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automatic modulation recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

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SENSORS AND MATERIALS 2021年第12期33卷 4229-4243页

作者： Hao, Xinyu Luo, Yu Ye, Qiubo He, Qi Yang, Guangsong Chen, Chin-Cheng Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Fujian Peoples R China Guizhou Radio & Televis Bur Guiyang 550000 Guizhou Peoples R China Fujian Shipping Res Inst Xiamen 361021 Fujian Peoples R China

With the application of various wireless communication technologies, the electromagnetic environment has become more complex, and the recognition of signal modulation has become increasingly difficult. In this paper, a hybrid model based on deep learning, which aims to quickly classify received modulated signals and help to plan spectrum resources, is proposed. The model is designed by considering the characteristics of convolutional neural networks (CNNs), global average pooling (GAP), gate recurrent units (GRUs), and other structures. Firstly, signal spatial features are extracted by convolution using a CNN, the dimension of the high dimensional feature map is reduced by GAP, then the signal temporal correlation is extracted using GRUs. Finally, modulation modes are classified in the softmax layer to classify and recognize the modulation modes of the received signal. Experimental results show that the average recognition rate of the model was 60.64% and the maximum recognition rate was 90%. The proposed method not only improves the recognition performance but also enhances the interpretability of the network.

关键词： convolutional neural networks global average pooling gate recurrent units automatic modulation recognition

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automatic modulation recognition Based on a DCN-BiLSTM Network

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SENSORS 2021年第5期21卷 1577-1577页

作者： Liu, Kai Gao, Wanjun Huang, Qinghua Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model's recognition rate for the 11 modulation signals can reach 90%.

关键词： automatic modulation recognition deep complex network convolutional neural network bidirectional long short-term memory network

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automatic modulation recognition based on sample-transferable and branch-scalable method for signals in complex multipath channel

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Digital Signal Processing 2025年 166卷

作者： Yitong Lu Shujuan Hou Shiyi Yuan Qin Zhang Yazhe He Shouzhi Wang School of Information and Electronics Beijing Institute of Technology Beijing 100081 Beijing China

At present, there are a large number of mature deep learning related studies on automatic modulation recognition (AMR) for signals in the additive white Gaussian noise (AWGN) or fixed multipath channel. However, in actual communication environments, the AMR method is required to have strong generalization ability due to the complexity and variability of multipath channels. Thus, we propose a sample-transferable and branch-scalable method suitable for signals in different multipath channels. According to the generation principle of multipath signals, we first estimate the multipath signals based on the direction of arrival (DOA) estimation algorithm to obtain characteristic parameters such as the number of paths and the direction of arrival. Then we decompose the multipath signals into multi-branch single-path signals using the estimation results. On this basis, we propose a multi-branch neural network trained with signals in the AWGN channel, with the decomposed multi-branch single-path signals serving as inputs. Hence, sample transfer from the training signals in the AWGN channel to the test signals in the multipath channel can be realized, significantly improving the generalization ability of the network. Moreover, we introduce the attention mechanism module to perform feature-level fusion on multi-branch signals, and use multipath signals to obtain additional recognition gain compared to single-path signals. In response to the uncertainty of multipath number in complex multipath channel environments, we propose a branch-scalable dynamic neural network (BSDNN) with novel “dual-branch training, multi-branch recognition”, and realize the recognition of multipath signals with arbitrary path number using the network structure trained with dual-branch signals. The experimental results show that our proposed BSDNN trained with the dual-branch signals in the AWGN channel can successfully transfer to modulation recognition of multipath signals with any number of paths. Fu

关键词： automatic modulation recognition Branch-scalable dynamic neural network Complex multipath fading channel Sample transfer

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Deep learning based automatic modulation recognition: Models, datasets, and challenges

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DIGITAL SIGNAL PROCESSING 2022年第0期129卷

作者： Zhang, Fuxin Luo, Chunbo Xu, Jialang Luo, Yang Zheng, Fu-Chun Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4RN England Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen Peoples R China

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 abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed. (C) 2022 Elsevier Inc. All rights reserved.

关键词： automatic modulation recognition Deep learning Neural networks modulation

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Sparsely Connected CNN for Efficient automatic modulation recognition

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第12期69卷 15557-15568页

作者： Tunze, Godwin Brown Huynh-The, Thien Lee, Jae-Min Kim, Dong-Seong Kumoh Natl Inst Technol Sch Elect Engn Gumi Si 39177 Gyeongsangbuk D South Korea

This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines two types of sparse convolutional layers-depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network more lightweight. The network architecture leverages sparsely connected convolutional layers in three principal modules: speed-accuracy tradeoff (SAT), deep feature extraction and processing (DFEP), and generic feature extraction (GFE) data pre-processing module. For a good tradeoff between complexity and accuracy, SAT deploys depthwise convolutional layers to enrich the relevant features outputted by the former GFE module. In addition to SAT, DFEP employs a cascade of regular grouped convolutional layers for mining more discriminative features from SAT via a multilayer transformation module. This cascade structure aims to prevent a loss of essential details of the signal as the network becomes deeper. Additionally, skip connections are deployed between sub-blocks within SAT and DFEP to allow inter-module feature sharing and to handle inter-block features loss. Experimental results on the RadioML2018.01A dataset indicate that SCGNet achieves an overall recognition accuracy of around 94.39% at a signal-to-noise ratio of +20 dB.

关键词： automatic modulation recognition convolutional neural network grouped convolutional layer intelligent receiver sparse convolutional layers

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Deep Learning for Robust automatic modulation recognition Method for IoT Applications

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IEEE ACCESS 2020年 8卷 117689-117697页

作者： Zhang, Tingping Shuai, Cong Zhou, Yaru Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

In the scenarios of non-cooperative wireless communications, automatic modulation recognition (AMR) is an indispensable algorithm to recognize various types of signal modulations before demodulation in many internet of things applications. Convolutional neural network (CNN)-based AMR is considered as one of the most promising methods to achieve good recognition performance. However, conventional CNN-based methods are often unstable and also lack of generalized capabilities under varying noise conditions, because these methods are merely trained on specific dataset and can only work at the corresponding noise condition. Hence, it is hard to apply these methods directly in practical systems. In this paper, we propose a CNN-based robust automatic modulation recognition (RAMR) method to recognize three types of modulation signals, i.e., frequency shift key (FSK), phase shift key (PSK), and quadrature amplitude modulation (QAM). The proposed method is trained on a mixed dataset for extracting common features under varying noise scenarios. Simulation results show that our proposed generalized CNN-based architecture can achieve higher robustness and convenience than conventional ones.

关键词： Feature extraction Signal to noise ratio modulation Training Testing Estimation Convolution automatic modulation recognition deep learning convolutional neural network the Internet of Things

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A Spatiotemporal Multi-Channel Learning Framework for automatic modulation recognition

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IEEE WIRELESS COMMUNICATIONS LETTERS 2020年第10期9卷 1629-1632页

作者： Xu, Jialang Luo, Chunbo Parr, Gerard Luo, Yang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4RN Devon England Univ East Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England

automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D) convolutional, two-dimensional (2D) convolutional and long short-term memory (LSTM) layers to extract features more effectively from a time and space perspective. Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation (16-QAM) and 64-QAM.

关键词： Feature extraction modulation Signal to noise ratio Training Data mining Convolution Deep learning automatic modulation recognition deep learning multi-channel

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Deep Learning-Based automatic modulation recognition Method in the Presence of Phase Offset

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IEEE ACCESS 2020年 8卷 42841-42847页

作者： Shi, Jie Hong, Sheng Cai, Changxin Wang, Yu Huang, Hao Gui, Guan Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol Zijin Coll Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China Yangtze Univ Sch Elect & Informat Jingzhou 434023 Peoples R China

automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in realistic communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

关键词： Feature extraction modulation OFDM Convolutional neural networks Wireless communication Telecommunications Deep learning convolutional neural network automatic modulation recognition phase offset

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automatic Digital modulation recognition Based on Genetic-Algorithm-Optimized Machine Learning Models

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IEEE ACCESS 2022年 10卷 50265-50277页

作者： Ansari, Sam Alnajjar, Khawla A. Saad, Mohamed Abdallah, Saeed El-Moursy, Ali A. Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates

recognition of the modulation scheme is the intermediate step between signal detection and demodulation of the received signal in communication networks. automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors. In general, several properties of the received signal are extracted and employed for AMR. Selecting the appropriate features has a significant impact on increasing the efficiency of AMR. In this paper, we implement and compare digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naive Bayes (NB) algorithms. In addition, the optimal parameters of each model are obtained by utilizing a genetic algorithm (GA). A series of studies are conducted in this work in order to determine the efficiency of different algorithms in identifying modulated signals with commonly used digital modulations. Numerous computer simulations are performed in the presence of additive white Gaussian noise (AWGN) with a signal-to-noise ratio (SNR) ranging from -10 dB to 30 dB. The simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs. In addition, the convergence rates of the models are enhanced.

关键词： modulation Digital modulation Frequency shift keying Signal to noise ratio Genetic algorithms Feature extraction Machine learning automatic modulation recognition classification feature extraction genetic algorithm machine learning

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Generalized automatic modulation recognition method based on distributed learning in the presence of data mismatch problem

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PHYSICAL COMMUNICATION 2021年 48卷 101428-101428页

作者： Wang, Juan Gui, Guan Sari, Hikmet Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

Deep learning-based automatic modulation recognition (DL-AMR) methods are mainly based on centralized learning and decentralized learning. These methods have been developed for many applications in heterogeneous wireless networks (HWNs). However, the centralized DL-AMR methods involve high communications cost and computation cost and the decentralized DL-AMR methods are not robust since these methods are based on the assumption of uniform data distribution. In the practical HWNs, most sub-networks are independent and their heterogeneous datasets often do not match. In order to solve these problems, we propose a generalized AMR (GAMR) method based on distributed learning by considering the data mismatch scenario. First, each sub-network trains its own model by means of initialization model download from fusion center and formative modulated datasets. Second, subnetworks upload model weight of all sub-networks to the fusion center for re-training a generalized model, which will be immediately distributed to sub-networks for updating local model. Repeat these two steps until the global model converges successfully. Finally, simulation results are given to confirm advantages of the proposed GAMR method in different scenarios of HWNs. (C) 2021 Elsevier B.V. All rights reserved.

关键词： automatic modulation recognition Distributed learning Data mismatch Generalization ability Heterogeneous wireless networks

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