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International Journal of Computer Applications

Syndrome Weight Decision based Genetic Algorithm Decoder for LDPC Codes

Hasna Chaibi, Ahlam Berkani, My Ahmad

Published Oct 15, 2015• 3 citations
Publisher

Abstract

Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances when solving large optimization problems .This article introduces a new Decoder based on Genetic Algorithm and the Syndrome Weight decision (GADSW) for decoding Low Density Parity Check (LDPC) codes.The performances of (GADSW) decoder are very good compared to sumproduct decoder, which prove its efficiency.

DOI
10.5120/ijca2015906403

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Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances when solving large optimization problems .This article introduces a new Decoder based on Genetic Algorithm and the Syndrome Weight decision (GADSW) for decoding Low Density Parity Check (LDPC) codes.The performances of (GADSW) decoder are very good compared to sumproduct decoder, which prove its efficiency.
Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders.
In order to improve the decoding performance of low density parity check (LDPC) codes, a sum-product genetic decoding (SPGD) algorithm based on genetic algorithm (GA) is proposed. By means of the classical decoding algorithm of sum-product algorithm (SPA) of LDPC codes, variable node information generated by each hard decision of iteration is regarded as individuals that constitute initial population of GA. The decoded optimal solutions are produced by a series of genetic operations. Simulation results show that under the premise of almost no increase in complexity, the proposed SPGD algorithm has the superior decoding error correction performance, better practicability and higher net coding gain (NCG).
This paper analyzes the correlation between the error weights and the syndrome weights during the sum product algorithm (SPA) decoding process of low density parity check codes (LDPC) in order to improve the error performance and the decoding speed. In this paper, we propose a modified SPA decoding algorithm to improve the decoding performance of the non-decoded codewords and investigate if this new algorithm takes indeed the best decoding decision for each codeword. The effect of the averaged decoding over the error weights and syndrome weights are analyzed as well as their correlation. The correlation coefficients between them are computed and a weight enumerator polynomial is used to jointly represent the error and syndrome weight distribution for each codeword.
We present in this paper a new method for improving the decoding of the LDPC (low-density parity check) codes by optimising the log-SPA (sum-product algorithm). Currently, the LDPC codes give very good results with iterative decoding algorithm, log-SPA, based on the propagation confidence BP (belief propagation). To improve the existing log-SPA, we propose a decoding algorithm (log-SPA modified), which minimises the error decoding of words that could not be previously decoded. This algorithm is based on the dependency between the weight of the syndrome and the weight of the error sequence generated during iterations of decoding. Simulations illustrate the performance of our approach compared with classical LDPC code based on log-SPA.
Low-density parity-check (LDPC) codes have been widely considered as error-correcting codes for next generation communication systems. A good LDPC decoder design requires both implementation friendly LDPC codes and efficient decoder architectures. The quality of LDPC code is crucial in determining the coding gain and implementation complexity of LDPC hardware decoders. This paper presented a genetic algorithm (GA) based LDPC code search algorithm with hardware considerations. Regular quasi-cyclic LDPC codes are used due to its friendliness to hardware implementation. Our hardware architecture design schedules pipeline LDPC decoding operation to boost the hardware utilization efficiency (HUE) of LDPC decoder. A LDPC decoder with a block size of 12288 is implemented in FPGA to validate our code and architecture design.
Error correction plays a crucial role when transmitting data from the source to the destination through a noisy channel. It has found many applications in television broadcasting services, data transmission in radiation harsh environment (e. g. space probes or physical experiments) or memory storages influenced by Single Event Effects (SEE). Low Density Parity Check (LDPC) codes provide an important technique to correct these errors. The parameters of error correction depend both on the decoding algorithm and on the LDPC code given by the parity-check matrix. Therefore, a particular design of the paritycheck matrix is necessary. Moreover, with the development of high performance computing, the application of genetic optimization algorithms to design the parity-check matrices has been enabled. In this article, we present the application of the genetic optimization algorithm to produce error correcting codes with special properties, especially the burst types of errors. The results show the bounds of correction capabilities for various code lengths and various redundancies of LDPC codes. This is particularly useful when designing systems under the influence of noise combined with the application of the error correction codes.