Blind Digital Modulation Identification for MIMO Systems in Railway Environments With High-Speed Channels and Impulsive Noise

Kharbech, Sofiane; Dayoub, Iyad; Zwingelstein-Colin, Marie; Simon, Eric Pierre

doi:10.1109/tvt.2018.2834869

Cited by 24 publications

(15 citation statements)

References 24 publications

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“…The ANN-based AMR algorithm in [20] was proposed for MIMO systems, and it was then modified in [21] by considering the time variation in the channel over an observation interval. A sliding window simplified constant modulus algorithm (SW-SCMA) was used during the blind channel estimation phase.…”

Section: B Ml-amr Methods For Mimo Systemsmentioning

confidence: 99%

“…The second algorithm assumes that the channel matrix is perfectly known at the receiver side and it uses the Zero-Forcing (ZF) equalization to recover the transmitted signal symbols before features extraction. In the last one, the receiver has no prior knowledge of the channel matrix and it is blindly estimated by SCMA likewise in [20]. It is worth mentioning that the final decision was made using a majority rule by fusing all individual decisions.…”

Section: B Ml-amr Methods For Mimo Systemsmentioning

confidence: 99%

“…Many existing works employed ML classifiers in order to improve the classification accuracy of AMR for both SISO and MIMO systems. Numerous researches were proposed based on ANNs [15], [20]- [22], Extreme Learning Machine (ELM) [23]- [25] as well as Support Vector Machine (SVM) [26], [27]. In this section, the presented ML classifiers are considered to be different from those in the DL subset.…”

Section: Machine Learning In Amr Domain For Wireless Communicationsmentioning

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 Ml-amr Methods For Mimo Systemsmentioning

confidence: 99%

Section: B Ml-amr Methods For Mimo Systemsmentioning

confidence: 99%

Section: Machine Learning In Amr Domain For Wireless Communicationsmentioning

confidence: 99%

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 simulation results showed that their algorithm could obtain excellent performance, even at low SNRs and with a very short observation interval. To deal with the BMC problem and the two major constraints in the railway transmission environment (i.e., the high speeds and impulsive nature of the noise), Kharbech et al [ 23 ] proposed a feature-based process of blind identification that includes three parts: impulsive noise mitigation, feature extraction, and classification. By analyzing the correlation functions of the received signals for certain modulation formats, Mohamed et al resolved the BMC problem in single- and multiple-antenna systems operating over frequency-selective channels in [ 24 ] and the BMC problem in the Alamouti space-time block code (STBC) System [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Jiang

Wang

et al. 2021

Sensors

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Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

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Cognitive Radio Contribution to Meeting Vehicular Communication Needs of Autonomous Vehicles

Krief¹,

Aniss²,

Berbineau³

et al. 2021

Intelligent Network Management and Control

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Blind Digital Modulation Identification for MIMO Systems in Railway Environments With High-Speed Channels and Impulsive Noise

Cited by 24 publications

References 24 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

Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Cognitive Radio Contribution to Meeting Vehicular Communication Needs of Autonomous Vehicles

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