Compressive Sensing for Ultra-Wideband Channel Estimation: on the Sparsity Assumption of Ultra-Wideband Channels

Abstract

Due to the sparse structure of ultra-wideband (UWB) multipath channels there has been a considerable amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. The main consideration of the related studies is to propose different implementations of the CS theory for the estimation of UWB channels which are assumed to be sparse. In this study we investigate the suitability of standardized UWB channel models to be used with the CS theory. In other words we question the sparsity assumption of realistic UWB multipath channels. For that we particularly investigate the effects of IEEE 802.15.4a UWB channel models and the selection of channel resolution both on channel estimation and system performances from a practical implementation point of view. In addition we compare the channel estimation performance with the Cramer-Rao lower bound for various channel models and number of measurements. The study shows that although UWB channel models for residential environments (e.g. channel models CM1 and CM2) exhibit a sparse structure yielding a reasonable channel estimation performance channel models for industrial environments (e.g. CM8) may not be treated as having a sparse structure due to multipaths arriving densely. Furthermore it is shown that the sparsity increased by channel resolution can improve the channel estimation performance significantly at the expense of increased receiver processing. Copyright (c) 2013 John Wiley & Sons Ltd.