Convolutional Neural Network Aided Signal Modulation Recognition in OFDM Systems

Hong, Sheng Chiong; Wang, Yu; Pan, Yuwen; Gu, Hao; Liu, Miao; Yang, Jie; Gui, Guan

doi:10.1109/vtc2020-spring48590.2020.9128455

Cited by 17 publications

(12 citation statements)

References 30 publications

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“…In [ 86 ], the authors here present a CNN-based MC approach for the identification of OFDM signals, which is linked to a CNN that is trained on I and Q samples. The suggested CNN-MC technique is made up of two parts: three convolutional layers and four fully connected layers.…”

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

Self Cite

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“…In the PR test, based on the higher-order cumulant extracted from the received signal, KNN and DT are selected as classifiers to identify the modulation type. Besides, the CNN-based classifier proposed in [18], called CNN-MI, is also used to compare with our deep learning model. With the same hardware condition and the datasets in As shown in Figure 7, It is clear that the highest curve comes from our network model, whose performance is far ahead of the other classifiers selected in this paper.…”

Section: The Proposed Rsn-mi Versus Other Dsmi Classifiermentioning

confidence: 99%

“…The result is that a trained network cannot effectively identify the modulation type of the signal which is generated from other channel environments, so it cannot be used in actual wireless communication systems. Besides, almost all of the current communication systems are built based on OFDM technology, but there are few OFDM related DSMI studies [17][18][19]. Therefore, it is of great research value to find an effective DSMI method suitable for multicarrier OFDM communication system.…”

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confidence: 99%

Deep learning‐based digital signal modulation identification under different multipath channels

Zhang

et al. 2021

IET Communications

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Deep learning (DL) has been applied to digital signal modulation identification (DSMI) due to its powerful feature learning ability. However, most of the existing DL-based DSMI methods are limited to specific experimental scene relating to the additive white Gaussian noise (AWGN) channel or static multipath channel. The result is that the trained network has deteriorative identification accuracy when the channel conditions change unless retrained. To solve the problem, this paper proposes a DSMI method suitable for orthogonal frequency division multiplexing (OFDM) under different multipath channels, including the variation of delay, path number and channel coefficient. This method can accurately detect the modulation feature rather than the channel's to identify the modulation type, thus reducing the network training amount. The method is divided into two parts. Firstly, traditional signal processing methods are combined, including various channel estimators and equalisers to compensate for the channel. Then a robust DL network, RSN-MI, is designed as a classifier. Unlike other DL-based DSMI methods, the influence of signal processing algorithms on DSMI performance are focused on rather than model parameters. Besides, the proposed classifier is compared with the DSMI classifier in other contributions. The results show that the classifier works better in different multipath channels. INTRODUCTIONDigital signal modulation identification (DSMI) is of great significance in wireless communication systems. As a necessary step between signal detection and demodulation, it has attracted more and more attention. The main task of DSMI is to identify the modulation type of the received signal without using any prior knowledge or using a small amount of prior knowledge, which lays the foundation for subsequent signal demodulation and information acquisition. DSMI plays an important role in both military and civil fields [1], especially in military fields, modulation identification is the prerequisite of jamming and monitoring enemy communications. It can also be used for spectrum detection and signal confirmation etc. in civil fields. Two traditional digital signal modulation identification approaches have been extensively studied: one is the maximum likelihood (ML) approach, and the other is the pattern recognition (PR) approach [2]. The maximum likelihood approach is based on the likelihood function of received signal, and the likelihood ratio is compared with the appropriate threshold value to achieve the best classification effect under the minimum errorThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…e feature extraction backbone is a convolutional neural network, AlexNet, which has won in the ImageNet classification competition [5,6], and various convolutional neural networks (VGGNet, GoogLeNet, ResNet) proposed by other scholars have brought deep learning into a new stage [7][8][9]. ese convolution extraction and information fusion operations not only have broad applications in the field of image processing, but also play an important role in signal recognition and modulation, optical communication, natural language processing, financial big data, and other fields [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research on Object Detection Algorithm Based on Multilayer Information Fusion

Baoyuan

Shen

Sun

2020

Mathematical Problems in Engineering

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At present, object detectors based on convolution neural networks generally rely on the last layer of features extracted by the feature extraction network. In the process of continuous convolution and pooling of deep features, the position information cannot be completely transferred backward. This paper proposes a multiscale feature reuse detection model, which includes the basic feature extraction network DenseNet, feature fusion network, multiscale anchor region proposal network, and classification and regression network. The fusion of high-dimensional features and low-dimensional features not only strengthens the model's sensitivity to objects of different sizes but also strengthens the transmission of information, so that the feature map has rich deep semantic information and shallow location information at the same time, which significantly improves the robustness and detection accuracy of the model. The algorithm is trained and tested in Pascal VOC2007 dataset. The experimental results show that the mean average precision of the objects in the dataset is 73.87%. At the same time, compared with the mainstream faster RCNN and SSD detection models, the mean average precision of object detection algorithm based on DenseNet is improved by 5.63% and 3.86%, respectively.

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Convolutional Neural Network Aided Signal Modulation Recognition in OFDM Systems

Cited by 17 publications

References 30 publications

A Survey of Blind Modulation Classification Techniques for OFDM Signals

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Deep learning‐based digital signal modulation identification under different multipath channels

Research on Object Detection Algorithm Based on Multilayer Information Fusion

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