On the utility of sixth-order cyclic cumulants for RF signal classification

Spooner, Chad M.

doi:10.1109/acssc.2001.987051

Cited by 103 publications

(68 citation statements)

References 9 publications

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“…The concept of AMR is to classify an unknown modulation by comparing it to hypothetical schemes [1]. As shown in previous literature, higher-order cyclic cumulants (CCs)-inheriting the signal selectivity of higher-order cumulants [2]-have been confirmed as suitable discriminating features for AMR [3][4][5][6][7][8][9]. However, to perform a CC-based AMR, a large amount of signal symbols and an extremely higher sampling rate are both required to achieve an acceptable performance, which definitely results in computational complexity and a heavy sampling burden.…”

Section: Introductionmentioning

confidence: 96%

Automatic Modulation Recognition Using Compressive Cyclic Features

Xie

Wan

2017

Algorithms

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Higher-order cyclic cumulants (CCs) have been widely adopted for automatic modulation recognition (AMR) in cognitive radio. However, the CC-based AMR suffers greatly from the requirement of high-rate sampling. To overcome this limit, we resort to the theory of compressive sensing (CS). By exploiting the sparsity of CCs, recognition features can be extracted from a small amount of compressive measurements via a rough CS reconstruction algorithm. Accordingly, a CS-based AMR scheme is formulated. Simulation results demonstrate the availability and robustness of the proposed approach.

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Section: Introductionmentioning

confidence: 96%

Automatic Modulation Recognition Using Compressive Cyclic Features

Xie

Wan

2017

Algorithms

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“…In the work of Marchand et al [9], a feature based on fourth-and second-order CCs was used for quadrature PSK (QPSK), 16-QAM and 64-QAM signal classification. Spooner [10,11] employed CC-based features, with order up to six, for the classification of PSK, QAM, and minimum shift keying signals. Features based on fourth-, sixth-, and eighth-order CCs were proposed in [12] to classify rectangular QAM signals.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the classification algorithm chosen in the second step, different preprocessing tasks are required. For the second step, two general classes of MC algorithms can be crystallized, which rely on likelihood based (LB) techniques [4][5][6][7], or feature-based (FB) methods [8][9][10][11][12][13][14][15][16][17][18]. In the LB approach, MC is treated as a multiple composite hypothesis testing problem, and computation of the likelihood function of the received signal is required.…”

Section: Introductionmentioning

confidence: 99%

Cyclostationarity-Based Modulation Classification of Linear Digital Modulations in Flat Fading Channels

Dobre

Abdi

Bar-Ness

et al. 2009

Wireless Pers Commun

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Modulation classification is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications. In this correspondence, higher-order cyclic cumulants (CCs) are explored to discriminate linear digital modulations in flat fading channels. Single-and multi-antenna CC-based classifiers are investigated. These benefit from the robustness of the CC-based features to unknown phase and timing offset. Furthermore, the latter provides significant performance improvement due to spatial diversity used to combat the fading effect. Classifier performances are investigated under a variety of channel conditions. In addition, analytical closed-form expressions for the cyclic cumulant polyspectra of linearly digitally modulated signals affected by fading, carrier frequency and timing offsets, and additive Gaussian noise are derived, along with a condition for the oversampling factor to avoid aliasing in the cycle and spectral frequency domains. Part of this work was published in a preliminary form and presented at the IEEE Military Communication Conference (MILCOM) 2003 and 2005, USA, under the titles "Higher-order cyclic cumulants for high order modulation classification," and "Selection combining for modulation recognition in fading channels," respectively. O. A. Dobre (B)

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“…The feature-based algorithms use features of a signal that are distinct for each modulation type. The pattern recognition or the decision theory-based algorithms are then used for classification [18][19][20][21][22][23]. This GRA-based AMC method utilizes the alpha profile, (a-profile), for a signal feature.…”

Section: Introductionmentioning

confidence: 99%

Grey systems theory applications to wireless communications

Amanna

Price

Thamvichai

2011

Analog Integr Circ Sig Process

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This paper discusses grey systems theory (GST) applications in wireless communications and highlights its potential to cognitive radio. GST consists of information theory concepts and practical algorithms developed to address situations where information is incomplete and affected by random uncertainties. Two GST concepts, grey relational analysis (GRA) and grey model (GM) prediction theory are discussed. GRA provides a method to quantify the similarity between a reference data series and set of data while GM is used for modeling time series data and enables prediction of future values with limited data points and unknown probability distributions. These two techniques are surveyed with respect to their applications to wireless communications. Their application to predictive Cognitive Radio and as a similarity measure for case based reasoning cognitive engines is highlighted. A GRA based Automatic Modulation Classification (AMC) algorithm is applied to digital communications signals with preliminary results shown in simulation.

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On the utility of sixth-order cyclic cumulants for RF signal classification

Cited by 103 publications

References 9 publications

Automatic Modulation Recognition Using Compressive Cyclic Features

Automatic Modulation Recognition Using Compressive Cyclic Features

Cyclostationarity-Based Modulation Classification of Linear Digital Modulations in Flat Fading Channels

Grey systems theory applications to wireless communications

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