Bayesian Compressive Sensing with Polar-Distributed Low-Density Sensing Matrices

被引:0
|
作者
Shin, Seungshik [1 ]
Shin, Sang-Yun [1 ]
Jang, Min [1 ]
Kim, Sang-Hyo [1 ]
机构
[1] Sungkyunkwan Univ, Coll Informat & Commun Engn, Suwon 440746, South Korea
来源
TENCON 2012 - 2012 IEEE REGION 10 CONFERENCE: SUSTAINABLE DEVELOPMENT THROUGH HUMANITARIAN TECHNOLOGY | 2012年
关键词
SIGNAL RECOVERY;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Unlike general random independent identically distributed (i.i.d.) signal, sparse signal in compressive sensing is not i.i.d. and its representation consists of significant coefficients and near-zero coefficients. With consideration of the signal characteristics used in the design method of low density parity check matrix, we propose a design method of low density sensing matrix (LDSM) for the Bayesian compressive sensing framework. Good LDSM is obtained by assuming a two-state mixture Gaussian signal model, by using polar-degree-distributed variable nodes and allocating high degree nodes to the significant coefficients. Simulation results showed that the polar-distributed LDSM results in 35.1% lower mean square error than irregular LDSM which is conventionally optimized in the channel coding problem, even though the noise threshold of the polar-distributed LDSM over BI-AWGN is much lower than the conventionally optimized LDSM.
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页数:5
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