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—In this paper, the ensembles of repeat multiple– accumulate codes (RA m), which are obtained by interconnecting a repeater with a cascade of m accumulate codes through uniform random interleavers, are analyzed. It is proved that the average spectral shapes of these code ensembles are equal to 0 below a threshold distance m and, moreover, they form a(More)
—Algorithms and dynamics over networks often involve randomization, and randomization may result in oscillating dynamics which fail to converge in a deterministic sense. In this paper, we observe this undesired feature in three applications, in which the dynamics is the randomized asynchronous counterpart of a well-behaved synchronous one. These three(More)
—The 0//1-regularized least squares approach is used to deal with linear inverse problems under sparsity constraints, which arise in mathematical and engineering fields, e.g., statistics , signal processing, machine learning, and coding theory. In particular, multi-agent models have been recently emerged in this context to describe diverse kinds of(More)
— This paper regards the relative localization problem in sensor networks. We study a randomized algorithm, which is based on input-driven consensus dynamics and involves pairwise " gossip " communications and updates. Due to the ran-domness of the updates, the state of this algorithm ergodically oscillates around a limit value. Exploiting the ergodicity of(More)
In this paper we study a novel model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process. We refer to this communication model as " gos-siping ". Second, agents are not completely open-minded, but instead take into account their initial(More)
—In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we(More)
— This paper deals with the problem of simultaneously classifying sensors and estimating hidden parameters in a network with communication constraints. In particular, we consider a network where sensors measure a common parameter with different precision rank. The goal of each unit is to estimate the unknown parameter and its own specific type through local(More)
—In this paper we derive exact formulae of the input– output weight enumerators for truncated convolutional encoders. Although explicit analytic expressions can be computed for relatively small code lengths, they become prohibitively complex to calculate as the truncation length increases. By applying Hayman-like techniques, we present an accurate and easy(More)
—In this paper we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences(More)
In this paper, we address the problem of simultaneous classification and estimation of hidden parameters in a sensor network with communications constraints. In particular, we consider a network of noisy sensors which measure a common scalar unknown parameter. We assume that a fraction of the nodes represent faulty sensors, whose measurements are poorly(More)