Gabor Hannak

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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)
Most modern wireless multi-user networks suffer from undesired interference that impairs the data transmission over the individual radio links. In order to maximize the data throughput in such systems, several interference mitigation schemes have been investigated recently. Interference alignment stands out as one of the most promising ones, able to attain(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)
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)
Interference Alignment (IA) is a linear precoding scheme for the K-user interference channel with high signal to noise ratio. Ideally, interference is completely suppressed and each user is able to achieve half of the single-user multiple-input multiple-output (MIMO) degrees of freedom. We use the Vienna MIMO testbed to evaluate the feasibility of IA in(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)