A hybrid of machine learning based early termination and optimal scaling factor scheme for 5G LDPC codes

Machine Learning (ML) has proven to be an effective tool for optimizing several aspects of channel codes in communication systems. In this work, ML-based schemes have been adapted to the 5G New Radio (NR) Low-Density Parity Check (LDPC) codes to help them meet the stringent requirements of ultra-reliable low-latency communications (URLLC) in 5G NR. Furthermore, enhancements to the modulation schemes have also been applied to attempt to outperform the complex and robust Layered Belief Propagation Algorithm (LBPA) using the lower complexity Normalized Min-Sum Algorithm (NMSA) decoder. By combining the ML-based schemes and the enhanced modulation schemes, NMSA effectively provided [Formula: see text] gains of up to 1 dB and latency reductions of up to 0.53 ms compared to the conventional Layered Belief Propagation Algorithm.