LDPC码自适应量化最小和算法  

Adaptive Quantization Minimum-sum Algorithm for LDPC Codes

作　　者：周华[1] 马凌峻 李文杰 ZHOU Hua;MA Lingjun;LI Wenjie(School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044

出　　处：《计算机与数字工程》2025年第1期11-14,20,共5页Computer & Digital Engineering

基　　金：国家自然科学基金项目(编号:61771248)资助。

摘　　要：在LDPC码的译码方式中,和积译码算法由于复杂度过高难以实际应用。最小和译码作为和积译码的简化,是目前LDPC译码器设计的主要算法。量化过程将浮点数据转化为定点数据,是译码器设计的重要步骤。但是由于量化比特的限制,使用传统的量化方法会导致错误平层的出现。为此,提出了一种自适应量化最小和算法,在每次迭代译码后利用奇偶校验的结果自适应增加量化步长,使量化器能够更好地适应迭代译码的特性。仿真结果表明,该算法可以有效地抑制错误平层现象,相比于传统的量化算法译码性能在中高信噪比时提高了约0.2 dB,节省了1 bit的存储空间。In the decoding method of LDPC codes,the sum-product decoding algorithm is difficult to be practically applied due to its high complexity.As a simplification of sum-product decoding,min-sum decoding is the main algorithm in the design of LDPC decoders at present.The quantization process converts floating-point data to fixed-point data which is an important step in de⁃coder design.However,the use of traditional quantization methods will lead to the appearance of erroneous levels due to the limita⁃tion of quantization bits.To this end,an adaptive quantization minimum-sum algorithm is proposed.After each iterative decoding,the result of parity check is used to adaptively increase the quantization step size so that the quantizer can better adapt to the charac⁃teristics of iterative decoding.The simulation results show that the algorithm can effectively suppress the error leveling phenomenon.The decoding performance is improved by about 0.2 dB at medium and high SNR compared with the traditional quantization algo⁃rithm and the storage space of 1 bit is saved.

关 键 词：LDPC码 最小和译码 自适应量化 

分 类 号：TN919.3[电子电信—通信与信息系统]