To improve the decoding performance of Low Density Parity Check (LDPC) code, an efficient decoding algorithm based on min-sum algorithm is proposed.
针对低密度奇偶校验(LDPC)译码算法性能低的问题，提出一种基于最小和的高效译码算法。
The predefined summarization methods, such as min , sum , and Max , are built using the more general-purpose reduce method.
预定义的汇总方法，例如min 、sum和max，是用更通用的reduce构建的。
Merge operators are SUM, MINUS, PRODUCT, DIVIDE, MAX, MIN, and NOP (returns data from the first cube in the virtual cube definition).
合并运算符包括SUM、MINUS、PRODUCT、DIVIDE、MAX、MIN和NOP(从虚拟数据集定义的第一个数据集返回数据)。
The model fully illustrated the stochastic characteristic of WMN with the property that the input-output relation and sum functions of this model were bounded by a min-plus convolution function.
该流量模型的特征是其求和函数及其输入输出函数的边界函数均受限于一个最小加卷积函数。
The model fully illustrated the stochastic characteristic of WMN with the property that the input-output relation and sum functions of this model were bounded by a min-plus convolution function.
该流量模型的特征是其求和函数及其输入输出函数的边界函数均受限于一个最小加卷积函数。
应用推荐