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

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.

References in corpus (4)

• Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage
• Placement Optimization of UAV-Mounted Mobile Base Stations
• Joint Traffic-Aware UAV Placement and Predictive Routing for Aerial Networks
• UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels

Cited by in corpus (4)

• Traffic and Obstacle-aware UAV Positioning in Urban Environments Using Reinforcement Learning
• UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels
• A Framework to Develop and Validate RL-Based Obstacle-Aware UAV Positioning Algorithms
• Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks