A Novel Modulation-Sensing Method

Popoola, Jide Julius; Olst, Rex van

doi:10.1109/mvt.2011.941893

Cited by 37 publications

(22 citation statements)

References 30 publications

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“…For this reason, we propose to employ ANN to identify the primary users' presence by identifying the modulation type of the primary user. Our study is different from the studies in [13] and [14], as our approach detects primary user signal while cognitive users are communicating. This is very helpful for cognitive users, as nodes can perform sensing while communicating.…”

Section: Introductionmentioning

confidence: 81%

“…As indicated in the section I, previous works [7], [13], [14] have shown that one of the best candidates for signal identification are higher order moments and cumulants of the received signal. For N samples of any signal x, higher moment of order k is defined by…”

Section: A Feature Extractionmentioning

confidence: 93%

“…For this reason, ANN is important for signal detection in cognitive settings. For instance, in [13], [14] the authors used ANN classifier to detect primary user signal.…”

Section: Introductionmentioning

confidence: 99%

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Blind primary user identification in MIMO cognitive networks

Ghosh¹,

Hamouda²,

Dayoub³

2013

2013 IEEE International Conference on Communications (ICC)

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Early detection of primary users presence is one of the most important tasks for cognitive communication. Also, in cognitive settings cognitive nodes may receive signals from primary users and from other cognitive users simultaneously. For such scenario, we propose primary user signal detection using modulation class identification method. We consider multiple transmit and multiple receive antennas for cognitive nodes. We employ Artificial Neural Network (ANN) for the modulation identification purpose. The proposed algorithm works as higher order moments and cumulants are calculated from the received signal samples at each of the receiving branches of cognitive nodes. After this step, these features are fed to the ANN to determine the presence of primary users. Final identification decision is drawn using the decision from all receiving branches. We also present numerical results of our algorithm and compare these results with the theoretical results of the energy detection algorithm.Index Terms-Cognitive networks, MIMO, blind identification, artificial neural networks.

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Section: Introductionmentioning

confidence: 81%

Section: A Feature Extractionmentioning

confidence: 93%

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Blind primary user identification in MIMO cognitive networks

Ghosh¹,

Hamouda²,

Dayoub³

2013

2013 IEEE International Conference on Communications (ICC)

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“…This, of course, should occur without harming the activities of PUs, and SUs should vacate particular spectrum bands upon PUs' request without any untoward delay or disturbance. Specifically, CR systems not only need to perform spectrum sensing (SS) in the interested frequency bands but also need to interrogate the sensed signals to obtain additional information, such as the modulation scheme, in order to design improved dynamic spectrum access (DSA) strategies [18,19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

Salam

Sheriff

et al. 2019

IEEE Trans. Veh. Technol.

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“…Therefore, in order to utilize the spectrum better, the Federal Communications Commission (FCC) is currently revising traditional license based policies and is considering a new spectrum sharing strategies using cognitive radio (CR). CR is one of the most promising new paradigms for the efficient use of the radio spectrum by opportunistically finding and using empty frequency bands [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Hosseinzadeh

2014

J. of Syst. Eng. Electron.

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Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio (SNR), and classifies all forms of primary user's signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise (AWGN) channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

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A Novel Modulation-Sensing Method

Cited by 37 publications

References 30 publications

Blind primary user identification in MIMO cognitive networks

Blind primary user identification in MIMO cognitive networks

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

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