Multi-Depth Adaptive Networks For Wireless Interference Identification

Wireless interference identification (WII) is a promising technology for non-cooperative communication systems in both civilian and military scenarios. With the rapid development of artificial intelligence, deep learning (DL) based WII methods have been proposed. However, the existing networks based on DL do not have the adaptive ability, and this situation may cause a waste of computing resources. In this paper, we propose a novel Multi-Depth Adaptive Network (MDANet), and it can adaptively determine the forward propagation depth according to the difficulty of input samples in inference. Specifically, we firstly divide a given convolutional neural network (CNN) into several blocks, and each block has its own output of classification by attaching a fully connected layer. The mechanism of confidence is introduced to enable the network to dynamically select depth of forward propagation and allocate appropriate computational resources depending on the complexity of samples during test time. In addition, we improve the recognition ability of early blocks through three proposed approaches. Experiments demonstrate that the MDANet can reduce the calculation cost and prediction time significantly without the performance loss.