Service-Oriented AoI Modeling and Analysis for Non-Terrestrial Networks

TL;DR

This paper develops a service-oriented model for analyzing the Age of Information in non-terrestrial networks, considering satellite and UAV dynamics, to improve data freshness assessment in future 6G IoT applications.

Contribution

It introduces a novel hybrid ground node distribution model and derives AoI metrics accounting for satellite visibility and cross-traffic, addressing gaps in terrestrial-focused AoI analysis.

Findings

Derived average AoI for different network scenarios.

Analyzed impact of network parameters on AoI performance.

Enhanced understanding of AoI in satellite-UAV-ground node systems.

Abstract

To achieve truly seamless global intelligent connectivity, non-terrestrial networks (NTN) mainly composed of low earth orbit (LEO) satellites and drones are recognized as important components of the future 6G network architecture. Meanwhile, the rapid advancement of the Internet of Things (IoT) has led to the proliferation of numerous applications with stringent requirements for timely information delivery. The Age of Information (AoI), a critical performance metric for assessing the freshness of data in information update systems, has gained significant importance in this context. However, existing modeling and analysis work on AoI mainly focuses on terrestrial networks, and the distribution characteristics of ground nodes and the high dynamics of satellites have not been fully considered, which poses challenges for more accurate evaluation. Against this background, we model the ground nodes as a hybrid distribution of Poisson point process (PPP) and Poisson cluster process (PCP) to capture the impact of ground node distribution on the AoI of status update packet transmission supported by UAVs and satellites in NTN, and the visibility and cross-traffic characteristics of satellites are additionally considered. We derived the average AoI for the system in these two different situations and examined the impact of various network parameters on AoI performance.