Neural Network Coding
Author(s)
Liu, Litian; Solomon, Amit; Salamatian, Salman; Medard, Muriel
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© 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.
Date issued
2020-06Department
Massachusetts Institute of Technology. Research Laboratory of Electronics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
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