Augmented Convolutional Neural Networks with Transformer for Wireless Interference Identification
Pengyu Wang, Yufan Cheng, Binhong Dong · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
As electromagnetic environments are more and more complex, wireless interference identification (WII) is becoming vital for non-cooperative communication systems in both civilian and military scenarios. With the enormous success of deep learning (DL), methods that optimize convolutional neural networks (CNNs) for WII have been proposed. However, due to the intrinsic characteristics of CNNs, the existing networks are difficult to capture long-range feature dependencies, causing the low recognition accuracy and the high computational complexity. Motivated by the success of transformers in natural language processing (NLP) domain, we propose an augmented convolutional neural network with transformer (ACNNT), which combines both the advantages of CNNs and transformers to simultaneously strengthen locality and establish long-range dependencies. Specifically, the ACNNT has multiple stages, and every stage consists of convolutional layers and transformer module to model local and long-range dependencies of context, respectively. At the end of the network, a classification token is used for classification. A channel attention (CA) module is proposed to further improve the expressive ability of the transformers. Extensive experiments demonstrate that the proposed method leads to performance improvement as compared to conventional DL based methods.