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Adaptive Characterization of Jitter Noise in Sampled High-Speed Signals



Kevin J. Coakley, Chih-Ming Wang, Paul D. Hale, Tracy S. Clement


We estimate the root-mean-square (RMS) value of timing jitter noise in simulated signals similar to measured high-speed sampled signals. The simulated signals are contaminated by additive noise, timing jitter noise, and time shift errors. Before estimating the RMS value of the jitter noise, we align the signals (unless there are no time shift errors) based on estimates of the relative shifts from cross-correlation analysis. We compute the mean and sample variance of the aligned signals based on repeated measurements at each time sample. We estimate the derivative of the average of the aligned signals at each time sample based on a regression spline model. Our estimate of the RMS value of the jitter noise depends on estimated derivatives and sample variances at time samples where the magnitude of the estimated derivative exceeds a selected threshold. Our estimate is generally biased. Using a parametric bootstrap approach, we adaptively adjust the estimate based on an estimate of this bias. We apply our method to real data collected at NIST. We study how results depend on the derivative threshold.
IEEE Transactions on Instrumentation and Measurement
No. 5


adaptive, bias-correction, bootstrap, derivative estimation, high-speed, jitter, optoelectronics, regression spline


Coakley, K. , Wang, C. , Hale, P. and Clement, T. (2003), Adaptive Characterization of Jitter Noise in Sampled High-Speed Signals, IEEE Transactions on Instrumentation and Measurement, [online], (Accessed April 15, 2024)
Created October 1, 2003, Updated February 19, 2017