In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based ...In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.展开更多
This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We b...This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We built a dataset of radar echoes with four comb jamming types and five non-comb interference types.A dual-channel method creates 2D images preserving both magnitude and phase information from the signal spectrum.A CNN classifier with convolutional blocks,batch normalization,and dropout achieves 99.75%accuracy with 1.5%false alarm rate after only 7 training epochs.展开更多
文摘In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.
文摘This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We built a dataset of radar echoes with four comb jamming types and five non-comb interference types.A dual-channel method creates 2D images preserving both magnitude and phase information from the signal spectrum.A CNN classifier with convolutional blocks,batch normalization,and dropout achieves 99.75%accuracy with 1.5%false alarm rate after only 7 training epochs.
