Competitive Learning-Based Clock Parameters Estimation for PTP Synchronization With Unknown Delay Distributions

Clock synchronization is a crucial requirement for coordinated activities in distributed networks. As an effective solution, Precision Time Protocol (PTP) is tailored to provide tight synchronization which, however, suffers from packet delay variation (PDV). Therefore, it is a challenging task to mitigate the uncertainties of PDV so that clock skew and offset can be estimated with higher accuracy and robustness. Under the assumption of delay symmetry, PDV is caused by unavailable prior information about the statistical distributions of stochastic delay. This paper investigates a robust estimator that can achieve joint estimation for clock skew and offset under delay with unknown distributions by employing the competitive learning-based rival penalized expectation maximization (RPEM) algorithm to learn the unknown probability density function (pdf) of stochastic delay. Moreover, for the asymmetric delay scenario, besides the unknown delay distributions, delay asymmetry is another important influence on synchronization accuracy. Therefore, the optimal invariant robust estimator is developed to simultaneously handle the performance degradation caused by delay asymmetry and unknown delay distributions. The estimator can even cope with the scenario where the random delays in the uplink and downlink follow different distributions. The effectiveness of the estimation schemes is validated by the computer simulations.