Time-stampless adaptive nonuniform sampling for stochastic signals
Files
First author draft
Date
2012-10
Authors
Feizi, Soheil
Goyal, Vivek K.
Medard, Muriel
Version
First author draft
OA Version
Citation
Soheil Feizi, Vivek K Goyal, Muriel Medard. 2012. "Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals." IEEE Transactions on Signal Processing, Volume 60, Issue 10, pp. 5440 - 5450. https://doi.org/10.1109/tsp.2012.2208633
Abstract
In this paper, we introduce a time-stampless adaptive nonuniform sampling (TANS) framework, in which time increments between samples are determined by a function of the m most recent increments and sample values. Since only past samples are used in computing time increments, it is not necessary to save sampling times (time stamps) for use in the reconstruction process. We focus on two TANS schemes for discrete-time stochastic signals: a greedy method, and a method based on dynamic programming. We analyze the performances of these schemes by computing (or bounding) their trade-offs between sampling rate and expected reconstruction distortion for autoregressive and Markovian signals. Simulation results support the analysis of the sampling schemes. We show that, by opportunistically adapting to local signal characteristics, TANS may lead to improved power efficiency in some applications.