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Systems engineering and electronics

Design of degree sequence for irregular LDPC codes on PSO

Linhua Ma

Published Jan 1, 2010• 0 citations
OpenAlex

Abstract

The algorithm of threshold calculation for low-density parity-check(LDPC) codes is investigated.The design steps of degree-sequence which has higher threshold and the process of constrained problem are discussed in detail.A method of degree-sequence design for LDPC on particle swarm optimization(PSO) is presented.The analysis and comparison of estimation result and some capacity-approaching degree-sequences are also given.

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The algorithm of threshold calculation for low-density parity-check(LDPC) codes is investigated.The design steps of degree-sequence which has higher threshold and the process of constrained problem are discussed in detail.A method of degree-sequence design for LDPC on particle swarm optimization(PSO) is presented.The analysis and comparison of estimation result and some capacity-approaching degree-sequences are also given.
This paper presents a new design methodology/process for low-density parity-check codes (LDPC). To minimize the gap to Shannon limit, the particle swarm optimizer is applied to optimize the variable and check node degree distribution lambda and p respectively in case of irregular LDPC codes. discrete fast density evolution (FDE) is used (as the analysis technique) to compute the threshold value of LDPC code and the Shannon limit is evaluated based on Butman and McEliece formula. The results conducted show that, our proposed distributions with low degrees of (lambda, p) outperform other comparable distributions.
The optimization method of degree distributions for irregular LDPC codes is key to get codes which exhibit a performance extremely close to Shannon capacity.The paper propose a practical method,which uses a simplification Gaussian Approximation (GA) algorithm to calculate the threshold quickly,then get the best degree distributions by adaptive particle swarm optimizer (APSO) with special restrictions.Simulation results shows that such a method has excellent optimization ability with low complexity,so it is of suggestive significance to the construction of excellent LDPC in both wireless and optical communication system.
For irregular LDPC codes,the density evolution method can track the messages to find out the noise threshold, enabling optimization of the degree distribution. A new optimized scheme for irregular LDPC codes is proposed in this paper, which is combining Zhengya He's modified particle swarm optimization (PSO) algorithm with discrete density evolution (DDE) for the offset BP-based decoding algorithm. Several optimized degree distribution pairs and corresponding threshold of LDPC codes over Rician channel were given using the proposed scheme. The performance of the optimized LDPC codes was also analyzed. Simulation results shows that the proposed scheme is effective.
Because the structure of check matrix of fast encodable irregular LDPC code is exceptional,normal design technique of optimization of degree distribution can not work well in this case.In this paper,a method of optimization of fast encodable irregular low-density parity-check codes' degree distribution is discussed based on differential evolution with particular restrictions.The principle of density evolution of irregular LDPC codes is explained.Gaussian approximation to program the evolution proceeding and differential evolution are analysed,and the particular restrictions on degree distribution are discussed.Simulation results show that some good distribution sequences are better than that reported before.
A low-complexity algorithm is proposed in this paper in order to optimize irregular low-density parity-check(LDPC)codes.The algorithm proposed can calculate the noise threshold by means of a one-dimensional density evolution and search the optimal degree profiles with fast-convergence differential evolution,so that it has a lower complexity and a faster convergence speed.Simulation results show that the irregular LDPC codes optimized by the presented algorithm can also perform better than Turbo codes at moderate block length even with less computation cost.