A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs
A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs
复制标题
一种用于5G网络资源优化的混合模糊遗传算法
DOI：
10.1109/tvt.2019.2915194
复制
发表时间：
2019-07-01
影响因子：
6.8
通讯作者：
Jamalipour, Abbas
中科院分区：
文献类型：
--
作者：
Khan, Ammara Anjum;Abolhasan, Mehran;Jamalipour, Abbas
在5G驱动的vanet中，为了支持用户多样化的服务质量需求和动态的资源需求，网络资源需要灵活、可扩展的资源分配策略。当前的异构车辆网络以连接为中心的思维方式进行设计和部署，将固定的资源分配给一个单元，而不考虑交通状况、静态覆盖和容量。本文提出了一种混合模糊逻辑引导遗传算法（H-FLGA）的软件定义网络控制器方法，以解决5G驱动vanet的多目标资源优化问题。该方法实现了面向服务的观点，提出了5G vanet中五种不同的网络资源优化场景。在此基础上，根据客户的服务需求类型，利用模糊推理系统对多目标的权重进行优化。与遗传算法相比，该方法具有多目标代价函数的最小值。仿真结果表明，与其他方案相比，该方案的端到端时延最小。所提出的方法将帮助网络服务提供商根据用户的动态客户需求实现以客户为中心的网络基础设施。
To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.