A Deconvolution Algorithm for Ultra Wideband Channel Modeling Based on Compressive Sensing

A deconvolution algorithm based on Compressive Sensing (CS) is proposed for the post-processing of Ultra WideBand (UWB) channel modeling using frequency-domain measurements. A window with Gaussian transition band is used to extract the measurements according to the UWB frequency regulation policy of China. The time-domain waveform of the quasi-Gaussian window is used as the apriori information of the CS based deconvolution algorithm. The deconvolution results are with high-resolution characteristic. Furthermore, flexible zero-padding of windowing and the design of parameterized waveform dictionary lead to different resolutions of the deconvolution results. Matching Pursuit (MP) algorithm is used as the reconstruction algorithm of CS. Both LOS and NLOS measurements of offices are exploited to demonstrate that the proposed CS based deconvolution algorithm can achieve comparable performance with CLEAN algorithm using fewer samples.