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

SC of polar codes based normalized MS algorithm

Alaa A. Hasan, Ian Marsland

Published Oct 1, 2017• 2 citations
Publisher

Abstract

In this paper, the principle of normalized minimum-sum (NMS) polar decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, performances approach to that of the sum-product (SP) algorithm can be achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results for successive cancellation (SC) polar codes with code length moderately long validate the proposed approximation.

DOI
10.1109/iemcon.2017.8117164

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In this paper, the principle of normalized minimum-sum (NMS) polar decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, performances approach to that of the sum-product (SP) algorithm can be achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results for successive cancellation (SC) polar codes with code length moderately long validate the proposed approximation.
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The Successive Cancellation decoder cannot provide satisfactory error correcting performance for finite length polar codes. The error performance can be improved by using Successive Cancellation Flip decoders at the expense of higher complexity. In line with this, a novel decoding approach has been developed in this paper, where distributed parity bits are used to divide the codeword into multiple segments having equal probability of error occurrence, allowing early termination. The proposed Segmented Parity Successive Cancellation Flip (SP-SC Flip) Decoder achieves lower normalized complexity and average decoding latency compared to some related existing decoders, without degrading the error correcting performance. Further improvement in decoder performance is observed when code words are divided into a greater number of segments.
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The NMS algorithm is most commonly used in the design of LDPC code decoders. The result of the smallest-sub-smallest operation in the minimum sum algorithm is actually only two values. According to this feature, this paper proposes an algorithm that uses two correction factors to normalize and correct the minimum value and the second minimum value. The normalized minimum sum algorithm have two correction factors λ1 and λ2, where λ1 normalizes the second smallest value, and λ2 normalizes the minimum value. Both the DN-MS algorithm and the NMS algorithm are approximate forms of the logarithmic domain BP algorithm, and have exactly the same amount of calculation. But the calculation result of the check node in the DN-MS algorithm is closer to the check node in the log domain BP algorithm, so it has better decoding performance.