Compressed sensing enabled narrowband interference mitigation for IR-UWB systems

Abstract

Compressed sensing (CS) is an emerging theory that enables the reconstruction of sparse signals from a small set of random measurements. Because of the sparsity of impulse radio ultra-wideband (IR-UWB) signals in the time domain, CS makes it possible to operate at sub-Nyquist rates for IR-UWB communications where Nyquist sampling represents a formidable challenge. However, strong narrowband interference (NBI) still seriously affects the system. In this paper, by observing that the NBI signal is approximately sparse in the discrete Fourier transform (DFT) domain, a novel NBI estimation and mitigation scheme is proposed. By estimating the subspace of NBI and then feeding back the NBI nullspace, a compressive measurement matrix is designed to mitigate the NBI effectively while collecting useful signal energy. Theoretical analysis and simulation results show that NBI can be effectively mitigated using sub-Nyquist samples of a received signal in the IR-UWB communication system based on CS.