Experimental results for M-FSK signal classification and parameter estimation

Wang, Hongfei; Dobre, Octavia A.; Li, Cheng; Inkol, R.

doi:10.1109/i2mtc.2013.6555722

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…random variables with values drawn from A , one can express the statistical average in (14) and easily obtain (5). Then, after simple mathematical manipulations, taking the Fourier transform of m r (t, τ ), applying properties of the Fourier transform, and using (4), (6), and (7) can be easily obtained.…”

Section: Discussionmentioning

confidence: 99%

“…For the FSK signal, 1 the analytical expressions of the second-order time-varying moment, CM, and the set of CFs are given in (5), (6), and (7), respectively; the proof is provided in the Appendix.…”

Section: Signal Model and Its Statistical Characterizationmentioning

confidence: 99%

“…Blind signal identification comprises transmission scheme identification, modulation classification, and estimation of various signal parameters (e.g., symbol period). While the transmission scheme identification, modulation classification, and estimation of parameters such as phase and frequency offset, have been extensively studied (see, e.g., [1]- [6]), symbol period estimation has received less attention and motivates the work presented in this letter.…”

Section: Introductionmentioning

confidence: 99%

“…. , v max (in case f d is unknown, one can blindly estimate it with the algorithm in[5],[6]);C = V v max ; DVP = {V 1 , V 2 , . .…”

mentioning

confidence: 99%

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Blind Cyclostationarity-Based Symbol Period Estimation for FSK Signals

Wang

Dobre

et al. 2015

IEEE Commun. Lett.

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In this letter, we propose an algorithm for blind estimation of the symbol period of frequency-shift-keying (FSK) signals affected by fading channels. The algorithm exploits the property of the second-order cyclic moment magnitude to exhibit a change in the peak pattern at a delay equal to the symbol period, which we prove analytically. It has the advantage that it requires neither channel estimation nor carrier recovery, while providing a good performance.Index Terms-Symbol period estimation, second-order cyclostationarity, frequency-shift-keying (FSK), fading channel. 1558-2558

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Section: Discussionmentioning

confidence: 99%

Section: Signal Model and Its Statistical Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…. , v max (in case f d is unknown, one can blindly estimate it with the algorithm in[5],[6]);C = V v max ; DVP = {V 1 , V 2 , . .…”

mentioning

confidence: 99%

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Blind Cyclostationarity-Based Symbol Period Estimation for FSK Signals

Wang

Dobre

et al. 2015

IEEE Commun. Lett.

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“…In 2003, Dobre used higher-order cyclic cumulants to identify ASK, PSK, and QAM [77]. In [78,79], cyclostationarity-based classification of M-FSK was performed using the first-order cycle moment corresponding to zero at frequencies other than cycle frequencies (CFs). Here, the number of first-order CFs was the feature extracted from the incoming signal and utilized to determine the modulation order, M, of an FSK received signal.…”

Section: Transform Features For Mrmentioning

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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