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

Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing