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J. W. H. Kao, S. M. Berber and A. Bigdeli, “A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion,” Advances in Artificial Intelligence, Vol. 2009, 2009, Article ID: 356120. doi:10.1155/2009/356120

has been cited by the following article:

• TITLE: Efficient Information Set Decoding Based on Genetic Algorithms

  AUTHORS: Ahmed Azouaoui, Idriss Chana, Mostafa Belkasmi

  KEYWORDS: Genetic Algorithms (GA); Error Correcting Codes; RS Codes; Information Set Decoding; Chase Algorithm

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.5 No.7, July 25, 2012

  ABSTRACT: In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the generating matrix G. Then we present a new soft-decision decoding based on HDGA and the Chase algorithm (SDGA). The performance of some binary and non-binary Linear Block Codes are given for HDGA and SDGA over Gaussian and Rayleigh channels. The performances show that the HDGA decoder has the same performances as the Berlekamp-Massey Algorithm (BMA) in various transmission channels. On the other hand, the performances of SDGA are equivalent to soft-decision decoding using Chase algorithm and BMA (Chase-BMA). The complexity of decoders proposed is also discussed and compared to those of other decoders.