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In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data.

In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.

In this paper we consider the generalized approxi- mate message passing (GAMP) algorithm for recovering a sparse signal from modulo samples of randomized projections of the unknown signal. The modulo samples are obtained by a self-reset (SR) analog to digital converter (ADC). Additionally, in contrast to previous work on SR ADC, we consider a scenario where the compressed sensing (CS) measurements (i.e., randomized projections) are sent through a communication channel, namely an additive white Gaussian noise (AWGN) channel before being quantized by a SR ADC.

This work proposes a direct method to generate phase shift keying (PSK) symbols with desired correlation properties by mapping complex Gaussian random variables. The relationship between the cross-correlation of Gaussian and PSK symbols is derived in closed-form. This non-iterative approach outputs finite-alphabet constant-modulus waveforms capable of matching desired transmit beampatterns.

The rapid convergence rate, high fidelity learning outcome and low computational cost are key targets in solving the learning problem of the complex physical system. Guided

Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results.