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I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have acknowledged all the sources of information which have been used in the thesis. Abstract Most modern wireless multiuser networks suffer from undesired interference that impairs the data transmission over the individual radio links. In order to(More)
We propose a novel graphical model selection (GMS) scheme for high-dimensional stationary time series, based on a natural generalization of the graphical LASSO (gLASSO) introduced originally for GMS based on i.i.d. samples. This method estimates the conditional independence graph (CIG) of a time series or discrete time process from a finite length(More)
We consider the problem of inferring the conditional independence graph (CIG) of a multivariate stationary dicrete-time Gaussian random process based on a finite length observation. Using information-theoretic methods, we derive a lower bound on the error probability of any learning scheme for the underlying process CIG. This bound, in turn, yields a(More)
—In this work we aim to solve the compressed sensing problem for the case of a complex unknown vector by utilizing the Bayesian-optimal structured signal approximate message passing (BOSSAMP) algorithm on the jointly sparse real and imaginary parts of the unknown. By introducing a latent activity variable, BOSSAMP separates the tasks of activity detection(More)
Average consensus is a well-studied method for distributed averaging. The convergence properties of average consensus depend on the averaging weights. Examples for commonly used weight designs are Metropolis-Hastings (MH) weights and constant weights. In this paper , we provide a complete convergence analysis for a generalized MH weight design that(More)
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