Pre-filtering Ultra-wideband Channel Estimation Based on Compressed Sensing

Abstract

Compressed sensing or compressed sampling (CS) has been receiving a lot of interest as a promising method for sparse signal recovery from fewer sensors, while the complexity is not high. Because of the sparsity of the Ultra-Wideband (UWB) channel, CS has been used to the UWB channel estimation to reduce demands and complexity of high-speed analog-to-digital (A/D) converters. In this paper, pre-filtering method for UWB channel estimation based on the theory of CS is developed. An FIR filter is adopted at the transmitter, so that we get a measurement matrix, a quasi-Toeplitz matrix, leading to a better reconstruction accuracy of CS. Furthermore, there is no need to add a measurement matrix element in the receiver, which magnifies the noise in the UWB channel. That also makes the receiver simplified. Extensive simulations show that the proposed approach can successfully suppress the additive white Gaussian noise (AWGN). Finally, when the UWB channel estimation method we proposed is used in the correlation detection of UWB signals, it develops better performance of the bit error rate (BER) at lower sampling rate.