The ultra-dense network (UDN) based on mobile edge computing (MEC) is an important technology, which can achieve the low-latency of 5G communications and enhance the quality of user experience. However, how to improve the task offloading efficiency is a hot topic of UDN under the constraint on the limited wireless resources. In this article, we propose a heuristic task offloading algorithm HTOA to optimize the delay and energy consumption of offloading tasks in UDN. Firstly, a convex programming model for MEC resource allocation is established, which aims to obtain the optimal allocation set of resources for offloading tasks, and optimize the execution delay of offloading tasks. Followed by, the problem of joint channel allocation and user upload power control is solved by the greedy strategy and golden section method, which aims to optimization the delay and energy consumption of task upload data. Compared with the random task offloading algorithm, numerical simulations show that the algorithm HTOA can effectively reduce the delay and energy consumption of task offloading, and perform better as the number of users increases.

中文翻译：

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超密集网络中的联合无线资源管理和任务卸载

基于移动边缘计算（MEC）的超密集网络（UDN）是一项重要技术，可以实现5G通信的低时延，提升用户体验质量。然而，在有限的无线资源的约束下，如何提高任务卸载效率是UDN的热门话题。在本文中，我们提出了一种启发式任务卸载算法 HTOA 来优化 UDN 中卸载任务的延迟和能耗。首先，建立了MEC资源分配的凸规划模型，旨在获得分流任务的最优资源分配集，优化分流任务的执行延迟。其次，通过贪心策略和黄金分割法解决联合信道分配和用户上传功率控制问题，旨在优化任务上传数据的延迟和能耗。与随机任务卸载算法相比，数值仿真表明，HTOA算法能够有效降低任务卸载的延迟和能耗，并且随着用户数量的增加表现更好。