NRCS-CE: A Noise-Resistance UWB Channel Estimation Method for WSN and IoT Applications

Ultra-wideband (UWB) technology is a potential candidate solution for Wireless Sensor Network (WSN) and Internet of Things (IoT) applications due to the advantages of high-speed communication and accurate positioning. Channel estimation plays a key role in UWB systems, especially in low signal-to-noise ratio (SNR) environments. In this paper, a noise-resistance channel estimation method based on compressed sensing is proposed for accurate estimation of UWB channels contaminated by noise. The proposed algorithm based on compressed sensing relies on the fact that the multipath signals of UWB have a sparse representation in the time domain or other domains. However, the reconstruction of multipath signals is difficult owing to the additive noise and noise-folding effect. In order to improve the noise robustness, UWB multipath signals are sparse representation by using the orthogonal basis of an eigen-based dictionary. Three approaches are applied to the proposed noise-resistance algorithm, including average pilot noise reduction, denoising whitening measurement, and sparsity adaptive matching pursuit (SAMP) algorithm. The improved relative residue is proposed to calculate the iteration termination condition of SAMP. Simulation results demonstrate that the algorithm provides good noise-resistance performances in terms of normalized mean square error (NMSE) of channel estimation and the bit error rate of Rake receivers, especially in low SNR environments.