基于LDPC码的BP译码改进算法研究
发布时间：2019-01-03 19:20
【摘要】：随着时代的发展,通信在人们的日常生活中变得越来越重要。低密度奇偶校验码(LDPC Codes,Low Density Parity-Check Codes)作为一类逼近Shannon限的纠错码,以其优良的性能已经成为Turbo码的有力竞争者,成为信道编码领域中的一个研究热点。在LDPC码所有译码算法中,BP算法是最常用的。在理论和实际中,BP算法都有优异的性能。虽然BP算法有很多的优势,但是其还是有很高的复杂度,在实现中的译码效率仍需提高。BP算法的复杂度主要体现在:在每次迭代过程中,都需要计算全部的比特和校验信息,所以,在每次迭代过程中,BP算法需要的计算量是相同的。但是随着迭代次数的增加,每次迭代过程中纠正的比特数却越来越少;另外,BP算法只有在译码成功或者迭代次数达到规定的最大迭代次数时才停止译码,但是在进行一定次数的迭代以后,一些没有正确译出的比特即使进行更多的迭代也不能正确译出。由上面提到的这些可以知道,BP算法虽然优良,但是还有一定的改进空间。在众多BP算法的改进算法中,FC算法通过在后续迭代中停止更新可靠性高的节点,从而降低算法的复杂度;NSPC算法通过利用满足奇偶校验要求的比特数spcN来提前预判一个码字是否译码失败,从而来减少迭代次数,以此来减少算法的计算量。但是由于FC算法的性能表现不好,本文首先在FC算法的基础上,对其进行了改进。然后本文将改进后的FC算法和NSPC算法相结合,提出了FCES算法。FCES算法既可以像FC算法一样减少每次迭代中需要更新的节点数量,又可以像NSPC算法一样减少算法的平均迭代次数,另外,FCES算法的性能优于FC算法和NSPC算法。经过仿真和分析,我们得出:与BP算法相比,FCES算法在性能损失不大的基础上,大大降低了译码的复杂度,提高了译码效率和收敛速度。
[Abstract]:With the development of the times, communication becomes more and more important in people's daily life. Low density parity check (LDPC Codes,Low Density Parity-Check Codes) codes, as a class of error-correcting codes that approach the Shannon limit, have become a powerful competitor of Turbo codes due to their excellent performance, and have become a research hotspot in the field of channel coding. Of all the decoding algorithms for LDPC codes, the BP algorithm is the most commonly used. In theory and practice, BP algorithm has excellent performance. Although the BP algorithm has many advantages, it still has a high complexity. The decoding efficiency in the implementation still needs to be improved. The complexity of the BP algorithm is mainly reflected in: in each iteration, all bits and check information need to be calculated. Therefore, in each iteration, the BP algorithm needs the same amount of computation. However, with the increase of the number of iterations, the number of bits corrected in each iteration process is less and less. In addition, the BP algorithm only stops decoding when the decoding is successful or the number of iterations reaches the specified maximum number of iterations, but after a certain number of iterations, Some bits that are not correctly translated will not be translated correctly even if more iterations are carried out. From the above mentioned, we can know that the BP algorithm is good, but there is still some room for improvement. Among the improved BP algorithms, the FC algorithm reduces the complexity of the algorithm by stopping updating the nodes with high reliability in subsequent iterations. The NSPC algorithm can reduce the number of iterations by using the bit number spcN which meets the parity check requirement to predict whether a codeword decoding fails in advance and thus to reduce the computational complexity of the algorithm. But because the performance of FC algorithm is not good, this paper improves the algorithm based on FC algorithm. Then, by combining the improved FC algorithm with the NSPC algorithm, a FCES algorithm is proposed. The FCES algorithm can not only reduce the number of nodes that need to be updated in each iteration as FC algorithm, but also reduce the average number of iterations of the algorithm like NSPC algorithm. In addition, the performance of FCES algorithm is better than that of FC algorithm and NSPC algorithm. Through simulation and analysis, we conclude that compared with BP algorithm, FCES algorithm greatly reduces the complexity of decoding and improves the efficiency and convergence speed of decoding on the basis of less performance loss.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.22
[Abstract]:With the development of the times, communication becomes more and more important in people's daily life. Low density parity check (LDPC Codes,Low Density Parity-Check Codes) codes, as a class of error-correcting codes that approach the Shannon limit, have become a powerful competitor of Turbo codes due to their excellent performance, and have become a research hotspot in the field of channel coding. Of all the decoding algorithms for LDPC codes, the BP algorithm is the most commonly used. In theory and practice, BP algorithm has excellent performance. Although the BP algorithm has many advantages, it still has a high complexity. The decoding efficiency in the implementation still needs to be improved. The complexity of the BP algorithm is mainly reflected in: in each iteration, all bits and check information need to be calculated. Therefore, in each iteration, the BP algorithm needs the same amount of computation. However, with the increase of the number of iterations, the number of bits corrected in each iteration process is less and less. In addition, the BP algorithm only stops decoding when the decoding is successful or the number of iterations reaches the specified maximum number of iterations, but after a certain number of iterations, Some bits that are not correctly translated will not be translated correctly even if more iterations are carried out. From the above mentioned, we can know that the BP algorithm is good, but there is still some room for improvement. Among the improved BP algorithms, the FC algorithm reduces the complexity of the algorithm by stopping updating the nodes with high reliability in subsequent iterations. The NSPC algorithm can reduce the number of iterations by using the bit number spcN which meets the parity check requirement to predict whether a codeword decoding fails in advance and thus to reduce the computational complexity of the algorithm. But because the performance of FC algorithm is not good, this paper improves the algorithm based on FC algorithm. Then, by combining the improved FC algorithm with the NSPC algorithm, a FCES algorithm is proposed. The FCES algorithm can not only reduce the number of nodes that need to be updated in each iteration as FC algorithm, but also reduce the average number of iterations of the algorithm like NSPC algorithm. In addition, the performance of FCES algorithm is better than that of FC algorithm and NSPC algorithm. Through simulation and analysis, we conclude that compared with BP algorithm, FCES algorithm greatly reduces the complexity of decoding and improves the efficiency and convergence speed of decoding on the basis of less performance loss.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.22
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