Abstract:

Network coding technology can effectively improve the network throughput.However,traditional network coding involves a high complexity in both encoding and decoding and it is difficult to adapt to the influence of dynamic factors such as environmental noise,which leads easily to decoding distortion.In recent years,researchers have introduced neural networks to optimize the network coding process,but in high-resolution image transmission,the existing neural network coding schemes have an insufficient ability to capture high-dimensional spatial information,resulting in large communication and computation overhead.To solve this problem,this paper proposes a joint source deep learning Network coding scheme that uses a two-dimensional Convolutional Neural Network(CNN) to parameter-design the encoder and decoder of each network node,which captures deep spatial structure information and reduces the computational complexity of network nodes.At the source node,the convolution layer operation is used to reduce the dimension of the transmission data and improve the data transmission rate;At the intermediate node,the data from the two sources are received and compressed by CNN coding for single channel transmission;At the destination node,the received data is decoded using a CNN to increase the dimension and restore the original image.Experimental results show that under different channel bandwidth occupancy ratios and channel noise levels,the proposed scheme shows an excellent decoding performance in peak signal-to-noise ratio and structural similarity.

Key words: network coding, deep learning, convolutional neural networks, high resolution image, image communication