信息与电子工程2012，Vol.10Issue(4)：396-400,405,6.
基于因子图的马尔可夫压缩感知
Markov compressive sensing in Cognitive Radio using Factor Graph
摘要
Abstract
Because the requirement that the underlying signals should be sparse in compressive sensing is in line with the opportunistic spectrum occupancy of cognitive radio(CR), methods have been proposed to apply compressive sensing in solving problems in CR. With this approach, an even more flexible and diversified sensing strategy instead of the conventional scheme such as filter-bank sensing mode can be casted. This paper assumes the underlying sparse signal as a first-order Markov process and models the spectrum sensing as Bayesian inference of the targeted signal. This kind of probabilistic model can be visualized by Factor Graph, which connects forward compressive sensing and backward signal rebuilding through message passing among nodes. This connection generates circles in Factor Graph and hence produces an iterative inference algorithm. Experimental result demonstrates the better performance of this algorithm.关键词
认知无线电/压缩感知/贝叶斯推理/马尔可夫过程/因子图Key words
cognitive radio/ compressive sensing/ Bayesian inference/ Markov process/ Factor Graph分类
信息技术与安全科学引用本文复制引用
汪振兴,杨涛..基于因子图的马尔可夫压缩感知[J].信息与电子工程,2012,10(4):396-400,405,6.基金项目
National Science Foundation of China(No.60972024,No.60872059) （No.60972024,No.60872059）
the Doctoral Programs Foundation of Ministry of Education of China(the Doctoral Programs Foundation of Ministry of Education of China) （the Doctoral Programs Foundation of Ministry of Education of China）
NSTMP of China under Grant(NSTMP of China under Grant) （NSTMP of China under Grant）
