Designing interleaver for high dimensional data transmission using hybrid GSO

Abstract

Interleaver plays key role in preserving the performance of turbo encoding systems. Under high dimensional data transmission, the interleaver design often goes complex. This paper presents a hybrid meta-heuristic search algorithm by combining renowned Genetic Algorithm (GA) and Group Search Optimizer (GSO) in the name of hybrid GSO (HGSO). The HGSO is emphasized to operate in high dimensional space so that the interleaver design is expected to be robust under high dimensional data transmission. The hybridization embodies the mutation operator of GA in the GSO scanning process. This improves the exploration process of GSO to enable faster convergence. Experiments are conducted at higher order data bits and the performance of HGSO is demonstrated. A statistical report is prepared from the observed results to illustrate the reliability of the outcome accomplished by HGSO over the other methods.