Efficient systematic turbo polar decoding based on optimized scaling factor and early termination mechanism

Abstract

In this paper, an efficient early termination (ET) mechanism for systematic turbo-polar code (STPC) based on optimal estimation of scaling factor (SF) is proposed. The gradient of the regression line which best fits the distance between a priori and extrinsic information is used to estimate the SF. The multiplication of the extrinsic information by the proposed SF presents effectiveness in resolving the correlation issue between intrinsic and extrinsic reliability information traded between the two typical parallel concatenated soft-cancellation (SCAN) decoders. It is shown that the SF has improved the conventional STPC by about 0.3 dB with an interleaver length of 64 bits, and about 1 dB over the systematic polar code (SPC) at a bit error rate (BER) of . A new scheme is proposed as a stopping criterion, which is mainly based on the estimated value of SF at the second component decoder and the decoded frozen bits for each decoding iteration. It is shown that the proposed ET results in halving the average number of iterations (ANI) without adding considerable complexity. Moreover, the modified codes present comparable results in terms of BER to the codes that utilize fix number of iterations.

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

“…The systematic turbo-polar code (STPC) with an early termination (ET) mechanism that Hamad et al [26] proposed is a notable development in the field of polar codes. This mechanism employs an ideal scaling factor (SF) estimation technique, which has been shown to enhance the BER performance of STPC.…”

Turbo encoder and decoder chip design and FPGA device analysis for communication system

“…To calculate α and β values the Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm is applied. The SISO decoders perform the turbo decoding process and are referred to as a log-likelihood ratio (LLR) [26]. For the encoded sequence, di = [d1, d2…dN], and generated codeword against each sequence di = [d1, d2…dN], the LLR is articulated as (1).…”

“…The MAP algorithm [26] comprises addition processes and multiplication processes. For a large classification, the logarithm and estimate are applied, and the (3) for Max log-MAP is specified as (4).…”