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.

翻译：具备5G连接能力的无人机服务是一个新兴领域，具有广泛的应用前景。运营商控制的无人机飞行和手动静态飞行配置是制约无人机服务规模化广泛应用的主要限制因素。多项服务依赖于无人机与蜂窝网络保持优质连接，而在预设飞行路径中维持这种连接具有挑战性。本文通过提出一种确保连通性的无人机深度强化学习路径规划框架（DUPAC）来解决这些局限性。在无人机飞行过程中，DUPAC根据距离和信号质量指标，从既定起点到目的地确定最优航线。基于Unity框架的模拟真实城市场景评估验证了DUPAC的可行性与性能。结果表明，DUPAC在仅增加2%飞行距离的情况下，实现了与基准方法相当的自主飞行路径，同时在整个飞行过程中平均保持优于9%的连接质量。