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arXiv · 2512.14776

Low-Complexity Channel Estimation for Internet of Vehicles AFDM Communications With Sparse Bayesian Learning

Xiangxiang Li·Haiyan Wang·Yao Ge·Xiaohong Shen·Miaowen Wen·Shun Zhang·Yong Liang Guan
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Abstract

Affine frequency division multiplexing (AFDM) has been considered as a promising waveform to enable high-reliable connectivity in the internet of vehicles. However, accurate channel estimation is critical and challenging to achieve the expected performance of the AFDM systems in doubly-dispersive channels. In this paper, we propose a sparse Bayesian learning (SBL) framework for AFDM systems and develop a dynamic grid update strategy with two off-grid channel estimation methods, i.e., grid-refinement SBL (GR-SBL) and grid-evolution SBL (GE-SBL) estimators. Specifically, the GR-SBL employs a localized grid refinement method and dynamically updates grid for a high-precision estimation. The GE-SBL estimator approximates the off-grid components via first-order linear approximation and enables gradual grid evolution for estimation accuracy enhancement. Furthermore, we develop a distributed computing scheme to decompose the large-dimensional channel estimation model into multiple manageable small-dimensional sub-models for complexity reduction of GR-SBL and GE-SBL, denoted as distributed GR-SBL (D-GR-SBL) and distributed GE-SBL (D-GE-SBL) estimators, which also support parallel processing to reduce the computational latency. Finally, simulation results demonstrate that the proposed channel estimators outperform existing competitive schemes. The GR-SBL estimator achieves high-precision estimation with fine step sizes at the cost of high complexity, while the GE-SBL estimator provides a better trade-off between performance and complexity. The proposed D-GR-SBL and D-GE-SBL estimators effectively reduce complexity and maintain comparable performance to GR-SBL and GE-SBL estimators, respectively.

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Xiangxiang Li, Haiyan Wang, Yao Ge, Xiaohong Shen, Miaowen Wen, Shun Zhang, Yong Liang Guan. 2025-12-16. Low-Complexity Channel Estimation for Internet of Vehicles AFDM Communications With Sparse Bayesian Learning. https://arxiv.org/abs/2512.14776

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