An Interpolation-Based Blind CFO Estimator for Faster-Than-Nyquist Signaling Over LEO Satellite Channel
In this article, an interpolation-based blind carrier frequency-offset (CFO) estimator is proposed for faster-than-Nyquist (FTN) system in low-Earth-orbit satellite channel. According to maximum-likelihood criterion, we can obtain the CFO estimation problem formulation from the joint probability density function of output samples of matched filter. After simplifying, the CFO estimation problem can be equivalently solved by nonlinear least-square approach. Based on this, we have designed the objective function for CFO estimation of FTN. The physical meaning of designed objective function can be seen as the periodogram of the data samples. The peak location of the periodogram of the data samples is corresponding to the CFO estimate of FTN. Then, the objective function is proved to be approximated as a parabolic curve. By maximizing the designed objective function, an interpolation-based blind CFO estimator is proposed without iterated procedure. Finally, simulation results demonstrated that our proposed interpolation-based CFO estimator has lower computation complexity, and outperforms traditional estimators.
Authors
- Senbai Zhang (ORCID: https://orcid.org/0000-0001-8579-2251)
- Zhixiang Gao (ORCID: https://orcid.org/0000-0001-6327-4613)
- Xin Lin (ORCID: https://orcid.org/0000-0001-7456-5846)
- Hehao Niu (ORCID: https://orcid.org/0000-0002-6423-3482)
- Xiaohu Liang (ORCID: https://orcid.org/0000-0003-0409-0675)
- Hao Liang (ORCID: https://orcid.org/0000-0002-7201-372X)
- Aijun Liu (ORCID: https://orcid.org/0000-0001-7235-9424)
Institutions
- National University of Defense Technology (CN)
- PLA Army Engineering University (CN)
Publication Details
- Journal
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1109/taes.2024.3461683
- Citations
- 2
- Primary Topic
- Wireless Communication Networks Research
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Jiangsu Province