Ultra Wideband Channel Estimation Based on Adaptive Bayesian Compressive Sensing with Weighted Eigen Dictionary

The sampling rate is too high to be accomplished for channel estimation in Ultra Wideband (UWB) Systems. Due to the sparse structure of UWB channels, compressive sensing (CS) is suitable for UWB channel estimation. Capitalizing on the sparseness of random UWB channels in the basis of eigen functions, eigen-dictionary has been adopted. While the contribution of every atoms to the reconstruction of the channels is different, we develop a weighted eigen-dictionary. Combining with Bayesian compressive sensing (BCS), the proposed Ultra Wideband channel estimation with weighted eigen dictionary could improve the estimation performance with lower sampling rate.