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Computer Science > Information Theory

arXiv:2202.01972 (cs)
[Submitted on 4 Feb 2022]

Title:Hybrid Neural Coded Modulation: Design and Training Methods

Authors:Sung Hoon Lim, Jiyong Han, Wonjong Noh, Yujae Song, Sang-Woon Jeon
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Abstract:We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). The inner code is designed using a deep neural network (DNN) which takes the channel coded bits and outputs modulated symbols. For training the DNN, we propose to use a loss function that is inspired by the generalized mutual information. The resulting constellations are shown to outperform the conventional quadrature amplitude modulation (QAM) based coding scheme for modulation order 16 and 64 with 5G standard LDPC codes.
Subjects: Information Theory (cs.IT); Machine Learning (cs.LG)
Cite as: arXiv:2202.01972 [cs.IT]
  (or arXiv:2202.01972v1 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2202.01972

Submission history

From: Sung Hoon Lim [view email]
[v1] Fri, 4 Feb 2022 05:04:15 UTC (426 KB)
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