Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals

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.

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