This workspace describe the tools and results described in the paper [1].
The master branch contains the main source and MWE notebook to run an experiment using the proposed workflow. The notebook 'study_auto_encoder.ipynb' contain and details the steps to setup and run a complete study. The notebook 'plot_utils.ipynb' provides the tools to plot the corresponding results.
Each of the other branches of the repository describe one of the experiment presented in the paper:
- V.A. (8,4) Code: Illustration of the Proposed Concept:.............................................branch ae-8-4
- V.B.1. Model Performance During Training:..........................................................branch ae-31-16-training-study
- V.B.2. Training Repeatability:.....................................................................branch ae-31-16-repeatability
- V.B.3. Correlation Between Decoders Types and their Performances:..................................branch ae-31-16-repeatability
- V.C. (31,11) Codes: Auto-encoder Training Procedure and Ablation Study:............................branch ae-31-11
- V.D. (63,36) & (63,45) Codes: Scalability and Algorithmic Complexity:..............................branch ae-63-36 and ae-63-45
- V.E.1 Comparison with Short (64,k) LDPC Codes - Impact of the Rate:................................branch ae-LDPC
- V.E.2 Comparison with State-of-the-Art (128,64) Codes - Impact of the Number of Iterations:........branch ae-128-64
[1] G. Larue, L. -A. Dufrene, Q. Lampin, H. Ghauch and G. Rekaya, "Neural Belief Propagation Auto-Encoder for Linear Block Code Design," in IEEE Transactions on Communications, 2022, doi: 10.1109/TCOMM.2022.3208331.
Copyright (c) 2022 Orange
Authors: Guillaume Larue [email protected], Quentin Lampin [email protected], Louis-Adrien Dufrene [email protected]
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