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This manuscript investigates channel capacity under mismatched stochastic likelihood decoding. We derive Feinstein- and Verd\'u-Han-style bounds on the error probability coded communication. These are used to obtain a general…

Information Theory · Computer Science 2026-04-21 Francesc Molina , Albert Guillen i Fabregas

We derive a single-letter upper bound to the mismatched-decoding capacity for discrete memoryless channels. The bound is expressed as the mutual information of a transformation of the channel, such that a maximum-likelihood decoding error…

Information Theory · Computer Science 2021-02-16 Ehsan Asadi Kangarshahi , Albert Guillén i Fàbregas

This paper shows that, with high probability, randomly punctured Reed-Solomon codes over fields of polynomial size achieve the list decoding capacity. More specifically, we prove that for any $\epsilon>0$ and $R\in (0,1)$, with high…

Information Theory · Computer Science 2025-09-03 Zeyu Guo , Zihan Zhang

List decoding of insertions and deletions in the Levenshtein metric is considered. The Levenshtein distance between two sequences is the minimum number of insertions and deletions needed to turn one of the sequences into the other. In this…

Information Theory · Computer Science 2017-11-20 Antonia Wachter-Zeh

It is known that the Shannon capacity of the q-ary symmetric channel (qSC) is the same as the list-decoding capacity of an adversarial channel, raising the question of whether there is a formal (and black-box) connection between the two. We…

Information Theory · Computer Science 2024-10-29 Francisco Pernice , Oscar Sprumont , Mary Wootters

We study the list decodability of different ensembles of codes over the real alphabet under the assumption of an omniscient adversary. It is a well-known result that when the source and the adversary have power constraints $ P $ and $ N $…

Information Theory · Computer Science 2021-09-30 Yihan Zhang , Shashank Vatedka

In this work, we consider the list-decodability and list-recoverability of codes in the zero-rate regime. Briefly, a code $\mathcal{C} \subseteq [q]^n$ is $(p,\ell,L)$-list-recoverable if for all tuples of input lists $(Y_1,\dots,Y_n)$ with…

Information Theory · Computer Science 2023-09-06 Nicolas Resch , Chen Yuan , Yihan Zhang

In this work, we study the problem of list decoding of insertions and deletions. We present a Johnson-type upper bound on the maximum list size. The bound is meaningful only when insertions occur. Our bound implies that there are binary…

Information Theory · Computer Science 2020-02-18 Tomohiro Hayashi , Kenji Yasunaga

It is well known that a random q-ary code of rate \Omega(\epsilon^2) is list decodable up to radius (1 - 1/q - \epsilon) with list sizes on the order of 1/\epsilon^2, with probability 1 - o(1). However, until recently, a similar statement…

Information Theory · Computer Science 2013-07-11 Mary Wootters

A binary code of blocklength $n$ and codebook size $M$ is called an $(n,M)$ code, which is studied for memoryless binary symmetric channels (BSCs) with the maximum likelihood (ML) decoding. For any $n \geq 2$, some optimal codes among the…

Information Theory · Computer Science 2023-07-06 Yanyan Dong , Shenghao Yang

In the paper, the Levenshtein's sequence reconstruction problem is considered in the case where at most $t$ substitution errors occur in each of the $N$ channels and the decoder outputs a list of length $\mathcal{L}$. Moreover, it is…

Information Theory · Computer Science 2022-11-17 Ville Junnila , Tero Laihonen , Tuomo Lehtilä

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm remain unclear. Previous works only compare decoding…

In an unreliable single-hop broadcast network setting, we investigate the throughput and decoding-delay performance of random linear network coding as a function of the coding window size and the network size. Our model consists of a source…

Information Theory · Computer Science 2015-03-17 B. T. Swapna , Atilla Eryilmaz , Ness B. Shroff

Error-correcting codes are one of the most fundamental objects in pseudorandomness, with applications in communication, complexity theory, and beyond. Codes are useful because of their ability to support decoding, which is the task of…

Information Theory · Computer Science 2024-08-28 Shashank Srivastava

We consider the problem of determining the zero-error list-decoding capacity of the $q/(q-1)$ channel studied by Elias (1988). The $q/(q-1)$ channel has input and output alphabet consisting of $q$ symbols, say, $Q = \{x_1,x_2,\ldots,…

Information Theory · Computer Science 2018-02-26 Siddharth Bhandari , Jaikumar Radhakrishnan

We prove the following results concerning the list decoding of error-correcting codes: (i) We show that for \textit{any} code with a relative distance of $\delta$ (over a large enough alphabet), the following result holds for \textit{random…

Information Theory · Computer Science 2010-01-13 Atri Rudra , Steve Uurtamo

We study certain combinatorial aspects of list-decoding, motivated by the exponential gap between the known upper bound (of $O(1/\gamma)$) and lower bound (of $\Omega_p(\log (1/\gamma))$) for the list-size needed to decode up to radius $p$…

Information Theory · Computer Science 2015-03-20 Venkatesan Guruswami , Srivatsan Narayanan

The fundamental limits of channels with mismatched decoding are addressed. A general formula is established for the mismatch capacity of a general channel, defined as a sequence of conditional distributions with a general decoding metrics…

Information Theory · Computer Science 2013-10-01 Anelia Somekh-Baruch

We demonstrate a decoding scheme for nested lattice codes which is able to decode a list of a particular size which contains the transmitted codeword with high probability. This list decoder is analogous to that used in random coding…

Information Theory · Computer Science 2010-10-04 Yiwei Song , Natasha Devroye

We give a new construction of algebraic codes which are efficiently list decodable from a fraction $1-R-\eps$ of adversarial errors where $R$ is the rate of the code, for any desired positive constant $\eps$. The worst-case list size output…

Information Theory · Computer Science 2015-03-20 Venkatesan Guruswami , Chaoping Xing