A novel scaling and early stopping mechanism for LTE turbo code based onn regression analysis

摘要

In this paper, a new extrinsic information scaling and early stoppingn mechanism for long term evolution (LTE) turbo code is proposed. An scaling factor is obtained by computing the Pearson's correlationn coefficient between the extrinsic and a posteriori log-likelihood ration (LLR) at every half-iteration. Additionally, two new stopping criterian are proposed. The first one uses the regression angle which is computedn at each half-iteration and is applied at low E (b)/N (0). The sec