The Effect of Channel Models on Compressed Sensing Based UWB Channel Estimation

Abstract

Ultra-wideband (UWB) multipath channels are assumed to have a sparse structure as the received consecutive pulses arrive with a considerable time delay and can be resolved individually at the receiver. Due to this sparse structure, there has been a significant amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. There are various implementations of the CS theory for the UWB channel estimation based on the assumption that the UWB channels are sparse. However, the sparsity of a UWB channel mainly depends on the channel environment. Motivated by this, in this study we investigate the effect of UWB channel environments on the CS based UWB channel estimation. Particularly, we consider the standardized IEEE 802.15.4a UWB channel models and study the channel estimation performance from a practical implementation point of view. The study shows that while UWB channel models for residential environments (e. g., 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. The results of this study are important as it determines the suitability of different channel models to be used with the CS theory.