The informeddynamicscheduling (IDS) strategies, in which the edge message with the maximum message-residual is updated preferentially, achieve remarkable error-correction performance when applied to low-density pari...
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The informeddynamicscheduling (IDS) strategies, in which the edge message with the maximum message-residual is updated preferentially, achieve remarkable error-correction performance when applied to low-density parity-check (LDPC) codes. However, the IDS strategies incur inferior convergence in iterative decoding owing to the greedy problem, which is called the update-relayed trend here. In order to solve the greediness, two locally informed dynamic scheduling algorithms based on the law of large numbers are proposed. The proposed decoding algorithms use random select of check nodes over a predefined update range (RSPUR) which effectively suppresses the propagation of the update-relayed trend and accordingly restrains the forming of multi-update cycles. Moreover, the decoding algorithm is further improved based on random select of check nodes over an adjustable update range (RSCAR). The update ranges are selected based on the law of large numbers. Therefore, the computational resources can be allocated more equitably by increasing iterations. Simulation results show that both the proposed algorithms achieve excellent performance in terms of throughput and convergence with low decoding complexity over the Additive White Gaussian Noise (AWGN) and the fading channels compared to the previous IDS strategies. Hence, the proposed algorithms behave excellently over the wireless channels.
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