Design of Polynomial Function based Blind Equalizer using Particle Swarm Optimization

In this paper, we propose a new particle swarm optimization (PSO) based training algorithm for the blind adaptive equalizer which uses polynomial cost function. The proposed blind equalizer with polynomial cost function trained with PSO (BEPC-PSO) is different from the conventional algorithm of constant modulus which is based on the modulus of symbols and is unable to recover the carrier phase. In contrast, BEPC-PSO can achieve automatic carrier phase recovery along with significantly improved performance. Simulations are performed to evaluate the performance of the BEPC-PSO on the finite impulse response (FIR) voice band channel. Compared with the other blind equalizers the BEPC-PSO produces a better convergence rate and a much lower inter-symbol interference (ISI) is achieved, resulting in a significant increase in the efficiency of equalization.