A Low-Complexity Joint Fractional Delay and Doppler Frequency Estimator for AFDM-Enabled Vehicular LEO-ICAN Systems
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917513297657856 |
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| author | Chen, Zhenyu Xiao, Ke Tang, Xiaomei Lei, Jing Yuan, Muzi Sun, Guangfu |
| author_facet | Chen, Zhenyu Xiao, Ke Tang, Xiaomei Lei, Jing Yuan, Muzi Sun, Guangfu |
| contents | Low-Earth-orbit (LEO) satellites and vehicle-to-everything (V2X) networks are driving integrated communication and navigation (ICAN) toward next-generation intelligent transportation. Affine frequency division multiplexing (AFDM) is a promising waveform for high-mobility LEO scenarios owing to its Doppler robustness, simple modulation, and low pilot overhead. However, applying existing high-accuracy AFDM fractional delay-Doppler estimators to LEO-ICAN entails substantial search or inference complexity, while the spectrum-wrapping-induced envelope structure in line-of-sight (LOS)-dominated channels remains underexploited. This paper analyzes and exploits the spectrum-wrapping-induced envelope structure of the fractional AFDM response, and proposes a low-complexity joint estimator that combines minimum-entropy fractional Doppler estimation with closed-form fractional delay estimation. Simulation results show that the proposed estimator approaches the root Cramér--Rao lower bound (RCRLB) and achieves root-mean-square error (RMSE) performance comparable to that of matched filtering (MF), matched filtering with generalized Fibonacci search (MF-GFS), and off-grid sparse Bayesian learning (OG-SBL), while requiring substantially lower computational complexity and runtime. This favorable accuracy-complexity profile highlights the potential of the proposed estimator for real-time ICAN processing in high-mobility LEO-assisted vehicular networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Low-Complexity Joint Fractional Delay and Doppler Frequency Estimator for AFDM-Enabled Vehicular LEO-ICAN Systems Chen, Zhenyu Xiao, Ke Tang, Xiaomei Lei, Jing Yuan, Muzi Sun, Guangfu Signal Processing Low-Earth-orbit (LEO) satellites and vehicle-to-everything (V2X) networks are driving integrated communication and navigation (ICAN) toward next-generation intelligent transportation. Affine frequency division multiplexing (AFDM) is a promising waveform for high-mobility LEO scenarios owing to its Doppler robustness, simple modulation, and low pilot overhead. However, applying existing high-accuracy AFDM fractional delay-Doppler estimators to LEO-ICAN entails substantial search or inference complexity, while the spectrum-wrapping-induced envelope structure in line-of-sight (LOS)-dominated channels remains underexploited. This paper analyzes and exploits the spectrum-wrapping-induced envelope structure of the fractional AFDM response, and proposes a low-complexity joint estimator that combines minimum-entropy fractional Doppler estimation with closed-form fractional delay estimation. Simulation results show that the proposed estimator approaches the root Cramér--Rao lower bound (RCRLB) and achieves root-mean-square error (RMSE) performance comparable to that of matched filtering (MF), matched filtering with generalized Fibonacci search (MF-GFS), and off-grid sparse Bayesian learning (OG-SBL), while requiring substantially lower computational complexity and runtime. This favorable accuracy-complexity profile highlights the potential of the proposed estimator for real-time ICAN processing in high-mobility LEO-assisted vehicular networks. |
| title | A Low-Complexity Joint Fractional Delay and Doppler Frequency Estimator for AFDM-Enabled Vehicular LEO-ICAN Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.04316 |