Neural Network Coding
Name
Neural_Network_Coding.pdf
Description
Accepted version
Size
1008.19 KB
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Author(s)
Liu, Litian
Solomon, Amit
Salamatian, Salman
Medard, Muriel
Date Issued
June 2020
Journal
IEEE International Conference on Communications
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
2020. "Neural Network Coding." IEEE International Conference on Communications, 2020-June.
Version
Author's final manuscript
Abstract
© 2020 IEEE. In this paper we introduce Neural Network Coding (NNC), a data-driven approach to joint source and network coding. In NNC, the encoders at each source and intermediate node, as well as the decoder at each destination node, are neural networks which are all trained jointly for the task of communicating correlated sources through a network of noisy point-to-point links. The NNC scheme is application-specific and makes use of a training set of data, instead of making assumptions on the source statistics. In addition, it can adapt to any arbitrary network topology and power constraint. We show empirically that, for the task of transmitting MNIST images over a network, the NNC scheme shows improvement over baseline schemes, especially in the low-SNR regime.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICC40277.2020.9149399
