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In this paper, we delineate how the contraction coefficient of the strong data processing inequality for KL divergence can be used to learn likelihood models. We then present an alternative formulation that forces the input KL divergence to vanish, and achieves a contraction coefficient equivalent to the squared maximal correlation using a linear algebraic solution. To analyze the performance loss...
The completion time for the dissemination of information to all nodes in a network plays a critical role in the design and analysis of communication systems. In this work, we analyse the completion time of data dissemination in a shared loss (i.e., unreliable links) multicast tree, at the limit of large number of nodes. Specifically, analytic expressions for upper and lower bounds on the expected...
The ever-increasing size of wireless networks poses a significant computational challenge for policy optimization schemes. In this paper, we propose a technique to reduce the dimensionality of the value iteration problem, and thereby reduce computational complexity, by exploiting certain structural properties of the logical state transition network. Specifically, our method involves approximating...
In this paper, we propose a compressive and collaborative sensing (CCS) algorithm for distributed robotic networks to build scalar field map. A collaborative control law is utilized to steer the robots to move on the field while avoiding collision with each other and with obstacles. At each time instant, the robots collect, add measurements within their sensing range and exchange data with their neighbors...
Regeneration codes with exact-repair property for distributed storage systems is studied in this paper. For exact-repair problem, the achievable points of (α,β) tradeoff match with the outer bound only for minimum storage regenerating (MSR), minimum bandwidth regenerating (MBR), and some specific values of n, k, and d. Such tradeoff is characterized in this work for general (n, k, k), (i.e., k = d)...
Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into the foundation of new AI-based services on the Internet. Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend...
It has been shown that there is a duality between the linear network coding solution and the entropic vectors induced by collection of subspaces in a vector space over a finite field (dubbed linearly constructed entropic vectors). The region of all linearly constructed vectors, coincides with the set of all representable polymatroids. For any integer polymatroid, there is an associated matroid, which...
We consider the design of an incentive mechanism for a principal, who is assumed to be not trustworthy, to collect informative data from privacy-sensitive individuals. The principal offers payments to incentivize participation and informative data reporting. The individuals are strategic and take into account both the payment and the cost for privacy loss during data reporting. Due to privacy concerns,...
In this work, we consider a K-user Gaussian wiretap multiple-access channel (GW-MAC) in which each transmitter has an independent confidential message for the receiver. There is also an external eavesdropper who intercepts the communications. The goal is to transmit the messages reliably while keeping them confidential from the eavesdropper. To accomplish this goal, two different approaches have been...
An information flow problem discusses how to transport information over a complicated network. It is known that a linear network code plays an essential role in a certain type of information flow problems, but it is not understood clearly how contributing linear network codes are for other types of information flow problems. One basic problem concerning this aspect is the solvability of information...
This work considers the compressed sensing (CS) of i.i.d. signals with sparse measurement matrices and belief-propagation (BP) reconstruction. In general, BP reconstruction for CS requires the passing of messages that are distributions over the real numbers. To implement this in practice, one typically uses either quantized distributions or a Gaussian approximation. In this work, we use density evolution...
We investigate the role of relays in multiple access channels (MACs) with bursty user traffic, where intermittent data traffic restricts the users to bursty transmissions. Specifically, we examine a K-user bursty MIMO Gaussian MAC with a relay, where bursty traffic of each user is governed by a Bernoulli random process. As our main result, we characterize the degrees of freedom (DoF) region. To this...
This paper considers a 2-player strategic game for network routing under link disruptions. Player 1 (defender) routes flow through a network to maximize her value of effective flow while facing transportation costs. Player 2 (attacker) simultaneously disrupts one or more links to maximize her value of lost flow but also faces cost of disrupting links. This game is strategically equivalent to a zero-sum...
These Proceedings contain papers that were presented at the Fifty-Third Annual Allerton Conference on Communication, Control, and Computing which was held at Allerton House, Monticello, Illinois, on September 30–October 2, 2015. As in past years, the Conference was sponsored by the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering of the University of Illinois...
Emerging applications including semantic information processing impose priorities on the possible realizations of information sources, so that not all source sequences are important. This paper proposes an initial framework for optimal lossless compression of subsets of the output of a discrete memoryless source (DMS). It turns out that, the optimal source code may not index the conventional source-typical...
In this paper, we consider the problem of estimating change-points in a high dimensional linear regression model. In the model considered, the linear coefficients have high dimensions, are sparse, and undergo multiple changes in the given data samples. Our goal is to estimate the number and locations of change-points and sparse coefficients in each of the intervals between change-points. We develop...
This paper considers subspace recovery in the presence of outliers in a decentralized setting. The intrinsic low-dimensional geometry of the data is exploited to substantially reduce the processing and communication overhead given limited sensing and communication resources at the sensing nodes. A small subset of the data is first selected. The data is embedded into a random low-dimensional subspace...
In distributed cloud storages fault tolerance is maintained by regenerating the lost coded data from the surviving clouds. Recent studies suggest using maximum distance separable (MDS) network codes in cloud storage systems to allow efficient and reliable recovery after node faults. MDS codes are designed to use a substantial number of repair nodes and rely on centralized management and a static fully...
Today's mobile devices almost exclusively connect to infrastructures for communications and information access; but advances such as AirDrop and WiFi Direct are bringing device-to-device communication to the forefront of mobile computing. Self-organizing ad hoc mobile networks have a wide range of applications in scenarios where infrastructure is not available or with limited bandwidth, such as communications...
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