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arXiv · 2112.12378

Density Evolution Analysis of the Iterative Joint Ordered-Statistics Decoding for NOMA

Chentao Yue·Mahyar Shirvanimoghaddam·Alva Kosasih·Giyoon Park·Ok-Sun Park·Wibowo Hardjawana·Branka Vucetic·Yonghui Li
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Abstract

In this paper, we develop a density evolution (DE) framework for analyzing the iterative joint decoding (JD) for non-orthogonal multiple access (NOMA) systems, where the ordered-statistics decoding (OSD) is applied to decode short block codes. We first investigate the density-transform feature of the soft-output OSD (SOSD), by deriving the density of the extrinsic log-likelihood ratio (LLR) with known densities of the priori LLR. Then, we represent the OSD-based JD by bipartite graphs (BGs), and develop the DE framework by characterizing the density-transform features of nodes over the BG. Numerical examples show that the proposed DE framework accurately tracks the evolution of LLRs during the iterative decoding, especially at moderate-to-high SNRs. Based on the DE framework, we further analyze the BER performance of the OSD-based JD, and the convergence points of the two-user and equal-power systems.

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Chentao Yue, Mahyar Shirvanimoghaddam, Alva Kosasih, Giyoon Park, Ok-Sun Park, Wibowo Hardjawana, Branka Vucetic, Yonghui Li. 2021-12-23. Density Evolution Analysis of the Iterative Joint Ordered-Statistics Decoding for NOMA. https://arxiv.org/abs/2112.12378

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