Trajectory-Aware Rate Adaptation for Flying Networks

TL;DR

This paper introduces TARA, a trajectory-aware rate adaptation algorithm for UAV-based flying networks, which predicts future channel conditions based on node movement to enhance wireless throughput.

Contribution

The paper presents a novel rate adaptation method that leverages UAV trajectory information to improve wireless link performance in flying networks.

Findings

Up to 53% throughput increase with TARA.

Average 14% improvement over conventional algorithms.

Effective across 100 different UAV trajectories.

Abstract

Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes' movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT.