Neural Network Coding
Neural Network Coding
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神经网络编码
DOI：
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发表时间：
2020
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通讯作者：
M. Médard
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作者：
Litian Liu;Amit Solomon;Salman Salamatian;M. Médard
在本文中，我们介绍了神经网络编码（NNC），数据驱动的联合源和网络编码的方法。在NNC中，每个源和中间节点处的编码器以及每个目的地节点处的解码器都是神经网络，它们都是联合训练的，用于通过有噪声的点对点链路的网络来传送相关源的任务。NNC方案是特定于应用程序的，并使用训练数据集，而不是对源统计数据进行假设。此外，它可以适应任何任意的网络拓扑结构和功率约束。我们的经验表明，对于通过网络传输MNIST图像的任务，NNC方案显示出优于基线方案的改进，特别是在低SNR的制度。
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.