In order to improve the error code performance of Low Density Parity Check (LDPC) code Offset Min-Sum (OMS) algorithm, a low complexity OMS algorithm for 5G LDPC codes was proposed based on 5G NR standard. Aiming at the problem that the offset factor value calculation in the traditional algorithm is not accurate enough, the density evolution was used to obtain a more accurate offset factor value, which was used to the check node updating in order to enhance the performance of OMS algorithm. And the obtained offset factor value was approximated by using the linear approximation method, so as to reduce the complexity of the algorithm while ensuring decoding performance. For the influence of the variable node oscillation phenomenon on the decoding, the Log-Likelihood Ratio (LLR) message values before and after node updating were weighted, so the oscillation of the variable node was reduced, and the convergence speed of the decoder was improved. The simulation results show that compared with Normalized-Min-Sum (NMS) algorithm and OMS algorithm, the proposed algorithm improves the decoding performance by 0.3-0.5 dB when the Bit-Error Rate (BER) is 10 ^-5, and the average iteration times reduced by 48.1% and 24.3% respectively. At the same time, the difference between the performance of the proposed algorithm and LLR-BP (Log-Likelihood Ratio-Belief Propagation) algorithm performance is only nearly 0.1 dB.

Narrow Band Internet of Things (NB-IoT) technology is developing rapidly. Compared with the original wireless communication, the spectrum bandwidth of NB-IoT is only 180 kHz. Therefore, how to use resources or spectrum more efficiently (ie. resource allocation and scheduling) becomes a key issue for NB-IoT technology. In order to solve this problem, the related factors of NB-IoT uplink resource scheduling were analyzed, including resource allocation, power control and uplink transmission gap, and different options for comparison to select the optimal scheme were provided. In addition, modulation and coding scheme and the selection of the number of repeated transmissions were also analyzed in detail. A greedy-stable selection modulation and coding strategy based on different coverage levels and power headroom report were proposed, with which modulation and coding level was initially selected. A compensation factor was introduced to select the number of retransmissions and the update of the modulation and coding level. Finally, the proposed scheme was simulated. The simulation results show that the proposed scheme can save more than 56% of the activity time and 46% of the resource consumption compared with the direct transmission method.