A Research on Automatic Modulation Recognition with the Combination of the Rough Sets and Neural Network

Hua-kui, Wang; Zhang, Bin; Wu, Juanping; Han, Yan; Wu, Xiaowei; Jia, Ruo-si

doi:10.1109/pcspa.2010.201

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…When the modulated signal has a constant amplitude, such as M-FSK and M-PSK, the γ max value must be zero. Therefore, γ max can be used to discriminate between M-ASK and M-QAM in addition to constant amplitude M-FSK and M-PSK digital modulation schemes [4,31,33,[35][36][37][38][39]. The average value of γ max is used to discriminate among continuous phase FSK, Gaussian FSK, and Gaussian minimum shift keying (GMSK) [40].…”

Section: Kurtosis Of the Normalized Centered Instantaneous Amplitudementioning

confidence: 99%

“…Therefore, these features are used to classify the order of M-ASK and M-QAM [42]. In [35,38,43], σ aa is used to discriminate between the orders of ASK. σ a feature is helpful for recognizing ASK from PSK [31].…”

Section: Kurtosis Of the Normalized Centered Instantaneous Amplitudementioning

confidence: 99%

“…Some studies discussed the complexity analysis of the classifier used in MR in different approaches. For the ANN classifier in [38], the rough sets used for the selected features with the NN reduced the network complexity. A combination of the rough sets and neural network has smaller training time.…”

Section: Complexity Analysis For Classifiersmentioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

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Section: Kurtosis Of the Normalized Centered Instantaneous Amplitudementioning

confidence: 99%

Section: Kurtosis Of the Normalized Centered Instantaneous Amplitudementioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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Neural Network Classification of SDR Signal Modulation

Stebel

Krumnikl

Moravec

et al. 2016

Computer Information Systems and Industrial Management

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Part 3: Images, Visualization, ClassificationInternational audienceWith the rising popularity of Software Defined Radios (SDR), there is a strong demand for automatic detection of the modulation type and signal parameters. Automatic modulation classification is an approach to identify the modulation type and its parameters such as the carrier frequency or symbol rate. In electronic warfare, it enables real-time signal interception and processing. In civil applications, it can be used, e.g., by the amateur radio operators to automatically set the transceiver to the appropriate modulation and communication protocol. This paper presents a modulation classification driven by a neural network. A set of signal features are provided as an input of the neural network. The paper discusses the relevance of different signal features and its impact on the success rate of the neural network classification. The proposed approach is tested on both artificial and real samples captured by the SDR

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New AMR(Automatic Modulation Recognition) algorithm based on the phase difference tendency of unknown communication signal

Kim,

Ryu,

Kim

et al. 2023

2023 14th International Conference on Information and Communication Technology Convergence (ICTC)

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A Research on Automatic Modulation Recognition with the Combination of the Rough Sets and Neural Network

Cited by 4 publications

References 6 publications

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Neural Network Classification of SDR Signal Modulation

New AMR(Automatic Modulation Recognition) algorithm based on the phase difference tendency of unknown communication signal

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