In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-of...In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.展开更多
针对近红外光谱分析中存在的高维数据降维、多重共线性及模型稀疏性问题,提出一种基于正则偏最小二乘RPLS(Regularization Partial Least Squares)的近红外光谱波长变量选择方法。该方法在偏最小二乘回归模型中同时引入L1和L2范数罚正则...针对近红外光谱分析中存在的高维数据降维、多重共线性及模型稀疏性问题,提出一种基于正则偏最小二乘RPLS(Regularization Partial Least Squares)的近红外光谱波长变量选择方法。该方法在偏最小二乘回归模型中同时引入L1和L2范数罚正则项,使模型产生稀疏性,通过交替迭代算法求解主成分载荷系数的稀疏解,实现光谱数据降维和重要波长变量的自动选择。对当归近红外光谱进行正则偏最小二乘波长选择实验。结果表明,与CARS(Competitive Adaptive Reweighted Sampling)随机蛙跳等变量选择方法相比,正则偏最小二乘方法在选择变量数及模型的预测能力方面均具有一定的优势。展开更多
文摘In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.
