The Price of Distributed: Rate Loss in the CEO Problem

Atalik, Arda; Köse, Alper; Gastpar, Michael

doi:10.1109/ciss53076.2022.9751153

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) Previous Rate Loss Upper Bound1

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“…Note that the new upper bound is tighter than the general upper bound and its dependence on the number of users M is more prominent at low distortions. For a detailed analysis on the rate loss in the AWGN CEO problem, we refer to [31].…”

Section: ) Previous Rate Loss Upper Boundmentioning

confidence: 99%

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Atalik

Köse

Gastpar

2022

IEEE Trans. Signal Process.

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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 conditional mean in the case of finite-variance input signals and additive Gaussian noise. The main result is a new lower bound in terms of the differential entropies of the input signal and the noisy observation. The main results are also extended to the vector Gaussian channel and to the natural exponential family. Various other properties such as upper bounds, asymptotics, Taylor series expansion, and connection to Fisher Information are obtained. Two applications of the lower bound in the remote-source coding and CEO problem are discussed.

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Section: ) Previous Rate Loss Upper Boundmentioning

confidence: 99%