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Proposal for LDPC Code Design System Using Multi-Objective Optimization and FPGA-Based Emulation

  • Yukari Ishida
  • Hirotaka Nosato
  • Eiichi Takahashi
  • Masahiro Murakawa
  • Isamu Kajitani
  • Tatsumi Furuya
  • Tetsuya Higuchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5216)

Abstract

The paper proposes a low density parity check (LDPC) code design system to facilitate the design of communication systems using LDPC codes for error correction. The proposed LDPC code design system has three advantages (utilization of MOGA to search codes, speed enhancement achieved through parallelization and FPGAs, and employment of more precise simulation models) and solves problems encountered when LDPC codes are used in practical applications. Preliminary evaluation results for the proposed system are presented, which demonstrate that the system can function successfully.

Keywords

error-correcting code LDPC multi-objective optimization MOGA communication channel model ISI FPGA emulation parallelization MPI PC cluster 

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References

  1. 1.
    Gallager, R.: Low-Density Parity-Check Codes. MIT Press, Cambridge (1963)Google Scholar
  2. 2.
    Mackay, D., Neal, R.M.: Near shannon limit performance of low density parity check codes. Electron Lett. 32(18), 1645–1646 (1996)CrossRefGoogle Scholar
  3. 3.
    Lin, S., Costello, J.D.: Error Control Coding, 2nd edn. PEARSON Prentice Hall (2004)Google Scholar
  4. 4.
    Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press (1963)Google Scholar
  5. 5.
    Seki, K., Itabashi, T., Higuchi, T., Kasai, Y., Takahashi, E.: Performance evaluation of low latency LDPC code. Technical report, IEEE P802.3an July 2004 Plenary (2004)Google Scholar
  6. 6.
    Coello, C.C.: A comprehensible survey of evolutionary -based multi-objective optimization techniques. Knowledge and Information Systems 1(3), 269–308 (1999)Google Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report 97003, University of Illinois (1997)Google Scholar
  9. 9.
    Forum, M.P.I.: MPI-2: Extensions to the message-passing interface (2003), http://www.mpi-forum.org
  10. 10.
    Culler, D.E., Singh, J.P., Gupta, A.: Parallel Computer Archtecture, A Hardware/Software Approach. Morgan Kaufmann, San Francisco (1999)Google Scholar
  11. 11.
    Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3, 493–530 (1989)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yukari Ishida
    • 1
  • Hirotaka Nosato
    • 1
  • Eiichi Takahashi
    • 2
  • Masahiro Murakawa
    • 2
  • Isamu Kajitani
    • 2
  • Tatsumi Furuya
    • 1
  • Tetsuya Higuchi
    • 2
  1. 1.Toho UniversityChibaJapan
  2. 2.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan

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