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 multistage Wiener filter (MWF) outperforms the full rank Wiener filter in low sample support environments. However, the MWF adaptive process should be stopped at an optimum stage to get the best performance. There are two methods to stop the MWF adaptive process. One method is to calculate until the final full-stage, and the second method is to terminate at r-stage less than full-stage. The computational load is smaller in the latter method, however, a performance degradation is caused by an additional or subtractive stage calculation. Therefore, it is very important for the r-stage calculation to stop an adaptive process at the optimum stage. In this paper, we propose a simple method based on a cross-correlation coefficient to stop the MWF adaptive process. Because its coefficient is calculated by the MWF forward recursion, the optimum stage is determined automatically and additional calculations are avoided. The performance was evaluated by simulation examples, demonstrating the superiority of the proposed method.

We propose a new stopping criterion for decoding LDPC codes which consists of a measure of decoder behaviors and a decision rule to predict decoding failure. We will show that the proposed measure, the number of satisfied check nodes, does not need (or minimizes) additional complexity, and the decision rule is efficient and more importantly channel independent, which was not possible in the previous work.