Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing
Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Mean time to loose lock of phase tracking by particle filtering
Signal Processing - Special section: Distributed source coding
Particle filtering equalization method for a satellite communication channel
EURASIP Journal on Applied Signal Processing
Recognition of noisy images by PLL networks
Signal Processing
IEEE Transactions on Signal Processing
Monte Carlo solutions for blind phase noise estimation
EURASIP Journal on Wireless Communications and Networking - Special issue on synchronization in wireless communications
Bayesian phase tracking for multiple pulse signals
Signal Processing
On-line Hybrid Cramér-Rao bound for oversampled dynamical phase and frequency offset estimation
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Approximate expressions for Cramer-Rao bounds of code aided QAM dynamical phase estimation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Extended Kalman Filter for oversampled dynamical phase offset estimation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Smoothing PLLs for QAM dynamical phase estimation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Bias-free phase tracking with linear and nonlinear systems
IEEE Transactions on Wireless Communications
Resource efficient implementation of a 10Gb/s radio receiver baseband in FPGA
Proceedings of the 10th FPGAworld Conference
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This paper studies the problem of tracking a Brownian phase with linear drift observed to within one digital modulation and one additive white Gaussian noise. This problem is of great importance as it models the problem of carrier synchronization in digital communications. The ultimate performances achievable for this problem are evaluated and are compared to the performances of three solutions of the problem. The optimal filter cannot be explicitly calculated and one goal of the paper is to implement it using recent sequential Monte-Carlo techniques known as particle filtering. This approach is compared to more traditional loops such as the Costas loop and the decision feedback loop. Moreover, since the phase has a linear drift, the loops considered are second-order loops. To make fair comparisons, we exploit all the known information to put the loops in their best configurations (optimal step sizes of the loops). We show that asymptotically, the loops and the particle filter are equivalent in terms of mean square error. However, using Monte-Carlo simulations we show that the particle filter outperforms the loops when considering the mean acquisition time (convergence rate), and we argue that the particle filter is also better than the loops when dealing with the important problem of mean time between cycle slips.