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

In this paper, a new extrinsic information scaling and early stopping mechanism for long term evolution (LTE) turbo code is proposed. A scaling factor is obtained by computing the Pearson’s correlation coefficient between the extrinsic and a posteriori log-likelihood ratio (LLR) at every half-iteration. Additionally, two new stopping criteria are proposed. The first one uses the regression angle which is computed at each half-iteration and is applied at low E _b/N ₀. The second one uses Pearson’s correlation coefficient and is applicable for high E _b/N ₀ values. The performance of the proposed scheme was compared against an existing scaling and stopping mechanism based on the sign difference ratio (SDR) technique as well as conventional LTE turbo code. Simulations have been performed with both quadrature phase shift keying (QPSK) modulation and 16-quadrature amplitude modulation (QAM) together with code rates of 1/3 and 1/2. The results demonstrate that the proposed scheme outperforms both the conventional scheme and that employing the SDR-based scaling and stopping mechanism in terms of BER performance and average number of decoding iterations. The performance analysis using EXIT charts for each scheme shows higher initial output mutual information for input mutual information of zero. Better convergence is also demonstrated with the wider tunnel for the proposed scheme. Additionally, the computational complexity analysis demonstrates a significant gain in terms of the average number of computations per packet with the different modulation and coding schemes while still gaining in terms of error performance.