Multi-User Signal Classification via Spectral Correlation

Hong, Steven; Like, Eric C.; Wu, Zhiqiang; Tekin, Cem

doi:10.1109/ccnc.2010.5421830

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…It is showed following that the estimated result of cyclic spectrum density (CSD) of several typical modulation signals using FAM algorithm by MATLB [6] [7]:…”

Section: Simulation and Resultsmentioning

confidence: 99%

Method of modulation recognition of typical communication satellite signals based on cyclostationary

Wang

2013

2013 ICME International Conference on Complex Medical Engineering

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The recognition of communication satellite signals' modulation types is indispensable in so many applications, especially in satellite communication field and is an important aspect of the category in pattern recognition. This paper demonstrates the cyclostationary method is appropriate for modulation recognition. With the advantages of better anti-noise property, containing more information and revealing the nature of the cyclostationary signal. In this paper, some new characteristic parameters are advanced and verified by simulation.

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“…It is showed following that the estimated result of cyclic spectrum density (CSD) of several typical modulation signals using FAM algorithm by MATLB [6] [7]:…”

Section: Simulation and Resultsmentioning

confidence: 99%

Method of modulation recognition of typical communication satellite signals based on cyclostationary

Wang

2013

2013 ICME International Conference on Complex Medical Engineering

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“…Although significant research has been conducted on AMC algorithms, one of the issues that has not yet been addressed adequately is how to detect and classify signals from more than one user within a certain frequency band [12]. This is an obstacle that needs to be addressed in the military domain and is certain to become an issue in the future of commercial CR networks.…”

Section: Introductionmentioning

confidence: 98%

“…For multi-user AMC, the cylostationary characteristics combined with adaptive K-means clustering were used to classify simultaneous combinations of analog modulations and QPSK digital modulation in [12]. However, Cyclostatinary characteristics fail to classify higher order PSK and QAM modulations since their characteristics are inseparable.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Modulation Classification of multiple signals in Cognitive Radio Networks

Abdelbar

Tranter

Bose

2014

2014 IEEE International Conference on Communications (ICC)

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Automatic Modulation Classification (AMC) is an important component in Cognitive Radio (CR) Networks. Multiuser AMC classifies the modulation schemes of simultaneous multiple unknown transmitters. In addition, cooperation among multiple CR receivers for modulation classification offers significant improvements in classification performance and overcomes the detrimental channel effects that degrades the single CR classifier performance. In this paper, a novel centralized softcombining data fusion algorithm based on the joint probability distribution of fourth order cumulants is presented for cooperative modulation classification. Fourth order cumulants of the received signals are calculated as discriminating features for different modulation schemes at each CR node and sent to a centralized data Fusion Center (FC). The FC chooses the modulation scheme that maximizes the joint probability of the estimated cumulants. As compared to independent receiver classification, cooperative classification results are significantly improved under the same multi-path environment.

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“…2 illustrates such an intelligent spectrum sensing engine. Cyclostationary analysis has been accepted as effective tool for electronic emission detection [6] [7], signal detection and spectrum sensing [8] [9], RF parameter estimation [10], and modulation detection [11][12][13] [14]. However, cyclostationary-based signal processing algorithm such as spectral correlation function (SCF) and spectral coherent function (SOF) requires extremely high resolution to fully observe the cyclic frequency features.…”

Section: Introductionmentioning

confidence: 99%

Real-time cyclostationary analysis for cognitive radio via software defined radio

Zhou¹,

Yang³

et al. 2012

2012 IEEE Global Communications Conference (GLOBECOM)

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Cyclostationary analysis such as spectral correlation function (SCF) and spectral coherence function (SOF) has been accepted as an important tool in signal detection and radio frequency (RF) parameter estimation in cognitive radios. However, cyclostationary analysis requires extremely high resolution to fully observe the cyclic frequency features, leading to very high computational complexity and difficulty in real time implementation, especially on software defined radios where computational capability is limited. In this paper, we implement and demonstrate a real time SCF/SOF RF signal analyzer by adopting previously proposed two-stage dynamic resolution SCF/SOF estimation method using software defined radios. Specifically, high resolution SCF/SOF calculation is only performed near the expected cyclic feature locations obtained through a low complexity coarse estimation employing spectral analysis. This cyclostationary analysis based RF signal analyzer is capable of sensing the existence of primary users and estimating the carrier frequency and symbol rate accurately. Using Universal Software Radio Peripheral (USRP) software defined radio platform and GNU radio software, we implement and demonstrate such a cyclostationary analysis based RF signal analyzer in real time and validate the performance in realistic wireless communication channels.

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Multi-User Signal Classification via Spectral Correlation

Cited by 8 publications

References 3 publications

Method of modulation recognition of typical communication satellite signals based on cyclostationary

Method of modulation recognition of typical communication satellite signals based on cyclostationary

Cooperative Modulation Classification of multiple signals in Cognitive Radio Networks

Real-time cyclostationary analysis for cognitive radio via software defined radio

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