一种基于量化预处理的低复杂度LDPC译码算法  

A Low-complexity LDPC Decoding Algorithm Based on Pre-processing Quantization

在线阅读下载全文

作  者:孙友明[1,2] 李神峰 黄奕俊 黎相成 覃团发[1,2] SUN Youming;LI Shenfeng;HUANG Yijun;LI Xiangcheng;QIN Tuanfa(School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China;Guangxi Key Laboratory of Multimedia Communication and Network Technology,Guangxi University,Nanning 530004,China)

机构地区:[1]广西大学计算机与电子信息学院,南宁530004 [2]广西多媒体通信与网络技术重点实验室,南宁530004

出  处:《电讯技术》2023年第3期410-415,共6页Telecommunication Engineering

基  金:国家自然科学基金资助项目(61961004,62261003,61862006);广西自然科学基金资助项目(2020GXNSFAA159074)。

摘  要:为了降低低密度奇偶校验(Low Density Parity Check,LDPC)码译码算法的复杂度,提出了一种基于量化预处理的LDPC迭代大数逻辑译码算法。该算法在迭代译码过程中,校验节点采用基于伴随式的信息处理方式,避免了外信息的计算;同时,变量节点基于回传的伴随式信息进行可靠度偏移大小的计算,并结合与当前码位相对应的调制映射信息进行可靠度偏移方向的设计。迭代更新时,变量节点采用基于信息匹配的可靠度更新规则。迭代前的量化预处理能避免实数乘法运算进入迭代过程,使其只涉及整数加法操作和逻辑操作。仿真结果表明,在保持译码性能的前提上,所提算法具有更低的译码复杂度。To reduce the computational complexity,an iterative majority-logic low density parity check(LDPC)decoding algorithm is proposed based on pre-processing quantization.In the proposed algorithm,syndrome-based message processing is employed at check nodes,which can avoid the extrinsic information computation.At variable nodes,the shifting-step of the reliability-message is computed according to the received syndrome message.Meanwhile,the shifting-direction is designed by combining the modulation information with respect to the current bit.Furthermore,the message-matching strategy is adopted in the iterative decoding.With the pre-processing quantization,the proposed algorithm can avoid the complicated real multiplication and only involves the integer addition and logic operations,resulting in low complexity.Simulation results show that the proposed algorithm has low decoding complexity while maintaining excellent decoding performance.

关 键 词:LDPC码 伴随式信息 外信息 量化预处理 

分 类 号:TN911.21[电子电信—通信与信息系统]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象