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We consider a generalization of the discrete memoryless channel, in which the channel probability distribution is replaced by a uniform distribution over clouds of channel output sequences. For a random ensemble of such channels, we derive…

Information Theory · Computer Science 2022-09-22 Sergey Tridenski , Anelia Somekh-Baruch

For most discrete memoryless channels, there does not exist a linear code for the channel which uses all of the channel's input symbols. Therefore, linearity of the code for such channels is a very restrictive condition and there should be…

Information Theory · Computer Science 2011-02-17 Aria Ghasemian Sahebi , S. Sandeep Pradhan

This paper considers the memoryless input-constrained binary erasure channel (BEC). The channel input constraint is the $(d,\infty)$-runlength limited (RLL) constraint, which mandates that any pair of successive $1$s in the input sequence…

Information Theory · Computer Science 2022-04-15 V. Arvind Rameshwar , Navin Kashyap

The zero-error capacity of a channel is the rate at which it can send information perfectly, with zero probability of error, and has long been studied in classical information theory. We show that the zero-error capacity of quantum channels…

Quantum Physics · Physics 2011-09-13 Toby S. Cubitt , Graeme Smith

The upper bound on the capacity of a 3-node discrete memoryless relay channel is considered, where a source X wants to send information to destination Y with the help of a relay Z. Y and Z are independent given X, and the link from Z to Y…

Information Theory · Computer Science 2012-10-26 Feng Xue

This work constructs codes that are efficiently decodable from a constant fraction of \emph{worst-case} insertion and deletion errors in three parameter settings: (i) Binary codes with rate approaching 1; (ii) Codes with constant rate for…

Information Theory · Computer Science 2016-05-17 Venkatesan Guruswami , Ray Li

In this paper, we investigate the optimal tradeoff between source and channel coding for channels with bit or packet erasure. Upper and Lower bounds on the optimal channel coding rate are computed to achieve minimal end-to-end distortion.…

Information Theory · Computer Science 2007-07-13 Sriram N. Kizhakkemadam , Panos Papamichalis , Mandyam Srinath , Dinesh Rajan

This paper provides a new instance of quantum deletion error-correcting codes. This code can correct any single quantum deletion error, while our code is only of length 4. This paper also provides an example of an encoding quantum circuit…

Quantum Physics · Physics 2020-01-24 Manabu Hagiwara , Ayumu Nakayama

We consider the wiretap channel, where the individual channel uses have memory or are influenced by an adversary. We analyze the explicit and computationally efficient construction of information-theoretically secure coding schemes which…

Cryptography and Security · Computer Science 2024-04-03 Esther Hänggi , Iyán Méndez Veiga , Ligong Wang

I. This paper is devoted to the problem of error detection with quantum codes. In the first part we examine possible problem settings for quantum error detection. Our goal is to derive a functional that describes the probability of…

Quantum Physics · Physics 2007-05-23 Alexei Ashikhmin , Alexander Barg , Emanuel Knill , Simon Litsyn

This article considers the performance of digital communication systems transmitting messages over finite-state erasure channels with memory. Information bits are protected from channel erasures using error-correcting codes; successful…

Information Theory · Computer Science 2013-09-10 Santhosh Kumar , Jean-Francois Chamberland , Henry D. Pfister

When digital data are transmitted over a noisy channel, it is important to have a mechanism allowing recovery against a limited number of errors. Normally, a user string of 0's and 1's, called bits, is encoded by adding a number of…

Information Theory · Computer Science 2019-08-28 Mario Blaum

We characterize the capacity for the discrete-time arbitrarily varying channel with discrete inputs, outputs, and states when (a) the encoder and decoder do not share common randomness, (b) the input and state are subject to cost…

Information Theory · Computer Science 2022-05-16 Yihan Zhang , Sidharth Jaggi , Michael Langberg , Anand D. Sarwate

We study faulty successive cancellation decoding of polar codes for the binary erasure channel. To this end, we introduce a simple erasure-based fault model and we show that, under this model, polarization does not happen, meaning that…

Information Theory · Computer Science 2015-02-10 Alexios Balatsoukas-Stimming , Andreas Burg

The quantum analog of the classical erasure channel provides a simple example of a channel whose asymptotic capacity for faithful transmission of intact quantum states, with and without the assistance of a two-way classical side channel,…

Quantum Physics · Physics 2008-11-26 Charles H. Bennett , David P. DiVincenzo , John A. Smolin

In extension of the bit commitment task and following work initiated by Crepeau and Kilian, we introduce and solve the problem of characterising the optimal rate at which a discrete memoryless channel can be used for bit commitment. It…

Cryptography and Security · Computer Science 2007-05-23 Andreas Winter , Anderson C. A. Nascimento , Hideki Imai

We derive the capacity region of the degraded broadcast channel (DBC) subject to the constraint that the communication is not detected by an adversary, the Warden. Our capacity result is in a computable form and numerical results show that…

Information Theory · Computer Science 2026-05-12 Yossef Steinberg , Michèle Wigger

The problem of characterising the zero-error capacity region for multiple access channels even in the noiseless case has remained an open problem for over three decades. Motivated by this challenging question, a recently developed theory of…

Information Theory · Computer Science 2019-10-29 Ghassen Zafzouf , Girish N. Nair , Jamie S. Evans

We introduce the Random Subsequence Model, a spin glass model on pairs of random strings $(X,Y) \in \{0,1\}^N \times \{0,1\}^M$ whose partition function counts subsequence embeddings of $Y$ into $X$. We study two variants: the null model,…

Information Theory · Computer Science 2026-04-09 Ryan Jeong , Francisco Pernice

The zero-error capacity of a discrete classical channel was first defined by Shannon as the least upper bound of rates for which one transmits information with zero probability of error. The problem of finding the zero-error capacity $C_0$,…

Information Theory · Computer Science 2020-09-24 Holger Boche , Christian Deppe