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Sequential decoding
About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.
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Journal Article•
TL;DR: This work presents rational rate k/n punctured convolutional codes with good performance that improve the free distance and (or) weight spectra over previously reported codes with the same parameters.
Abstract: We present rational rate k/n punctured convolutional codes (n up to 8, k=1, /spl middot//spl middot//spl middot/, n-1, and constraint length /spl nu/ up to 8) with good performance. Many of these codes improve the free distance and (or) weight spectra over previously reported codes with the same parameters. The tabulated codes are found by an exhaustive (or a random) search.
66 citations
24 Jun 2007
TL;DR: It is shown that both recoverability and minimum decoding overhead of any MPG code with MR property depend only on the configuration, where decoding overhead is defined as the additional number of symbols to access, in order to decode the lost data symbols.
Abstract: In this paper, we study the maximally recoverable (MR) property for multi-protection group (MPG) codes. MPG codes with MR property achieve the best erasure recoverability given configurations, where a configuration represents the structural relationship between data and parity symbols. We present construction and decoding algorithms for MPG codes with MR property. We show that both recoverability and minimum decoding overhead of any MPG code with MR property depend only on the configuration, where decoding overhead is defined as the additional number of symbols to access, in order to decode the lost data symbols.
66 citations
Book•
10 Dec 2007
TL;DR: In this article, the authors present a classification of problems on Coding in networks and propose a solution to overcome these problems using error control codes at different layers of the network, including linear block codes and cyclic codes.
Abstract: Preface.1 Problems Facing Error Control Coding in Data Networks.1.1 International Recommendations on Using Error Control Coding at Different Network Layers.1.2 Classification of Problems on Coding in Networks.2 Block Codes.2.1 Main Definitions.2.2 Algebraic Structures.2.3 Linear Block Codes.2.4 Cyclic Codes.2.5 Bounds on Minimum Distance.3 General Methods of Decoding of Linear Codes.3.1 Minimum Distance Decoding.3.2 Information Set Decoding.3.3 A Supercode Decoding Algorithm.3.4 The Complexity of Decoding in the Channel with Independent Errors.4 Codes with Algebraic Decoding.4.1 Hamming Codes.4.2 Reed-Solomon Codes.4.3 BCH Codes.4.4 Decoding of BCH Codes.4.5 The Sudan Algorithm and its Extensions.5 Decoding of LDPC Codes.5.1 Low-Density Parity-Check Codes.5.2 LDPC Constructions.5.3 Estimating the Minimum Distance of EG-LDPC Codes.5.4 Burst-Error-Correcting LDPC Codes.5.5 Decoding Schemes of LDPC Codes.5.6 Simulation Results in AWGN.Appendix 5.A Euclidean Geometries.6 Convolutional Codes and Turbo-Codes.6.1 Convolutional Codes Representation and Encoding.6.2 Viterbi Decoding Algorithm.6.3 List Decoding.6.4 Sequential Decoding.6.5 Parallel-Concatenated Convolutional Codes and Soft Input Soft Output Decoding.6.6 SISO Decoding Algorithms.7 Coding of Messages at the Transport Layer of the Data Network.7.1 Decreasing the Message Delay with the help of Transport Coding.7.2 Transmission of Message during Limited Time.7.3 Transmission of Priority Messages without using Priority Packets .7.4 Estimation of the Effectiveness of Transport Coding for the Nonexponential Model of Packet Delay.8 Providing Security of Data in a Network with the Help of Coding Methods.8.1 Public-Key Cryptography.8.2 Codebased Cryptosystems: McEliece and Niederreiter.8.3 Cryptosystems Based on Full Decoding.8.4 Further Development of Codebased Cryptosystems.8.5 Codebased Cryptosystems and RSA: Comparison and Perspectives.8.6 Codebased Signature.9 Reconciliation of Coding at Different Layers of a Network.9.1 Transport Coding in a Network with Unreliable Channels.9.2 Reconciliation of Channel and Transport Coding.9.3 Use of Tornado Codes for Reconciliation of Channel and Transport Coding.9.4 Development of Coding Methods at the Presentation Layer.9.5 Reconciliation of Coding at Neighbour Layers of a Network.Index.
66 citations
TL;DR: A modified algorithm for decoding of low-density parity-check codes over finite-state binary Markov channels that outperforms systems in which the channel statistics are not exploited in the decoding, even when the channel parameters are not known a priori at the decoder.
Abstract: We propose a modified algorithm for decoding of low-density parity-check codes over finite-state binary Markov channels. The proposed approach clearly outperforms systems in which the channel statistics are not exploited in the decoding, even when the channel parameters are not known a priori at the decoder.
66 citations
TL;DR: Information set decoding (ISC) as discussed by the authors is an algorithm for decoding any linear code and has been shown to be logarithmically exact for virtually all codes, with the case of complete minimum distance decoding and bounded hard decision decoding.
Abstract: Information set decoding is an algorithm for decoding any linear code. Expressions for the complexity of the procedure that are logarithmically exact for virtually all codes are presented. The expressions cover the cases of complete minimum distance decoding and bounded hard-decision decoding, as well as the important case of bounded soft-decision decoding. It is demonstrated that these results are vastly better than those for the trivial algorithms of searching through all codewords or through all syndromes, and are significantly better than those for any other general algorithm currently known. For codes over large symbol fields, the procedure tends towards a complexity that is subexponential in the symbol size. >
66 citations