Abstract: In order to further narrow the gap between min-sum (MS) algorithm and belief propagation (BP) algorithm, and to improve the decoding performance of normalized minsum (NMS) algorithm, an improved normalized minimum sum LDPC decoding algorithm based on residual difference layer is proposed. Firstly, the overestimation problem of MS algorithm is quantitatively analyzed. BP algorithm and MS algorithm are used to test the ratio characteristics of node LLR messages, and the corresponding normalization factors are calculated. To reduce decoding complexity, a weighted average processing is adopted according to the variation in the optimal normalization factor. Additionally, to reduce the average number of iterations and accelerate decoding convergence, the proposed algorithm uses the residual characteristics of the check node information to prioritize updates in layers with larger residual values. The layers are dynamically rearranged between iterations. Simulation results show that the proposed RB_LINMS algorithm achieves a performance gain of approximately 0.26 dB in decoding, compared with the traditional NMS algorithm at the bit error rate 10^-5, and reducer the average number of iterations by up to 33.20%. Therefore, with a slight increase in complexity, it offers faster convergence and improved decoding performance.

Key words: 5G, low density parity check, normalized min-sum, layered decoding