High-Throughput Dynamic Scheduling for Belief-Propagation Decoding of LDPC Codes

Low-density parity-check (LDPC) codes, usually decoded using the informed dynamic scheduling (IDS) algorithms, are well applicable to the 5th-generation communication systems (5G). However, the IDS algorithm is greedy because of the update-relayed trend. The update-relayed trend leads to the unreasonable allocation of decoding resources, which exerts a huge impact on the decoding performance. To solve this greedy problem, we proposed the decoding algorithm based on random select of check nodes with a predefined update range (RSPUR). By decoding with a narrow update range, the RSPUR algorithm effectively suppresses the update-relayed trend and allocates the decoding resources in a more rational way. Simulation results indicate that the proposed algorithm achieves excellent error-correction performance and high throughput compared to the previous IDS algorithms.