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.
翻译:本文展示了迭代联合频道参数( carrier 阶段、 Doppler 轮值和 Doppler 率) 的算法, 以及使用分布式接收器对受多普勒 和 Doppler 率影响的频道的传输进行估算和解码。 此算法是使用一个分布式接收器, 将总产品算法( SPA) 应用到一个系数图中, 代表信息符号和频道参数的后传分布 。 在本文中, 我们展示了两种方法 : 处理合成产品算法的棘手信息 。 在第一种方法中, 我们用序列过滤器过滤器过滤器来估计未知参数 。 我们还提出了一个微粒子微调方法, 以便改进趋同。 在第二种方法中, 我们用随机行走量算法模型来比较模型, 以随机行进算法和多普勒斯比值 。 我们用Weighted Bayesian Cramer- Raounds (WBCRBRBs) 来计算 。 多普勒转换和多普勒略度估测算法,, 以所有基于 IMS 度测算法 的精确测算法 。