A novel PSO based blind channel estimation and equalization

As an alternative technique to well-known constant modulus algorithm (CMA), a Decision Feedback Equalizer via Channel Matched Filter (CMF-DFE) based blind channel estimation and equalization algorithm is proposed in this paper. The proposed technique employs Particle Swarm Optimization (PSO) in training, where the conventional CMA and least mean squares (LMS) based training algorithms are found slow and their convergence strictly depend on the step size parameter. On the other hand, if the PSO training algorithm is employed, it provides a faster convergence and less sensitivity to training parameters. Thus, a rapid converging high performance blind channel estimation and equalization method is obtained, as it is compared to CMF-DFE based blind LMS and CMA algorithm. Here, the price is paid for more complexity, however the obtained performance promises to compete with reference training sequence based parameter estimation techniques.