Using fuzzy clustering and TTSAS algorithm for modulation classification based on constellation diagram

Ahmadi, Negar

doi:10.1016/j.engappai.2009.05.006

Cited by 18 publications

(11 citation statements)

References 12 publications

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“…Eight signals including 4 kinds of QAM and MPSK signals, respectively, are introduced for identification capability demonstration. Two-Threshold Sequential Algorithmic Scheme (TTSAS) [20], fuzzy algorithm [20] and the -centre algorithm, are adopted for performance comparison. Simulations and measurements show the effectiveness of the proposed method than the other three counterparts.…”

Section: Advanced K-means Methodmentioning

confidence: 99%

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

et al. 2016

Mobile Information Systems

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Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

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Section: Advanced K-means Methodmentioning

confidence: 99%

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

et al. 2016

Mobile Information Systems

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“…The simulation results demonstrated that such algorithms acquired 100% classification success rate for 4-QAM, 16QAM, 32QAM, and 64QAM at 10 dB SNR. In [107], two threshold sequential algorithmic schemes and PR were proposed to identify QAM and PSK. Classification was conducted by utilizing the constellation of the received signal through fuzzy clustering, and hierarchical clustering algorithms were used for classification.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

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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“…The SNR in this Letter is defined as E s / N 0 , where E s is the energy per symbol and N 0 is the power spectral density of the Gaussian noise. Since [2, 3] use oversampling and an SNR per bit definition, the SNR in these studies is recalculated to SNR per symbol for better comparison. Table 1 clearly shows that the proposed utility function outperforms, for example, the ‘fuzzy’ algorithm by at least 3 dB.…”

Section: Simulationmentioning

confidence: 99%

“…In [2], a combination of the ‘K‐means’ and ‘K‐centre’ algorithms is used for AMC and the standard deviation of the calculated prototypes is used for the AMC decision. In [3], a fuzzy ‘K‐means’ algorithm is implemented and the AMC decision is based on a Hamming neural network. Both references show that the performance of the applied clustering algorithms is mainly dependent on the defined ‘cluster validity criterion’ [4].…”

Section: Unsupervised Clusteringmentioning

confidence: 99%

Automatic modulation classification technique for radio monitoring

Weber¹,

Mitrouchev²,

Felhauer³

2015

Electron. lett.

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The automatic classification of the modulation format of a detected signal is the intermediate step between signal detection and demodulation. If neither the transmitted data nor other signal parameters such as the frequency offset, phase offset and timing information are known, then automatic modulation classification (AMC) is a challenging task in radio monitoring systems. The approach of clustering algorithms is a new trend in AMC for digital modulations. A novel algorithm called 'highest constellation pattern matching' is introduced to identify quadrature amplitude modulation and phase shift keying signals. The obtained simulation and measurement results outperform the existing algorithms for AMC based on clustering. Finally, it is shown that the proposed algorithm works in a real monitoring environment.

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Using fuzzy clustering and TTSAS algorithm for modulation classification based on constellation diagram

Cited by 18 publications

References 12 publications

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

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

Automatic modulation classification technique for radio monitoring

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