Signal Reconstruction Errors in Jittered Sampling

Abstract

Abstract-One of the most significant types of error in digital signal processing (DSP) systems working with wideband signals is the error introduced by the analog-to-digital (AD) and digital-to-analog (DA) converters. This paper presents an accurate and simple method to evaluate the performance of AD/DA converters affected by clock jitter, which is based on the analysis of the mean square error (MSE) between the reconstructed signal and the original one. Using an approximation of the linear minimum MSE (LMMSE) filter as reconstruction technique, we derive analytic expressions of the MSE. In particular, through asymptotic analysis, we are able to simply evaluate the performance of digital signal reconstruction as a function of the clock jitter, number of quantization bits, signal bandwidth and sampling rate.

Efficient Parametric Signal Estimation From Samples With Location Errors

“…We implement the estimator in (19) iteratively by using the estimate a t ILE obtained above to calculate…”

“…Hence tolerating higher time jitter through post processing presents an opportunity for lowering the power of acquisition subsystems. In [17]- [19], [22] the authors characterize error due to time jitter and study estimation error of bandlimited signals in the presence of jitter. In [17] the author develops a linear interpolator for processing bandlimited signals with correlated and uncorrelated time jitter.…”

Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

“…Due to the lack of attention to nonlinear postprocessing, it is not readily apparent from the literature that these linear estimators are far from optimal. The effects of jitter on linear MMSE reconstruction of bandlimited signals are discussed in [10] and extended to the asymptotic case and multidimensional signals in [11]. More recently, [12] uses a second-order Taylor series approximation to perform weighted least-squares fitting of a jittered random signal.…”

“…The squared errors of the results are normalized relative to the result for initialization with the zero-jitter LMMSE in (22), so that the squared error of the result for initialization with this linear estimator is 0 dB. The parameters , , , , , and are determined using (10) and (11) timator derived in [4]. The MSE performances are plotted for different values of , and to demonstrate the effect of increasing , increasing , or decreasing on the relative MSE performances.…”

“…The V 1/2 2 values are normalized by the final value for each curve. The parameters αx, βx, αz, βz, αw, and βw are determined using (10) and (11). The rate of convergence depends on the choice of parameters, as demonstrated in the above plots.…”