Genetic algorithm optimization for blind channel identification with higher order cumulant fitting

An important family of blind equalization algorithms identify a communication channel model based on fitting higher order cumulants, which poses a nonlinear optimization problem. Since higher order cumulant-based criteria are multimodal, conventional gradient search techniques require a good initial estimate to avoid converging to local minima. We present a novel scheme which uses genetic algorithms to optimize the cumulant fitting cost function. A microgenetic algorithm implementation is adopted to further enhance computational efficiency. As is demonstrated in computer simulation, this scheme is robust and accurate and has a fast convergence performance.