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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Helmy, Islam Choi, Wooyeol Chosun University Dept. of Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350324136

automatic modulation classification (AMC) is one of the foremost challenges in multi-input multi-output (MIMO) systems. In literature, various approaches have been presented where the deep learning (DL) models show remarkable performance. However, the existing methods mainly focus on training using large datasets, which is insignificant in high-mobility environments where rapid changes occur. In this study, we investigate the performance of existing DL models for small datasets and reduce the complexity by eliminating the equalizer. We apply two DL models to estimate the channel modulation among four modulation techniques. We also vary the number of transmitters for different datasets where the number of receiver antennas is 4 and 8, respectively. Furthermore, we verify the signal-To-noise ratio (SNR) of 0 to 40 dB. The results show that the convolutional neural network (CNN) performs better than the multi-layer perceptron (MLP) applied to the different datasets. © 2024 IEEE.

关键词： automatic modulation classification deep learning MIMO

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MULTI-TASK LEARNING FOR RADAR SIGNAL CHARACTERISATION

MULTI-TASK LEARNING FOR RADAR SIGNAL CHARACTERISATION

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

作者： Huang, Zi Pemasiri, Akila Denman, Simon Fookes, Clinton Martin, Terrence Queensland Univ Technol Brisbane Australia Revolut Aerosp Eagle Farm Australia

ISBN: (纸本)9798350302615

Radio signal recognition is a crucial task in both civilian and military applications, as accurate and timely identification of unknown signals is an essential part of spectrum management and electronic warfare. The majority of research in this field has focused on applying deep learning for modulation classification, leaving the task of signal characterisation as an understudied area. This paper addresses this gap by presenting an approach for tackling radar signal classification and characterisation as a multi-task learning (MTL) problem. We propose the IQ Signal Transformer (IQST) among several reference architectures that allow for simultaneous optimisation of multiple regression and classification tasks. We demonstrate the performance of our proposed MTL model on a synthetic radar dataset, while also providing a first-of-its-kind benchmark for radar signal characterisation.

关键词： Multi-task Learning Radio Signal Recognition Radar Signal Characterisation automatic modulation classification Radar Dataset Transformer

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automatic modulation Recognition: An FPGA Implementation

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IEEE COMMUNICATIONS LETTERS 2022年第9期26卷 2062-2066页

作者： Kumar, Satish Mahapatra, Rajarshi Singh, Anurag Dr SPM Int Inst Informat Technol Dept Elect & Commun Engn Raipur 493661 Chhattisgarh India

Very recently, convolution neural network (CNN) based deep-learning (DL) models have been used in automatic modulation classification (AMC) systems and achieved superior performance. However, the huge CNN model size and floating-point weights and activation make deployment of such systems very complex with limited hardware resources such as FPGAs. In this work, we have designed CNN-based AMC schemes for the complex-valued temporal radio signal domain and implemented them on an FPGA platform. This work focuses on quantized CNN and low precision mathematics to solve the problem of bigger model size and floating-point weights and activations. However, the low precision weights, activation, and quantized CNN significantly impact the model's accuracy. To overcome this, we proposed an RU-based AMC scheme and used an iterative pruning-based training mechanism to maintain the overall accuracy above a certain threshold while decreasing the model size on hardware. The proposed scheme achieves at least 1.4% higher accuracy and takes only 40% hardware resources than the baseline. The RFSoc implemented model achieves a real-time throughput of 527k classification per second with a latency of 7.5 mu s.

关键词： automatic modulation classification convolution neural network iterative pruning quantization FPGA

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A Robust Cooperative modulation classification Scheme with Intra-sensor Fusion for the Time-correlated Flat Fading Channels

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DEFENCE SCIENCE JOURNAL 2020年第1期70卷 60-65页

作者： Markovic, Goran Sokolovic, Vlada Univ Belgrade Sch Elect Engn Belgrade Serbia Univ Def Belgrade Mil Acad Belgrade Serbia

Networks with distributed sensors, e.g. cognitive radio networks or wireless sensor networks enable large-scale deployments of cooperative automatic modulation classification (AMC). Existing cooperative AMC schemes with centralised fusion offer considerable performance increase in comparison to single sensor reception. Previous studies were generally focused on AMC scenarios in which multipath channel is assumed to be static during a signal reception. However, in practical mobile environments, time-correlated multipath channels occur, which induce large negative influence on the existing cooperative AMC solutions. In this paper, we propose two novel cooperative AMC schemes with the additional intra-sensor fusion, and show that these offer significant performance improvements over the existing ones under given conditions.

关键词： automatic modulation classification Multi-sensor fusion Decision fusion Time-correlated fading channels

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Deep Learning-Based Anomaly Detection using Hybrid Loss 14

Deep Learning-Based Anomaly Detection using Hybrid Loss

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Onyekwelu, Michael Jang, Mingyu Yoon, Dongweon Hanyang University Department of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Recently, deep learning-based (DL) automatic modulation classification (AMC) has been extensively studied in cooperative and non-cooperative communication contexts such as cognitive radio and spectrum surveillance. One of the drawbacks of DL-based AMC is its susceptibility to anomalous or interfering signals. In this paper, we propose a DL-based anomaly detection for AMC, utilizing an autoencoder to process the in-phase and quadrature components of a received signal. In order to detect anomalies, we employ a hybrid loss, a combination of the autoencoder's reconstruction loss and the Mahalanobis distance of the latent space embedding of the training vector and each input instance. Through computer simulations, we show that the proposed model has superior detection performance with less computational complexity compared to the conventional DL-based model. © 2023 IEEE.

关键词： Anomaly Detection Autoencoder automatic modulation classification

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Probabilities of False Alarm and Detection for the First-Order Cyclostationarity Test: Application to modulation classification

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IEEE COMMUNICATIONS LETTERS 2020年第1期24卷 57-61页

作者： Serbes, Ahmet Cukur, Huseyin Qaraqe, Khalid Yildiz Tech Univ Elect & Commun Engn Dept TR-34220 Istanbul Turkey Texas A&M Univ Dept Elect Doha Qatar Texas A&M Univ Comp Engn Dept Doha Qatar

automatic modulation classification is one of the most challenging problems of cognitive radio and has significant commercial and military application. This letter focuses on blind modulation classification of first-order cyclostationary signals under a Gaussian channel. A detailed theoretical analysis of the first-order cyclostationarity test has been performed. We present the theoretical expressions of the probability of false alarm P-fa and the probability of detection P-d as a function of the selected threshold for the first-order cyclostationarity test based constant false alarm rate detector. The P-d is presented for only AM and M-FSK signals, which are the only signals that possess first-order cyclostationary features. Theoretical formulations presented in this letter are validated by extensive simulation results.

关键词： First-order cyclostationarity test blind modulation identification automatic modulation classification

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automatic modulation classification Using an 8PSK Demodulator for Variants of QPSK

Automatic Modulation Classification Using an 8PSK Demodulato...

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6th International Symposium on Telecommunications (IST) with Emphasis on Information and Communication Technology

作者： Farhang, Mohsen Bahramgiri, Hossein Dehghani, Hamid Malek Ashtar Univ Technol Dept Elect & Elect Engn Tehran Iran

ISBN: (纸本)9781467320733

In this paper a feature-based modulation classification algorithm is developed for discriminating PSK signals. The candidate modulation types are assumed to be QPSK, OQPSK, pi/4 DQOSK and 8PSK. The proposed method applies an 8PSK baseband demodulator in order to extract required features from observed symbols. The received signal with unknown modulation type is demodulated by an 8PSK demodulator whose output is considered as a finite state machine with different states and transitions for each candidate modulation. Estimated probabilities of particular transitions constitute the discriminating features. The obtained features are given to a classifier which decides on the modulation type of the received signal, according to the patterns of candidate modulations. In order to evaluate the performance of the proposed method, the probability of correct classification is computed with different number of observed symbols and SNR conditions by carrying out several simulations. The results show that the proposed method offers more accurate classification compared to previous methods for classifying variants of QPSK.

关键词： automatic modulation classification feature extraction pattern recognition variants of QPSK bayes classifier

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ElecDaug: Electromagnetic Data Augmentation for Model Repair based on Metamorphic Relation 7

ElecDaug: Electromagnetic Data Augmentation for Model Repair...

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37th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： He, Jiawei Bao, Zhida Zhang, Quanjun Sun, Weisong Liu, Jiawei Fang, Chunrong Lin, Yun Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Harbin Engn Univ Harbin Peoples R China

ISBN: (纸本)9781450394758

With the application of deep learning (DL) in signal detection, improving the robustness of classification models has received much attention, especially in automatic modulation classification (AMC) of electromagnetic signals. A large amount of electromagnetic signal data is required to obtain robust models in the training and testing process. However, the high cost of manual collection and the issue of low quality of automatically generated data contribute to the AMC model's defects. Therefore, it is essential to generate electromagnetic data by data augmentation. In this paper, we propose a novel electromagnetic data augmentation tool, namely ElecDaug, which directs the metamorphic process by electromagnetic signal characteristics to achieve automatic data augmentation. Based on electromagnetic data pre-processing, transmission or time-frequency domains characteristic metamorphic, ElecDaug can augment the data samples to build robust AMC models. Preliminary experiments show that ElecDaug can effectively augment available data samples for model repair. The video is at https://***/x5g6IVX_Q3s. Documentation and source code can be found here: https://***/ehhhhjw/tool_***.

关键词： Model Repair Data Augmentation Metamorphic Relation automatic modulation classification

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An Effective Radio Frequency Signal classification Method Based on Multi-Task Learning Mechanism 96

An Effective Radio Frequency Signal Classification Method Ba...

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IEEE 96th Vehicular Technology Conference (VTC-Fall)

作者： Liu, Hongwei Hao, Chengyao Peng, Yang Wang, Yu Ohtsuki, Tomoaki Gui, Guan NJUPT Coll Telecommun & Informat Engn Nanjing Peoples R China Keio Univ Dept Informat & Comp Sci Yokohama Kanagawa Japan

ISBN: (纸本)9781665454681

With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1% relative to the single-task learning (STL) network. The keras code is released at https://***/LiuK1288/lhw-000.

关键词： Radio frequency fingerprint automatic modulation classification deep learning multi-task learning

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Multi-Channel Learning with Preprocessing for automatic modulation Order Separation 27

Multi-Channel Learning with Preprocessing for Automatic Modu...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Sumen, Gizem Celebi, Burak Ahmet Kurt, Gunes Karabulut Gorcin, Ali Basaran, Semiha Tedik TUBITAK Informat & Informat Secur Res Ctr BILGEM Kocaeli Turkey Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey Polytech Montreal Dept Elect Engn Polygrames Res Ctr Montreal PQ Canada Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781665497923

automatic modulation classification (AMC) with deep learning (DL) based methods has been studied in recent years and improvements have been shown in many studies;however, it has been difficult to design a classifier that can distinguish modulation orders such as 16-QAM and 64-QAM, with high accuracy. In this study, the distinction performance of 16-QAM and 64-QAM modulation orders increased by feeding the features obtained during the preprocessing stage to the multi-channel convolutional long short-term deep neural network (MCLDNN). Simulation results indicate performance improvements, particularly at the low SNR region. Furthermore, the proposed method can be extended for the separation of other orders of QAM and other digital modulations.

关键词： automatic modulation classification convolutional neural network cumulant deep learning feature extraction

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