Iterative Joint Parameters Estimation and Decoding in a Distributed Receiver for Satellite Applications and Relevant Cramer-Rao Bounds

TL;DR

This paper introduces an iterative algorithm for joint estimation of channel parameters and decoding in satellite communication receivers, utilizing sum-product algorithms, particle filtering, and phase tracking, with derived Cramer-Rao bounds.

Contribution

It proposes two novel methods for joint parameter estimation in distributed satellite receivers, including particle filtering with particle fine-tuning and phase tracking with polynomial regression, along with new bounds.

Findings

Phase tracking slightly outperforms particle filtering in accuracy.

Particle filtering has lower computational complexity.

Derived Cramer-Rao bounds are accurate for all SNR levels.

Abstract

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.