LAUSR

In this contribution, a novel uni-cycle Genetic Algorithm (GA) is proposed as a learning tool to optimize the coefficients of an adaptive linear equalizer. The meaning of evolution concept is understood quite different in comparison with the regular GA, originally not designed to solve dynamic optimization problems: just like in nature, where the environmental conditions tend to evolve, the channel state is a subject to continuous change so does the optimal set of equalizer coefficients, represented by the individuals’ chromosomes in the proposed uni-cycle GA. The algorithm is designed with the aim to work in the real time. To meet such requirement, it considers only one generation per one signaling interval. As the results of the simulation study show, the proposed solution achieves superior performance over the Least Mean Square (LMS) algorithm. Furthermore, the uni-cycle GA possesses good convergence properties and a fast-tracking capability.