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

Authors :
Yue, Chentao
Shirvanimoghaddam, Mahyar
Kosasih, Alva
Park, Giyoon
Park, Ok-Sun
Hardjawana, Wibowo
Vucetic, Branka
Li, Yonghui
Publication Year :
2021

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.<br />Comment: 30 Pages, 12 Figures

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2112.12378
Document Type :
Working Paper