Shannon Bounds on Lossy Gray-Wyner Networks

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

We study the hitting probabilities of the solution to a system of d stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive (d-6)\documentclass[12pt]{minimal} \usepackage{ ...

Information theory has allowed us to determine the fundamental limit of various communication and algorithmic problems, e.g., the channel coding problem, the compression problem, and the hypothesis testing problem. In this work, we revisit the assumptions ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

This paper considers the problem of distributed lossy compression where the goal is to recover one or more linear combinations of the sources at the decoder, subject to distortion constraints. For certain configurations, it is known that codes with algebra ...

We consider nonlinear parabolic stochastic PDEs on a bounded Lipschitz domain driven by a Gaussian noise that is white in time and colored in space, with Dirichlet or Neumann boundary condition. We establish existence, uniqueness and moment bounds of the r ...

Commitment is a key primitive which resides at the heart of several cryptographic protocols. Noisy channels can help realize information-theoretically secure commitment schemes; however, their imprecise statistical characterization can severely impair such ...

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model upd ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

The conditional mean is a fundamental and important quantity whose applications include the theories of estimation and rate-distortion. It is also notoriously difficult to work with. This paper establishes novel bounds on the differential entropy of the co ...

Highly energy-efficient wireless sensor nodes are a prerequisite for a sustainable operation of the Internet of things. Therefore, classical approaches for system design based on digital signal processing are not a viable solution, but system design has to ...

Noninvasive estimation of axon diameter with diffusion MRI holds the potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent studies use powder averaging to account for complex white matter a ...

We study the existence and propagation of singularities of the solution to a one-dimensional linear stochastic wave equation driven by an additive Gaussian noise that is white in time and colored in space. Our approach is based on a simultaneous law of the ...

In this thesis, we study the stochastic heat equation (SHE) on bounded domains and on the whole Euclidean space

\R^d.

We confirm the intuition that as the bounded domain increases to the whole space, both solutions become arbitrarily close to one another ...

We revise the proof of low-rate upper bounds on the reliability function of discrete memoryless channels for ordinary and list-decoding schemes, in particular Berlekamp and Blinovsky's zero-rate bound, as well as Blahut's bound for low rates. The available ...

In the distributed remote (CEO) source coding problem, many separate encoders observe independently noisy copies of an underlying source. The rate loss is the difference between the rate required in this distributed setting and the rate that would be requi ...

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the diff ...

It has been shown that the coherent detection of long range (LoRa) signals only provides marginal gains of around 0.7 dB on the additive white Gaussian noise (AWGN) channel. However, ALOHA-based massive Internet-of-Things systems, including LoRa, often ope ...

Shannon Bounds on Lossy Gray-Wyner Networks

Chat with Graph Search

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

Spectral Estimators for High-Dimensional Matrix Inference

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Quantization for Decentralized Learning Under Subspace Constraints

Distributed Lossy Computation with Structured Codes: From Discrete to Continuous Sources

Parabolic stochastic PDEs on bounded domains with rough initial conditions: moment and correlation bounds

On the (Im)possibility of Commitment over Gaussian Unfair Noisy Channels

Federated Linear Bandit Learning via Over-the-air Computation

Distributionally Robust Linear Quadratic Control

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Spike-Based Sensing and Communication for Highly Energy-Efficient Sensor Edge Nodes

Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

Propagation of singularities for the stochastic wave equation

Localization errors of the stochastic heat equation

A Revisitation of Low-Rate Bounds on the Reliability Function of Discrete Memoryless Channels for List Decoding

The Price of Distributed: Rate Loss in the CEO Problem

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Differential Entropy of the Conditional Expectation under Gaussian Noise

On the Advantage of Coherent LoRa Detection in the Presence of Interference