구성부호의 연접방법에 따른 직렬연접 길쌈부호의 성능

Performance of Serial Concatenated Convolutional Codes according to the Concatenation Methods of Component Codes

  • 배상재 (경북대학교 전자전기공학부) ;
  • 이상훈 (경북대학교 전자전기공학부) ;
  • 주언경 (경북대학교 전자전기공학부)
  • 발행 : 2002.01.01

초록

본 논문에서는 AWGN 채널환경에서 직렬연접 길쌈부호(serial concatenated convolutional codes; SCCC)의 세 가지 형태에 대한 성능을 비교 및 분석한다. 모의실험 결과 낮은 신호 대 잡음비(signal-to-noise ratio; SNR) 영역에서는 첫 번째 형태의 성능이 가장 우수하였다. 그러나 높은 SNR 영역에서는 세 번째 형태의 성능이 가장 우수함을 아 수 있었다. 그리고 첫 번째 형태에서는 SNR과 반복복호 횟수를 증가시키더라도 성능이 더 이상 향상되지 않는 오류마루(error floor)가 발생하였다. 그러나 두 번째와 세 번째 형태는 높은 SNR에서 반복복호를 5회 이상 수행하더라도 성능이 계속 향상되며 오류마루가 나타나지 않았다. 그리고 SNR이 증가할수록 세 가지 직렬연접 길쌈부호의 BER 성능은 각각의 상위경계(upper bound) 성능에 근접해짐을 알 수 있었다. 또한 자유거리(free distance)가 가장 큰 세 번째 직렬연접 길쌈부호가 세 가지 구조 중에서 가장 우수한 상위경계 성능을 나타내었다.

In this paper, the performance of three types of serial concatenated convolutional codes (SCCC) in AWGN (additive white Gaussian noise) channel is compared and analyzed. As results of simulations, it can be observed that Type I shows the best error performance at lower signal-to-noise ratio (SNR) region. However, Type III shows the best error performance at higher SNR region. It can be also observed the error floor that the performance cannot be improved even though increasing of the number of iterations and SNR at Type I. However, the performance of Type II and Type III are still improved over the five iterations at higher SNR without error floor. And BER performance of three types can be closed to upper bound of three types with increase of SNR. It can be also observed that the upper bound of Type III shows the best performance among the three types due to the greatest free distance.

키워드

참고문헌

  1. C. Berrou and A. Glavieux, 'Near optimum error correcting coding and decoding: Turbo-codes,' lEEE Trans. Commun., vol. 44, no. 10, pp. 1261-1271, Oct. 1996 https://doi.org/10.1109/26.539767
  2. L. R. Bahl, J. Cocke, F. Jelinek, and J. Raviv 'Optimal decoding of linear code for minizing symbol error rate,' IEEE Trans. Inform. Theory, vol. 20, no. 2, pp. 284-287, Mar. 1974
  3. S. S. Pietrobon and A. S. Barbulescu, 'A simplification of the modified Bahl decoding algorithm for systematic convolutional codes,' Proc. IEEE ISITA'94, Sydney, Australia, pp. 1073-1077, Nov. 1994
  4. P. Robertson, E. Villenm, and P. Hoeher, 'A comparison of optimal and sub-optimal MAP decoding algorithms operating in log domain,' Proc. IEEE ICC'96, Dallas, Texas, vol. 2, pp. 1009-1013, June 1995
  5. S. Benedetto, D. Divsalar, G. Montorsi, and F. Pollara, "Soft-output decoding algorithms for continuous decoding of parallel concatenated convolutional codes,' Proc. IEEE ICC'96, Dallas, Texas, vol. 2, pp. 112-117, June 1996
  6. S. Benedetto, D. Divsalar, G. Montorsi, and F. Pollara, 'Soft-output decoding algorithms in iterative decoding of turbo codes,' The TDA Progress report 42-124, Pasadena, California, pp. 63-87, Feb. 15, 1996
  7. J. Hagenauer, "The turbo principle: tutorial introduction and state of the art,' Proc. Int. Symp. on Turbo Codes and Retated Topics, Brest, France, pp. 1-11, Sep. 1997
  8. S. Benedeao and G. Montorsi, 'Unveiling turbo codes: Some results on parallel concatenated coding schemes,' 1EEE Trans. Inform. Theory, vol. 42, no. 2, pp. 409-428, Mar. 1996 https://doi.org/10.1109/18.485713
  9. D. Divsalar and F. Pollar, 'On the design of Tinbo codes,' JPL TDA Progress Report 42-123, Pasadena, California, pp. 99-121, Nov. 1995
  10. S. Benedetto, G. Montorsi, D. Divsalar, and F. Pollara, 'Serial concatenation of interleaved codes: Performance analysis, design, and iterative decoding,' JPL TDA Progress Report, Pasadena, California, vol. 42, pp. 1-26, Aug. 1996
  11. S. Benedetto and G. Montorsi, 'Sehal concatenation of block and convolutional codes,' Electron. Lett., vol. 32, pp 887-888, May 1996 https://doi.org/10.1049/el:19960621
  12. S. Benedetto, G. Montorsi, D. Divsalar, and F. Pollara, 'Serial concatenadon of interleaved codes: Performance analysis, design, and iterative decoding,' IEEE Trans. Inform. Theory, vol. 44, no. 3, pp. 909-926, May 1998 https://doi.org/10.1109/18.669119
  13. A. Ambroze, G. Wade, and M. Tomlinson, 'Iterative MAP decoding for serial concatenat-ed convolutional codes,' Proc. 1EE Commun., vol. 145, no. 2, pp. 53-59, Apr. 1998 https://doi.org/10.1049/ip-com:19981876
  14. B. Vucetic and J. Yuan, Turbo Codes Principles and Applications, Kluwer Academic, Mass., 2000
  15. L. C. Perez, J. Seghers, and D. J. Costello, 'A distance spectrum interpretation of Turbo codes,' 1EEE Trans. Inform. Theory, vol. 42, no. 6, pp. 1698-1709, Nov. 1996 https://doi.org/10.1109/18.556666
  16. S. Dolinar and D. Divsalar, 'weight distribu-tions for turbo codes using random and nonrandom permutations,' The TDA Progress report 42-122, Pasadena, California, pp. 56-65, Aug. 15, 1995
  17. J. Rolf and Sh.Z. Kamil, fundamentals of ConvoIutionaI Coding, IEEE Press, NJ., 1999
  18. P. Robertson, 'Illuminating the structure of parallel concatenated recursive systematic (turbo) codes,' Proc. IEEE Gtobecom '94, San Fransisco, California, pp. 1298-1303, Nov. 1994
  19. A. J. Viterbi and J. K. Omura, Principles of Digitat Communication and Coding, McGraw Hill Book Co., NY., 1979