J4 ›› 2013, Vol. 40 ›› Issue (1): 123-128+176.doi: 10.3969/j.issn.1001-2400.2013.01.022

• 研究论文 • 上一篇    下一篇

粒子滤波盲均衡译码联合算法

李浩;王润亮;彭华   

  1. (信息工程大学 信息工程学院,河南 郑州  450002)
  • 收稿日期:2011-09-13 出版日期:2013-02-20 发布日期:2013-03-28
  • 通讯作者: 李浩
  • 作者简介:李浩(1986-),男,信息工程大学博士研究生,E-mail: leo.lihao@163.com.
  • 基金资助:

    国家自然科学基金资助项目(61072046)

Particle filter algorithm for joint blind equalization and decoding

LI Hao;WANG Runliang;PENG Hua   

  1. (Inst. of Info. Eng., Info. Eng. Univ., Zhengzhou  450002, China)
  • Received:2011-09-13 Online:2013-02-20 Published:2013-03-28
  • Contact: LI Hao

摘要:

粒子滤波在解决信道盲辨识和盲均衡问题上具有收敛快、抗深衰信道等优势.在粒子滤波盲均衡算法的基础上,依据卷积码的马氏性特点进行建模,提出了一种直接对信息序列做重要性采样的粒子滤波盲均衡和卷积码译码联合算法.同时,提出了噪声功率递推计算的方法,并将其应用于联合算法中噪声功率参数的自适应调整.仿真结果表明,相比分离算法,联合算法的收敛性能和误码率性能都有明显提高,而自适应调整功率参数的算法则降低了运算复杂度.

关键词: 粒子滤波, 信道盲辨识, 盲均衡, 联合盲均衡译码

Abstract:

Particle filtering (PF) is particularly useful in dealing with the blind channel identification and blind equalization for its fast convergence and its outstanding performance of resisting multiple-path fading channels. Considering the Markov chain property of convolutional codes, the signal model is modified and a particle filter algorithm for joint blind equalization and decoding of convolutional code is introduced which samples the information sequence directly instead of the coded sequence. An iterative method to approximate the noise power is proposed, which is applied to the joint algorithm to adjust the parameter of noise power adaptively. The proposed algorithm is simulated. The simulation result shows that the convergence of the joint algorithm is faster and the bit error rate (BER) is lower that of the separate algorithm. And the adaptive adjustment algorithm reduces the computational complexity.

Key words: particle filtering, blind channel identification, blind equalization, joint blind equalization and decoding