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The analysis of random coding error exponents pertaining to erasure/list decoding, due to Forney, is revisited. Instead of using Jensen's inequality as well as some other inequalities in the derivation, we demonstrate that an exponentially…

Information Theory · Computer Science 2016-11-17 Neri Merhav

We derive the optimum second-order coding rates, known as second-order capacities, for erasure and list decoding. For erasure decoding for discrete memoryless channels, we show that second-order capacity is $\sqrt{V}\Phi^{-1}(\epsilon_t)$…

Information Theory · Computer Science 2014-04-22 Vincent Y. F. Tan , Pierre Moulin

A lower bound on the maximum likelihood (ML) decoding error exponent of linear block code ensembles, on the erasure channel, is developed. The lower bound turns to be positive, over an ensemble specific interval of erasure probabilities,…

Information Theory · Computer Science 2019-01-23 Enrico Paolini , Gianluigi Liva

We construct a new family of explicit codes that are list decodable to capacity and achieve an optimal list size of $O(\frac{1}{\epsilon})$. In contrast to existing explicit constructions of codes achieving list decoding capacity, our…

Information Theory · Computer Science 2025-02-12 Fernando Granha Jeronimo , Tushant Mittal , Shashank Srivastava , Madhur Tulsiani

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

Inner and outer bounds are derived on the optimal performance of fixed length block codes on discrete memoryless channels with feedback and errors-and-erasures decoding. First an inner bound is derived using a two phase encoding scheme with…

Information Theory · Computer Science 2020-01-03 Baris Nakiboglu , Lizhong Zheng

Motivated by the significant performance gains which polar codes experience under successive cancellation list decoding, their scaling exponent is studied as a function of the list size. In particular, the error probability is fixed and the…

Information Theory · Computer Science 2014-09-23 Marco Mondelli , S. Hamed Hassani , Rüdiger Urbanke

The performance of maximum-likelihood (ML) decoded binary linear block codes is addressed via the derivation of tightened upper bounds on their decoding error probability. The upper bounds on the block and bit error probabilities are valid…

Information Theory · Computer Science 2007-07-13 M. Twitto , I. Sason , S. Shamai

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

We propose two types of universal codes that are suited to two asymptotic regimes when the output alphabet is possibly continuous. The first class has the property that the error probability decays exponentially fast and we identify an…

Information Theory · Computer Science 2024-09-10 Masahito Hayashi

We analyze the list-decodability, and related notions, of random linear codes. This has been studied extensively before: there are many different parameter regimes and many different variants. Previous works have used complementary styles…

Information Theory · Computer Science 2017-04-11 Atri Rudra , Mary Wootters

We study the problem of universal decoding for unknown discrete memoryless channels in the presence of erasure/list option at the decoder, in the random coding regime. Specifically, we harness a universal version of Forney's classical…

Information Theory · Computer Science 2017-06-23 Wasim Huleihel , Nir Weinberger , Neri Merhav

Exponential error bounds for the finite-alphabet interference channel (IFC) with two transmitter-receiver pairs, are investigated under the random coding regime. Our focus is on optimum decoding, as opposed to heuristic decoding rules that…

Information Theory · Computer Science 2008-10-14 Raul Etkin , Neri Merhav , Erik Ordentlich

In this paper, we establish the list-decoding capacity theorem for sum-rank metric codes. This theorem implies the list-decodability theorem for random general sum-rank metric codes: Any random general sum-rank metric code with a rate not…

Information Theory · Computer Science 2025-03-14 Yang Liu , Anna Baumeister , Antonia Wachter-Zeh

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

A Lagrange-dual (Gallager-style) lower bound is derived for the error exponent function of the typical random code (TRC) pertaining to the i.i.d. random coding ensemble and mismatched stochastic likelihood decoding. While the original…

Information Theory · Computer Science 2019-08-13 Neri Merhav

A missing piece in quantum information theory, with very few exceptions, has been to provide the random coding exponents for quantum information-processing protocols. We remedy the situation by providing these exponents for a variety of…

Quantum Physics · Physics 2015-09-30 Naresh Sharma

The list-decodable code has been an active topic in theoretical computer science.There are general results about the list-decodability to the Johnson radius and the list-decoding capacity theorem. In this paper we show that rates,…

Information Theory · Computer Science 2022-05-31 Hao Chen

We analyze random coding error exponents associated with erasure/list Slepian-Wolf decoding using two different methods and then compare the resulting bounds. The first method follows the well known techniques of Gallager and Forney and the…

Information Theory · Computer Science 2013-05-27 Neri Merhav

In this paper q-ary Raptor codes under ML decoding are considered. An upper bound on the probability of decoding failure is derived using the weight enumerator of the outer code, or its expected weight enumerator if the outer code is drawn…

Information Theory · Computer Science 2016-11-21 Francisco Lázaro , Gianluigi Liva , Enrico Paolini , Gerhard Bauch