A Network Coding Scheme Transmitting Specific Data and Universal Data Based on Deep Learning

For a wireless sensor network (WSN), we proposed a network coding scheme that adopts a neural network (NN) instead of methods based on Shannon theory, and this scheme can transmit specific data such as images of specific data sets. In further, it also can transmit universal data with the channel code. In the source, the specific data or the code word is sent out to a network performing network coding, where the universal data are encoded into the code word with Reed Solomon code and Polar code. In the intermediate nodes, the network coding based on Shannon theory is replaced with a multilayer perceptron, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). In the sink, we trained a NN with the received massage as the input and the original data as the label. The trained NN includes the sublayers, which are the subneural networks in every node. We test the new transmission scheme for some datasets and channel code such as Reed Solomon code in a WSN environment, and it is proven to be effective.