Blind recognition of low-density paritycheck(LDPC)codes has gradually attracted more attention with the development of military and civil communications.However,in the case of the paritycheck matrices with relatively ...Blind recognition of low-density paritycheck(LDPC)codes has gradually attracted more attention with the development of military and civil communications.However,in the case of the paritycheck matrices with relatively high row weights,the existing blind recognition algorithms based on a candidate set generally perform worse.In this paper,we propose a blind recognition method for LDPC codes,called as tangent function assisted least square(TLS)method,which improves recognition performances by constructing a new cost function.To characterize the constraint degree among received vectors and paritycheck vectors,a feature function based on tangent function is constructed in the proposed algorithm.A cost function based on least square method is also established according to the feature function values satisfying the parity-check relationship.Moreover,the minimum average value in TLS is obtained on the candidate set.Numerical analysis and simulation results show that recognition performances of TLS algorithm are consistent with theoretical results.Compared with existing algorithms,the proposed method possesses better recognition performances.展开更多
LDPC(Low Density Parity Check)码是无线通信系统中高效的信道编码技术,并已经在第五代移动通信等系统中应用,码率兼容则是在应用中遇到的重要问题之一。码率兼容的关键在于在不增加译码复杂度的情况下,实现低码率到高码率的任意变化...LDPC(Low Density Parity Check)码是无线通信系统中高效的信道编码技术,并已经在第五代移动通信等系统中应用,码率兼容则是在应用中遇到的重要问题之一。码率兼容的关键在于在不增加译码复杂度的情况下,实现低码率到高码率的任意变化。论文提出了一种码率兼容多元LDPC码的比特级打孔算法。该算法首先将多元符号矩阵转换为二元比特矩阵,其次利用最小环路检测算法检测每个比特变量节点所在环路大小,并选择大环路比特变量节点进行打孔。仿真结果表明,针对码长256、码率0.5的非规则四元LDPC码及码长155、码率0.4得规则LDPC码,论文所提算法均大约有0.1dB~0.25 dB的增益。展开更多
现有的低密度奇偶校验码(Low Density Parity Check,LDPC)闭集识别方法中,通过直接采用后验概率构建模型计算结果累加的方式会导致在较小码字数量时识别性能较差,通过中心极限定理分布拟合的参数分析方法在部分条件下偏离实际分布,从而...现有的低密度奇偶校验码(Low Density Parity Check,LDPC)闭集识别方法中,通过直接采用后验概率构建模型计算结果累加的方式会导致在较小码字数量时识别性能较差,通过中心极限定理分布拟合的参数分析方法在部分条件下偏离实际分布,从而无法获得更高的识别性能。基于这些问题,文中提出了一种基于分布参数的LDPC闭集识别方法,该方法通过对软判决序列在LD模型中理论分布进行期望分析,得出了对不同行重校验向量的理论分布参数,通过对参数的数值分析,给出了实际分布参数与理论分布参数差异性的量化模型,并基于该模型识别出对应校验矩阵。实验结果表明,当码字数量较少时,该方法在多行重校验矩阵中的识别性能优于现有的后验概率识别算法。展开更多
基金Fundamental Research Funds for the Central Universities under Grant 3072025YC0802the National Natural Science Foundation of China under Grant 62001138Heilongjiang Provincial Natural Science Foundation of China under Grant LH2021F009。
文摘Blind recognition of low-density paritycheck(LDPC)codes has gradually attracted more attention with the development of military and civil communications.However,in the case of the paritycheck matrices with relatively high row weights,the existing blind recognition algorithms based on a candidate set generally perform worse.In this paper,we propose a blind recognition method for LDPC codes,called as tangent function assisted least square(TLS)method,which improves recognition performances by constructing a new cost function.To characterize the constraint degree among received vectors and paritycheck vectors,a feature function based on tangent function is constructed in the proposed algorithm.A cost function based on least square method is also established according to the feature function values satisfying the parity-check relationship.Moreover,the minimum average value in TLS is obtained on the candidate set.Numerical analysis and simulation results show that recognition performances of TLS algorithm are consistent with theoretical results.Compared with existing algorithms,the proposed method possesses better recognition performances.
文摘LDPC(Low Density Parity Check)码是无线通信系统中高效的信道编码技术,并已经在第五代移动通信等系统中应用,码率兼容则是在应用中遇到的重要问题之一。码率兼容的关键在于在不增加译码复杂度的情况下,实现低码率到高码率的任意变化。论文提出了一种码率兼容多元LDPC码的比特级打孔算法。该算法首先将多元符号矩阵转换为二元比特矩阵,其次利用最小环路检测算法检测每个比特变量节点所在环路大小,并选择大环路比特变量节点进行打孔。仿真结果表明,针对码长256、码率0.5的非规则四元LDPC码及码长155、码率0.4得规则LDPC码,论文所提算法均大约有0.1dB~0.25 dB的增益。
文摘现有的低密度奇偶校验码(Low Density Parity Check,LDPC)闭集识别方法中,通过直接采用后验概率构建模型计算结果累加的方式会导致在较小码字数量时识别性能较差,通过中心极限定理分布拟合的参数分析方法在部分条件下偏离实际分布,从而无法获得更高的识别性能。基于这些问题,文中提出了一种基于分布参数的LDPC闭集识别方法,该方法通过对软判决序列在LD模型中理论分布进行期望分析,得出了对不同行重校验向量的理论分布参数,通过对参数的数值分析,给出了实际分布参数与理论分布参数差异性的量化模型,并基于该模型识别出对应校验矩阵。实验结果表明,当码字数量较少时,该方法在多行重校验矩阵中的识别性能优于现有的后验概率识别算法。
