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For the additive Gaussian noise channel with average codeword power constraint, sparse superposition codes and adaptive successive decoding is developed. Codewords are linear combinations of subsets of vectors, with the message indexed by…

Information Theory · Computer Science 2010-06-22 Andrew R Barron , Antony Joseph

We propose a compression-based version of the empirical entropy of a finite string over a finite alphabet. Whereas previously one considers the naked entropy of (possibly higher order) Markov processes, we consider the sum of the…

Information Theory · Computer Science 2011-04-05 Paul M. B. Vitányi

We propose a new framework for reasoning about information in complex systems. Our foundation is based on a variational extension of Shannon's information theory that takes into account the modeling power and computational constraints of…

Machine Learning · Computer Science 2020-02-26 Yilun Xu , Shengjia Zhao , Jiaming Song , Russell Stewart , Stefano Ermon

The Shannon sampling theorem for bandlimited wide sense stationary random processes was established in 1957, which and its extensions to various random processes have been widely studied since then. However, truncation of the Shannon series…

Functional Analysis · Mathematics 2014-01-21 Wenjian Chen , Haizhang Zhang

This paper derives bounds for two omnipresent information theoretic measures, the Shannon entropy and its complementary dual, the extropy. Based on a large size data set from a logconcave model, the said bounds are obtained for the entropy…

Statistics Theory · Mathematics 2025-07-21 Konstantinos Zografos

We study the problem of secure joint source-channel coding for multimodal semantic sources transmitted over noisy wiretap channels. The source model consists of $m$ modalities (e.g., image, audio, and sensor data), all represented as random…

Information Theory · Computer Science 2026-05-26 Denis Kozlov , Mahtab Mirmohseni , Rahim Tafazolli

Semiconstrained systems were recently suggested as a generalization of constrained systems, commonly used in communication and data-storage applications that require certain offending subsequences be avoided. In an attempt to apply…

Information Theory · Computer Science 2016-10-25 Ohad Elishco , Tom Meyerovitch , Moshe Schwartz

The Shannon entropy, one of the cornerstones of information theory, is widely used in physics, particularly in statistical mechanics. Yet its characterization and connection to physics remain vague, leaving ample room for misconceptions and…

Statistical Mechanics · Physics 2021-07-28 Gabriele Carcassi , Christine A. Aidala , Julian Barbour

It was recently shown that the lossless compression of a single source $X^n$ is achievable with a notion of strong locality; any $X_i$ can be decoded from a constant number of compressed bits, with a vanishing in $n$ probability of error.…

Information Theory · Computer Science 2022-09-28 Shashank Vatedka , Venkat Chandar , Aslan Tchamkerten

The design and implementation of error correcting codes has long been informed by two fundamental results: Shannon's 1948 capacity theorem, which established that long codes use noisy channels most efficiently; and Berlekamp, McEliece, and…

Information Theory · Computer Science 2024-10-30 Ken R. Duffy , Muriel Médard , Wei An

In this work, the problem of transmitting an i.i.d Gaussian source over an i.i.d Gaussian wiretap channel with an i.i.d Gaussian side information is considered. The intended receiver is assumed to have a certain minimum SNR and the…

Information Theory · Computer Science 2010-08-03 Ghadamali Bagherikaram , Konstantinos N. Plataniotis

The paper examines relationships between the conditional Shannon entropy and the expectation of $\ell_{\alpha}$-norm for joint probability distributions. More precisely, we investigate the tight bounds of the expectation of…

Information Theory · Computer Science 2020-08-24 Yuta Sakai , Ken-ichi Iwata

We introduce the (private) entropy of a directed graph (in a new network coding sense) as well as a number of related concepts. We show that the entropy of a directed graph is identical to its guessing number and can be bounded from below…

Combinatorics · Mathematics 2007-11-28 Soren Riis

Generalization error bounds are critical to understanding the performance of machine learning models. In this work, we propose a new information-theoretic based generalization error upper bound applicable to supervised learning scenarios.…

Information Theory · Computer Science 2021-01-11 Gholamali Aminian , Laura Toni , Miguel R. D. Rodrigues

Reliable data-driven estimation of Shannon entropy from small data sets, where the number of examples is potentially smaller than the number of possible outcomes, is a critical matter in several applications. In this paper, we introduce a…

Machine Learning · Computer Science 2025-12-12 Gabriel F. A. Bastos , Jugurta Montalvão

For the discrete-time AWGN channel with a power constraint, we give an alternative derivation of Shannon's sphere-packing upper bound on the optimal block error exponent and prove for the first time an analogous lower bound on the optimal…

Information Theory · Computer Science 2023-07-26 Sergey Tridenski , Anelia Somekh-Baruch

Slepian-Wolf theorem is a well-known framework that targets almost lossless compression of (two) data streams with symbol-by-symbol correlation between the outputs of (two) distributed sources. However, this paper considers a different…

Information Theory · Computer Science 2012-06-20 Ahmad Beirami , Faramarz Fekri

This paper is the part-II of the previous paper and introduces the world of Yuen's concept. In the theory of cryptology, the Shannon impossibility theorem states that the upper bound of the security of a plaintext against a ciphertext-only…

Quantum Physics · Physics 2025-04-25 Osamu Hirota

We compare the elementary theories of Shannon information and Kolmogorov complexity, the extent to which they have a common purpose, and where they are fundamentally different. We discuss and relate the basic notions of both theories:…

Information Theory · Computer Science 2020-07-21 Peter Grunwald , Paul Vitanyi

A fundamental quantity of interest in Shannon theory, classical or quantum, is the optimal error exponent of a given channel W and rate R: the constant E(W,R) which governs the exponential decay of decoding error when using ever larger…

Quantum Physics · Physics 2023-09-26 Joseph M. Renes