J4 ›› 2014, Vol. 41 ›› Issue (5): 13-17.doi: 10.3969/j.issn.1001-2400.2014.05.003

• 研究论文 • 上一篇    下一篇

译码转发中继信道下双层延长LDPC码设计

刘洋1,2;李京娥1,2;李颖1,2   

  1. (1. 西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西 西安  710071;
    2. 电信科学研究院 无线移动通信国家重点实验室,北京  100083)
  • 收稿日期:2013-05-29 出版日期:2014-10-20 发布日期:2014-11-27
  • 通讯作者: 刘洋
  • 作者简介:刘洋(1988- ),女,西安电子科技大学博士研究生,E-mail: xdyanger@126.com.
  • 基金资助:

    973计划资助项目(2012CB316100);国家自然科学基金资助项目(61072064, 61201140, 61301177)

Design of bilayer lengthened LDPC codes for decode-and-forward in relay channels

LIU Yang1,2;LI Jing'e1,2;LI Ying1,2   

  1. (1. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an  710071, China;
    2. State Key Lab. of Wireless Mobile Communications, China Academy of Telecommunication Technology, Beijing  100191, China)
  • Received:2013-05-29 Online:2014-10-20 Published:2014-11-27
  • Contact: LIU Yang

摘要:

译码转发中继系统中双层延长低密度奇偶校验(LDPC)码设计,一般采用双层密度进化算法,固定下层变量节点度分布来搜索上层变量节点度分布,复杂度很高.针对这一问题,提出了高斯近似算法对上下层变量节点度分布进行整体优化,以达到信源到目的和信源到中继两条链路上的传输速率同时最大的目的,复杂度较低.仿真结果表明,文中设计的双层延长LDPC码的译码阈值与理论限的间隔较小,误码性能与利用双层密度进化算法搜索到的最优码集接近.

关键词: 中继信道, 低密度奇偶校验码, 高斯近似, 整体优化

Abstract:

The bilayer density evolution algorithm is always used to design bilayer lengthened LDPC codes for the decode-and-forward relay system. The general approach is to fix the lower variable degree distribution and then find an upper variable degree distribution, which has higher complexity. To solve this problem, the Gaussian approximation algorithm is proposed to implement the overall optimization for the lower and upper variable degree distributions. The proposed algorithm aims at maximizing the rates of the source-to-relay and the source-to-destination link simultaneously, which has lower complexity. Simulation results show that the gap between the convergence threshold and the theoretical limit of the proposed LDPC codes is smaller and that BER performance is almost the same as that of the ensembles obtained by bilayer density evolution.

Key words: relay channel, LDPC codes, Gaussian approximation, global optimization

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