Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi-UAV-enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs-Edge-Cloud three-layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed-integer non-linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficient Q -learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three-layer network architecture achieves a 5% benefits compared with the traditional two-layer network architecture in terms of MUs' energy and time consumptions.

中文翻译：

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问多UAV的云边缘计算网络的基于学习的计算分载

最近，无人飞行器（UAV）被认为是为移动用户（MU）提供广泛覆盖和中继服务的飞行平台。移动边缘计算（MEC）被开发为一种新的范例，旨在提高未来网络中MU的体验质量。基于无人机的高度灵活性和可控性，作者研究了一种支持多UAV的MEC系统，其中无人机具有计算资源，可以为MU提供卸载任务，目的是减少MU的总消耗。时间和精力。考虑到远程云中心中丰富的计算资源，他们提出了MUs-Edge-Cloud三层网络体系结构，UAV在其中发挥飞边服务器的作用。基于此框架，他们将计算分流问题表述为混合整数非线性规划问题，通常难以获得最优解。为了解决这个问题，他们建议问 基于学习的计算卸载算法（QCOA），以减少优化问题的复杂性。数值结果表明，提出的QCOA优于基准卸载策略（例如，随机卸载，遍历卸载）。此外，就MU的能源和时间消耗而言，与传统的两层网络体系结构相比，提出的三层网络体系结构可实现5％的收益。