  Enabling cellular connectivity for drones introduces a wide set of challenges
and opportunities. Communication of cellular-connected drones is influenced by
3-dimensional mobility and line-of-sight channel characteristics which results
in higher number of handovers with increasing altitude. Our cell planning
simulations in coexistence of aerial and terrestrial users indicate that the
severe interference from drones to base stations is a major challenge for
uplink communications of terrestrial users. Here, we first present the major
challenges in co-existence of terrestrial and drone communications by
considering real geographical network data for Stockholm. Then, we derive
analytical models for the key performance indicators (KPIs), including
communications delay and interference over cellular networks, and formulate the
handover and radio resource management (H-RRM) optimization problem.
Afterwards, we transform this problem into a machine learning problem, and
propose a deep reinforcement learning solution to solve H-RRM problem. Finally,
using simulation results, we present how the speed and altitude of drones, and
the tolerable level of interference, shape the optimal H-RRM policy in the
network. Especially, the heat-maps of handover decisions in different drone's
altitudes/speeds have been presented, which promote a revision of the legacy
handover schemes and redefining the boundaries of cells in the sky.
