Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure

Zhang, Zufan; Luo, Hao; Wang, Chun; Gan, Chenquan; Xiang, Yong

doi:10.1109/tvt.2020.3030018

Cited by 153 publications

(74 citation statements)

References 32 publications

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“…In [ 89 ], a CNN long short-term memory (CNN-LSTM) based dual-stream structure for MC is developed. The first stream extracts local raw temporal characteristics from raw signals, while the second stream learns knowledge from amplitude and phase data.…”

Section: Artificial Intelligence-based Approach To MCmentioning

confidence: 99%

“…Various MC algorithms for the OFDM systems were carried out in [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]. The algorithms for multiple-input multiple-output and OFDM (MIMO-OFDM) systems based on deep neural network (DNN) and Gibbs sampling are investigated in [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

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Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

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Section: Artificial Intelligence-based Approach To MCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

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“…Recently, researchers in wireless communications have started to apply deep neural networks to cognitive radio tasks with some success [19]- [33]. The authors in [19], [24] demonstrated that convolutional neural networks (CNNs) trained on time domain in-phase and quadrature (IQ) data significantly outperform conventional expert feature-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

Liao¹,

Zhao²,

Gu³

et al. 2021

IEEE Access

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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 temporal sensitivity of recurrent neural networks. Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio range above -10 dB, and achieves significantly improved classification accuracy from 80% to 92.1% at high signal to noise ratio range, while drastically reduces the average training and prediction time by approximately 74% and 67%, respectively. Response patterns learned by the proposed architecture are visualized to better understand the physics of the model. Furthermore, a comparative study is performed to investigate the impacts of various sequential convolutional recurrent neural network structure settings on classification performance. A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.INDEX TERMS automatic modulation classification, convolutional neural networks, cognitive radio, deep learning, recurrent neural networks, spectrum monitoring.

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“…17, the original I/Q data and the fourth-order cumulant (FOC) were combined to represent the modulated signal, and a deep CNN network and a deep long short-term memory (LSTM) network scheme were proposed to identify the modulated signal with a recognition rate of nearly 90%. (17) Zhang et al (18) proposed a CNN-LSTM dual-stream structure in which each stream was composed of a CNN and LSTM. The features learned from the two streams mutually interacted in pairs to increase the diversity of features and improve performance.…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

Hao¹,

Luo²,

Ye³

et al. 2021

Sensors and Materials

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Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure

Cited by 153 publications

References 32 publications

A Survey of Blind Modulation Classification Techniques for OFDM Signals

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

Automatic Modulation Recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

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