In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a GNU radio-based data set that mimics the imperfections in a real wireless channel and uses 10 different modulation types. A Convolutional Neural Network (CNN) architecture was then developed and shown to achieve performance that exceeds that of expert-based approaches. Here, we continue this line of work and investigate deep neural network architectures that deliver high classification accuracy. We identify three architectures - namely, a Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term Memory neural network (LSTM), and a deep Residual Network (ResNet) - that lead to typical classification accuracy values around 90% at high SNR. We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy. To this end, we demonstrate the performance of Principal Component Analysis in significantly reducing the training time, while maintaining good performance at low SNR. We also investigate subsampling techniques that further reduce the training time, and pave the way for online classification at high SNR. Finally, we identify representative SNR values for training each of the candidate architectures, and consequently, realize drastic reductions of the training time, with negligible loss in classification accuracy.
Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Unmanned System Research Institute, Northwestern Polytechnical University（西北工业大学无人系统研究院）；School of Electronics And Information, Northwestern Polytechnical University（西北工业大学电子与信息学院）；Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki（希腊亚里士多德大学电气与计算机工程系）
National University of Defense Technology（国防科学技术大学）
Currently, deep learning methods with stacking small size convolutional filters are widely used for automatic modulation classification (AMC). In this report, we find some experienced improvements by using large kernel size for convolutional deep convolution neural network based AMC, which is more efficient in extracting multi-scale features of the raw signal I/Q sequence data. Also, Squeeze-and-Excitation (SE) mechanisms can significantly help AMC networks to focus on the more important features of the signal. As a result, we propose a multi-scale feature network with large kernel size and SE…
DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Electronics And Information, Northwestern Polytechnical University（西北工业大学电子与信息学院）；Unmanned System Research Institute, Northwestern Polytechnical University（西北工业大学无人系统研究院）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；US Army Research Laboratory（美国陆军研究实验室）
This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning pre-training step, our framework achieves higher performance given smaller amounts of labeled data, thereby largely reducing the labeling burden of deep learning. We evaluate the performance of our semi-supervised framework on a public dataset. The evaluation results demonstrate that our semi-supervised approach significantly outperforms supervised frameworks thereby substan…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Utah State University（犹他州立大学）
Automatic modulation classification enables intelligent communications and it is of crucial importance in today's and future wireless communication networks. Although many automatic modulation classification schemes have been proposed, they cannot tackle the intra-class diversity problem caused by the dynamic changes of the wireless communication environment. In order to overcome this problem, inspired by face recognition, a novel automatic modulation classification scheme is proposed by using the multi-scale network in this paper. Moreover, a novel loss function that combines the center loss …
Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of differ…
Informatics and Information Security Research Center (BİLGEM), TÜBİTAK（土耳其科学技术研究理事会信息学与信息安全研究中心）；Istanbul Technical University（伊斯坦布尔技术大学）；Balıkesir University（巴勒克埃西尔大学）；Yıldız Technical University（耶尔德兹技术大学）；Boğaziçi University（海峡大学）
Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and major improvements are reported. In this paper, a novel convolutional neural network (CNN) classifier model is proposed to classify modulation classes in terms of their families, i.e., types. The proposed classifier is robust against realistic wireless channel impairments and in re…
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Kostas Research Institute at Northeastern University（东北大学科斯塔斯研究所）；Northeastern University（东北大学）
Science and Technology on Electronic Information Control Laboratory（电子信息控制科技实验室）
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as the role of front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks are developed to take complementary advantage of parallel computing capability of convolutional neural networks and tempora…
Graduate Institute of Electrical Engineering, National Taiwan University（台湾大学电机工程学研究所）
To develop intelligent receivers, automatic modulation classification (AMC) plays an important role for better spectrum utilization. The emerging deep learning (DL) technique has received much attention in AMC due to its superior performance in classifying data with deep structure. In this work, a novel polar-based deep learning architecture with channel compensation network (CCN) is proposed. Our test results show that learning features from polar domain (r-theta) can improve recognition accuracy by 5% and reduce training overhead by 48%. Besides, the proposed CCN is also robust to channel fa…
Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed for AMC. However, the computation cost is very high, which makes them inappropriate for beyond the fifth generation wireless communication networks that have stringent requirements on the classification accuracy and computing time. In order to tackle those challenges, a novel involution enabled AMC scheme is proposed by using the bottleneck structure of the residual networks. Invol…
School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China（电子科技大学长三角研究院（湖州））
Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes. Recently, the emerging deep learning (DL) research has facilitated high-performance DL-AMR approaches. However, most DL-AMR models only focus on recognition accuracy, leading to huge model sizes and high computational complexity, while some lightweight and low-complexity models struggle to meet the accuracy requirements. This letter proposes an efficient DL-AMR model based on phase parameter estimation and transformation, with convolutional neural networ…
Southern University of Science and Technology（南方科技大学）
Recent learning-based image classification and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals are crucial for modulation mode recognition, this paper proposes a time-frequency attention mechanism for a convolutional neural network (CNN)-based modulation recognition framework. The proposed time-frequency attention module is designed to learn which channel, frequency and time…
Ultralight Signal Classification Model for Automatic Modulation Recognition
Multiverse Computing（多元宇宙计算公司）；DAS Photonics（DAS光子学公司）；DIPC (Donostia International Physics Center)（多诺斯蒂亚国际物理中心（DIPC））；Ikerbasque Foundation for Science（伊克尔巴斯克科学基金会）
Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification ab…
Joint Signal Detection and Automatic Modulation Classification via Deep Learning
Future Network of Intelligence Institute (FNii)（未来智能网络研究所（FNii））；School of Science and Engineering (SSE)（科学与工程学院（SSE））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）
In this paper, we have proposed a novel algorithm for identifying the modulation scheme of an unknown incoming signal in order to mitigate the interference with primary user in Cognitive Radio systems, which is facilitated by using Automatic Modulation Classification (AMC) at the front end of Software Defined Radio (SDR). In this study, we used computer simulations of analog and digital modulations belonging to eleven classes. Spectral based features have been used as input features for Sequential Minimal Optimization (SMO). These features of primary users are stored in the database, then it m…
Sichuan University（四川大学）；Rutgers University – New Brunswick（罗格斯大学新伯朗士威校区）；Chengdu University of Information Technology（成都信息工程大学）
Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR. Based on expert experience and spectrograms, they not only increase the difficulty of preprocessing, but also consume a lot of memory. In order to directly use in-phase and quadrature (IQ) data obtained by the receiver and enhance the efficiency of network extraction features to improve the recognition rate of modulation mode, this paper proposes a new network structure called Fully Dense Neural Network (FDNN). This network uses residual blocks to extract featu…
Due to the over-fitting problem caused by imbalance samples, there is still room to improve the performance of data-driven automatic modulation classification (AMC) in noisy scenarios. By fully considering the signal characteristics, an AMC scheme based on adaptive fusion network (AFNet) is proposed in this work. The AFNet can extract and aggregate multi-scale spatial features of in-phase and quadrature (I/Q) signals intelligently, thus improving the feature representation capability. Moreover, a novel confidence weighted loss function is proposed to address the imbalance issue and it is imple…
School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；School of Computer Science and Engineering, University of Science and Technology, Nanjing（南京理工大学计算机科学与工程学院）
In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, conventional DL-based approaches suffer from heavy training overhead, memory overhead, and computat…
Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show t…
Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box a…
BR-FiLM: Bounded Residual Channel-Quality Conditioning for Automatic Modulation Recognition
Virginia Commonwealth University（弗吉尼亚联邦大学）；Fairfield University（费尔菲尔德大学）；South Dakota State University（南达科他州立大学）；Stevens Institute of Technology（史蒂文斯理工学院）
