.png){kind=icon}
.png){kind=icon}
Return
Link Quality Classifier with Compressed Sensing Based on l1-l2 Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2011.082911.111611.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Network tomography is an inference technique for internal network characteristics from end-to-end measurements. In this letter, we propose a new network tomography scheme to classify communication links into lower or higher quality classes according to their link loss rates. The two-class classification is achieved by the estimation of link loss rates via compressed sensing, which is an emerging theory to obtain a sparse solution from an underdetermined linear system, with regarding link loss rates in the higher quality class as 0. In the proposed scheme, we implement compressed sensing with an l(1)-l(2) optimization, where the cost function is defined as a sum of l(1) and l(2) norms with a mixing parameter, which enables us to control the threshold between the lower and higher quality classes.
Keywords:
Network tomography
compressed sensing
l(1)-l(2) optimization
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W