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IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences

A Stopping Criterion for Symbol Flipping Decoding of Non-Binary LDPC Codes

Zhanzhan Zhao, Xiaopeng Jiao, Jianjun Mu, Qingqing Li

Published May 9, 2021• 0 citations
Publisher

Abstract

A properly designed stopping criterion for iterative decoding algorithms can save a number of iterations and lead to a considerable reduction of system latency. The symbol flipping decoding algorithms based on prediction (SFDP) have been proposed recently for efficient decoding of non-binary low-density parity-check (LDPC) codes. To detect the decoding frames with slow convergence or even non-convergence, we track the number of oscillations on the value of objective function during the iterations. Based on this tracking number, we design a simple stopping criterion for the SFDP algorithms. Simulation results show that the proposed stopping criterion can significantly reduce the number of iterations at low signal-to-noise ratio regions with slight error performance degradation.

DOI
10.1587/transfun.2021eal2009

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A properly designed stopping criterion for iterative decoding algorithms can save a number of iterations and lead to a considerable reduction of system latency. The symbol flipping decoding algorithms based on prediction (SFDP) have been proposed recently for efficient decoding of non-binary low-density parity-check (LDPC) codes. To detect the decoding frames with slow convergence or even non-convergence, we track the number of oscillations on the value of objective function during the iterations. Based on this tracking number, we design a simple stopping criterion for the SFDP algorithms. Simulation results show that the proposed stopping criterion can significantly reduce the number of iterations at low signal-to-noise ratio regions with slight error performance degradation.
The symbol flipping decoding algorithms based on prediction (SFDP) for non-binary LDPC codes perform well in terms of error performances but converge slowly when compared to other symbol flipping decoding algorithms. In order to improve the convergence rate, we design new flipping rules with two phases for the SFDP algorithms. In the first phase, two or more symbols are flipped at each iteration to allow a quick increase of the objective function. While in the second phase, only one symbol is flipped to avoid the oscillation of the decoder when the objective function is close to its maximum. Simulation results show that the SFDP algorithms with the proposed flipping rules can reduce the average number of iterations significantly, whereas having similar performances when compared to the original SFDP algorithms.
To speed up the convergence rate of non-binary low-density parity check codes and reduce decoding computational complexity of decoding algorithm, a weighted symbol flipping decoding algorithm based on iterative stopping criteria is proposed. The algorithm determines the position and amplitude of the flipped symbol according to the weight of the information node and the flipping function in the check equation, and selects the optimal value of the weighting factor through simulation. At the same time, the iteration stop criterion is introduced, and the iteration is stopped by setting the number of parity equations without satisfying the different signal-to-noise ratio. The simulation results show that the channel additive white Gauss noise, the algorithm ensures the invariant properties of weighted symbol flipping decoding algorithm under the condition that can accelerate the convergence speed, reduce the average number of iterations, so the decoding performance and complexity trad-off between effective.
This paper investigates the application of gradient descent with momentum in symbol flipping decoding algorithms based on prediction (SFDP) for non-binary low-density parity-check (NB-LDPC) codes. The momentum added in the objective function of SFDP algorithms can provide inertia to the decoding process by considering the flipping states in the past iterations. Simulation results show that the proposed momentum-based SFDP algorithms perform significantly better than the original SFDP algorithms with low extra complexity. Furthermore, to lower the error floor of momentum-based SFDP algorithms, we also introduce artificial noises into the objective functions of momentum-based SFDP algorithms to help the iterative decoding escape from local optimum.
Symbol flipping decoding (SFD) of non-binary low-density parity-check (NB-LDPC) codes has lower implementation complexity at a cost of performance degradation, when compared with other belief propagation decoding algorithms. This paper proposes modified SFD algorithms based on prediction (SFDP) for NB-LDPC codes by introducing random penalty items into the flipping metrics. Two types of noise with uniform and Gaussian distributions are considered. A probabilistic analysis shows theoretically that the randomly penalized SFDP algorithms can be advantageous in the correct flipping probability over their original versions. Simulation results show that the proposed penalizing methods exhibit significant performance gains for both regular and irregular NB-LDPC codes with different rates and alphabet sizes in both the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC).
A two-stage hybrid iterative decoding algorithm with an efficient stopping criterion for nonbinary low-density parity-check (LDPC) codes is proposed, which combines weighted symbol-flipping (WSF) algorithm and fast Fourier transform q-ary sum-product algorithm (FFT-QSPA). The first WSF decoding would be stopped in advance by analyzing the trend of the number of unsatisfied checks. If the first stage decoding is stopped or failed, the second powerful FFT-QSPA is activated. The proposed decoding with the efficient stopping achieves error performance as good as that of FFT-QSPA with a low complexity, and converges faster than hybrid WSF (HWSF) algorithm.
A dynamic multi-symbol (DM) flipping scheme is proposed for various symbol flipping decodings of non-binary low-density parity-check (NB-LDPC) codes. This new approach divides the whole decoding process into multiple stages according to the number of iterations and allows a different maximum number of symbols to be flipped in each stage. Numerical analysis reveals that the proposed multi-symbol flipping scheme can yield a higher probability of correct flipping with the dynamic flipping threshold. The proposed DM scheme can be highly parallelized, extensively improving the decoder throughput over existing symbol flipping decoding algorithms based on prediction (SFDP). Numerical results show that the DM scheme can significantly enhance the error correction capability and achieve faster convergence speed with little increase in average computational complexity.