集成技术Issue(2)：66-74,9.
基于分层 Dirichlet 过程的频谱利用聚类和预测
Spectrum Utilization Clustering and Prediction Based on Hierarchical Dirichlet Process
刘阳阳 1戴明威 2黄晓霞1
作者信息
- 1. 中国科学院深圳先进技术研究院 深圳 518055
- 2. 中国科学院大学 北京 100049
- 折叠
摘要
Abstract
Cognitive radio networks achieve spectrum sharing by utilizing the idle periods of licensed bands via dynamic spectrum access technique. Spectrum characterization and prediction help perform more efficient spectrum sensing and then optimize spectrum access strategy. In the paper, UTD-HDP, a nonparametric Bayesian model, was introduced by extending the standard HDP(Hierarchical Dirichlet Process) to perform utilization data clustering and distribution parameters estimation. Using this model, we characterized the features of spectrum utilization adaptively and predicted the future spectrum utilization with high accuracy.关键词
频谱利用特征提取/频谱利用预测/分层Dirichlet过程/Gibbs采样Key words
spectrum utilization feature extraction/spectrum utilization prediction/hierarchical Dirichlet process/Gibbs sampling分类
信息技术与安全科学引用本文复制引用
刘阳阳,戴明威,黄晓霞..基于分层 Dirichlet 过程的频谱利用聚类和预测[J].集成技术,2015,(2):66-74,9.
