In deterministic networks where relay nodes are stationary, combined relay selection is an efficient relay selection scheme, which is capable of achieving the near-optimal outage performance with much lower system complexity. However, the efficiency of combined relay selection is only validated upon fixed topological settings. To investigate the performance of combined relay selection in spatially random networks (SRNs), we employ the Poisson point process to model the dynamical nature of cooperative networks and study the outage performance of the proposed system. It is surprising that the equivalence principle of combined relay selection appearing in deterministic networks does not hold in SRNs.

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Network traffic classification is significant due to the fast growth of the number of internet users. The traditional way of classifying the large number of traffic generated by these users is becoming less effective. Therefore, many researchers made a network traffic classifier based on deep learning. However, those classifiers do not provide far better results and perform poorly when dealing with encrypted information. This paper tries to approach highly accurate and robust results in both encrypted and unencrypted networks by using machine learning algorithms. The algorithm used is the convolutional neural network (CNN). The performance of the proposed CNN is compared with that of the classical LeNet-5 network. Experimental results show that the classifier based on the proposed CNN performed better when dealing with both encrypted and unencrypted datasets, achieving a maximum average accuracy of 83.55%. Moreover, it is not sensitive to hyper-parameter choices, indicating its superiority in robustness. Compared with traditional network classifiers, the network classifier based on CNN can improve accuracy and improve stability.