  In this study, an optimization model for offline scheduling policy of
low-density parity-check (LDPC) codes is proposed to improve the decoding
efficiency of the belief propagation (BP). The optimization model uses the
number of messages passed (NMP) as a metric to evaluate complexity, and two
metrics, average entropy (AE), and gap to maximum a posteriori (GAP), to
evaluate BP decoding performance. Based on this model, an algorithm is proposed
to optimize the scheduling sequence for reduced decoding complexity and
superior performance compared to layered BP. We validated the proposed
algorithm on LDPC codes constructed following 5G New Radio, which resulted in a
reduction of decoding complexity of more than 20$\%$ compared to LBP.
