Link Quality Classifier with Compressed Sensing Based on l1-l2 Optimization

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.