Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

Zhang, Tingping; Cong, Shuang; Zhou, Yaru

doi:10.1109/access.2020.2981130

Cited by 23 publications

(15 citation statements)

References 48 publications

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“…CNN and LSTM were employed in [46] for modulation scheme classification. The method in [47] was proved to have higher robustness against SNR variation and less memory consuming than the benchmark methods. Similarly, the algorithm in [48] was proposed for varying noise regimes with less computational complexity and likewise smaller model sizes.…”

Section: ) Cnn-based Methodsmentioning

confidence: 99%

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Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

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The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

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Section: ) Cnn-based Methodsmentioning

confidence: 99%

“…carrier frequency, baud rate, offset timing, etc.) [52], while others are limited to a small number of modulation schemes [47], [48]. Besides, some algorithms have considerable computational complexity [42] and cannot be used in real-time applications.…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

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“…In [22] HybridNet is proposed to show the enhanced performance, which exploits both CNN and a bidirectional gated recurrent unit to capture temporal depecdencies. In [23] a CNN-based AMC method is proposed, where the architecture is designed to improve the generalized capability under varying noise conditions. In [24] a multi-stream CNN is proposed, which shows the network architecture is extended horizontally to extract diverse key features and to mitigate the over-fitting problem.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

Kim

Nwadiugwu

et al. 2021

IEEE Access

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In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as 2 × N size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as 4 × N size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from 4 × N to 2 × N by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range. INDEX TERMSAutomatic modulation classification, Deep learning model, Convolution neural network, Frame extension, Cognitive radio.

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“…Feature extraction based on deep learning is the key part of the modulation recognition method. Many neural networks have been proposed to achieve better performance [13]. In 2012, Hinton uses AlexNet to perform gesture recognition in the ILSVRC competition and won the championship.…”

Section: Introductionmentioning

confidence: 99%

A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

Gao

Wang

et al. 2021

IEEE Access

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The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based on flying fish swarm algorithm. First, Short-Time Fourier Transform, Choi-Williams Distribution, and Cyclic Spectrum are combined to complete multi-channel signal processing. Second, AlexNet, VGGNet, GoogLeNet, and ResNet are transferred to realize feature extraction. Third, the support vector machine classifies the modulations after dimension reduction and feature fusion. Finally, the flying fish swarm is proposed to optimize the signal processing methods, the types of networks, the layers of networks, the dimensions of features, and the parameters of the support vector machine. The method can accurately recognize BPSK, QPSK, OQPSK, 8PSK, 4ASK, QAM16, QAM32, and QAM64. The simulation results show that the average recognition rate of modulation is 94.5% at SNR of 0 dB and 84.7% at SNR of -4 dB. Besides, the proposed modulation recognition method possesses good robustness under low SNR conditions.INDEX TERMS Modulation recognition, deep learning, flying fish swarm algorithm, multi-channel signal processing, feature extraction.

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Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

Cited by 23 publications

References 48 publications

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

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