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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Li, Yingkai Wang, Shufei Zhang, Yibin Huang, Hao Wang, Yu Zhang, Qianyun Lin, Yun Gui, Guan NJUPT Coll Telecommun & Informat Engn Nanjing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9798350387414

In the context of emerging 6G technology challenges, this paper introduces the LSMFF-AMC approach, leveraging multimodal feature fusion (MFF) with Long-Short range attention (LSRA) to enhance automatic modulation classification(AMC). The method significantly boosts classification accuracy by employing convolutional neural networks (CNN) for diverse modal feature extraction and integrating LSRA for comprehensive feature combination. Our experiments demonstrate an increase in accuracy from 88% to nearly 97%, outperforming traditional single-modal approaches. Additionally, a convergence analysis of the training loss function reveals LSMFF-AMC's superior and faster convergence compared to standard AMC methods.

关键词： automatic modulation classification multimodal feature fusion convolutional neural networks long-short range attention

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Deep Learning Enabled Algorithm for automatic modulation classification

Deep Learning Enabled Algorithm for Automatic Modulation Cla...

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IEEE Space, Aerospace and Defence Conference (SPACE)

作者： Singh, Brahmjit Shalu Prakash, Chandra NIT Kurukshetra ECE Dept Kurukshetra Haryana India NIT Kuruksketra ECE Dept Kurukshetra Haryana India ISRO Space Applicat Ctr Ahmadabad Gujarat India

ISBN: (纸本)9798350367393;9798350367386

In this paper, we present a joint convolutional neural network and gated recurrent unit based deep leaning model for recognition of modulation schemes in blind RF environment. The raw I/Q samples from RadioML2016.10b dataset are used as the input data. The proposed model offers significantly improved accuracy of the modulation schemes at the low SNR region ranging from -2 dB value to 4 dB. Through numerical results, it is shown that the proposed model yields recognition accuracy upto 99% at 0 dB SNR. The average accuracy achieved is 93.3%, which is a significant improvement in the recognition performance.

关键词： Wireless communication modulation methods automatic modulation classification Deep leaning

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Deep Learning-Based automatic modulation classification for Composite Modulated Radar Signal Using Time-Frequency Image 34

Deep Learning-Based Automatic Modulation Classification for ...

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34th International Telecommunication Networks and Applications Conference

作者： Song, Geonho Jeon, Ganghyuk Yoon, Dongweon Hanyang Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9798350379129;9798350379112

automatic modulation classification (AMC) of radar signals is a crucial technique in modern electromagnetic warfare. Recently, various AMC methods using deep learning (DL) have been proposed. This paper focuses on an DL-based AMC for composite modulated radar signals, leveraging the time-frequency image (TFI) of the received radar signals. We propose a novel TFI generation method that can reduce distortion to enhance classification performance. The proposed method first generates a grayscale TFI from the received radar signal and reduces its noise via temporal marginalization and the mu-law function. The resulting image is then standardized and clipped along the frequency axis for pattern enhancement. Through computer simulations on various DL models, we show that the proposed method outperforms the conventional ones in terms of classification accuracy.

关键词： Radar signal automatic modulation classification time-frequency image deep learning

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automatic modulation classification: Cauchy-Score-function-based cyclic correlation spectrum and FC-MLP under mixed noise and fading channels

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DIGITAL SIGNAL PROCESSING 2022年第0期126卷 103476-103476页

作者： Luan, Shengyang Gao, Yinrui Liu, Tao Li, Jiayuan Zhang, Zhaojun Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China

automatic modulation classification (AMC), also termed blind signal modulation recognition, plays a critical role in various civilian and military applications. Although existing approaches have made substantial contributions in this area, most fail to simultaneously consider mixed noise, different channel modes and low-power scenarios. In this paper, a novel cyclic correlation spectrum based on the Cauchy Score function is first proposed as the robust pattern for the outliers in the Gaussian and non-Gaussian mixed noise. Besides, its properties are studied and proved for further potential applications in wireless signal processing. Furthermore, a modified lightweight neural network, termed feature-coupling multi-layer perceptron (FC-MLP), is designed to avoid the potential risk of overfitting and meet the needs when applied in low-power chips. In addition, a novel AMC method is proposed under a dual-layer decision tree framework, and different patterns are adopted in different layers to make full use of the robustness of the proposed pattern and the information embedded in the time sequence. Meanwhile, different classifiers are also chosen according to the characteristics of the patterns. In the simulations, state-of-the-art machine learning techniques, neural networks and patterns are employed as comparison candidates to verify the superiority of the proposed pattern, the lightweight classifier and the novel AMC approach in the scenario of Rayleigh channel or Rician channel with additive mixed noise. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： automatic modulation classification Cauchy Score function Mixed noise Rayleigh channel Rician channel

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High-Reliability Envelope-Based automatic modulation classification Using Deep Learning

High-Reliability Envelope-Based Automatic Modulation Classif...

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Wireless and Microwave Technology Conference (WAMICON)

作者： Abu Khater, Mohammad Peroulis, Dimitrios Philowave LLC W Lafayette IN 47906 USA Purdue Univ Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350344943

This paper presents a novel method for deep learning-based automatic modulation classification (AMC). The method relies on coupling the input signal, and extracting the envelope and the frequency information, as input features to the neural network. The main advantage of this method, compared to traditional IQ-based detection, is that it is more robust since there is no demodulation process which can suffer from non-linear imperfections. This is critical if the target signal is a highpower interferer. The presented method is tested, with hardware implementation, and is capable of detecting the modulation type and rate with an average of 99.6% accuracy. The time required for the modulation classification is approximated to be 9.7 mu s. The behavior of the system in the presence of noise is also tested and presented. Compared to the state-of-the-art, this method provides more information about the interferer (modulation and rate), in a shorter time, without compromising the accuracy.

关键词： Interference detection automatic modulation classification machine learning neural network edge AI

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automatic modulation classification Using Fractional Low Order Cyclic Spectrum and Deep Residual Networks in Impulsive Noise

Automatic Modulation Classification Using Fractional Low Ord...

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8th IEEE MTT-S International Wireless Symposium (IWS) part of China Microwave Week

作者： Ma, Jitong Jiang, Fan Dalian Maritime Univ Informat Sci & Technol Coll Dalian Peoples R China Natl Ctr Ocean Stand & Metrol Tianjin Peoples R China

ISBN: (纸本)9781665435277

automatic modulation classification (AMC) plays an important role in cognitive radio, surveillance and electronic warfare systems. However, due to the presence of impulsive noise in wireless communication systems, conventional AMC methods which are based on Gaussian noise assumption usually exhibit poor performance in impulsive noise environment. Aiming at realizing efficient AMC with impulsive noise, a novel AMC method based on fractional low order cyclic spectrum (FLOCS) and deep residual networks (ResNets) is proposed in this paper. In this method, FLOCS can effectively suppress impulsive noise, and meanwhile the discriminating features can also be extracted from FLOCS for AMC. Then, on the basis of these features, deep residual networks are employed to classify different modulation types. Monte Carlo simulation results demonstrate that the proposed AMC method can achieve superior classification performance in impulsive noise environment.

关键词： automatic modulation classification impulsive noise fractional low order cyclic spectrum (FLOCS)

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Collaborative automatic modulation classification via Deep Edge Inference for Hierarchical Cognitive Radio Networks

Collaborative Automatic Modulation Classification via Deep E...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： He, Chaowei Dong, Peihao Zhou, Fuhui Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Peoples R China

ISBN: (纸本)9798350378412

In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of the transmission overhead, data privacy, and computation load. In this article, an edge learning (EL) based framework jointly mobilizing the edge device and the edge server for intelligent co-inference is proposed to realize the collaborative automatic modulation classification (C-AMC) between them. A spectrum semantic compression neural network (SSCNet) with the lightweight structure is designed for the edge device to compress the collected raw data into a compact semantic message that is then sent to the edge server via the wireless channel. On the edge server side, a modulation classification neural network (MCNet) combining bidirectional long short-term memory (Bi-LSTM) and multi-head attention layers is elaborated to determine the modulation type from the noisy semantic message. By leveraging the computation resources of both the edge device and the edge server, high transmission overhead and risks of data privacy leakage are avoided. The simulation results verify the effectiveness of the proposed C-AMC framework, significantly reducing the model size and computational complexity.

关键词： Edge learning cognitive radio automatic modulation classification spectrum semantics

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KGAMC: A Novel Knowledge Graph Driven automatic modulation classification Scheme 59

KGAMC: A Novel Knowledge Graph Driven Automatic Modulation C...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Li, Yike Yuan, Lu Zhou, Fuhui Wu, Qihui Al-Dhahir, Naofal Wong, Kai-Kit Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Zhejiang Lab Hangzhou Peoples R China Univ Texas Dallas Dallas TX USA UCL London England

ISBN: (纸本)9781728190549

automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of blind signals, which consumes longer computation time and ignores the interpretability of the model learning process. To solve these problems, we propose a novel knowledge graph (KG) driven AMC (KGAMC) scheme by training the networks under the guidance of domain knowledge. A modulation knowledge graph (MKG) with the knowledge of modulation technical characteristics and application scenarios is constructed and a relation-graph convolution network (RGCN) is designed to extract knowledge of the MKG. This knowledge is utilized to facilitate the signal features separation of the data-oriented model by implementing a specialized feature aggregation method. Simulation results demonstrate that KGAMC achieves superior classification performance compared to other benchmark schemes, especially in the low signal-to-noise ratio (SNR) range. Furthermore, the signal features of the high-order modulation are more discriminative, thus reducing the confusion between similar signals.

关键词： automatic modulation classification data-and-knowledge dual-driven knowledge graph feature aggregation

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LEARNABLE STATISTICAL MOMENTS POOLING FOR automatic modulation classification 49

LEARNABLE STATISTICAL MOMENTS POOLING FOR AUTOMATIC MODULATI...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Harper, Clayton Thornton, Mitchell A. Larson, Eric C. Southern Methodist Univ Dallas TX 75205 USA

ISBN: (纸本)9798350344868;9798350344851

We introduce a differentiable statistical moment aggregation layer, enabling networks to learn the optimal method of statistical moment pooling for automatic modulation classification. Statistical pooling, a cornerstone of convolutional networks, consolidates activations into fixed-length representations. Traditionally, this entails mean, variance, and higher-ordered statistics pooling defined as fixed hyperparameters. By enabling the statistics layer to become differentiable, networks are able to optimize the method of statistical aggregations, transcending predefined hyperparameters. With our approach, the statistical moment order is differentiable. Our results demonstrate learned statistical moments are able to outperform fixed-moments-improving modulation classification performance of a time-domain signal.1

关键词： Statistical moments automatic modulation classification deep learning learnable pooling

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automatic modulation classification of radar signals utilizing X-net

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DIGITAL SIGNAL PROCESSING 2022年第0期123卷 103396-103396页

作者： Chen, Kuiyu Zhang, Jingyi Chen, Si Zhang, Shuning Zhao, Huichang Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

automatic modulation classification (AMC) of radar signals has long been a challenge, especially in the area of electronic reconnaissance, where collecting and labeling numerous signal samples are usually harsh and impracticable. In this article, a novel recognition network is proposed for detecting radiation signals under intense noise background with only simulation samples for model training. Owing to the X-shaped structure, the recognition network is named X-net. A residual learning convolutional denoising autoencoder (RLCDAE) and a supplementary classification network based on noise level estimation (NLE) constitute the X-net. Via pre-training of RLCDAE, the robustness against noise is enhanced. Then, a supplementary classification network further improves the recognition performance under low signalto-noise ratios (SNRs). To evaluate the method, comparative experiments with some excellent algorithms on noise immunity and recognition accuracy are conducted. The cognitive system can still recognize ten kinds of radar signals with an overall precision of 96% even when the SNR is -8 dB. Furthermore, simulation samples trained model is verified by measured data. Outstanding performance proves the effectiveness and superiority of the proposed method on cognitive radar signals under low SNRs.& nbsp;(C) 2022 Elsevier Inc. All rights reserved.

关键词： automatic modulation classification Radar signals Residual learning convolutional denoising autoencoder Supplementary classification networks Noise level estimation Measured data

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