Turbo Blind Equalization Based on Particle Filter

Particle filter (PF), which is based on the Bayesian theory, 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. Under the low SNR conditions the bit error rates (BER) of Turbo blind equalization are much lower. In order to get good BER performance in multiple-path fading channels, the particle filter algorithm for blind equalization is modified. The important sampling function of particle filtering exploits the prior information of the particles and the soft input soft output (SISO) particle filter equalization algorithm is proposed. Considering the structure of Turbo blind equalization, a new Turbo blind equalization based on particle filter is proposed, which makes use of the channel coding gain. Therefore, the performance of equalization and channel identification are improved. The simulation result shows that compared to the particle filter equalization algorithm, the bit error rates (BER) of the proposed algorithm have a gain of about 1dB, and the frame error rates (FER) have a gain of above 3dB. By analyzing, there are hardly error bits under the condition of accurate estimation of the channel coefficients.