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.

中文翻译：

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使用强化学习管理的 MEC 无人机设计 5G 网络切片扩展

5G 系统中延迟受限应用的网络切片需要网络边缘​​的计算设施来保证处理连接设备生成的数据流时的超低延迟，这对于数据量更大、距离边缘更远的情况来说是一个挑战。网络。为了应对这一挑战，我们建议使用配备多接入边缘计算 (MEC) 设施的无人机 (UAV) 扩展 5G 网络切片。然而，机载计算元件 (CE) 会消耗无人机的电池电量，从而影响其飞行时间。我们提出了一个框架，其中系统控制器 (SC) 可以打开和关闭 UAV 的 CE，并有可能将工作卸载到其他 UAV，以最大化根据功耗、工作损失和产生的延迟定义的目标函数。这个框架的管理是通过强化学习来实现的。引入了系统的马尔可夫模型以启用强化学习并为系统参数的选择提供指导。一个用例被认为是证明所提出的框架所取得的收益并讨论数值结果。