论文标题
迭代的联合参数估计和在分布式接收器中用于卫星应用和相关的cramer-rao边界
Iterative Joint Parameters Estimation and Decoding in a Distributed Receiver for Satellite Applications and Relevant Cramer-Rao Bounds
论文作者
论文摘要
本文提出了一种迭代关节通道参数(载波相位,多普勒偏移和多普勒速率)的算法,并使用分布式接收器对受多普勒偏移和多普勒速率影响的通道进行了传输的解码。该算法是通过将汇总算法(SPA)应用于代表信息符号的关节A后验分布和通道参数的因子图来得出的。在本文中,我们提出了两种处理总结算法的棘手消息的方法。在第一种方法中,我们使用具有顺序重要性采样(SIS)的粒子过滤来估计未知参数。我们还提出了一种微调颗粒的方法,以改善收敛性。在第二种方法中,我们使用随机步行相模型近似模型,然后进行相跟踪算法和多项式回归算法以估计未知参数。我们为关节载体相,多普勒偏移和多普勒速率估计得出加权的贝叶斯cramer-rao边界(WBCRB),这些估计值考虑了估计参数的先前分布,并且对于所有考虑的信号与噪声比(SNR)值(SNR)值是准确的下限。数值结果(位错误率(BER)和参数估计的均方误差(MSE))表明,随机步行模型的相位跟踪略优于粒子过滤。但是,粒子过滤的计算成本低于基于随机步行模型的方法。
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 sum-product algorithm. In the first approach, we use particle filtering with sequential importance sampling (SIS) 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 (WBCRBs) 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 (SNR) values. Numerical results (of bit error rate (BER) and the mean-square error (MSE) 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.