粒子滤波盲均衡译码联合算法

doi:10.3969/j.issn.1001-2400.2013.01.022

Abstract

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

LI Hao;WANG Runliang;PENG Hua. Particle filter algorithm for joint blind equalization and decoding[J].J4, 2013, 40(1): 123-128+176.

References

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Particle filter algorithm for joint blind equalization and decoding

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