Cumulant based automatic modulation classification of QPSK, OQPSK, 8-PSK and 16-PSK

Das, Dibyajyoti; Bora, Prabin Kumar; Bhattacharjee, Ratnajit

doi:10.1109/comsnets.2016.7439996

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…Instead of using the timing synchronization points per symbol for calculation, all FIGURE 12 The P CI as a function of the power ratio h 2 1 ∕h 2 2 the sampled points per symbol are taken into consideration in the derivation of the generalized theoretical values. Therefore it is more robust in blind environment for the release of the demand for prior knowledge of timing information and symbol period [20][21][22][23][24]. Moreover, a novel multipath interference detection method is proposed based on the derived generalized HOS features.…”

Section: Discussionmentioning

confidence: 99%

A high order statistics based multipath interference detection method

2021

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Multipath interference commonly exists in wireless communication, navigation and radar systems, which may cause severe signal fading and bit error rate (BER) performance degradation. Therefore the detection of multipath interference is urgently needed. This paper proposes a high order statistics (HOS) based multipath interference detection method, since the HOS of the received signal show distinct difference when multipath interference exists. Moreover, the generalized theoretical values of the moments and cumulants, which release the demand for prior knowledge of timing information and symbol period, are deduced in this paper. It makes the proposed HOS features based detection method robust in blind environment. Computer simulations are performed to verify the proposed method.

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Section: Discussionmentioning

confidence: 99%

A high order statistics based multipath interference detection method

2021

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“…In the second module of the FB-AMC, some methods are reported for decision making [10]. Several common approaches, such as decision tree (DT) [22][23][24]30], artificial neural networks (ANNs) [27,32], machine learning (ML) and support vector machine (SVM) [25,29,34−36], k-nearest neighbour (KNN) [26,33], genetic programming (GP) [25,26,31], PDF-based algorithm [11], particle swarm optimization (PSO) [36], principal component analysis (PCA) [27], and combinations of some techniques have been used for decision making. The decision making schemes of these algorithms can be seen in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Automatic modulation classification using modulation fingerprint extraction

Norolahi

Azmi

Ahmadi

2021

J. of Syst. Eng. Electron.

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An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.

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“…HOS features are used to classify M-PSK and M-QAM modulation types, which are robust against AWGN [12,40,48,[51][52][53][54][55]. Furthermore, the multipath channel effects can be easily modelled using the HOS features [12,31,56,57], which are robust to frequency, phase offset, and timing errors [52,53,58]. In [48,49], the extracted features from ratio and absolute of HOC are used as a characteristic parameter for classifying between M-FSK and M-PSK, M-ASK, and 16 QAM, and between M-PSK and M-ASK [59].…”

Section: Statistical Features For Mrmentioning

confidence: 99%

“…In [48,49], the extracted features from ratio and absolute of HOC are used as a characteristic parameter for classifying between M-FSK and M-PSK, M-ASK, and 16 QAM, and between M-PSK and M-ASK [59]. In [53], the authors used fourth-order cumulants to recognize M-PSK. They divided their algorithm into two steps.…”

Section: Statistical Features For Mrmentioning

confidence: 99%

“…However, M-PSK and the required increased number of samples were not identified to enhance classification accuracy. Previous studies [48,49,52,53,58,60,88] used HOS as the features for DT, whereas [59] combined instantaneous and HOS as the features for DT. In [40], DT used a hierarchal hybrid by combining FB and LB, where the first stage was to discriminate among MFSK, PSK, and QAM subclasses depending on the variance threshold of instantaneous amplitude.…”

Section: Dtmentioning

confidence: 99%

See 1 more Smart Citation

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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Cumulant based automatic modulation classification of QPSK, OQPSK, 8-PSK and 16-PSK

Cited by 12 publications

References 10 publications

A high order statistics based multipath interference detection method

A high order statistics based multipath interference detection method

Automatic modulation classification using modulation fingerprint extraction

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

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