Journal of Communications and Networks

  • 제9권1호
  • /
  • Pages.1-10
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  • 2007
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  • 1229-2370(pISSN)
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  • 1976-5541(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지

Subsidiary Maximum Likelihood Iterative Decoding Based on Extrinsic Information

  • Yang, Fengfan (Department of Electronic Engineering, Nanjing University of Aeronautics & Astronautics) ;
  • Le-Ngoc, Tho (Department of Electrical & Computer Engineering, McGill University)
  • 발행 : 2007.03.31
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초록

This paper proposes a multimodal generalized Gaussian distribution (MGGD) to effectively model the varying statistical properties of the extrinsic information. A subsidiary maximum likelihood decoding (MLD) algorithm is subsequently developed to dynamically select the most suitable MGGD parameters to be used in the component maximum a posteriori (MAP) decoders at each decoding iteration to derive the more reliable metrics performance enhancement. Simulation results show that, for a wide range of block lengths, the proposed approach can enhance the overall turbo decoding performance for both parallel and serially concatenated codes in additive white Gaussian noise (AWGN), Rician, and Rayleigh fading channels.

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참고문헌

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