OPTIMIZATION OF HEURISTIC ALGORITHMS FOR IMPROVING BER OF ADAPTIVE TURBO CODES

The third component introduced in the turbo codes improved the code performance by providing very low error rates for a very wide range of block lengths and coding rates. But this increased the complexity and the parameters such as permeability and permittivity rates were constant and they could not perform well under noisy environments. This drawback was addressed in [1] by proposing A3DTC. The bit error rate was minimized by generating parameters based on noise and signal strengths. A performance comparison is done between the two heuristic algorithms i.e., Genetic Algorithm and Particle Swarm Optimization Algorithm [2] where a knowledge source using the two algorithms is generated. Under various noisy environments the experimental results compare the performance of the two algorithms. In this paper their performance is analyzed and optimization is done. The results show that genetic algorithm is able to give better performance when compared to particle swarm optimization algorithm.