Integrating UAV-Enabled Base Stations in 3D Networks: QoS-Aware Joint Fronthaul and Backhaul Design

TL;DR

This paper proposes a joint design framework for UAV-enabled 3D networks, optimizing drone placement and backhaul connections to enhance network performance and resilience in complex multi-layered environments.

Contribution

It introduces a novel clustering and genetic algorithm approach for strategic drone placement and backhaul design in 3D networks, addressing key connectivity and capacity challenges.

Findings

Algorithms outperform baseline schemes in network coverage and throughput.

Optimized drone placement improves backhaul connectivity.

Enhanced network resilience to demand fluctuations.

Abstract

The emerging concept of 3D networks, integrating terrestrial, aerial, and space layers, introduces a novel and complex structure characterized by stations relaying backhaul loads through point-to-point wireless links, forming a wireless 3D backhaul mesh. A key challenge is the strategic placement of aerial platform such as drone base stations (DBSs), considering the locations and service demands of ground nodes and the connectivity to backhaul gateway nodes for core network access. This paper addresses these complexities with a two-fold approach: a novel Agglomerative Hierarchical Clustering (HC) algorithm that optimizes DBS locations to satisfy minimum backhaul adjacency and maximum fronthaul coverage radius requirements; and a Genetic Algorithm (GA) that designs backhaul connections to satisfy the cumulative load across the network and maximize the throughput margin which translates to network resilience to increasing demands. Our results showcase the effectiveness of these algorithms against benchline schemes, offering insights into the operational dynamics of these novel 3D networks.