Genetic algorithm optimisation for maximum likelihood joint channel and data estimation

A novel blind equalisation scheme is developed based on maximum likelihood (ML) joint channel and data estimation. In this scheme, the joint ML optimisation is decomposed into a two-level optimisation loop. An e cient version of genetic algorithms (GAs), known as a micro GA, is employed at the upper level to identify the unknown channel model and the Viterbi algorithm (VA) is used at the lower level to provide the maximum likelihood sequence estimation of the transmitted data sequence. The proposed GA based scheme is accurate and robust, and has a fast convergence rate, as is demonstrated in simulation.