Abstract
In this study, we introduce a novel soft decoder, the first of its kind, for linear block codes, based on Simulated Annealing algorithm (SA). The main enhancement in our contribution which let our decoder over performs with large gain (about 3 dB at 710-4) the classical SA approach, is to take the most reliable information set of the received codeword as a start solution and also according to this reliability generate neighbor’s solutions. Besides, our algorithm performance is enhanced by reducing search space when we involve the code error correcting capability parameter. The performance of the designed algorithm is investigated through a parameter tuning process and then compared with other various decoding algorithms in terms of decoding performance and algorithmic complexity. Simulation results, show that our algorithm over performs its competitor decoders while keeping minimum computation cost. In fact, our algorithm has large gain over Chase-2 and GAMD, furthermore, it over performs the most efficient and up to date DDGA decoder by 2 dB at 10-5 for RS codes.
Highlights
After the emergence of error correcting codes theory, several researches were proposed to design "good" and practical decoders
We propose in this paper, a novel soft decoder based on Simulated Annealing Algorithm (SASD)
In our case we assume that our parameters are independent and for this reason, we run several simulations of performance which is the Bit Error (BER) expressed as function of Signal to Noise Ratio (SNR), with different SASD parameters values
Summary
After the emergence of error correcting codes theory, several researches were proposed to design "good" and practical decoders. Scientists have designed techniques that convert this problem into graph optimization These algorithms become impracticable or inefficient for large codes, the use of long code is important in communication design to approach channel limit as proven by Shannon’s landmark paper (Shannon, 1948). When metaheuristic and probabilistic algorithms became widely used and recognized as efficient approaches for hard optimization problems, in the first instance, hard decoding was coupled with these techniques to design good decoders by making hard decision on the received signal trying to find the most probable transmitted code using metaheuristic and probabilistic search. It is convenient to cite the work of Aylaj and Belkasmi (2015) by proposing a hard decoder based on SA method Such model discards all information about the reliability of the received signal and losing accuracy in decoding capability
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