基于分层 Dirichlet 过程的频谱利用聚类和预测

认知无线电网络通过动态频谱接入技术,利用授权频段的空闲时段实现频谱共享。对频谱利用特征的描述和未来利用率的预测有利于实现高效频谱感知算法,进而优化频谱接入策略。通过对标准的分层Dirichlet过程进行扩展,提出了一种跨信道的非参数贝叶斯模型UTD-HDP(UTD扩展的分层Dirichlet过程),用于无线频谱利用率数据的聚类分析和分布参数估计。利用该模型,可以自适应地描述无线频谱利用率的特征,实现了对未来时间频谱利用率的高精度预测。

Spectrum Utilization Clustering and Prediction Based on Hierarchical Dirichlet Process

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.