Reinforcement-Learning for Management of a 5G Network Slice Extension with UAVs

Some representative 5G application scenarios regard geographic areas very far from the structured core network, but are characterized by the need for processing huge amount of data that cannot be transmitted to multi-access edge (MEC) facilities installed at the edge of that network. To this purpose, this paper proposes to extend a 5G network slice with a fleet of UAVs, each providing computing facilities, and for this reason referred to as MEC UAVs. The paper proposes a cooperation between MEC UAVs belonging to the same fleet based on job offloading, aiming at minimizing power consumption due to active computer elements providing MEC, job loss probability and queueing delay. A Reinforcement Learning (RL) approach is used to support the System Controller in its decisions. A numerical analysis is presented to evaluate achieved performance.