Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 38, Issue 10, Pages 2356-2371Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2020.3000416
Keywords
5G mobile communication; Cloud computing; Reinforcement learning; Wireless communication; Unmanned aerial vehicles; Edge computing; Delays; 5G; network slicing; UAVs; reinforcement learning; Markov decision processes (MDP)
Funding
- PRIN project Liquid_Edge
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Network slices for delay-constrained applications in 5G systems require computing facilities at the edge of the network to guarantee ultra-low latency in processing data flows generated by connected devices, which is challenging with larger volumes of data, and larger distances to the edge of the network. To address this challenge, we propose to extend 5G network slices with Unmanned Aerial Vehicles (UAV) equipped with multi-access edge computing (MEC) facilities. However, onboard computing elements (CE) consume UAV's battery power thus impacting its flight duration. We propose a framework where a System Controller (SC) can turn on and off UAV's CEs, with the possibility of offloading jobs to other UAVs, to maximize an objective function defined in terms of power consumption, job loss, and incurred delay. Management of this framework is achieved by reinforcement learning. A Markov model of the system is introduced to enable reinforcement learning and provide guidelines for the selection of system parameters. A use case is considered to demonstrate the gain achieved by the proposed framework and discuss numerical results.
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