Time-Frequency Component-Aware Convolutional Neural Network for Wireless Interference Classification

In wireless communication systems, wireless interference classification (WIC) is considered as one of the most effective technologies to address the challenges brought by electromagnetic interference in military and civilian scenarios. Recently, deep learning (DL) based methods have dominated progress in the field of WIC. However, the most existing methods do not consider the redundancy of input samples, nor have the ability to adaptively allocate computational resources conditioned on the inputs. To this end, we propose time-frequency component-aware convolutional neural network (TFCCNN), and it allows the convolution calculation to be performed only at the locations where time-frequency components or important parts exist in the time-frequency image of interference signals, leading to reduce the superfluous computation. Furthermore, to further reduce the computational complexity, we introduce a novel adaptive forward propagation (AFP) algorithm, and the network can determine the depth of forward propagation according to the difficulty of the sample during inference. Experimental results demonstrate that the proposed method reduces the computational complexity by about 75% when the recognition accuracy is slightly improved compared to the traditional CNNs.