Automatic modulation type recognition

Druckmann, I.; Plotkin, E.I.; Swamy, M. N. S.

doi:10.1109/ccece.1998.682551

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…The overall recognition rate of these algorithms over 97% when SNR=10dB. Druckmann et al in [5] suggested algorithm to discriminate between analogue modulated signals (AM, FM, DSB and SSB). The overall recognition rate of this algorithm reached 95% at SNR>10dB.…”

Section: Introductionmentioning

confidence: 99%

New Algorithms for Automatic Modulation Recognition for Analogue Signals Using Multi Features

Dahap¹,

Ahmed²

2014

IJSPS

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In this paper, we suggested new algorithms to discriminate between eight analogue modulated signals (amplitude modulation (AM), frequency modulation (FM), double side band (DSB), lower side band (LSB), upper side band (USB), vestigial side band (VSB), combined (AM-FM) and carrier wave (CW)). The simulation results show that the overall recognition of the new algorithms over 97% when the signal to noise ratio (SNR)=0dB. These new algorithms not only achieve a better recognition rate, but also reduce the computational loads. 

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Section: Introductionmentioning

confidence: 99%

New Algorithms for Automatic Modulation Recognition for Analogue Signals Using Multi Features

Dahap¹,

Ahmed²

2014

IJSPS

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“…This has motivated the researcher to develop various digital modulation identification methods [1][2][3]. As the adaptive receiver can communicate with different communication standards like TDMA, CDMA and GSM, the identification of digital modulation type of a signal is to be optimized.…”

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confidence: 99%

“…The simulation for various methodologies was also carried out and reported. Modulation scheme recognition using the signal envelop method was described by Druckmann et al [3]. Lopatka et al [4] also adopted the approach incorporating fuzzy classification for 4DPSK, 16QAM and FSK schemes.…”

mentioning

confidence: 99%

“…The decision-theoretical approach is based on hypothesis testing for sourced hypotheses conditioned to a finite set of known candidate signals. The pattern recognition approach usually consist feature extraction, reduction of the feature space and classification based on the lower dimension feature space [2,3]. Once the modulation scheme is correctly identified, other operations, such as signal demodulation and information extraction, can be subsequently performed.…”

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confidence: 99%

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Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments

Periasamy¹,

Madheswaran²

2008

Wireless Pers Commun

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This paper proposes a neural network (NN) based intelligent decision making system for digital modulation classification using wavelet transform, histogram peak and higher order statistical moments. The decision making system is developed to classify the modulation schemes buried in additive white Gaussian noise and channel interference utilizing NN classifier. The performance is verified and validated for M-ary PSK, M-ary FSK, M-ary QAM and GMSK modulation schemes by examining the receiver operating characteristics, confusion matrix and probability of correct identification for various signal-to-noise ratios (SNR) and also for various decision parameters. The performance of the proposed system also has been compared with existing methods and found that this method can be considered as reliable classification method for Digital Modulation Scheme with lower SNR upto −5 dB.

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“…The simulation for various methodologies was done and compared. Modulation scheme recognition using the signal envelope method was described by Druckmann et al [3]. This method achieves better recognition rates at short data records and if the data records are longer, then this method fails to recognize to recognize.…”

Section: Introductionmentioning

confidence: 99%

Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters

Periasamy¹,

Madheswaran²

2008

Journal of Computer Networks and Communications

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A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.

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Automatic modulation type recognition

Cited by 13 publications

References 5 publications

New Algorithms for Automatic Modulation Recognition for Analogue Signals Using Multi Features

New Algorithms for Automatic Modulation Recognition for Analogue Signals Using Multi Features

Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments

Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters

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