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modulation recognition based on adaptive denoising residual neural network

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-18页

作者： Ma, Mingyue Zhen, Jiaqi Heilongjiang Univ Coll Elect Engn Harbin 150080 Peoples R China

The existing deep learning methods cannot accurately identify the modulation type of wireless signals in the circumstance of low signal-to-noise ratio (SNR). Therefore, the model based on adaptive denoising residual neural network is designed in this paper. At the input end, the pre-processing of the communication signal is carried out to extract the in-phase, orthogonal, amplitude, phase and frequency components of the complex signal as the recognition features. Then adaptive denoising module combining the soft threshold function and the improved channel attention mechanism removes the noise mixed in the signal. After that, the Inception-ResNet which is composed of Inception and residual structure is used to mine deep features. Finally the modulation recognition is realized by the full connection layer. The simulation results show that under the background of Gaussian white noise, its average recognition accuracy for common signals reaches about 94% and also performs well in the condition of Rician, Rayleigh and even unknown channel. Besides, the proposed model is better than visual geometry group-19 under low SNR, and has certain robustness to frequency offset and phase offset.

关键词： modulation recognition Deep learning Adaptive denoising Residual neural network Feature extraction

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modulation recognition for underwater acoustic communication based on hybrid neural network and feature fusion

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APPLIED ACOUSTICS 2024年 225卷

作者： Wang, Yang Shen, Tongsheng Wang, Tao Qiao, Gang Zhou, Feng Harbin Engn Univ Natl Key Lab Underwater Acoust Technol Harbin 150001 Peoples R China Acad Mil Sci Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Harbin Engn Univ Key Lab Marine Informat Acquisit & Secur Minist Ind & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China Harbin Engn Univ Sanya Nanhai Innovat & Dev Base Sanya 572024 Peoples R China

It is a huge challenge for underwater acoustic receivers to correctly identify modulation methods due to the complex underwater channel environment and severe noise interference. Combined with the lightweight network (SqueezeNet) and attention mechanism (SENet), a multi-attribute and multi-scale feature fusion model based on a hybrid neural network is proposed, which achieves efficient and accurate recognition for modulation modes. First, the wavelet time-frequency (WTF) spectrum, square power spectrum, and contour maps of cyclic spectrum are extracted as multi-attribute inputs for the network to reduce the impact of inherent defects in single attribute feature. Second, shallow and deep features based on the SqueezeNet model are obtained as multi-scale features, of which the key feature expression ability is enhanced by the SENet model to provide sufficient feature information for modulation recognition. The simulation experiments and sea trial data confirm that the suggested method demonstrates strong generalization capabilities and effectiveness when applied to underwater acoustic channels and environmental noise. In contrast to algorithms in existence, the method verifies superior recognition abilities.

关键词： Underwater acoustic communication modulation recognition Feature combination Hybrid neural network

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modulation recognition of Communication Signals Based on Cascade Network

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IEICE TRANSACTIONS ON COMMUNICATIONS 2024年第9期E107B卷 620-626页

作者： Hou, Yanli Liu, Chunxiao Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050018 Peoples R China

To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN (Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short- Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of - 10 to 15 dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10 dB SNR.

关键词： deep learning modulation recognition stacked denoising auto encoder CLDNN dilated convolution ECA mechanism

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modulation recognition with alpha-stable noise over fading channels

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WIRELESS NETWORKS 2024年第5期30卷 3795-3804页

作者： Zhang, Lingfei Hua, Liang Liu, Mingqian Shang, Bodong Zhang, Yarui Qinghai Nationalities Univ Coll Phys & Elect Informat Engn Xining Peoples R China Zhejiang Business Coll Hangzhou Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Virginia Tech Dept ECE Wireless VT Blacksburg VA 24061 USA

In this paper, we propose a novel method to recognize the digital modulation communication signals with alpha-stable noise over fading channels. Specifically, the probability density function of alpha-stable noise is determined by kernel density estimation, and the fading channel parameters are measured by utilizing the improved expectation conditional maximization algorithm. Then we leverage the compound hypothesis test model, which serves as a classifier, to implement modulation recognition. Simulation results show that the proposed method is robust to alpha-stable noise over fading channels. Moreover, the correct average recognition rates of the digital modulation communication signals under alpha-stable noise is more than 90%, when the generalized signal-to-noise ratio is 10dB.

关键词： Alpha-stable noise Expectation conditional maximization Fading channel Kernel density estimation modulation recognition

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modulation recognition network compression based on a randomly perturbation convolutional kernel activation mapping method

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WIRELESS NETWORKS 2024年第4期30卷 2143-2157页

作者： Zhao, Chengqiang Zhang, Jiashu Ni, Fan Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Sichuan Prov Key Lab Signal & Informat Proc Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China

Deep-learning-based automatic modulation recognition techniques have been extensively explored for wireless communication systems, because of their strong feature extraction and classification abilities of deep neural networks. Although the high recognition accuracy and low alarms, they also raise concerns about complexity and interpretability, which can affect the practical deployments. In this paper, we propose a randomly perturbation convolutional kernel activation mapping (RPCKAM) strategy to explain modulation recognition networks, and the RPCKAM-based filter pruning method to compress modulation recognition networks, combining the reasoning and interpretation of decisions made by modulation recognition networks with the compression of network models. The RPCKAM is used to evaluate the importance of convolutional kernels, and then the importance ranking is used to provide a basis for network pruning. Moreover, the effect of pruning is verified with VGG16 and ResNet34 as the baseline networks. The experimental results reflect that the RPCKAM-based convolutional kernel pruning can compress the model on a large scale while maintaining high accuracy, and can even improve the modulation recognition accuracy within a certain compression range. Specifically, the accuracy improvement is close to 1% at most in the VGG16, and it is also close to 0.5% at most in the ResNet34. Furthermore, by analyzing the performance changes before and after model compression, it is found that the compression effect of ResNet34 is better than that of VGG16, and the performance of the RPCKAM-based pruning method outperforms that of the Grad-CAM-based pruning method.

关键词： modulation recognition Interpretability Network pruning

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modulation recognition of underwater acoustic communication signals based on deep learning

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 1-17页

作者： Wang, Biao Yang, Heng Fang, Tao Jiangsu Univ Sci & Technol Ocean Coll Zhenjiang 212003 Peoples R China

In recent years, research on modulation signal recognition using deep learning (DL) has achieved significant success. However, the complex environment of underwater channels presents substantial challenges for modulation recognition. To address this issue, this paper proposes a multi-scale feature fusion hybrid model (HM) based on Gram angle field, Markov transition field, and recurrence plot (GMR). The hybrid model is used to fuse time domain and frequency domain features and integrate multi-scale features into low-dimensional features. Results from lake trials in Qiandao Lake demonstrate that the network achieves a recognition accuracy of 94.31% under real underwater acoustic channel conditions. Comparative experiments with other methods further prove the superiority of the proposed method across multiple evaluation metrics.

关键词： modulation recognition Feature fusion Underwater acoustic signal Deep learning

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modulation recognition Based on Denoising Bidirectional Recurrent Neural Network

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2437-2455页

作者： Du, Ruiyan Liu, Fulai Zhang, Lijie Ji, Yahui Xu, Jialiang Gao, Fan Northeastern Univ Qinhuangdao Engineer Optimizat & Smart Antenna Inst Qinhuangdao Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

modulation recognition is an important research area in wireless communication. It is commonly used in both military and civilian domains, such as spectrum detection and interference identification. Most existing modulation recognition algorithms have a better recognition performance at high signal noise ratio (SNR). However, when SNR decreases to - 10 dB or even lower, such as the battlefield and disaster areas and other harsh environment, the recognition rate may decrease dramatically. In order to solve this problem, a modulation recognition algorithm based on denoising bidirectional recurrent neural network is proposed. Firstly, the state memory ability of the signal reconstruction layer in the network is utilized to learn the temporal correlation of the modulated signal, the reconstruction of the received signal is completed and the noise in the modulated signal is suppressed. Then, the reconstructed signal is encoded and decoded by the feature reconstruction layer, in which the feature of reconstructed signal is compressed and reconstructed, thereby the influence of noise can be further reduced. Finally, the reconstructed features are identified and classified by the fully connected layer. Simulation results demonstrate that the proposed network can effectively suppress the noise in the signal. Compared with other existing algorithms, the proposed method has higher recognition accuracy in the low SNR environment.

关键词： modulation recognition Denoising bidirectional recurrent neural network Deep learning Dilated convolution

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modulation recognition with Frequency Offset and Phase Offset over Multipath Channels

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China Communications 2023年第10期20卷 58-69页

作者： Mingqian Liu Zhaoxi Wen Yunfei Chen Ming Li State Key Laboratory of Integrated Service Networks Xidian UniversityShaanxiXi’an 710071China Department of Engineering University of DurhamSouth RoadDH13LEDurhamUK Guilin Changhai Development Co. LtdGuangxiGuilin 541001China

modulation recognition becomes unreliable at low signal-to-noise ratio(SNR)over fading channel.A novel method is proposed to recognize the digital modulated signals with frequency and phase offsets over multi-path fading channels in this *** method can overcome the effects of phase offset,Gaussian noise and multi-path *** achieve this,firstly,the characteristic parameters search is constructed based on the cyclostationarity of received signals,to overcome the phase offset,Gaussian white noise,and influence caused by multi-path ***,the carrier frequency of the received signal is estimated,and the maximum characteristic parameter is searched around the integer multiple carriers and their ***,the modulation types of the received signal with frequency and phase offsets are classified using decision *** results demonstrate that the performance of the proposed method is better than the traditional methods when SNR is over 5dB,and that the proposed method is robust to frequency and phase offsets over multipath channels.

关键词： cyclic characteristics frequency and phase offset multi-path channels modulation recognition

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modulation recognition with Untrained Deep Neural Network for IoT and Mobile Applications

Modulation Recognition with Untrained Deep Neural Network fo...

引用

IEEE Radio and Wireless Symposium (RWS)

作者： Woo, Jongseok Jung, Kuchul Mukhopadhyay, Saibal Georgia Inst Technol Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9798350340457

This paper presents a novel denoising algorithm with the untrained deep neural network (DNN) for Radio Frequency (RF) signal modulation recognition considering the mobile and Internet of Things (IoT) applications. In denoising with the untrained DNN, which is called deep image prior (DIP), a DNN is used as a deep generative network to generate the less noisy signal from the pure noise. Considering denoiser for RF signal, which can't apply the denoising algorithm selectively, we propose a new DIP algorithm called hybrid DIP to improve the data reproducing capability when the noise level is low. The experimental results show that the proposed method can increase the classification accuracy of the received RF signals, especially with low signal-to-noise ratio (SNR), with minimal impact on the signal with high SNR.

关键词： modulation recognition DNN untrained DNN deep image prior deep decoder

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Radar modulation recognition of Intra-Pulse Overlapping Signals Based on Object Detection

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 1888-1900页

作者： Xu, Shuai Liu, Lutao Guo, Muran Harbin Engn Univ Sch Informat & Commun Engn Harbin 150000 Peoples R China

Existing radar modulation recognition research predominantly assumes the presence of a single intra-pulse signal, despite the frequent occurrence of overlapping or interleaving of two pulse signals. This article addresses the challenging problem of modulation recognition for overlapping intra-pulse signals, where the complexity arises from the stepwise growth in the number of samples due to the permutation of subsignals. For the first time, a series of object detection methods is employed to tackle this issue. Specifically, focusing on air-to-ground reconnaissance systems, we construct a time-frequency dataset comprising overlapping signals. Using this dataset, we successfully detect dual signals while training solely on single pulse signals. Mainstream frameworks, such as "You Only Look Once" (YOLO) and region-based convolutional neural network (RCNN) are explored, and an improved YOLOv7 algorithm is proposed, incorporating DCNv2 and explicit visual center block. Experimental results demonstrate a 91% recognition rate for overlapping signals at -4 dB, surpassing state-of-the-art methods. Notably, this article introduces a new dataset of overlapping radar signals, which can be publicly accessible at IEEEDataPort https://***/documents/radar-modulation-recognition-intra-pulse-overlapping-signals.

关键词： Radar modulation Reconnaissance Time-frequency analysis Accuracy YOLO Signal to noise ratio Air-to-ground reconnaissance systems modulation recognition overlapping signal "You Only Look Once" (YOLO)

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