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Open Access

Sparse Bayesian Learning Based Off-Grid Estimation of OTFS Channels with Doppler Squint

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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Abstract

Orthogonal Time Frequency Space (OTFS) modulation has exhibited significant potential to further promote the performance of future wireless communication networks especially in high-mobility scenarios. In practical OTFS systems, the subcarrier-dependent Doppler shift which is referred to as the Doppler Squint Effect (DSE) plays an important role due to the assistance of time-frequency modulation. Unfortunately, most existing works on OTFS channel estimation ignore DSE, which leads to severe performance degradation. In this letter, OTFS systems taking DSE into consideration are investigated. Inspired by the input-output analysis with DSE and the embedded pilot pattern, the sparse Bayesian learning based parameter estimation scheme is adopted to recover the delay-Doppler channel. Simulation results verify the excellent performance of the proposed off-grid estimation approach considering DSE.

References

[1]

V. S. Bhat, G. Harshavardhan, and A. Chockalingam, Input-output relation and performance of RIS-aided OTFS with fractional delay-Doppler, IEEE Commun. Lett., vol. 27, no. 1, pp. 337–341, 2023.

[2]

P. Raviteja, K. T. Phan, Y. Hong, and E. Viterbo, Interference cancellation and iterative detection for orthogonal time frequency space modulation, IEEE Trans. Wirel. Commun., vol. 17, no. 10, pp. 6501–6515, 2018.

[3]

G. D. Surabhi and A. Chockalingam, Low-complexity linear equalization for OTFS modulation, IEEE Commun. Lett., vol. 24, no. 2, pp. 330–334, 2020.

[4]

P. Raviteja, K. T. Phan, and Y. Hong, Embedded pilot-aided channel estimation for OTFS in delay-Doppler channels, IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 4906–4917, 2019.

[5]

L. Gaudio, M. Kobayashi, G. Caire, and G. Colavolpe, On the effectiveness of OTFS for joint radar parameter estimation and communication, IEEE Trans. Wirel. Commun., vol. 19, no. 9, pp. 5951–5965, 2020.

[6]
O. K. Rasheed, G. D. Surabhi, and A. Chockalingam, Sparse Delay-Doppler channel estimation in rapidly time-varying channels for multiuser OTFS on the uplink, in Proc. 2020 IEEE 91 st Vehicular Technology Conference (VTC-Spring), Antwerp, Belgium, 2020, pp. 1–5.
[7]

Z. Wei, W. Yuan, S. Li, J. Yuan, and D. W. K. Ng, Off-grid channel estimation with sparse Bayesian learning for OTFS systems, IEEE Trans. Wirel. Commun., vol. 21, no. 9, pp. 7407–7426, 2022.

[8]

L. Zhao, W. J. Gao, and W. B. Guo, Sparse Bayesian learning of delay-Doppler channel for OTFS system, IEEE Commun. Lett., vol. 24, no. 12, pp. 2766–2769, 2020.

[9]

Y. Liu, S. Zhang, F. Gao, J. Ma, and X. Wang, Uplink-aided high mobility downlink channel estimation over massive MIMO-OTFS system, IEEE J. Sel. Areas Commun., vol. 38, no. 9, pp. 1994–2009, 2020.

[10]

M. Li, S. Zhang, Y. Ge, F. Gao, and P. Fan, Joint channel estimation and data detection for hybrid RIS aided millimeter wave OTFS systems, IEEE Trans. Commun., vol. 70, no. 10, pp. 6832–6848, 2022.

[11]

F. Liu, Z. Yuan, Q. Guo, Z. Wang, and P. Sun, Message passing-based structured sparse signal recovery for estimation of OTFS channels with fractional Doppler shifts, IEEE Trans. Wirel. Commun., vol. 20, no. 12, pp. 7773–7785, 2021.

[12]

L. Zhao, J. Yang, Y. Liu, and W. Guo, Block sparse Bayesian learning-based channel estimation for MIMO-OTFS systems, IEEE Commun. Lett., vol. 26, no. 4, pp. 892–896, 2022.

[13]
X. Wang, X. Shi, J. Wang, and J. Song, On the Doppler squint effect in OTFS systems over doubly-dispersive channels: Modeling and evaluation, IEEE Trans. Wirel. Commun., vol. 22, no. 12, pp. 8781−8796, 2023.
[14]

C. Jin, Z. Bie, X. Lin, W. Xu, and H. Gao, A simple two-stage equalizer for OTFS with rectangular windows, IEEE Commun. Lett., vol. 25, no. 4, pp. 1158–1162, 2021.

[15]

A. Liao, Z. Gao, D. Wang, H. Wang, H. Yin, D. W. K. Ng, and M. S. Alouini, Terahertz ultra-massive MIMO-based aeronautical communications in space-air-ground integrated networks, IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1741–1767, 2021.

[16]
H. G. Lee, J. Kim, J. Joung, and J. Choi, Frequency-varying Doppler shift effect on wideband orthogonal time-frequency space systems, in Proc. 2023 Int. Conf. Electronics, Information, and Communication (ICEIC), Singapore, 2023, pp. 1–4.
[17]

K. P. Arunkumar and C. R. Murthy, Orthogonal delay scale space modulation: A new technique for wideband time-varying channels, IEEE Trans. Signal Process., vol. 70, pp. 2625–2638, 2022.

[18]

Z. Yang, L. Xie, and C. Zhang, Off-grid direction of arrival estimation using sparse Bayesian inference, IEEE Trans. Signal Process., vol. 61, no. 1, pp. 38–43, 2013.

Tsinghua Science and Technology
Pages 1821-1828
Cite this article:
Wang X, Shi X, Wang J. Sparse Bayesian Learning Based Off-Grid Estimation of OTFS Channels with Doppler Squint. Tsinghua Science and Technology, 2024, 29(6): 1821-1828. https://doi.org/10.26599/TST.2023.9010093

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Received: 06 May 2023
Revised: 23 August 2023
Accepted: 02 September 2023
Published: 19 March 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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