  Network slicing is a well assessed approach enabling virtualization of the
mobile core and radio access network (RAN) in the emerging 5th Generation New
Radio. Slicing is of paramount importance when dealing with the emerging and
diverse vertical applications entailing heterogeneous sets of requirements. 5G
is also envisioning Unmanned Aerial Vehicles (UAVs) to be a key element in the
cellular network standard, aiming at their use as aerial base stations and
exploiting their flexible and quick deployment to enhance the wireless network
performance. This work presents a UAV-assisted 5G network, where the aerial
base stations (UAV-BS) are empowered with network slicing capabilities aiming
at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of
users. The users belong to three heterogeneous categories of 5G service type,
namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency
communication (URLLC), and massive machine-type communication (mMTC). A first
application of multi-agent and multi-decision deep reinforcement learning for
UAV-BS in a network slicing context is introduced, aiming at the optimization
of the SLA satisfaction ratio of users through the joint allocation of radio
resources to slices and refinement of the UAV-BSs 2-dimensional trajectories.
The performance of the presented strategy have been tested and compared to
benchmark heuristics, highlighting a higher percentage of satisfied users (at
least 27% more) in a variety of scenarios.
