Cooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

在卸载计算中，移动边缘计算（MEC）和移动云计算（MCC）之间的协作可以提高具有计算密集型任务的用户设备（UE）的服务质量（QoS）。在本文中，为了最大程度地减少期望的费用，我们集中于如何将计算密集型任务从资源稀缺的UE卸载到接入点（AP）和云，以及AP的密度分配的问题。移动边缘。我们考虑了三种卸载计算模式，并着眼于每种模式的覆盖概率和相应的遍历率。最终的优化问题是目标函数和约束中的混合整数和非凸问题。我们提出了一种低复杂度的次优算法，称为凸优化和非线性规划迭代（ICONP）。数值结果验证了所提出算法的更好性能。最佳的计算比率和AP的密度分配有助于节省费用。