In this paper, two-dimensional (2-D) correction scheme is proposed to improve the performance of conventional Min-Sum (MS) decoding of regular low density parity check codes. The adopted algorithm to obtain the correc...In this paper, two-dimensional (2-D) correction scheme is proposed to improve the performance of conventional Min-Sum (MS) decoding of regular low density parity check codes. The adopted algorithm to obtain the correction factors is simply based on estimating the mean square difference (MSD) between the transmitted codeword and the posteriori information of both bit and check node that produced at the MS decoder. Semi-practical tests using software-defined radio (SDR) and specific code simulations show that the proposed quasi-optimal algorithm provides a comparable error performance as Sum-Product (SP) decoding while requiring less complexity.展开更多
The problem of improving the performance of min-sum decoding of low-density parity-check （LDPC） codes is considered in this paper. Based on min-sum algorithm, a novel modified min-sum decoding algorithm for LDPC cod...The problem of improving the performance of min-sum decoding of low-density parity-check （LDPC） codes is considered in this paper. Based on min-sum algorithm, a novel modified min-sum decoding algorithm for LDPC codes is proposed. The proposed algorithm modifies the variable node message in the iteration process by averaging the new message and previous message if their signs are different. Compared with the standard min-sum algorithm, the modification is achieved with only a small increase in complexity, but significantly improves decoding performance for both regular and irregular LDPC codes. Simulation results show that the performance of our modified decoding algorithm is very close to that of the standard sum-product algorithm for moderate length LDPC codes.展开更多
基于奇异值分解(Singular value decomposition,SVD)重构能够有效分离和抑制监测信号中的随机噪声分量,但其性能受限于轨迹矩阵的构造、有效分量评估选择等因素的影响。针对该问题,提出了一种基于最小互信息(Min mutual information,MMI...基于奇异值分解(Singular value decomposition,SVD)重构能够有效分离和抑制监测信号中的随机噪声分量,但其性能受限于轨迹矩阵的构造、有效分量评估选择等因素的影响。针对该问题,提出了一种基于最小互信息(Min mutual information,MMI)自适应累加奇异值子对(Sum singular value pairs,SSVP)优化框架并应用于机床轴承故障信号的特征提取。首先,采用反对角平均法计算奇异值(Singular value,SV)和奇异值向量,利用SV对子信号能量的表征能力得到奇异值子对(Singular value pairs,SVP);然后,基于MMI指标自适应获取最佳重构分量,避免了过降噪或欠降噪;同时,利用MMI和奇异值比(Singular value ratio,SVR)指标联合确定Hankel矩阵的最优分解维数。最后利用主轴故障轴承数据以及工业现场某加工中心进给系统轴承故障数据验证了MMI-SSVP方法的有效性。展开更多
针对LDPC(Low Density Parity Check)码分层(LBP:Layered Belief-Propagation)译码算法计算复杂度高、不易于硬件实现的问题,提出一种改进算法。该算法首先引入函数f(x)使LBP译码算法的计算复杂度大大降低;同时引入具体参数校正因子和...针对LDPC(Low Density Parity Check)码分层(LBP:Layered Belief-Propagation)译码算法计算复杂度高、不易于硬件实现的问题,提出一种改进算法。该算法首先引入函数f(x)使LBP译码算法的计算复杂度大大降低;同时引入具体参数校正因子和偏移因子,提升译码性能。仿真结果表明,改进后的算法相比LBP算法在计算复杂度降低的同时,也提升了译码性能,从而达到了易于硬件实现的目的。展开更多
文摘In this paper, two-dimensional (2-D) correction scheme is proposed to improve the performance of conventional Min-Sum (MS) decoding of regular low density parity check codes. The adopted algorithm to obtain the correction factors is simply based on estimating the mean square difference (MSD) between the transmitted codeword and the posteriori information of both bit and check node that produced at the MS decoder. Semi-practical tests using software-defined radio (SDR) and specific code simulations show that the proposed quasi-optimal algorithm provides a comparable error performance as Sum-Product (SP) decoding while requiring less complexity.
基金supported by the Major State Basic Research Development Program of China (2009CB320300)
文摘The problem of improving the performance of min-sum decoding of low-density parity-check （LDPC） codes is considered in this paper. Based on min-sum algorithm, a novel modified min-sum decoding algorithm for LDPC codes is proposed. The proposed algorithm modifies the variable node message in the iteration process by averaging the new message and previous message if their signs are different. Compared with the standard min-sum algorithm, the modification is achieved with only a small increase in complexity, but significantly improves decoding performance for both regular and irregular LDPC codes. Simulation results show that the performance of our modified decoding algorithm is very close to that of the standard sum-product algorithm for moderate length LDPC codes.
文摘基于奇异值分解(Singular value decomposition,SVD)重构能够有效分离和抑制监测信号中的随机噪声分量,但其性能受限于轨迹矩阵的构造、有效分量评估选择等因素的影响。针对该问题,提出了一种基于最小互信息(Min mutual information,MMI)自适应累加奇异值子对(Sum singular value pairs,SSVP)优化框架并应用于机床轴承故障信号的特征提取。首先,采用反对角平均法计算奇异值(Singular value,SV)和奇异值向量,利用SV对子信号能量的表征能力得到奇异值子对(Singular value pairs,SVP);然后,基于MMI指标自适应获取最佳重构分量,避免了过降噪或欠降噪;同时,利用MMI和奇异值比(Singular value ratio,SVR)指标联合确定Hankel矩阵的最优分解维数。最后利用主轴故障轴承数据以及工业现场某加工中心进给系统轴承故障数据验证了MMI-SSVP方法的有效性。
文摘针对LDPC(Low Density Parity Check)码分层(LBP:Layered Belief-Propagation)译码算法计算复杂度高、不易于硬件实现的问题,提出一种改进算法。该算法首先引入函数f(x)使LBP译码算法的计算复杂度大大降低;同时引入具体参数校正因子和偏移因子,提升译码性能。仿真结果表明,改进后的算法相比LBP算法在计算复杂度降低的同时,也提升了译码性能,从而达到了易于硬件实现的目的。
