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.

通过提出 DUPAC，一种基于深度强化学习的无人机路径规划框架来解决操作员控制的无人机飞行和手动静态飞行配置等问题，该框架在维持优秀无人机与蜂窝网络连接的同时，实现了从起点到终点的最佳路线确定，并在实验中展现了可行性和性能。