Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

TL;DR

This paper introduces DUPAC, a deep reinforcement learning framework that plans UAV paths in dense urban environments to ensure reliable 5G connectivity, improving signal quality with minimal additional distance.

Contribution

The paper presents a novel DRL-based UAV path planning method that guarantees connectivity, addressing limitations of manual control and static routes in urban settings.

Findings

DUPAC achieves near-optimal flight paths with only 2% longer distance.

Maintains 9% better connection quality compared to baseline methods.

Validated in simulated urban scenarios using Unity framework.

Abstract

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.