Abstract

Practically good error-correcting codes should have good parameters and efficient decoding algorithms. Some algebraically defined good codes, such as cyclic codes, Reed–Solomon codes, and Reed–Muller codes, have nice decoding algorithms. However, many optimal linear codes do not have an efficient decoding algorithm except for the general syndrome decoding which requires a lot of memory. Therefore, a natural question to ask is which optimal linear codes have an efficient decoding. We show that two binary optimal [ 36 , 19 , 8 ] linear codes and two binary optimal [ 40 , 22 , 8 ] codes have an efficient decoding algorithm. There was no known efficient decoding algorithm for the binary optimal [ 36 , 19 , 8 ] and [ 40 , 22 , 8 ] codes. We project them onto the much shorter length linear [ 9 , 5 , 4 ] and [ 10 , 6 , 4 ] codes over G F ( 4 ) , respectively. This decoding algorithm, called projection decoding, can correct errors of weight up to 3. These [ 36 , 19 , 8 ] and [ 40 , 22 , 8 ] codes respectively have more codewords than any optimal self-dual [ 36 , 18 , 8 ] and [ 40 , 20 , 8 ] codes for given length and minimum weight, implying that these codes are more practical.

Highlights

  • IntroductionCoding theory or the theory of error-correcting codes requires a lot of mathematical concepts but has wide applications in data storage, satellite communication, smart phone, and High Definition TV

  • Coding theory or the theory of error-correcting codes requires a lot of mathematical concepts but has wide applications in data storage, satellite communication, smart phone, and High Definition TV.The well known classes of codes include Reed-Solomon codes, Reed-Muller codes, turbo codes, LDPC codes, Polar codes, network codes, quantum codes, and DNA codes

  • A linear [n, k ] code C over GF (q) or Fq is a k-dimensional subspace of Fnq

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Summary

Introduction

Coding theory or the theory of error-correcting codes requires a lot of mathematical concepts but has wide applications in data storage, satellite communication, smart phone, and High Definition TV. The purpose of this paper is to show how to decode efficiently a binary optimal [36, 19, 8] linear code and a binary optimal [40, 22, 8] code by projecting them onto the much shorter length linear [9, 5, 4] and [10, 6, 4] codes over GF (4), respectively This decoding algorithm, which we will call (8−1). This decoding algorithm exploits the properties of codes with projection onto an additive code over GF (4) to locate the errors in the noisy codewords, assuming not more than 3 errors occurred.

Projection of Binary Linear Codes
Projection Decoding
Examples
C E36 with some error of weight up to three and have the ω
Conclusions

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