Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

Shi, Jihong; Hong, Sheng Chiong; Cai, Changxin; Wang, Yu; Huang, Hao; Gui, Guan

doi:10.1109/access.2020.2978094

Cited by 56 publications

(21 citation statements)

References 52 publications

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“…The proposed module employed an ANN in order to estimate both CFO and PO of received signals. In contrast to [41], this module eliminates the effects of both CFO and PO by shifting the signal frequency and phase prior to modulation recognition. The work reported in [53] exploited the attention mechanism for the fusion of the extracted multi-scale features.…”

Section: B: Classification Using Iq Samples From Existing Datasetsmentioning

confidence: 99%

“…Although DL-AMR methods outperform the traditional modulation recognition ones [127], but there is still a long way for signal recognition in real electromagnetic environments and practical applications. The performance of FB-based AMR methods tends to degrade badly with channel impairment effects such as the high-speed mobility and impulsive nature of noise [20], multipath fading effects [32], PO [41], CFO [52], and heavy noises and interferences [88]. However, the quality of service in the complex communication environment under low SNRs is hard to be guaranteed and, therefore, further investigation is required for the robustness of DL classifiers in a larger range of SNR [43].…”

Section: A Electromagnetic Environmentmentioning

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: B: Classification Using Iq Samples From Existing Datasetsmentioning

confidence: 99%

Section: A Electromagnetic Environmentmentioning

confidence: 99%

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

et al. 2021

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“…Ref. [20] proposes a robust CNN-based approach that can precisely classify four types of modulation, including BPSK, QPSK, 8PSK, and 16QAM in an orthogonal frequency division multiplexing (OFDM) system under the presence of Phase offset (PO). In [21], CNN and LSTM have been used to solve the AMC problem.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020

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Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

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“…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%

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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Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

Cited by 56 publications

References 52 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

An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

A Survey of Blind Modulation Classification Techniques for OFDM Signals

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