.png){kind=icon}
Return
Parallel-Serial Convolution Cascade Time-Frequency Attention Network for Wireless Interference Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2025.3596230.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Wireless interference recognition (WIR) techniques have proven to be effective anti-jamming solutions. With advancements in deep learning (DL), various DL-based methods have been explored for WIR. However, existing multi-feature input methods struggle to effectively fuse the different dimensional feature of interference signals, leading to suboptimal recognition performance. To address these challenges, we propose a parallel-serial convolution cascade time-frequency attention network (PSC-TFAN) for interference signal detection in complex environments. PSC-TFAN uses time-frequency images (TFIs) of interference signals as a single input, eliminating the need for complex multi-input designs. A parallel-serial convolution module is developed to extract local features of interference signals, complemented by an enhanced depthwise separable convolution (EDSC) to reduce computational overhead. Additionally, we introduce three embedding strategies to emphasize the complementary features of interference signals. A time-frequency attention module further combines these features to highlight relevant information while suppressing redundancy and noise. Experimental results demonstrate that PSC-TFAN achieves superior recognition accuracy and significantly lower computational complexity compared to state-of-the-art DL models, establishing its effectiveness in practical WIR applications.
Keywords:
Wireless interference recognition
convolutional neural networks
enhanced depthwise separable convolution
time-frequency attention
fusion strategy
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
Organization
Cited Papers
ShareSaveCompound Model of Navigation Interference Recognition Based on Deep Sparse Denoising Auto-encoder.png)
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)IF0
PREAI
ShareSaveFeature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal
PREAI
ShareSaveShareSaveA New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals
OAAI
OAAIShareSaveShareSaveHDCGD-CBAM: Satellite Interference Recognition Algorithm Based on Improved CLDNN and CBAMCHINA COMMUNICATIONS
CHINA COMMUNICATIONSIF3.1
PREAI
ShareSave
View more