PROJECT TITLE :

Ultrawideband Channel Estimation: A Bayesian Compressive Sensing Strategy Based on Statistical Sparsity

ABSTRACT:

To deal with the formidable sampling rate needed by Nyquist criterion, compressive sensing (CS) has been recently adopted for ultrawideband (UWB) channel estimation. During this paper, exploiting the statistical sparsity of real UWB signals in the basis shaped by eigenvectors, we tend to develop a replacement CS dictionary called eigendictionary, which permits the use of CS for UWB channel estimation. With respect to the eigendictionary, the expansion vector of UWB signals is sparse and exhibits an extra structure in the shape of statistically significant coefficients occurring in clusters. Capitalizing on this structure, we tend to propose 2 novel Bayesian CS (BCS) algorithms to efficiently reconstruct UWB signals from a small assortment of random projection measurements. Furthermore, by utilizing the common sparsity profile inherent in UWB signals, we extend the proposed Bayesian algorithms to multitask (MT) versions, that will simultaneously recover multiple UWB signals if on the market. Since the statistical affiliation between totally different UWB signals is exploited, the developed MT-BCS will obtain higher performance than the only-task version. Extensive simulations using real UWB data show that the proposed schemes considerably cut back the requirement on sampling rate and gift glorious performance compared with the ancient correlator and different CS-based channel estimation schemes.