Research on CS-based Channel Estimation Methods for UWB Communications

The theory of compressed sensing can be used to reconstruct sparse signals from fewer observations. According to the sparsity of UWB channels, a reduced sampling rate can be obtained at the detector based on compressed sensing frame. In this paper, a filter matrix estimation algorithm is proposed by designing the over-completed dictionary and observation matrix. Then, the Orthogonal Matching Pursuit (OMP), the Basis Pursuit De-noising (BPDN) and the Dantzig Selector (DS) are used to detect original signal to give the opinions for choosing suitable reconstruction algorithms. The simulation results in the IEEE 802.15.4a channel model show that the coherence detection based on the new channel estimation method outperforms the one based on random observation method for better bit error rate performances with a reduced sampling rate.