Effects of the Auto-Correlation of Delays on the Age of Information: A Gaussian Process Framework

TL;DR

This paper introduces a Gaussian process framework to analyze how auto-correlation in delays impacts the age of information (AoI), revealing that high dependence worsens AoI performance in complex systems.

Contribution

It proposes a novel stochastic process-based model for AoI analysis, extending beyond traditional queueing models, and examines the effect of delay dependence on AoI degradation.

Findings

High delay dependence degrades AoI performance.

Gaussian process model captures delay correlation effects.

Sensitivity analysis shows impact of second-order statistics.

Abstract

The age of information (AoI) has been studied actively in recent years as a performance measure for systems that require real-time performance, such as remote monitoring systems via communication networks. The theoretical analysis of the AoI is usually formulated based on explicit system modeling, such as a single-server queueing model. However, in general, the behavior of large-scale systems such as communication networks is complex, and it is usually difficult to express the delay using simple queueing models. In this paper, we consider a framework in which the sequence of delays is composed from a non-negative continuous-time stochastic process, called a virtual delay process, as a new modeling approach for the theoretical analysis of the AoI. Under such a framework, we derive an expression for the transient probability distribution of the AoI and further apply the theory of stochastic orders to prove that the high dependence of the sequence of delays leads to the degradation of AoI performance. We further consider a special case in which the sequence of delays is generated from a stationary Gaussian process, and we discuss the sensitivity of the AoI to second-order statistics of the delay process through numerical experiments.