Iterative Doppler Frequency Offset Estimation in Satellite High-Mobility Communications

Wang, Jiawei; Jiang, Chunxiao; Yang, Bo

doi:10.1109/jsac.2020.3005497

Cited by 31 publications

(6 citation statements)

References 49 publications

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“…Step 1. Calculate the autocorrelation function () Rm according to Equation (11) for the derivative frequency sequence (1 ) k z k L  with modulation information eliminated using Equation ( 4), then calculate the adjacent point difference of the autocorrelation sequence.…”

Section: Analysis Of Coarse Estimation Stagementioning

confidence: 99%

“…Step 4. Calculate the autocorrelation function (denoted as 1 () Rm) for the compensated signal sequence k z  using Equation (11), and calculate the adjacent point difference of the autocorrelation sequence.…”

Section: Analysis Of Coarse Estimation Stagementioning

confidence: 99%

“…Step 1. Calculate the autocorrelation function of the signal in Equation (17) using Equation (11), and the result can be denoted as 2 () Rm, 1 mN .…”

Section: Analysis Of Fine Estimation Stagementioning

confidence: 99%

“…The second method commonly used is the CA estimation algorithm of frequency offset. This approach obtains the optimal frequency offset estimation by using soft information of iterative decoding at the receiver under different codes [11][12][13]. Despite its high estimation accuracy, certain disadvantages of the algorithm must not be neglected, such as sophisticated calculation process and limited range of frequency offset estimation, for which reason many studies have focused on reducing the complexity of CA algorithms.…”

mentioning

confidence: 99%

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An Estimation Algorithm of Doppler Frequency for LEO Satellite Signals under Low SNR and Large Frequency Offset

Shi

Yao

Liu

et al. 2023

Preprint

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Accurate frequency offset estimation is the key to correct receiving of low-Earth-orbit (LEO) satellite signals. Under the circumstances of low signal-to-noise ratio (SNR) and large frequency offset, conventional estimation algorithms for frequency offset are limited by their estimation range and/or SNR threshold, for which reason the estimation accuracy cannot achieve the ideal error probability. Based on joint iterative method, this study proposes an improved joint estimation algorithm for frequency offset. Specifically, the coarse estimation stage takes advantage of the wide estimation range of M&M algorithm, thereby enlarging the estimation range of the proposed algorithm while improving the estimation accuracy of the M&M algorithm through conducting iterative estimation. The fine estimation stage integrates the high estimation accuracy of L&R algorithm to capture the residual frequency offset after accomplishing the coarse estimation, reducing the estimation error within low SNR region while lowering the SNR threshold of the proposed algorithm. The simulation results suggest that the proposed algorithm outperforms the commonly used conventional ones in estimating the frequency offset of LEO satellite signals with wide estimation range and low SNR threshold.

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Section: Analysis Of Coarse Estimation Stagementioning

confidence: 99%

Section: Analysis Of Coarse Estimation Stagementioning

confidence: 99%

“…Step 1. Calculate the autocorrelation function of the signal in Equation (17) using Equation (11), and the result can be denoted as 2 () Rm, 1 mN .…”

Section: Analysis Of Fine Estimation Stagementioning

confidence: 99%

mentioning

confidence: 99%

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An Estimation Algorithm of Doppler Frequency for LEO Satellite Signals under Low SNR and Large Frequency Offset

Shi

Yao

Liu

et al. 2023

Preprint

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show abstract

“…Achieving ultra-reliable, low-latency wireless communication is an important goal of SAGIN. Satellites and unmanned aerial vehicles (UAVs) participate in network construction and provide services as air users or base stations (BSs) [3]. As an emerging wireless network architecture and new service paradigm, SAGIN can provide a reliable, stable and robust service supply for end users worldwide.…”

Section: Introductionmentioning

confidence: 99%