ML-Based Forward Symbol Timing Offset Estimation for Burst Signals

Heng Wang,Hua Jiang,Kexian Gong

doi:10.1109/access.2020.3035733

Heng Wang, Hua Jiang + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3035733

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Abstract

In order to ensure the performance of burst signal demodulation, forward symbol timing offset estimation algorithm is usually used for symbol timing recovery. Aiming at the shortcomings of common forward symbol timing estimation algorithms, a new algorithm based on maximum likelihood estimation joint trigonometric polynomial interpolation is proposed in this article, which is suitable for the data of four samples per symbol and can resist large frequency offset. Hereafter, in order to make it more suitable for engineering practice, optimize it into an improved data-aided (DA) forward symbol timing offset estimation algorithm with multiple characteristics, e.g., moderate frequency offset capture range, insensitive to shaping coefficient, relatively low complexity, excellent estimation performance, flexible algorithm structure and less sample data required for calculation. The simulation results show that the performance of the improved algorithm in this article can approach the modified cramer-rao bound (MCRB) within the frequency offset capture range, under the conditions of low signal to noise ratio (SNR) and small shaping coefficient.

Highlights

Burst signals are widely used in various communication fields such as satellite communication and shortwave communication due to its high transmission rate, strong antiinterception ability and high identification difficulty
Aiming at the various shortcomings of the aforementioned current common forward symbol timing estimation algorithms, this paper proposes a new forward symbol timing estimation algorithm based on maximum likelihood estimation joint triangular polynomial interpolation in order to make it more suitable for engineering practice, improve it into a new form which is the final algorithm in this paper(the “C1” algorithm described below)
Even if the samples of 256 symbols are used for estimation, the estimation performance of AV N and W ang are still far away from MCRB in the case of low SNR and small shaping coefficient

Summary

INTRODUCTION

Burst signals are widely used in various communication fields such as satellite communication and shortwave communication due to its high transmission rate, strong antiinterception ability and high identification difficulty. The above NDA algorithms all have the following defects: (i) In the case of low SNR and small shaping coefficient, the estimation performance is always very poor; (ii) A large data sample size is required to obtain high-precision timing offset estimation results. H. Wang et al.: ML-Based Forward Symbol Timing Offset Estimation for Burst Signals atively excellent estimation performance than NDA algorithms, they receive little attention due to their complex structure, no resistance to frequency offset interference and need to insert auxiliary information into the transmission data to assist timing estimation. In order to enhance the channel adaptability of (5), author chooses to optimize the algorithm by “multi-level overlay smoothing”, the likelihood function can be expressed as follows [13], which is an improved burst detection algorithm based on the principle of conjugate differential correlation that can resist large frequency offset(hereafter abbreviated as “A” algorithm): Λ(r|v) ≈. As shown in Table., the complexity of the algorithm has again reduced and the function value still similar to the “sinc” function: Λ(r|v, τ) ≈

TRIGONOMETRIC POLYNOMIAL INTERPOLATION

CONCLUSION