标题: 异质无线网路中以乏晰Q-Learning为基础之网路选取机制
A Fuzzy Q-learning based Network Selection Scheme for LTE-A/Wi-Fi Heterogeneous Networks
作者: 游紹揚
張仲儒
You, Shao-Yang
Chang, Chung-Ju
電信工程研究所
关键字: 网路选取机制;乏晰Q学习法;异质网路系统;Network selection;Fuzzy Q-learning;Heterogeneous networks
公开日期: 2015
摘要: 随着智慧型手机与行动装置日益普及,透过无线网路行动上网的需求量增加,使得现有的基地台面临数据频宽不够服务大量使用者的问题。于是基地台业者开始布建许多比较平价低廉的小型基地台,以及在人口密集的定点布建Wi-Fi热点提供服务,期望结合LTE-A与Wi-Fi共存的异质网路系统,可以容纳更多使用者连线,提供更快速且稳定的服务品质,满足大量资料传输的需求。因此在异质网路中,如何根据使用者各自移动与通道状况及服务品质需求选取适合的无线网路连线,提供更高速的传输速率且容纳更多行动用户,成为一项重要的议题。
在本篇论文中,我们提出了结合模糊理论(fuzzy logic theory)与Q-learning学习法之网路选取机制,考量同时有LTE-A大型与小型基地台及电信业者布建之Wi-Fi覆盖范围的环境下,能够选择出最适当的网路连线,增加可容纳的使用者个数,提升整体系统传输速率。此机制考虑了三种系统参数:通道品质(SINR) ,负载状况(loading),以及移动速度(mobility),透过乏晰逻辑(fuzzy inference system)判断每个候选网路状况并且计算出相对应的Q-value值,最后选择Q-value值最大的为目标网路,接着Q学习演算法会根据服务品质需求(QoS requirement) ,不断地学习与修正乏晰系统规则,做出更佳的决策。模拟结果显示我们提出的方法,不仅能提升系统传输速率(throughput),同时能让使用者体验到良好的服务品质,且可以更适应网路环境的变化,经由学习机制做出最佳的网路选择。
To cope with the explosive growth in traffic demands for data transmission with limited network capacity, the Internet operators tend to develop the heterogeneous networks by setting up many small cell base stations or providing Wi-Fi access points as hotspots for service. As a result, how to find an effective mechanism to select the suitable target network for new arrival user to access in the heterogeneous wireless environment becomes an important issue nowadays.
In this thesis, a fuzzy Q-learning based network selection (FQNS) scheme is proposed to maximize system capacity, improve total throughput, and satisfy quality of service (QoS) requirements in LTE-A/Wi-Fi heterogeneous networks. Integrating the fuzzy logic with Q-learning algorithm, the FQNS scheme considers SINR factor, loading factor, and mobility factor to choose appropriate network. Through fuzzy inference system, we can know the candidate network states and calculate the corresponding Q-value. The target network, which has the maximum Q-value, could be selected for service. And then, the FQNS scheme will receive a reinforcement signal as a feedback from system to adaptively adjust the action decision and keep self-learning. Eventually, simulation results show that the FQNS scheme has better performance than the PVCS scheme, which means that system utilizes the FQNS scheme not only can achieve higher total throughput but also satisfy QoS requirements at the same time.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070260221
http://hdl.handle.net/11536/143003
显示于类别:Thesis