User-Load-Compatible Masking Schemes for Raptor-Like Protograph-Based LDPC Codes in Gaussian Multiple Access Channels
Na Gao, Yin Xu, Yihang Huang, Dazhi He, Hanjiang Hong, Chang Wen Chen, Wenjun Zhang · IEEE Transactions on Vehicular Technology · 2021
In this paper, the backward-compatible coding schemes are explored to achieve the near-capacity performances in dynamic LDPC-coded Gaussian multiple access channels (GMAC). Specifically, some user-load-compatible (ULC) masked edges in a raptor-like protograph are masked with the variation of user load u. To distinguish the edges in a protograph, a multi-user protograph-based extrinsic information transfer (MU-PEXIT) chart is developed. Based on this chart, an iterative edge selection algorithm combined with a theoretical simplification is proposed to facilitate the selection of independent masked edge sets (MESs) for varying u. To further decrease the complexity of the independent masking scheme, a nested version is presented, where the MESs for varying u are nested and derived from the same mother MES. Meanwhile, nested selection algorithms are provided to select the nested MESs. Extensive simulation results demonstrate that the masked 5G-NR LDPC codes can maintain near-capacity performances regardless of the variation of u. Besides, these ULC masked codes achieve comparable performances with the state-of-the-art LDPC codes which are specially re-designed in a static GMAC.