3 edition of **Performance Analysis of Linear Codes under Maximum-Likelihood Decoding** found in the catalog.

- 110 Want to read
- 31 Currently reading

Published
**June 20, 2006**
by Now Publishers Inc
.

Written in English

- Communications engineering / telecommunications,
- Technology,
- Technology & Industrial Arts,
- Science/Mathematics,
- Information Theory,
- Telecommunications,
- Computers-Information Theory,
- Technology / Telecommunications,
- Coding theory,
- Decoders (Electronics),
- Error-correcting codes (Information theory)

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 236 |

ID Numbers | |

Open Library | OL8811999M |

ISBN 10 | 1933019328 |

ISBN 10 | 9781933019321 |

algorithm [21], and the mRRD performance was obtained using parallel mRRD decoder [17]. We have a gap of dB to achieve maximum likelihood performance with our proposed decoder. Note, that the overall decoding time of our decoder is substantially smaller than the mRRD’s decoding time for the (63,36) code, with a factor of up to IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 3, MARCH Using Linear Programming to Decode Binary Linear Codes Jon Feldman, Martin J. Wainwright, Member, IEEE, and David R. Karger, Associate Member, IEEE Abstract—A new method is given for performing approximate maximum-likelihood (ML) decoding of an arbitrary binary linear.

their excellent performance under sum-product(SP) decoding (or message-passingdecoding). The primary research focus in this area to date has been on binary LDPC codes. Finite-length analysis of such LDPC codes under SP decoding is a difﬁcult ta sk. An approach to such an. This book has been written as lecture notes for students who need a grasp of the basic principles of linear codes. The scope and level of the lecture notes are considered suitable for under-graduate students of Mathematical Sciences at the Faculty of Mathematics, Natural Sciences and Information Technologies at the University of Primorska.

A new soft decision maximum-likelihood decoding algorithm, which generates the minimum set of candidate codewords by efficiently applying the algebraic decoder is proposed. As a result, the decoding complexity is reduced without degradation of performance. The new algorithm is tested and verified by simulation results. This chapter provides design, analysis, construction, and performance of the turbo codes, serially concatenated codes, and turbo-like codes including the design of interleavers in concatenation of codes. Also this chapter describes the iterative decoding algorithms for these codes.

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Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical Performance Analysis of Linear Codes under Maximum-Likelihood Decoding book, their performance analysis under ML decoding allows to predict their performance without resorting to computer.

Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding.

Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their Cited by: Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial Article (PDF Available) in Foundations and Trends® in Communications and Information Theory 3(1/2).

Performance Analysis of Linear Codes Under Maximum-likelihood Decoding: A Tutorial. Abstract. The preferred citation for this publication is I. Sason and S. Shamai, Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial, Foun-dation and Trends R in Communications and Information Theory, vol 3, no 1/2, pp 1–, Printed on acid-free paper ISBN: c I.

Sason and S. Shamai All rights Cited by: The preferred citation for this publication is I. Sason and S. Shamai, Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial, Foun-dation and Trends°R in Communications and Information Theory, vol 3, no 1/2, pp 1–, Printed on acid-free paper ISBN: °c I.

Sason and S. Shamai All rights. This article is focused on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for. Performance analysis of linear codes under maximum likelihood decoding at low rates.

Performance Analysis of Raptor Codes Under Maximum Likelihood Decoding Abstract: In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code.

By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds. Performance of space-time block codes can be improved using the coordinate interleaving of the input symbols from rotated M-ary phase shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) constellations.

This paper is on the performance analysis of coordinate-interleaved space-time codes, which are a subset of single-symbol maximum likelihood decodable linear space-time block.

Maximum Likelihood Decoding The ML decoding rule implicitly divides the received vectors into decoding regions known as Voronoi regions. TheVoronoiregion(i.e.,decisionregion)forthecodeword xn 1 2CisthesubsetofYn deﬁned by V(xn 1), fyn 1 2Y njW(yn 1 jx n 1) >W(yn 1 jxe n 1) 8ex n 1 2C;ex n 1 6= x n 1 g: Inthiscase.

Get this from a library. Performance analysis of linear codes under maximum-likelihood decoding: a tutorial. [Igal Sason; Shlomo Shamai] -- This article is focused on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding.

Though the ML decoding algorithm is prohibitively complex for most practical. Other topics studied are related to fundamental and simple block codes, the algebra of linear block codes, binary cyclic codes and BCH codes, decoding techniques for binary BCH codes, nonbinary BCH codes and Reed-Solomon codes, the performance of linear block codes with bounded-distance decoding an introduction to convolutional codes, maximum.

Having covered the techniques of hard and soft decision decoding, its time to illustrate the most important concept of Maximum Likelihood Decoding. Maximum Likelihood Decoding: Consider a set of possible codewords (valid codewords – set) generated by an encoder in the transmitter side.

We pick one codeword out of this set (call Read more Maximum Likelihood Decoding. Key words Product codes, split enumerator, weight enumerator, maximum likelihood performance 1 Introduction Linear product codes are widely used in many commu-nication and data storage systems.

Elias has introduced product codes and suggested decoding them in an it-erative fashion. The product code of two linear block codes is a linear block code.

In this chapter, we discussed the performance of codes under hard and soft decision decoding. For hard decision decoding, the performance of codes in the binary symmetric channel was discussed and numerically evaluated results for the bounded distance decoder compared to the full decoder were presented for a range of codes whose coset leader weight distribution is known.

In maximum-likelihood decoding of a convolutional code, we must find the code sequence x(D) that gives the maximum-likelihood P(y(D)|x(D)) for the given received sequence y(D).The Viterbi algorithm is a method for obtaining the path of the trellis that corresponds to the maximum-likelihood code sequence.

Consider the two paths x(D) and x'(D) in the trellis that diverge at node level 0 and. of block and convolutional codes in terms of maximum-likelihood analytical upper bounds. Section V is devoted to the presentation of a new iterative decoding algorithm and to its application to some signiﬁcant codes.

Performance comparison between SCCC’s and PCCC’s under suboptimum iterative decoding algorithms are presented in Section IV. Toshiyasu Matsushima's research works with citations and 1, reads, including: Bayes code for two-dimensional auto-regressive hidden Markov model and its application to lossless image.

These bounds are applied to various ensembles of turbo-like codes, focusing especially on repeat-accumulate codes and their recent variations which possess low encoding and decoding complexity and exhibit remarkable performance under iterative decoding.

Since the work of Shannon [], Maximum likelihood (ML) decoders have been it was established in the s that ML decoding of arbitrary linear codes is an NP-complete problem [], instead of seeking a universal, code book independent decoder, most codes are co-designed and developed with a specific decoder that is often an approximation of a ML decoder [3, 4].an arbitrary code (linear or nonlinear) using maximum likelihood decoding is studied on binary erasure channels (BECs) with arbitrary erasure probability 0linear codes, which are equivalent to a concatenation of several Hadamard linear codes.

Many universal decoding algorithms have been proposed for the decoding of linear binary block codes. The decoding algorithms in [4, 5] are based on the testing and re-encoding of the information bits as initially considered by Dorsch.

In particular, a list of the likely transmitted codewords is generated using the reliabilities of the.