Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals

TL;DR

This paper presents a novel time-stampless adaptive nonuniform sampling framework for stochastic signals, which adapts sampling based on recent data without storing timestamps, improving power efficiency and signal reconstruction.

Contribution

Introduction of a time-stampless adaptive sampling framework with greedy and dynamic programming schemes for stochastic signals, eliminating the need for timestamp storage.

Findings

TANS schemes effectively trade off sampling rate and reconstruction distortion.

Simulation results validate the performance analysis of the proposed schemes.

TANS can enhance power efficiency by adapting to local signal characteristics.

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.