A Parallel Sub-Iteration Scheduling Strategy for LDPC Decoding

In deep-space communications, conventional low-density parity check (LDPC) decoding algorithms suffer from slow convergence and high latency due to inefficient message scheduling. While residual-based layered belief propagation (RB-LBP) improves convergence, its serial scheduling limits hardware parallelism. Similarly, neural network-based decoders offer performance gains but impose prohibitive computational overheads on resource-constrained spaceborne platforms. To address these challenges, this paper proposes the parallel node-wise belief propagation (PNW-BP) algorithm. By employing a dynamic grouping strategy based on maximum residuals, PNW-BP constructs localized node sets for parallel message passing within sub-iterations. This strategy achieves intra-group parallelism while maintaining inter-group layered updates to preserve rapid convergence. Simulation results on the CCSDS standard (8176, 7154) LDPC code demonstrate that PNW-BP accelerates convergence, with no observable BER degradation under the evaluated conditions. For example, at Eb/N0=3.75dB and a target BER of 5×10−5, it requires only 8 iterations, whereas the NMSA, LBP, RB-LBP, and GS-BP require 37, 15, 16, and 20, respectively. Thus, PNW-BP provides an efficient solution for resource-constrained spaceborne applications.