A multi-template deconvolution algorithm based on compressed sensing for UWB channel modeling

Abstract

Deconvolution is a key operation for the postprocessing of ultra-wideband (UWB) channel modeling. Due to the wideband of the radio channel sounding pulse, the nature of the UWB channel can be frequency selective. This paper presents a multi-template compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling, considering the pulse distortion. UWB channels are a prime example of long and sparse channel impulse response (CIR). Furthermore, the design of a multi-template dictionary of parameterized waveforms that closely matches the waveform of multipath leads to that the UWB channel measurement signal is more compactly represented. The multi-path can be better identified by a multi-template dictionary. The Matching Pursuit (MP) algorithm is used as the signal reconstruction method of CS and outputs the CIR directly. Simulation results show that compared to CLEAN, the proposed multi-template CS-MP deconvolution algorithm can achieve a comparable performance with much fewer samplings.