超密集网络中基于移动边缘计算的任务卸载和资源优化

移动边缘计算(MEC)通过在无线网络边缘为用户提供计算能力，来提高用户的体验质量。然而，MEC的计算卸载仍面临着许多问题。该文针对超密集组网(UDN)的MEC场景下的计算卸载，考虑系统总能耗，提出卸载决策和资源分配的联合优化问题。首先采用坐标下降法制定了卸载决定的优化方案。同时，在满足用户时延约束下采用基于改进的匈牙利算法和贪婪算法来进行子信道分配。然后，将能耗最小化问题转化为功率最小化问题，并将其转化为一个凸优化问题得到用户最优的发送功率。仿真结果表明，所提出的卸载方案可以在满足用户不同时延的要求下最小化系统能耗，有效地提升了系统性能。

Computing Offloading and Resource Optimization in Ultra-dense Networks with Mobile Edge Computation

Mobile Edge Computing (MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks (UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user’s delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user’s optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users’ different delay requirements, and improve effectively the performance of the system.