A Genetic Algorithm for Optimizing TCM Encoder

Abstract

This article describes a genetic algorithm for the optimization of the Trellis Coded Modulation (TCM) schemes with a view to achieve a higher performance in the multipath fading channel. The use of genetic algorithms is motivated by the fact that they are capable of performing global searches to retrieve an approximate solution to an optimization problem and if the solution is unknown to provide one within a reasonable time lapse. The TCM schemes are indeed optimized by the Rouane and Costello algorithm but the latter has as major disadvantage high requirements in both computation time and memory storage. This is further exacerbated by an increase in the encoder rate, the number of memory piles and the depth of the trellis. We describe a genetic algorithm which is especially well suited to combinatorial optimization, in particular to the optimization of NP-complete problems for which the computation time grows with the complexity of the problem, in a non-polynomial way. Furthermore this opens up the possibility of using the method for the generation of codes for channel characteristics for which no optimization codes are yet known. Simulation results are presented, that show the evolutionary programming algorithm on several generations of populations which only exhibit a medium probability of exchanging genetic information.