  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.
