# Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

arXiv: https://arxiv.org/abs/2406.15225 · HTML: https://mlsanity.com/paper/2406.15225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G](https://mlsanity.com/paper/2105.04842/) (2021)
- [Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure](https://mlsanity.com/paper/2102.07087/) (2021)
- [Connectivity-Aware UAV Path Planning with Aerial Coverage Maps](https://mlsanity.com/paper/1905.05926/) (2019)
- [Cellular Coverage-Aware Path Planning for UAVs](https://mlsanity.com/paper/1905.03064/) (2019)
- [A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint](https://mlsanity.com/paper/2211.03696/) (2022)
- [Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control](https://mlsanity.com/paper/2004.11070/) (2020)

## Abstract (arXiv)

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.