In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices’ offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network’s communication rate and IoT networks’ total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices’ available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices’ scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing.

中文翻译：

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移动边缘计算网络的联合协作中继、资源分配和调度


在本文中，我们考虑基于物联网（IoT）的移动边缘计算（MEC）系统。 IoT 设备由接入点 (AP) 控制，接入点无法访问任何许可频谱。这限制了物联网设备的卸载能力，从而有效地减少了计算位数。在这种情况下，我们考虑频谱共享，其中物联网网络通过帮助主网络（拥有许可频谱）进行协作中继来获取许可频谱。我们的目标是通过联合优化中继选择、许可频谱分配、本地计算和卸载序列选择，最大化主网络的通信速率和物联网网络的计算位数之和。此外，我们还考虑了重要的约束，例如主网络的保证速率、物联网设备的可用能量以及可用的许可频谱资源块（RB）。我们观察到优化问题本质上是组合问题，当物联网设备的调度顺序被纳入公式中时，优化问题变得更加复杂。根据我们的分析，我们设计了一种最佳且计算效率高的算法。在结果部分，我们与相关基准系统进行比较，并展示我们提出的模型的有效性。此外，我们还展示了物联网和主要网络如何从频谱共享中受益。