...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Annales des Telecommunications >Improving spectrum efficiency in self-organized femtocells using learning automata and fractional frequency reuse
【24h】

Improving spectrum efficiency in self-organized femtocells using learning automata and fractional frequency reuse

机译：通过学习自动机和分数频率复用提高自组织毫微微小区的频谱效率

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

AbstractDeploying heterogeneous networks (HetNets) and especially femtocell technology improves indoor cell coverage and network capacity. However, since users install femtocells which usually reuse the same frequency band as macrocells, interference management is considered a main challenge. Recently, fractional frequency reuse (FFR) has been considered as a way to mitigate the interference in traditional as well as heterogeneous cellular networks. In conventional FFR methods, radio resources are allocated to macrocell/femtocell users only according to their region of presence ignoring the density of users in defined areas inside a cell. However, regarding the unpredictability of cellular traffic, especially on the femtocell level, smart methods are needed to allocate radio resources to the femtocells not only based on FFR rules, but also traffic load. In order to solve this problem, new distributed resource allocation methods are proposed which are based on learning automata (LA) and consider two levels of resource granularity (subband and mini-subband). Using the proposed methods, femto access points learn to choose appropriate subband and mini-subbands autonomously, regarding their resource requirements and the feedback of their users. The goal of the proposed methods is reduction of interference and improvement of spectral efficiency. Simulation results demonstrate higher spectral efficiency and lower outage probability compared to traditional methods in both fixed and dynamic network environments.
机译：摘要 部署异构网络（HetNets）尤其是毫微微小区技术可以改善室内小区的覆盖范围和网络容量。但是，由于用户安装的毫微微小区通常会重复使用与宏小区相同的频段，因此干扰管理被视为主要挑战。近来，分数频率复用（FFR）已被认为是减轻传统以及异构蜂窝网络中干扰的一种方式。在传统的FFR方法中，仅根据宏小区/毫微微小区用户的存在区域，将无线资源分配给宏小区/毫微微小区用户，而忽略小区内部定义区域中的用户密度。然而，关于蜂窝业务的不可预测性，特别是在毫微微小区级别，不仅需要基于FFR规则，而且还需要基于业务负载，需要智能方法为毫微微小区分配无线电资源。为了解决这个问题，提出了一种新的分布式资源分配方法，该方法基于学习自动机（LA），并考虑了两个级别的资源粒度（子带和微型子带）。使用所提出的方法，毫微微接入点会根据其资源需求和用户反馈来自主选择合适的子带和微型子带。所提出的方法的目的是减少干扰并提高频谱效率。仿真结果表明，在固定和动态网络环境中，与传统方法相比，频谱效率更高，中断概率更低。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号