
AU  - Xi Yang
AU  - Ting Liu
AU  - Yu Han
AU  - Shi Jin
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.19047
UR  - https://arxiv.org/abs/2609.19047
AB  - In this paper, we propose a hybrid-field channel tracking algorithm for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with mobility, leveraging the historical channel state information. The multiple path scenario is considered, and the line-of-sight path is coarsely estimated through the sparse peak search within a narrow window in the fractional Fourier domain based on the temporal continuity of the user's motion. Moreover, coarse estimations of the non-line-of-sight (NLoS) paths are determined by ensuring the scatterer survival probability, eliminating the necessity of detecting existing NLoS paths. Then, a Newton-based refinement is applied to the estimated paths before seeking possible new paths. Numerical results validate that the proposed algorithm achieves superior performance with low computational complexity.
ER  -