# IEEE Transactions on Information Theory

## Issue 3 • March 2019

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## Filter Results

Displaying Results 1 - 25 of 45

Publication Year: 2019, Page(s):C1 - C4
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• ### IEEE Transactions on Information Theory publication information

Publication Year: 2019, Page(s): C2
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• ### Asymptotic Coupling and Its Applications in Information Theory

Publication Year: 2019, Page(s):1321 - 1344
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A coupling of two distributions $P_{X}$ and $P_{Y}$ is a joint distribution $P_{XY}$ with marginal distributions equal to $P_{X}$ View full abstract»

• ### Intrinsic Capacity

Publication Year: 2019, Page(s):1345 - 1360
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Every channel can be expressed as a convex combination of deterministic channels with each deterministic channel corresponding to one particular intrinsic state. Such convex combinations are, in general, not unique, each giving rise to a specific intrinsic-state distribution. In this paper, we study the maximum and minimum capacities of a channel when the realization of its intrinsic state is caus... View full abstract»

• ### On the Evaluation of Marton’s Inner Bound for Two-Receiver Broadcast Channels

Publication Year: 2019, Page(s):1361 - 1371
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Marton’s inner bound is the best known achievable rate region for a general two-receiver discrete memoryless broadcast channel. In this paper, we establish improved bounds on the cardinalities of the auxiliary random variables appearing in this inner bound to the true rate region. We combine a perturbation technique, along with a representation using concave envelopes of information-theoretic func... View full abstract»

• ### Invariance of the Han–Kobayashi Region With Respect to Temporally-Correlated Gaussian Inputs

Publication Year: 2019, Page(s):1372 - 1374
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We establish that the multi-letter extension of the Han–Kobayashi achievable region with temporally correlated vector Gaussian inputs matches the Han–Kobayashi achievable region with scalar Gaussian inputs for the Gaussian interference channel. View full abstract»

• ### Combinatorial Entropy Power Inequalities: A Preliminary Study of the Stam Region

Publication Year: 2019, Page(s):1375 - 1386
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We initiate the study of the Stam region, defined as the subset of the positive orthant in ${R}^{2^{n}-1}$ that arises from considering the entropy powers of subset sums of $n$ independent random vectors in a Euclidean space of finite dimension. We show ... View full abstract»

• ### On the Entropy Power Inequality for the Rényi Entropy of Order [0, 1]

Publication Year: 2019, Page(s):1387 - 1396
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Using a sharp version of the reverse Young inequality, and a Rényi entropy comparison result due to Fradelizi, Madiman, and Wang (2016), the authors derive Rényi entropy power inequalities for log-concave random vectors when Rényi parameters belong to [0, 1]. Furthermore, the estimates are shown to be sharp up to absolute constants. View full abstract»

• ### Quickest Change Detection Under Transient Dynamics: Theory and Asymptotic Analysis

Publication Year: 2019, Page(s):1397 - 1412
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The problem of quickest change detection under transient dynamics is studied, where the change from the initial distribution to the final persistent distribution does not happen instantaneously, but after a series of transient phases. The observations within the different phases are generated by different distributions. The objective is to detect the change as quickly as possible, while controllin... View full abstract»

• ### Asymptotic Optimality of Mixture Rules for Detecting Changes in General Stochastic Models

Publication Year: 2019, Page(s):1413 - 1429
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The paper addresses a sequential changepoint detection problem for a general stochastic model, assuming that the observed data may be non-i.i.d. (i.e., dependent and non-identically distributed) and prior distribution of the change point is arbitrary. Tartakovsky and Veeravalli (2005), Baron and Tartakovsky (2006), and, more recently, Tartakovsky (2017) developed a general asymptotic theory of cha... View full abstract»

• ### Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes

Publication Year: 2019, Page(s):1430 - 1451
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In this paper, we consider the mixture of sparse linear regressions model. Let $\boldsymbol {\beta }^{(1)},\ldots, \boldsymbol {\beta }^{(L)}\in \mathbb {C} ^{n}$ be $L$ unknown sparse parameter vectors with a total of ... View full abstract»

• ### Sharp Oracle Inequalities for Stationary Points of Nonconvex Penalized M-Estimators

Publication Year: 2019, Page(s):1452 - 1472
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Many statistical estimation procedures lead to nonconvex optimization problems. Algorithms to solve these problems are often guaranteed to output a stationary point of the optimization problem. Oracle inequalities are an important theoretical instrument to assess the statistical performance of an estimator. Oracle results have focused on the theoretical properties of the uncomputable (global) mini... View full abstract»

• ### Non-Parametric Sparse Additive Auto-Regressive Network Models

Publication Year: 2019, Page(s):1473 - 1492
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Consider a multi-variate time series ${(X_{t})}_{t=0}^{T}$ , where $X_{t} \in \mathbb {R}^{d}$ which may represent spike train responses for multiple neurons in a brain, crime event data across multiple regions, and many others. An important challenge ass... View full abstract»

• ### On Information-Theoretic Characterizations of Markov Random Fields and Subfields

Publication Year: 2019, Page(s):1493 - 1511
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Let $X_{i}, i~\in V$ form a Markov random field (MRF) represented by an undirected graph $G = (V,E)$ , and $V'$ be a subset of $V$ View full abstract»

• ### Estimation Efficiency Under Privacy Constraints

Publication Year: 2019, Page(s):1512 - 1534
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We investigate the problem of estimating a random variable $Y$ under a privacy constraint dictated by another correlated random variable $X$ . When $X$ and View full abstract»

• ### Estimation of a Density From an Imperfect Simulation Model

Publication Year: 2019, Page(s):1535 - 1546
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Uncertainty quantification of a technical system can be done using density estimation. We usually start with a stochastic model, which is fitted to the technical system, and the density estimation is done using data from this stochastic model. However, in any application, such a stochastic model will not be perfect, and the estimation of the density should take into account the inadequacy of the s... View full abstract»

• ### Provable Dynamic Robust PCA or Robust Subspace Tracking

Publication Year: 2019, Page(s):1547 - 1577
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Dynamic robust principal component analysis (PCA) refers to the dynamic (time-varying) extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lie in a low-dimensional subspace that can change with time, albeit slowly. The goal is to track this changing subspace over time in the presence of sparse outliers. We develop and study a novel algorithm, which we call simple-ReProCS, b... View full abstract»

• ### Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices

Publication Year: 2019, Page(s):1578 - 1588
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One of the key issues in the acquisition of sparse data by means of compressed sensing is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper, we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensi... View full abstract»

• ### A Data-Dependent Weighted LASSO Under Poisson Noise

Publication Year: 2019, Page(s):1589 - 1613
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Sparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded by or arising from a Gaussian distribution. Poisson observations in particular are a characteristic feature of several real-world applications. Previous work on sparse Poisson inverse problems encountered several limiting technical hurdles. This p... View full abstract»

• ### Finite-Field Matrix Channels for Network Coding

Publication Year: 2019, Page(s):1614 - 1625
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In 2010, Silva et al. studied certain classes of finite-field matrix channels in order to model random linear network coding where exactly $t$ random errors are introduced. In this paper, we consider a generalization of these matrix channels where the number of errors is not required to be constant, indeed th... View full abstract»

• ### Joint Crosstalk-Avoidance and Error-Correction Coding for Parallel Data Buses

Publication Year: 2019, Page(s):1626 - 1638
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Communication in integrated circuits faces two major impediments: inter-wire capacitive coupling and noise. Coding can be used to address both these problems. So-called crosstalk-avoidance codes mitigate capacitive coupling, and traditional error-correction codes introduce resilience against channel errors. Unfortunately, crosstalk-avoidance and error-correction codes cannot be combined in a strai... View full abstract»

• ### Cooperative Repair: Constructions of Optimal MDS Codes for All Admissible Parameters

Publication Year: 2019, Page(s):1639 - 1656
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Two widely studied models of multiple-node repair in distributed storage systems are centralized repair and cooperative repair. The centralized model assumes that all the failed nodes are recreated in one location, while the cooperative one stipulates that the failed nodes may communicate but are distinct, and the amount of data exchanged between them is included ... View full abstract»

• ### List-Decodable Zero-Rate Codes

Publication Year: 2019, Page(s):1657 - 1667
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We consider list decoding in the zero-rate regime for two cases—the binary alphabet and the spherical codes in Euclidean space. Specifically, we study the maximal $\tau \in [{0,1}]$ for which there exists an arrangement of $M$ balls of relative Hamming r... View full abstract»

• ### Constructions of Linear Codes With One-Dimensional Hull

Publication Year: 2019, Page(s):1668 - 1676
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The hull of a linear code is defined to be the intersection of the code and its dual, and was originally introduced to classify finite projective planes. The hull plays an important role in determining the complexity of algorithms for checking permutation equivalence of two linear codes and computing the automorphism group of a linear code. It has been shown that these algorithms are very effectiv... View full abstract»

• ### BP-LED Decoding Algorithm for LDPC Codes Over AWGN Channels

Publication Year: 2019, Page(s):1677 - 1693
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A new method is presented for low-complexity near-maximum-likelihood (ML) decoding of low-density parity-check (LDPC) codes over the additive white Gaussian noise channel. The proposed method termed belief-propagation–list erasure decoding (BP-LED) is based on erasing carefully chosen unreliable bits performed in case of BP decoding failure. A strategy of introducing erasures into the received vec... View full abstract»

## Aims & Scope

IEEE Transactions on Information Theory publishes papers concerned with the transmission, processing, and utilization of information.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Alexander Barg

Department of Electrical and Computer Engineering and the Institute for Systems Research, University of Maryland

email: abarg-ittrans@ece.umd.edu