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IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

Performance of NMS decoding of SC polar codes based density evolution

Alaa A. Hasan, Ian Marsland

Published Oct 1, 2017• 1 citation
Publisher

Abstract

In this paper, the principle of density evolution (DE) combined with the normalized minimum-sum (NMS) decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, then almost the same behaviour of sum-product (SP) algorithm is achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results show the validation of the suggested algorithm as expected.

DOI
10.1109/iemcon.2017.8117161

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In this paper, the principle of density evolution (DE) combined with the normalized minimum-sum (NMS) decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, then almost the same behaviour of sum-product (SP) algorithm is achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results show the validation of the suggested algorithm as expected.
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In this paper, we present an improvement on the normalized min-sum (NMS) decoding algorithm for low density parity check codes. The modified NMS (M-NMS) algorithm introduces a more efficient adjustment for check node update computation. Taking the sub-minimum value among extrinsic information from the variable nodes into consideration, we make the correction factor varies with different iterations. Results show that, compared with standard min-sum (MS) and NMS decoding algorithms, the proposed method not only offers a better performance but also achieves a lower error floor in the high SNR region.
Normalized Min-Sum Algorithms (NMSA) are extensively employed in trading LDPC (Low Density Parity Check) decoders due to their low complexity and reasonable performance close to the performance of sum product algorithm in terms of Error Rate. Our work is focused on the enhancement of error rate performances of NMS algorithm for the decoding of LDPC decoding. In this context we have proposed a Layered Normalized Min-Sum algorithm (LNMS), which employs an adaptive normalization factor to ameliorate the reliability of the information transmitted during the decoding process. To perform this analysis study LDPC coded communication system has been examined over Weibull fading channels and was analyzed for different decoding techniques by the help of comparative computer simulations. Simulation results indicate that the proposed decoding algorithm can achieve better Bit Error Rate (BER) performances closer to SP algorithm with the preservation of the principal characteristics of the MS algorithm.
Low-density parity-check code (LDPC) is a kind of better error correction code which is played an important role in the reliability transmission of communication systems. In the aspect of decoding algorithm, the NMS algorithm becomes the mainstream algorithm dues to its lower complexity. what’s more, the Semi-serial decoding algorithm was put forward to reduce decoding iterations. However, the compensation factor of NMS algorithm is a fixed value and the Semi-serial decoding algorithm only considers one aspect of reducing iterations. In the actual decoding process, this value varies with the degree of the check node and the number of iterations. in order to solve the problems of these algorithms, this paper will propose a new improved algorithm. Firstly, we will adopt a dynamic compensation factor to improve performance of NMS algorithm on regular LDPC code. Then combined with the idea of Semi-serial decoding algorithm to reduce the number of iterations. The dynamic factor is firstly analyzed theoretically by density evolution theory. Then through the Monte Carlo experiment and the true LLR, the suitable dynamic compensation factors can be obtained which is changes with the number of iterations.