  The choice of the transmitting frequency to provide cellular-connected
Unmanned Aerial Vehicle (UAV) reliable connectivity and mobility support
introduce several challenges. Conventional sub-6 GHz networks are optimized for
ground Users (UEs). Operating at the millimeter Wave (mmWave) band would
provide high-capacity but highly intermittent links. To reach the destination
while minimizing a weighted function of traveling time and number of radio
failures, we propose in this paper a UAV joint trajectory and band switch
approach. By leveraging Double Deep Q-Learning we develop two different
approaches to learn a trajectory besides managing the band switch. A first
blind approach switches the band along the trajectory anytime the UAV-UE
throughput is below a predefined threshold. In addition, we propose a smart
approach for simultaneous learning-based path planning of UAV and band switch.
The two approaches are compared with an optimal band switch strategy in terms
of radio failure and band switches for different thresholds. Results reveal
that the smart approach is able in a high threshold regime to reduce the number
of radio failures and band switches while reaching the desired destination.
