| Summary: | 碩士 === 國立交通大學 === 電信工程研究所 === 104 === The packet delay variation (PDV) is usually a major error source in a packet-based synchronization system. The PDV at the queue buffer in each switching hub along the master-to-slave route presents a considerable uncertainty in the clock recovery system. A popular method to mitigate PDVs is to apply the minimum filter that uses only the packet with the least delay, i.e., we select, among the arriving packets, the packet with the minimum master and slave timestamp difference to compute the clock offset and discards all other packets. However, PDV does not always follow a distribution that is amenable to (sample-)minimum filtering. It has been shown that for congested networks, the sample-mean filter or the sample-maximum filter may yield better performance.
We consider two general parametric packet delay (PD) models. The first model assumes that the PD follows an Erlang distribution with unknown order parameter and traffic rate parameter, and the second model describes the PD by a mixed-Erlang distribution. For the first traffic model, we derive the generalized maximum likelihood estimator (GMLE) to jointly estimate the clock offset and model parameters. To estimate the clock offset with the second model, we invoke the concept of moment matching to estimate the mixing coefficients first and then find the solution with the least squared third moment error. This approach may fail to give an accurate estimate when a subset of the mixing coefficients become very small. To deal with this shortcoming, we add a model selection step by classing the mixing parameter combinations into seven cases (models). The model selection criterion is based on the Kullback-Leibler divergence (distance) between the sampled delay distribution and the quantized model distribution. Simulated numerical behaviors of the proposed synchronizers are presented. We find that, except for the special case when traffic rate parameter of the first model is very small, our estimators are unbiased and offer much improved mean performance than that of the minimum filter.
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