Measurement Matrix for Sparse Internet Data based Compressive Sampling

Compressive Sampling (CS) process consists of three main parts, namely sparse representation, measurement, and sparse reconstruction. The measurement matrix is used for sampling signal from the sparse signal representation process. The success of the reconstruction process is also strongly influenced by the selection of proper measurement matrix. One important factor in CS is the use of well-design measurement matrix. This paper compares the performance of measurement matrices which are Uniform, Normal, Binary, Half-normal, Log-normal, Binomial, Poisson, and Exponential matrix that applied to internet traffic data. At the right Restricted Isometric Constant (RIC), we evaluate the measurement matrix performance using Normalized Mean Square Error (NMSE) and Compression Ratio (CR). The results show that Binomial measurement matrix is superior with the smallest NMSE and the highest CR. This binomial measurement matrix is also capable of overcoming various patterns of data loss on the network.