Some theoretical and practical results on noisy signals recovery
We discuss some theoretical results and their applications to specific practical problems from wireless communications. We assume that we know the noisy version of the signal, which is sparse with respect to a given system of elements (dictionary), at a finite number of points and we want to approximately recover it. This problem of recovery of a noisy signal is closely related to the problem of establishing the Lebesgue-type inequalities for the corresponding algorithms and it motivates us to prove such inequalities. Under certain conditions on a dictionary (RIP-type condition, coherence condition) we obtain different kinds of the Lebesgue-type inequalities for the OMP and its version WOMP algorithms. The most important feature of our new theoretical result is the assumption that the dictionary has the RIP-type property instead of the assumption that it is the Riesz basis, which was used in the previous results. Our approach allows us to treat redundant (overcomplete) systems, which is important in applications. We consider the setting of sparse recovery for highly-coherent dictionaries that appear in OFDM setting for wireless communication. Our main example is the oversampled Fourier dictionary and the recovery of the frequency response of a sparse channel. We develop a general approach to such problems and we prove that under certain conditions the OMP algorithm recovers all dictionary elements with large coefficients, and estimate its accuracy (in NMSE metric) in terms of signal-to-noise ratio.