Research on QC-LDPC Decoding Method With Low Quantization Word Length Based on Adaptive Information Mapping in Passive Optical Network

Passive Optical Networks (PON) have advantages such as stable performance, convenient installation, high bandwidth, and resource saving, making them a mainstream network access technology with broad application prospects. The development of such as 8K video, digital twins, VR and other technologies causes explosive growth of network traffic and drives the next generation of PON to evolve towards higher rates, which results in the use of forward error correction (FEC) coding with higher coding gain to improve the power budget of PON. Quasi-cyclic low density parity check (QC-LDPC) codes are widely utilized in PON systems due to high coding gain and parallel encoding and decoding capabilities. However, the application of turbo-decode message passing (TDMP) decoding method is inevitably equated with high hardware complexity of the decoder caused by storing and processing massive information, which is one of the obstacles to PON evolution. Reducing the quantization word length is effective to reduce hardware complexity, but it also leads to saturation of decoding information, causing errors and affecting decoding performance. This work proposes an nonlinear mapping method which utilizes adaptive compression of decoding information through node saturation state monitoring during the decoding process, in order to ensure that the probability density of node information has a reasonable distribution. The simulation results indicate that the method can effectively mitigate the decline in decoding performance under low-word-length conditions.