Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting
<|reference_start|>Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting: 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.<|reference_end|>
@article{oh2024deep,
    title={Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting},
    author={Jiyong Oh, Syed M. Raza, Lusungu J. Mwasinga, Moonseong Kim, Hyunseung
  Choo},
    journal={arXiv preprint arXiv:2406.15225},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15225},
    primaryClass={cs.AI cs.RO eess.SP}
}