Compressed Sensing: A new approach to analyze the recovery algorithms based on UWB channel estimation

Compressed Sensing (CS) is a new mathematical concept, which can reconstruct the original signal accurately with lower Nyquist sampling. Besides, multipath arrivals in an Ultra-wideband (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely this signal sparsity of the impulse response of the UWB channel that is suitable for the application of Compressed Sensing theory. However, these multipath arrivals mainly depend on the channel models that generate different sparse levels (low-sparse or high-sparse) of the UWB channels according to which, the authors have analysed and chosen the best recovery algorithms which are suitable to the sparse level for each type of channel environment. Criteria for evaluating the algorithms are based on computational complexity, ability to reduce the sampling rate and processing time. In addition, the results of this study are an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.