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



  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:


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



关键词: 粒子滤波, 信道盲辨识, 盲均衡, 联合盲均衡译码


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