Evaluation of convolutional codes by error types simulation
Error types simulation (ETS)-a new method for performance evaluation of convolutional codes, is presented. The key idea is to consider types of channel error sequences separately and estimate the contribution of each type to the information bit error rate (I-BER). By averaging these contributions, weighted by the corresponding probability of occurrence of the type, we obtain the I-BER curve. We show that this approach yields an accurate estimate of the entire I-BER curve, while maintaining a computational complexity that is only a very small fraction of the complexity of a Monte-Carlo simulation for a single channel condition. The ETS is shown to outperform competing methods, in terms of both accuracy of the estimate, and the total computational complexity.