Abstract
Genetic algorithms offer very good performances for solving large optimization problems, especially in the domain of error-correcting codes. However, they have a major drawback related to the time complexity and memory occupation when running on a uniprocessor computer. This paper proposes a parallel decoder for linear block codes, using parallel genetic algorithms (PGA). The good performance and time complexity are confirmed by theoretical study and by simulations on BCH(63,30,14) codes over both AWGN and flat Rayleigh fading channels. The simulation results show that the coding gain between parallel and single genetic algorithm is about 0.7 dB at BER = 10﹣5 with only 4 processors.
Highlights
The error correcting codes began with the introduction of Hamming codes [1] in the same period that the remarkable work of Shannon [2]
This paper proposes a parallel decoder for linear block codes, using parallel genetic algorithms (PGA)
We have proposed two iterative decoding algorithms based on Genetic Algorithms (GAs), which can be applied to any arbitrary 3D binary product block codes, without the need of a Hard-In Hard-Out decoder
Summary
The error correcting codes began with the introduction of Hamming codes [1] in the same period that the remarkable work of Shannon [2]. In 2012, we made an extension of concatenated codes by passing to three dimensions [11] In this last work, we have proposed two iterative decoding algorithms based on GAs, which can be applied to any arbitrary 3D binary product block codes, without the need of a Hard-In Hard-Out decoder. We have showed that the two proposed decoders are less complex than both Chase-Pyndiah algorithm for codes with large correction capacity, and LBDA for large i parameter. We have developed a new decoder using parallel genetic algorithms, which runs on a parallel computer with multiple processors and a shared memory, or on a distributed system It reduces the time complexity and increases little performance.
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