Prototype of an Automatic Digital Modulation Classifier Embedded in a Real-Time Spectrum Analyzer

Vito, Luca De; Rapuano, Sergio; Villanacci, Maurizio

doi:10.1109/tim.2010.2045447

Cited by 23 publications

(9 citation statements)

References 31 publications

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“…There are several classification approaches based on feature extraction, e.g., instantaneous amplitude, phase, and frequency estimation [5], [6], wavelet transform [7]- [10], use of statistical features such as higher-order moments and cumulants [11]- [14], cyclostationary analysis [2], [15]- [17], and, application of information measures such as correntropy [18]. Particularly, the cyclostationary framework can be highlighted as a powerful method for feature extraction of signals in communication systems.…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Camara

Lima

et al. 2019

IEEE Access

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Cyclostationary analysis has several applications in communications, e.g., spectral sensing, signal parameter estimation, and modulation classification. Most of them consider the additive white Gaussian noise (AWGN) channel model, although wireless communication systems may also be subject to non-Gaussian interference and impulsive noise. In this context, the communication channel can be better modeled by heavy-tailed distributions, such as the non-Gaussian alpha-stable one. Some applications of the cyclostationary approach based on the spatial sign cyclic correlation function (SSCCF), fractional lower-order cyclic autocorrelation function (FLOCAF), and cyclic correntropy function (CCF) demonstrate that these are promising solutions for the analysis of signals in the presence of impulsive non-Gaussian noise. However, the investigation of functions above applied to digital modulation recognition in impulsive environments, and the comparison among them are topics that did not adequately explore yet. This work demonstrates that SSCCF is a particular case of the FLOCAF. Besides, a detailed analysis of the use of the FLOCAF and CCF is presented to obtain cyclostationary descriptors for the recognition of digital modulations BPSK, QPSK, 8-QAM, 16-QAM, and 32-QAM. Automatic modulation classification (AMC) architectures, based on the functions mentioned above, are also proposed. Besides, another contribution showed is that both the FLOCAF and CCF allow the symbol rate parameter estimation. The performances of AMC architectures were evaluated in the scenario with modulated signals contaminated with additive non-Gaussian alpha-stable noise. The results demonstrate that both architectures can classify signals in different contamination scenarios. However, the architecture based on the CCF is more efficient than the FLOCAF-based one.

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Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Camara

Lima

et al. 2019

IEEE Access

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“…Radar and digital communication links are intermittently verified by means of built-in noise sources that are activated during off-times [4]; alternatively, the noise can be superimposed to signals including modulated data to perform measurements of the bit error rate (BER) curves also during on-times. The performance of optoelectronic systems that transmit data through fiber cables is also tested using noise sources in conjunction with phase modulators and oscillators to produce jitter in test signals [5]- [6]. For advanced encryption applications, robust random occurrences of digital codes can be produced by sampling a voltage generated by a real noise source: these codes require much more efforts to be cracked by hackers with respect to the pseudorandom codes produced by calculus schemes [7].…”

Section: Introductionmentioning

confidence: 99%

Utilizing arbitrary waveform generators to produce noise with imposed spectral characteristics

et al. 2015

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<p>The reliability and performance of many systems mostly depend of their noise rejection capability. The behavior of systems that are prone to noise is therefore investigated since the early production stage, as well as routinely during periodical maintenance actions. Actually, there are very few noise sources available on the market, the majority of which is capable of generating only noise characterized by a uniform power spectral density in a limited bandwidth; generators capable of producing colored noise are instead very rare. In theory, arbitrary waveform generators could be deployed and suitably programmed to serve as noise sources. But, the technicians charged of test and measurement assignments will barely find application notes that support them in stepping from theory to practice.</p><p>This work presents tutorial notes related to the simulation of colored noise, and proposes an analog generator that exploits an arbitrary waveform generator as noise source. The proposed generator is capable of producing noise signals characterized by arbitrary power spectral densities.</p>

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“…El-Mahdy and Namazi [7] developed an LB approach for Rayleigh fading channels, with unknown signal parameters, such as timing and phase offset; the likelihood function was calculated by averaging over these unknowns. For the FB approach, various features were chosen for classification, such as wavelet transform magnitude [8], number of peaks in the signal spectrum [9], mean of the complex signal envelope [10], zero-crossing sequence [11], instantaneous frequency [12], and signal cyclostationarity [13]- [15]. All these works considered the simple case of the additive white Gaussian noise (AWGN) channel.…”

Section: Introductionmentioning

confidence: 99%

Experimental results for M-FSK signal classification and parameter estimation

Wang

Dobre

et al. 2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Blind signal classification and parameter estimation have important applications in both military and commercial communications. This paper proposes a cyclostationarity-based approach for M-FSK signal classification and estimation of the tone frequency spacing. Relying on the number and position of the first-order cycle frequencies (CFs), the proposed approach requires neither recovery of the carrier and symbol timing, nor estimation of the channel, and signal and noise powers. The performance of this algorithm was investigated using both laboratory and simulation experiments.

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Prototype of an Automatic Digital Modulation Classifier Embedded in a Real-Time Spectrum Analyzer

Cited by 23 publications

References 31 publications

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Utilizing arbitrary waveform generators to produce noise with imposed spectral characteristics

Experimental results for M-FSK signal classification and parameter estimation

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