Minimum mean-square error (MMSE) estimators of signals from samples corrupted by jitter (timing noise) and additive noise are nonlinear, even when the signal parameters and additive noise have normal distributions. This paper develops a stochastic algorithm based on Gibbs sampling and slice sampling to approximate the optimal MMSE estimator in this Bayesian formulation. Simulations demonstrate that this nonlinear algorithm can improve significantly upon the linear MMSE estimator, as well as the EM algorithm approximation to the maximum likelihood (ML) estimator used in classical estimation. Effective off-chip postprocessing to mitigate jitter enables greater jitter to be tolerated, potentially reducing on-chip ADC power consumption.
['Daniel S. Weller', 'Vivek K. Goyal']
Bayesian Post-Processing Methods for Jitter Mitigation in Sampling