OTFS-Based Super Resolution Channel Estimation in Multisatellite Coordinated Transmission

Cohesive clustered satellite (CCS) system can utilize multi-satellite coordinated transmission (MSCT) to enhance the sum rate and provide direct satellite-to-device connectivity, which is regarded as a key component for low Earth orbit (LEO) satellite-integrated Internet. Considering the high-mobility LEO satellites and fractional Doppler interference (FDI) due to the non-integer Doppler tap, we utilize orthogonal time frequency space (OTFS) modulation to mitigate the complex delay-Doppler effects on a linear time-varying (LTV) channel. Then, we analyze the impact of FDI and the block circulant matrix with circulant block (BCCB) structure on the OTFS channel matrix, and derive the approximate super resolution (SR) relationships between the integer and fractional Doppler channel matrices. Furthermore, to improve communication efficiency and reliability of MSCT, we propose an OTFS-based super resolution-fractional Doppler channel estimation (SR-FCE) scheme, and introduce an enhanced low-correlation-zone periodic sequence (ELPS) superimposed on the OTFS frame to lower the peak-to-average power ratio. In the coarse estimate stage of SR-FCE scheme, a low-resolution channel matrix is obtained via the threshold method, followed by the fractional Doppler network (FracNet) to extract FDI parameters for high-resolution channel matrix reconstruction. Simulation results validate the feasibility of the SR-FCE scheme in MSCT, and outperforms the state-of-the-art schemes in terms of normalized mean squared error and bit error rate.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-09-01
DOI
https://doi.org/10.1109/jiot.2025.3604565
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

OTFS-Based Super Resolution Channel Estimation in Multisatellite Coordinated Transmission

Jian Jiao, Zhiang Wu, Qinyu Zhang, Ye Wang et al.
IEEE Internet of Things Journal
Sparse and Compressive Sensing Techniques
article

OTFS-Based Super Resolution Channel Estimation in Multisatellite Coordinated Transmission

Jian Jiao, Zhiang Wu, Qinyu Zhang, Ye Wang, Siyuan Bai, Cheng Guo
article en

Abstract

Cohesive clustered satellite (CCS) system can utilize multi-satellite coordinated transmission (MSCT) to enhance the sum rate and provide direct satellite-to-device connectivity, which is regarded as a key component for low Earth orbit (LEO) satellite-integrated Internet. Considering the high-mobility LEO satellites and fractional Doppler interference (FDI) due to the non-integer Doppler tap, we utilize orthogonal time frequency space (OTFS) modulation to mitigate the complex delay-Doppler effects on a linear time-varying (LTV) channel. Then, we analyze the impact of FDI and the block circulant matrix with circulant block (BCCB) structure on the OTFS channel matrix, and derive the approximate super resolution (SR) relationships between the integer and fractional Doppler channel matrices. Furthermore, to improve communication efficiency and reliability of MSCT, we propose an OTFS-based super resolution-fractional Doppler channel estimation (SR-FCE) scheme, and introduce an enhanced low-correlation-zone periodic sequence (ELPS) superimposed on the OTFS frame to lower the peak-to-average power ratio. In the coarse estimate stage of SR-FCE scheme, a low-resolution channel matrix is obtained via the threshold method, followed by the fractional Doppler network (FracNet) to extract FDI parameters for high-resolution channel matrix reconstruction. Simulation results validate the feasibility of the SR-FCE scheme in MSCT, and outperforms the state-of-the-art schemes in terms of normalized mean squared error and bit error rate.

IEEE Internet of Things JournalVol. 12(22)
Peng Cheng Laboratory (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 75%
Sparse and Compressive Sensing Techniques
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OTFS-Based Super Resolution Channel Estimation in Multisatellite Coordinated Transmission — Jian Jiao, Zhiang Wu, et al. · IEEE Internet of Things Journal (2025) | TGRS Research Map | TGRS