Generation and Analysis of Constrained Random Sampling Patterns

TL;DR

This paper introduces statistical evaluation methods for random sampling pattern generators and proposes a new generator optimized for practical ADC constraints, demonstrating improved performance over existing methods.

Contribution

The paper presents a novel random sampling pattern generator tailored for practical ADC limitations, with comprehensive statistical evaluation and comparison to existing methods.

Findings

The proposed generator produces better random sampling patterns for event-driven ADCs.

Statistical methods effectively evaluate the quality of sampling pattern generators.

Implementation issues of practical random sampling patterns are discussed.

Abstract

Random sampling is a technique for signal acquisition which is gaining popularity in practical signal processing systems. Nowadays, event-driven analog-to-digital converters make random sampling feasible in practical applications. A process of random sampling is defined by a sampling pattern, which indicates signal sampling points in time. Practical random sampling patterns are constrained by ADC characteristics and application requirements. In this paper authors introduce statistical methods which evaluate random sampling pattern generators with emphasis on practical applications. Furthermore, the authors propose a new random pattern generator which copes with strict practical limitations imposed on patterns, with possibly minimal loss in randomness of sampling. The proposed generator is compared with existing sampling pattern generators using the introduced statistical methods. It is shown that the proposed algorithm generates random sampling patterns dedicated for event-driven-ADCs better than existed sampling pattern generators. Finally, implementation issues of random sampling patterns are discussed.