Television channel spectrum sensing using independent component analysis

Ferreira, P.I.L.; Fontgalland, Glauco; Albert, Bruno; Gurjão, Edmar C.

doi:10.1049/iet-com.2014.0505

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…To strengthen the conventional BSS-based spectrum sensing algorithms, Mukherjee et al [22] was devoted to combine BSS with prediction using hidden Markov model (HMM) for enhancing spectrum sensing performance. Ferreira et al [21] applied ICA to identify signal sources in a broad band of frequencies and to use this information for spectrum sensing.…”

Section: Reviews Of Cognitive Radio Assisted By Blind Source Sepamentioning

confidence: 99%

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Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Luo

Zhu

2019

IEEE Access

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Due to the ever-increasing wireless service requirements, the issues of a limited frequency resource and underutilized frequency allocation scheme as well as frequency crowd have come to become increasingly severe. It gives rise to being difficult to effectively eliminate the spectrum interference and fulfill an efficient spectrum access mechanism using the congested frequency band. For this purpose, this paper conceives a simultaneous transmission and sensing scheme based on the guided independent component analysis combined with the cumulant-based non-Gaussian criterion for fulfilling full-duplex cognitive radio (FD-CR). The philosophy of this proposed scheme is that the ICA based blind source separation principle is firstly utilized for blind separation of the observation mixtures, and then the separated signals are recognized through correlation processing and non-Gaussian criterion based on cumulant. In particular, the known secondary user signal is used as a signature signal for assisting ICA framework construction for blind extraction of signals. As a key component of machine learning, the ICA theory can contribute significantly to conquering the static sensing problem and residual self-interference influence in existing conventional cognitive radio systems. The simulation experiments and analysis corroborate the effectiveness of the proposed method and system design. In addition, the low complexity and performance enhancement of the proposed method are also validated by comparing with that of the two practical FD-CR schemes. INDEX TERMS Full-duplex, cognitive radio, blind source separation, independent component analysis, cumulant.

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Section: Reviews Of Cognitive Radio Assisted By Blind Source Sepamentioning

confidence: 99%

“…Numerous AJD algorithms have been developed that can be utilized for minimizing the cost function (21) to obtain V separation matrix. After the accomplishing this assignment, the separated signal can be gotten as…”

Section: Full-duplex Cognitive Radio Based On Gica and Cumlant Asmentioning

confidence: 99%

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Luo

Zhu

2019

IEEE Access

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“…the algorithm consists two processes of albefaction and orthogonal separation, assuming linear transformation matrices is represented by V and W respectively. The albefaction process of V is: ( 2 ) Assuming that CX represents the covariance matrix of X, Evalue and Evector represents the eigenvalue and eigenvector of CX respectively, then V is calculated by the following formula:…”

Section: Algorithmmentioning

confidence: 99%

“…Recently, Ferreira et al apply ICA to detach the available bands in the TV band spectrum [2], and considered every frequency band as a component recovered by ICA, then the free bands to reuse. On the other hand, ICA has the potential possibility to be used in the border of the complex electromagnetic environment.…”

Section: Introductionmentioning

confidence: 99%

The Anomaly Mixed Spectrum Signals Detection Based on ICA and KNN

Lü¹,

Huang²,

Yang³

2016

dtetr

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Independent Component Analysis is very effective for blind source separation of mixed signals, except detecting abnormal signal, presented an anomaly mixed spectrum detection method based on K Nearest Neighbor classification, using Independent Component Analysis to separate the mixed signal under Gaussian noise, and classifying the mixed signal based on training sample set, so as to find the abnormal signal mixed in the original signal. Simulation results showed that this method could not only separated signals effectively, but also improved the classification accuracy.

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Sub array technique applied to reducing the number of sensors in blind source separation

Ferreira

Fontgalland

2015

2015 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC)

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Television channel spectrum sensing using independent component analysis

Cited by 4 publications

References 20 publications

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

The Anomaly Mixed Spectrum Signals Detection Based on ICA and KNN

Sub array technique applied to reducing the number of sensors in blind source separation

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