@inproceedings{5c8e2e6bfcbb4054a78946c0cbefb05c,
title = "Sparse Bayesian Learning based AFDM Channel Estimation Exploiting Hierarchical Laplace Priors",
abstract = "As an emerging waveform suitable for high-speed communication scenarios, the Affine Frequency Division Multiplexing (AFDM) exhibits inherent sparsity of its affine frequency domain channel matrix. Exploiting such sparsity, we investigate the channel estimation in AFDM communication systems with Sparse Bayesian Learning (SBL). The channel estimation problem is first reformulated as a joint sparsifying dictionary learning and sparse signal recovery task. We then introduce an SBL framework by modeling the sparse signal prior using a hierarchical Laplace distribution, with the Expectation-Maximization (EM) algorithm employed to iteratively update the model parameters. Numerical results highlight the superiority of the proposed algorithm compared to conventional orthogonal matching pursuit (OMP) methods in terms of estimation error, and enhanced tolerance to off-grid effects.",
keywords = "Affine frequency division multiplexing (AFDM), Laplace prior, Sparse Bayesian Learning (SBL), channel estimation, compressive sensing",
author = "Shuntian Tang and Dongkai Zhou and Peng Liu and Ershuo Chen and Xinyi Wang and Jing Guo and Zesong Fei",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2025 ; Conference date: 10-08-2025 Through 13-08-2025",
year = "2025",
doi = "10.1109/ICCCWorkshops67136.2025.11148183",
language = "English",
series = "2025 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2025",
address = "United States",
}