Joint Computation Offloading, Power Allocation, and Channel Assignment for 5G-Enabled Traffic Management Systems
Joint Computation Offloading, Power Allocation, and Channel Assignment for 5G-Enabled Traffic Management Systems
复制标题
5G 流量管理系统的联合计算卸载、功率分配和信道分配
DOI：
10.1109/tii.2019.2892767
复制
发表时间：
2019-05-01
影响因子：
12.3
通讯作者：
Xia, Feng
中科院分区：
文献类型：
--
作者：
Ning, Zhaolong;Wang, Xiaojie;Xia, Feng
由于5G中对延迟敏感和任务关键型应用的需求不断增加，移动边缘计算有望响应并支持实时交互系统。然而，由于超低延迟和无处不在的连接的条件，构建一个支持5G的交通管理系统仍然是具有挑战性的。此外，边缘节点的计算资源和存储能力是有限的，因此计算卸载是实时流量管理的一个基本问题。提出了一种适用于5G网络实时流量管理的混合计算卸载框架。特别地，我们同时考虑了非正交多址接入和基于车对车的流量分流。所研究的问题被描述为一个联合任务分配、子信道分配和功率分配问题，目标是最大化总的卸载率。然后，我们证明了它的NP难性，并将其分解为三个子问题，可以迭代求解。绩效评估说明了我们框架的有效性。
Due to the ever-increasing requirements of delay-sensitive and mission-critical applications in 5G, mobile edge computing is promising to react and support real-time interactive systems. However, it is still challenging to construct a 5G-enabled traffic management system, owing to the qualification of ultra-low latency and ubiquitous connectivity. Furthermore, the computing resources and storage capacities of edge nodes are limited, thus computation offloading is a fundamental issue for real-time traffic management. This paper puts forward a hybrid computation offloading framework for real-time traffic management in 5G networks. Specially, we consider both nonorthogonal-multiple-access-enabled and vehicle-to-vehicle-based traffic offloading. The investigated problem is formulated as a joint task distribution, subchannel assignment, and power allocation problem, with the objective of maximizing the sum offloading rate. After that, we prove its NP-hardness and decompose it into three subproblems, which can be solved iteratively. Performance evaluations illustrate the effectiveness of our framework.