While there has been a lot of prior work on determining the information-theoretic limits of structure recovery in Markov random fields (MRFs), which are undirected graphical models, characterizing the information-theoretic limits of learning BNs (directed models) is important in its own right for the following reasons. First, unlike MRFs where the undirected graph corresponding to a dependence ...

The performance of broadband millimeter-wave (mmWave) RF architectures, is generally determined by mathematical concepts such as the Shannon capacity. These systems have also to obey physical laws such as the conservation of energy and the propagation laws. Taking the physical and hardware limitations into account is crucial for characterizing the actual performance of mmWave systems under cert...

In this paper, we study the informationtheoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite number of samples. We show that the minimum number of samples required by any procedure to recover the correct structure grows as Ω (m) and Ω ( k logm+ k 2 /m ) for non-sparse and sparse BNs respectively, where m is the n...

Multicell Joint Processing has emerged as a new paradigm of cooperative communications , which aims at pushing the capacity limits of cellular systems by eliminating inter-cell interference. Its operation is based on the concept of Base Station cooperation , which is enabled through wideband error-free low-latency links to a central signal processor. This processor is responsible for jointly en...

We analyze the information-theoretic limits for the recovery of node labels in several network models, including the stochastic block model, as well as the latent space model. For the stochastic block model, the non-recoverability condition depends on the probabilities of having edges inside a community, and between different communities. For the latent space model, the non-recoverability condi...

We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i.i.d. samples. While there have been recent results for specific graph classes, these involve fairly extensive technical arguments that are specialized to each specific graph class. In contrast, we isolate two key graph-structural ingredients that can th...