Availability of Aerial Heterogeneous Networks for Reliable Emergency Communications

TL;DR

This paper analyzes the impact of heterogeneity in delay constraints and mobility on network availability in aerial heterogeneous networks for emergency communications, proposing joint resource optimization to improve reliability.

Contribution

It derives a lower bound on network availability under unified resource allocation and introduces a joint optimization of resources to mitigate heterogeneity effects.

Findings

Heterogeneity significantly reduces network availability.

Joint optimization of user sharing and pilot length improves reliability.

Proposed method outperforms existing resource allocation policies.

Abstract

We investigate network availability (NA) in aerial heterogeneous networks (AHetNets) for effective emergency rescue, where diverse delay-constrained communication services must be provided to user equipments (UEs) with varying mobility. The heterogeneity in delay constraints and UE mobility introduces resource allocation conflicts and imbalances, which undermine communication reliability and challenge NA. Although unified resource allocation (URA) can mitigate these issues, it remains unclear whether NA can be sustained under such diverse conditions. To address this, we derive expressions for the lower bound (LB) on NA in AHetNets under URA. Our analysis reveals that extended heterogeneity significantly degrades the LB due to resource limitations-even when the heterogeneity stems from additional services under less stringent delay constraints (LSDC) or from UEs with lower mobility. To overcome this degradation, we formulate and solve a joint optimization problem for the number of UEs sharing time-frequency resources ($K$) and pilot length ($\xi$), aiming to enhance the LB by improving spatial, frequency, and temporal resource efficiency. Simulation results validate our analysis and demonstrate that jointly optimizing $K$ and $\xi$ enables AHetNets to achieve the target NA under greater heterogeneity, outperforming existing resource allocation policies.