Joint parameter estimation and decoding in a distributed receiver,International Journal of Satellite Communications and Networking

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Joint parameter estimation and decoding in a distributed receiver
International Journal of Satellite Communications and Networking ( IF 0.9 ) Pub Date : 2022-07-25 , DOI: 10.1002/sat.1460
Ahsan Waqas ₁ , Khoa Nguyen ₁ , Gottfried Lechner ₁ , Terence Chan ₁

Affiliation

This paper presents an algorithm for iterative joint channel parameter (carrier phase, Doppler shift, and Doppler rate) estimation and decoding of transmission over channels affected by Doppler shift and Doppler rate using a distributed receiver. This algorithm is derived by applying the sum-product algorithm (SPA) to a factor graph representing the joint a posteriori distribution of the information symbols and channel parameters given the channel output. In this paper, we present two methods for dealing with intractable messages of the SPA. In the first approach, we use particle filtering with sequential importance sampling for the estimation of the unknown parameters. We also propose a method for fine-tuning of particles for improved convergence. In the second approach, we approximate our model with a random walk phase model, followed by a phase tracking algorithm and polynomial regression algorithm to estimate the unknown parameters. We derive the Weighted Bayesian Cramer-Rao Bounds for joint carrier phase, Doppler shift, and Doppler rate estimation, which take into account the prior distribution of the estimation parameters and are accurate lower bounds for all considered signal-to-noise ratio values. Numerical results (of bit error rate and the mean-square error of parameter estimation) suggest that phase tracking with the random walk model slightly outperforms particle filtering. However, particle filtering has a lower computational cost than the random walk model-based method.

中文翻译：

分布式接收器中的联合参数估计和解码

本文提出了一种迭代联合信道参数（载波相位、多普勒频移和多普勒率）估计和使用分布式接收器对受多普勒频移和多普勒率影响的信道传输进行解码的算法。该算法是通过将和积算法 (SPA) 应用于表示给定信道输出的信息符号和信道参数的联合后验分布的因子图而得出的。在本文中，我们提出了两种处理 SPA 的棘手消息的方法。在第一种方法中，我们使用具有顺序重要性采样的粒子滤波来估计未知参数。我们还提出了一种微调粒子以提高收敛性的方法。在第二种方法中，我们用随机游走阶段模型来近似我们的模型，其次是相位跟踪算法和多项式回归算法来估计未知参数。我们推导出联合载波相位、多普勒频移和多普勒率估计的加权贝叶斯 Cramer-Rao 界限，它考虑了估计参数的先验分布，并且是所有考虑的信噪比值的准确下界。数值结果（误码率和参数估计的均方误差）表明使用随机游动模型的相位跟踪略优于粒子滤波。然而，粒子滤波比基于随机游走模型的方法具有更低的计算成本。和多普勒率估计，它考虑了估计参数的先验分布，并且是所有考虑的信噪比值的准确下限。数值结果（误码率和参数估计的均方误差）表明使用随机游动模型的相位跟踪略优于粒子滤波。然而，粒子滤波比基于随机游走模型的方法具有更低的计算成本。和多普勒率估计，它考虑了估计参数的先验分布，并且是所有考虑的信噪比值的准确下限。数值结果（误码率和参数估计的均方误差）表明使用随机游动模型的相位跟踪略优于粒子滤波。然而，粒子滤波比基于随机游走模型的方法具有更低的计算成本。

更新日期：2022-07-25

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