Gabor Hannak

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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 scheme for high-dimensional stationary time series or discrete time processes. The method is based on a natural generalization of the graphical LASSO algorithm, introduced originally for the case of i.i.d. samples, and estimates the conditional independence graph of a time series from a finite length observation.(More)
We formulate the recovery of a graph signal from noisy samples taken on a subset of graph nodes as a convex optimization problem that balances the empirical error for explaining the observed values and a complexity term quantifying the smoothness of the graph signal. To solve this optimization problem, we propose to combine the alternating direction method(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)
We propose a novel scheme to improve compressed sensing (CS)-based radio frequency identification (RFID) by exploiting multiple measurement vectors. Multiple measurement vectors are obtained by employingmultiple receive antennas at the reader or by separation into real and imaginary parts. Our problem formulation renders the corresponding signal vectors(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)
We consider overloaded (non-orthogonal) multiple access multiuser wireless communication systems with many transmitting devices and one central aggregation node, a typical scenario in e.g. machine-to-machine communications. The task of the central node is to detect the set of active devices and to separate and detect their data streams, whose number at any(More)