Joint Activity Detection and Channel Estimation via Bayesian Orthogonal Matching Pursuit

被引：0
|
作者：
Lee, Euitaek [1 ]
Ahn, Jinyoup [1 ]
Kim, Namik [1 ]
Lee, Kwang Bok [1 ]
机构：
[1] Seoul Natl Univ, INMC, Dept Elect & Comp Engn, Seoul, South Korea
来源：
2017 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC) | 2017年
关键词：
Massive machine-type communications; compressed sensing; bayesian orthogonal matching pursuit; MACHINE-TYPE COMMUNICATIONS;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we propose a novel method to jointly estimate the activities and channel coefficients of devices in massive machine-type communications (mMTC) systems. We formulate a maximum a posteriori probability (MAP) estimation problem by exploiting the statistical prior information including the channel distribution and device activity probability. The formulated MAP problem is solved by a greedy algorithm, named Bayesian orthogonal matching pursuit (BOMP). We observe that the proposed method improves the activity detection and channel estimation performance compared to the conventional methods.
引用
收藏
页码：310 / 312
页数：3
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