Classification of voiceband data signals using the constellation magnitude

Benvenuto, N.; Goeddel, T. W.

doi:10.1109/26.481227

Cited by 19 publications

(7 citation statements)

References 21 publications

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“…If 4QAM it is the last result and the recursive process is finished. Summarize what have said above, the hierarchical digital modulation recognition process can be illustrated as figure 6. The signals are classified into four subclasses by four-order cumulants, and recognize the signals of first and second subclass separately.…”

Section: B Frist Subclass: π/4dqpsk and 8pskmentioning

confidence: 99%

“…One the other hand, a number of feature-based classification schemes have been proposed. The features include the following attributes derived from: estimates of the instantaneous amplitude, frequency, and phase [5]; outputs from simple zero-memory nonlinearities [6]; the empirical characteristic function and the Radon transform [7]; moments of the extracted phase [8]; and the characteristic function of the phase [9]. In the context of classifying Mary frequency-shift keying (MFSK) signals, a method based on higher order correlations is studied in [10]; a method based on the moments of the "I-Q image" is proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical digital modulation recognition based on higher-order cumulants

Han¹,

Wei²,

Song³

et al. 2012

2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control

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A simple method, based on elementary high-order cumulants, is proposed for the classification of digital modulation schemes. It is shown that cumulant-based classification is particularly effective when used in a hierarchical scheme, enabling separation into subclasses at low signal-to-noise ratio with small sample size. The modulation of BPSK, QPSK, OQPSK, 8PSK, π/4DQPSK, 16APK, 16QAM and 64QAM will be recognized base on four and six order cumulants of signals. The efficiency of the algorithm is proved in theory. Computer simulations show that the percent correct classification of the proposed recognition algorithm is better than 90% except the 64QAM when E b /N 0 >10dB.

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Section: B Frist Subclass: π/4dqpsk and 8pskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical digital modulation recognition based on higher-order cumulants

Han¹,

Wei²,

Song³

et al. 2012

2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control

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show abstract

“…Shimbo [5] proposed a method using the joint moments with phase offset for the classification among BPSK, QPSK and 16QAM. Although amplitude distributions to classify various QAM signals were introduced by [6], this method needs high SNR and a number of samples. Another method of amplitude moments [7] requires a number of samples, as well as the method using amplitude distributions, for example, 5000 at SNR=12[dB] with a possible optimization.…”

Section: Introductionmentioning

confidence: 99%

Modulation classification based on amplitude and cosine moments

Ohara

Masato

Oka

et al. 2009

2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

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“…In order to classify modulations with reasonable complexity, modulation classification methods based on moments and cumulants were developed. Phase moments approach was proposed to classify MPSK in [5], and amplitude moments approach was presented to classify V.29 signals in [6], [7]. In [8], [9], a classification method based on joint moments of amplitude and phase were developed to improve the classification performance.…”

Section: Introductionmentioning

confidence: 99%

A modulation classification using amplitude moments in OFDM systems

Shimbo

Oka

2010

2010 International Symposium on Information Theory &Amp; Its Applications

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In adaptive modulation systems, the receiver should identify the modulation employed at the transmitter. This paper presents a new modulation classification method based on amplitude moments and correlation between subcarriers in Orthogonal Frequency Division Multiplexing (OFDM) systems. In the OFDM systems, a frequency offset causes inter-carrier interference (ICI), and the amplitude moments of received signal include ICI components. The proposed method estimates and removes the ICI components using the correlation between subcarriers. Simulation results of classification error probability show that the proposed method is robust to frequency offset. I. INTRODUCTIONAdaptive modulation is an important technique to improve communication efficiency in time-varying channel conditions. In the adaptive modulation systems, a transmitter selects the best modulation against the channel condition, and a receiver has to identify the modulation employed by the transmitter. Since modulation classification is an intermediate step between signal detection and demodulation, a misclassification results in burst errors on the demodulated data. Therefore, the receiver should identify the modulation accurately. The statistical information of the received signal is important and useful to identify the modulation without side information.The receiver has difficulties in regenerating a reference carrier frequency with sufficient accuracy from the received signal, because a channel condition changes over time. In orthogonal frequency division multiplexing (OFDM) systems, the carrier frequency offset causes inter-carrier interference (ICI).Many works were devoted to the statistical information in a modulation classification. Likelihood ratio methods were proposed in [1], [2], [3], where M-ary Phase Shift Keying (MPSK) was identified by an estimated likelihood function. The likelihood ratio method was extended to include unknown signal level in [4], and showed a good performance especially in low signal-to-noise power ratio (SNR). However, these methods have computational complexity. In order to classify modulations with reasonable complexity, modulation classification methods based on moments and cumulants were developed. Phase moments approach was proposed to classify MPSK in [5], and amplitude moments approach was presented to classify V.29 signals in [6], [7]. In [8], [9], a classification method based on joint moments of amplitude and phase were

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Classification of voiceband data signals using the constellation magnitude

Cited by 19 publications

References 21 publications

Hierarchical digital modulation recognition based on higher-order cumulants

Hierarchical digital modulation recognition based on higher-order cumulants

Modulation classification based on amplitude and cosine moments

A modulation classification using amplitude moments in OFDM systems

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