Joint symbol detection and timing estimation using particle filtering

The paper addresses joint estimation of the timing epoch and detection of the transmitted symbols in a digital communication system. Most timing recovery techniques found in the literature are either approximately or heuristically derived, since optimal estimators are analytically intractable. Our approach to the problem relies on modeling the symbol timing as an autoregressive process. In this way, the digital communication system can be mathematically represented by a dynamic system in state-space form and the sequential Monte Carlo (SMC) methodology can be applied. SMC algorithms are powerful tools for Bayesian estimation that are based on representing the posterior distribution of the system state by a discrete measure with random support. This representation can be updated recursively, as new information becomes available, allowing for optimal estimation of both the transmitted symbols and their timing.