Resident Population Density-Inspired Deployment of K-tier Aerial Cellular Network

TL;DR

This paper proposes an inhomogeneous PPP-based model for UAV deployment in multi-tier aerial networks, optimizing coverage by aligning UAV distribution with user density variations.

Contribution

It introduces a novel inhomogeneous PPP model for UAV placement that accounts for user density gradients and derives optimal deployment strategies for enhanced coverage.

Findings

Derived analytical expressions for coverage probability.

Optimized UAV distribution parameters for maximum coverage.

Validated the effectiveness of inhomogeneous deployment strategies.

Abstract

Using unmanned aerial vehicles (UAVs) to enhance network coverage has proven a variety of benefits compared to terrestrial counterparts. One of the commonly used mathematical tools to model the locations of the UAVs is stochastic geometry (SG). However, in the existing studies, both users and UAVs are often modeled as homogeneous point processes. In this paper, we consider an inhomogeneous Poisson point process (PPP)-based model for the locations of the users that captures the degradation in the density of active users as we move away from the town center. In addition, we propose the deployment of aerial vehicles following the same inhomogeneity of the users to maximize the performance. In addition, a multi-tier network model is also considered to make better use of the rich space resources. Then, the analytical expressions of the coverage probability for a typical user and the total coverage probability are derived. Finally, we optimize the coverage probability with limitations of the total number of UAVs and the minimum local coverage probability. Finally we give the optimal UAV distribution parameters when the maximum overall coverage probability is reached.