Joint Traffic and Obstacle-aware UAV Positioning Algorithm for Aerial Networks

TL;DR

The paper tackles reliable, high-throughput UAV-based aerial networks in obstacle-rich environments by jointly addressing traffic demand and LoS constraints. It introduces the Traffic- and Obstacle-aware UAV Positioning Algorithm (TOPA), which minimizes the aggregate required capacity $C(t_k)=\sum_{i=1}^{N-1}C_i(t_k)$ under power, capacity, and LoS constraints, using Friis-based distance bounds and an intersection-of-spheres approach to define a feasible placement subspace, solved via the GEKKO optimizer. TOPA’s efficiency is validated through ns-3 simulations, demonstrating throughput improvements up to 100% while preserving fairness across diverse scenarios and traffic patterns. The results illustrate the practical viability of obstacle-aware, traffic-driven UAV placement for urban aerial networks and point toward future work in computer-vision-based positioning and multi-UAV deployments.

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly used as cost-effective and flexible Wi-Fi Access Points (APs) and cellular Base Stations (BSs) to enhance Quality of Service (QoS). In disaster management scenarios, UAV-based networks provide on-demand wireless connectivity when traditional infrastructures fail. In obstacle-rich environments like urban areas, reliable high-capacity communications links depend on Line-of-Sight (LoS) availability, especially at higher frequencies. Positioning UAVs to consider obstacles and enable LoS communications represents a promising solution that requires further exploration and development. The main contribution of this paper is the Traffic- and Obstacle-aware UAV Positioning Algorithm (TOPA). TOPA takes into account the users' traffic demand and the need for LoS between the UAV and the ground users in the presence of obstacles. The network performance achieved when using TOPA was evaluated through ns-3 simulations. The results show up to 100% improvement in the aggregate throughput without compromising fairness.

Figures (8)

• Figure 1: Flying network consisting of UAV positioned to provide LoS wireless connectivity. The users are served by the UAV, which is connected to the LTE Base Station (BS).
• Figure 2: Two-dimensional (2D) representation of the target positioning subspace ($P_s$), which is determined by the intersection of LoS constraints derived from the obstacle and the traffic demand spheres centered at each UE.
• Figure 3: Scenario A -- two UEs and four possible positions. Position 1 is the optimal solution defined by TOPA, which allows LoS with all UEs, while Position 2 is the baseline, which is located five meters above the middle of the rooftop of the building.
• Figure 4: Scenario B -- four groups of UEs located on different sides of the building. Each group is represented by a square with dimensions of $1m \times 1m$ and associated traffic demand that is presented by $\lambda_i, i\in \{1, ..., N-1\}$.
• Figure 5: Scenario A - Aggregate throughput measured on UAV, where $\lambda_1 {=} 2 \times \lambda_2$.