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IET International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN)

Decoding Algorithm of LDPC Codes Based on Genetic Algorithm

Sheng Huang, Kai Tian, Fangfang Tian, Jianguo Yuan

Published Jan 1, 2013• 5 citations
Publisher

Abstract

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).

DOI
10.1049/cp.2013.2387

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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).
In this letter, we analyze the performance of two improved Sum-Product (SP) based decoding algorithms for LDPC codes. In order to improve the decoding operation and fast convergence decoder At First, Using Averaging and using bits in the wrong equations for improve the bit node update And then by using a genetic algorithm. We corrected value bits decoding mistake is detected and value soft decision is less than 2. The decoding performance of GA-SP algorithm is superior to original SP algorithms by about 2 dB and is very close to the performance of ML decoding for (2640, 1320) Tanner LDPC code.
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.
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.
In this paper, an improved low-complexity sum-product decoding algorithm is presented for low-density parity-check (LDPC) codes. In the proposed algorithm, reduction in computational complexity is achieved by utilizing fast Fourier transform (FFT) with time shift in the check node process. The improvement in the decoding performance is achieved by utilizing an optimized integer constant in the variable node process. Simulation results show that the proposed algorithm achieves an overall coding gain improvement ranging from 0.04 to 0.46 dB. Moreover, when compared with the sum-product algorithm (SPA), the proposed decoding algorithm can achieve a reduction of 42%–67% of the total number of arithmetic operations required for the decoding process.