Sparse Channel Modelling Using Multi-Measurement Vector Compressive Sensing

Channel sparsity is well exploited for channel estimation, but there is very limited work on sparse channel modelling, which studies and characterizes the statistical properties of sparse channel coefficients. In this paper, we study sparse channel modelling using real measured channel data in off-body signal propagation. We propose multi-measurement vector based compressive sensing algorithms for extracting sparse channel coefficients, study the statistical properties of these extracted coefficients, and develop an algorithm for generating simulated channels using the statistical sparse model. The proposed method can be directly applied to other channel measurements, and is very useful for channel simulation and developing advanced sparse channel estimation schemes.