Decoding codes from curves and cyclic codes - Decoding Codes from Curves and Cyclic Codes

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  • Decoding codes from curves and cyclic codes

    Citation for published version (APA): Duursma, I. M. (1993). Decoding codes from curves and cyclic codes. Eindhoven: Technische Universiteit Eindhoven.

    DOI: 10.6100/IR399251

    Document status and date: Published: 01/01/1993

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  • Decoding Codes from Curves and Cyclic Codes

    Iwan M. Duursma

  • Deco ding

    Codes from Curves

    and Cyclic Codes

  • Decoding Codes from Curves and Cyclic Codes


    ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven. op gezag van de Rector Magnificus, prof. dr. J .H. van Lint, voor een commissie aangewezen door het College van Dekanen in het openbaar te verdedigen op

    maandag 13 september 1993 om 16.00 uur


    Iwan Maynard Duursma geboren te Bussum

    druk: wibro dissertatiedrukkeril. helmond.

  • Dit proefschrift is goedgekeurd door de promotoren prof. dr. J. H. van Lint en prof. dr. H. Stichtenoth

    copromotor: dr. G. R. Pellikaan


    Duursma, Iwan Maynard

    Decoding codes from curves and cydic codes/ Iwan Maynard Duursma. - Eindhoven : Technische Universiteit Eindhoven Proefschrift Eindhoven. Met lit. opg. ISBN 90-386-0212-X Trefw. : coderingstheorie / algebraïsche meetkunde.

  • Pref ace

    The thesis describes results of my research on decoding linear codes. This research has been carried out at the Eindhoven University of Technology in the period September 1990 March 1993. It was supported by the Nether- lands Organization for Scientific Research (NWO), through the foundation Stichting Mathematisch Centrum. Some of the results have found their way to international journals. In the order in which they have appeared as preprints they are:

    1. "Algebraic decoding using special divisors," IEEE Transactions on Information Theory, volume 39, March 1993.

    2. "On the decoding procedure of Feng and Rao," Proceedings Algebraic and Combinatorial Coding Theory 111 Conference, Voneshta Voda, Bulgaria, June 1992.

    3. "Majority coset decoding," IEEE Transactions on Information Theory, volume 39, May 1993.

    4. with R. Kötter, "Error-locating pairs for cyclic codes," preprint Eindhoven-Linköping, submitted for publication, March 1993.

    The papers [1] and [3] each work towards a single theorem, given on page 39 and page 52 respectively of this thesis. The paper [4] has four theorems, on pages 71, 77, 78 and 78. In addition, the tables at pages 86 and 87 give explicit decoding procedures. The introduction further explains the topic and the contributions of the thesis.

    I would like to thank the Discrete Mathematics group for the stimula- ting working environment, professor J.H. van Lint for his support and his comments on the preprints and R. Pellikaan for numerous discussions and managing the NWO project. Thanks also to professor G. van der Geer for introducing me to codes from algebra.ic curves and to professor H. Stichtenoth and professor R. Schoof for further discussions on algebra.ic curves. To all, I express my gratitude for judging the final manuscript. For the joint work on cyclic codes, I am grateful to the coauthor R. Kötter.

    1 thank the NWO for its financial support. For further support and for hos- pitality I wish to thank the University of Trento and professor R. Schoof, and the University of Linköping and professor T. Ericson.

    May, 1993. Iwan Duursma


  • VI

  • Contents



    1 Algebraic decoding

    1 A unified description 1.1 Error-locating pairs . 1.2 Error-locating functions 1.3 Error-correcting pairs . . 1.4 Example: projective RM-code 1.5 Additional methods . . . . . .

    II Decoding codes from curves

    2 Basic algorithm 2.1 Notation ... 2.2 Description . 2.3 Sufficient conditions . 2.4 Decoding and approximation . 2.5 Improvements . . . . . . . . .

    3 Modified algorithm 3.1 Special divisors 3.2 Main lemma . . . 3.3 Description . . . 3.4 Additional lemma . 3.5 Plane curves . . . .

    4 Majority coset decoding 4.1. Coset decoding . . . . 4.2 Two-dimensional syndromes 4.3 A majority scheme ...






    3 3 5 7 8



    17 17 19 23 25 27

    35 35 38 39 42 43

    47 48 49 51

  • 4.4 Description 53 4.5 Comparison 56 4.6 Example .. 60

    111 Decoding cyclic codes 67

    5 The BCH-bound and beyond 69 5.1 Notation ....... 69 5.2 Decoding BCH-codes . 70 5.3 Recurrences " " " " " " 73 5.4 Correcting more errors 74

    6 Pairs from MDS-codes 77 6.1 A class of MDS-codes . .. " " .. 77 6.2 Construction of pairs . " .. " .. 78 6.3 Fundamental iterative algorithm . 79 6.4 Reducing complexity ....... 81

    7 Applications 85 7.1 Codes of length less than 63 85 7.2 Sequences of codes " " ... 89

    Bibliography 91

    Samenvatting (summary in Dutch) 95

    Curriculum vitae 96


  • Introd uction

    The decoding problem has its roots in communica.tion theory. Two elec- tronic devices exchange information and due to noise or otherwise it will happen that the information received differs from the information sent. It is then assumed that the difference is in general small. In any case it is intended to have small differences by a proper design of the devices and if possible of the channel that connects them. The decoding problem is to attach the proper interpretation to the received information. If the pos- sibilities for the received information are few, the interpretation can be attached to these once and for all and stored in a table. We consider sit- uations where such tables are not feasible. Without a table, i.e. without deciding about interpretations beforehand, one needs a set of rules that can be applied any time information is received. Obviously, one prefers the set of rules to be small and the rules to be such that they can be carried out quickly. These two characteristics determine to a certain extent the size and the speed of a decoding device.

    Decoding is not only a problem of the receiver. The sender and receiver together determine how information is to be sent. In air-traffic control it is common use to avoid "yes" and "no" and to say "affirm" and "negative" instead. Sender and receiver have agreed upon this and it greatly enhances the reliability of communication. Similarly, with two electronic devices, the information will be encoded before it is transmitted. The encoding should improve the reliability of the communication and allow easy decoding by the receiver.

    The problem has a fruitful translation into mathematics:, encoded message and received message are associated with suitable strings of let- ters. The strings of letters are easily transferred into the language of the electronic devices (i.e. strings of zeros and ones) and the rules for encoding and decoding can be formulated in terms of operations that can be carried out by a microprocessor. A small example is obtained with messages of length two that use three different letters A, B, and C. The nine possible messages are:

    AA, BA, CA,

    AB, BB, CB,

    AC, BC, cc.

    The messages are very rnuch alike and by changing one letter a is transferred into a different me