Adaptive Offset Min-Sum Decoder for 5G-NR BG2 LDPC Codes Using Genetic Algorithm

This paper proposes a more efficient resource-constrained decoding algorithm for Adaptive Offset Min-Sum (A-OMS) low-density parity check (LDPC) optimized decoder parameters in fifth-generation New Radio (5G-NR) Base Graph 2 codes (BG2). To curb the challenge of observing the all-time finest offset to use the irregular BG2 structure, a genetic algorithm (GA) is utilized to enhance the respective offset aspect in the updating of check nodes to achieve the required objective of creating a near-optimal solution offset aspect. Depending on the GA, the bit error rate (BER) can achieve optimal performance over a wide range of signal-to-noise ratios (SNRs), which is a metaheuristic optimizer. These results of the simulation indicate that this proposed optimized decoder outperforms the conventional fixed-offset OMS by 0.2–0.5 dB in terms of belief propagation (BP) decoding performance, coding gains, and a BER that are 0.3–0.6 dB and a BER of 4.6, respectively. The proposed method achieves BP at about 35 percent decoding computation. In the case of 5G-NR next-generation receivers, the suggested approach will provide a fair trade-off between the implementation efficiency and performance.