Eigenvalue-Based Multiple Antenna Spectrum Sensing: Higher Order Moments

Sedighi, Saeid; Taherpour, Abbas; Gazor, Saeed; Khattab, Tamer

doi:10.1109/twc.2016.2640299

Cited by 39 publications

(18 citation statements)

References 35 publications

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“…Eigenvalue-based detection [14]- [20] is one of the most recent and promising techniques. Likewise energy detection, it can be applied to either narrowband or wideband signals.…”

Section: A Related Workmentioning

confidence: 99%

Performance of Blind Cooperative Spectrum Sensing under Nonuniform Signal and Noise Powers

Costa¹,

Bomfin²,

Guimarães³

et al. 2018

JCIS

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Abstract-This paper addresses the performance, the report channel traffic and the computational complexity of two blind centralized cooperative spectrum sensing schemes under the effect of unequal and dynamical noise and received signal powers. The first one is the generalized likelihood ratio test, applying decision fusion, and weighted and non-weighted data fusion of samples and eigenvalues. The second one is the circular folding cooperative power spectral density split cancellation method, applying data fusion. It is demonstrated that different configurations of the system parameters may influence the above three metrics in different extents, meaning that none of the techniques is capable of overcoming the others in all metrics simultaneously. As a consequence, the choice of the most suitable technique must be made to match the specific scenario to which the technique is intended to be applied. The results presented in this paper serve as guidelines for assisting this choice.Index Terms-Circular folding cooperative power spectral density split cancellation, cognitive radio, data fusion, decision fusion, eigenvalue fusion, generalized likelihood ratio test, nonuniform noise, spectrum sensing, unequal signal-to-noise ratio, dynamical noise.

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“…Eigenvalue-based detection [14]- [20] is one of the most recent and promising techniques. Likewise energy detection, it can be applied to either narrowband or wideband signals.…”

Section: A Related Workmentioning

confidence: 99%

Performance of Blind Cooperative Spectrum Sensing under Nonuniform Signal and Noise Powers

Costa¹,

Bomfin²,

Guimarães³

et al. 2018

JCIS

View full text Add to dashboard Cite

show abstract

“…And the work at [13] is based on a Separating Function Estimation Test (SFET) framework to establish test statistics from higher order moments of eigenvalues. Despite the fact that the performances of [10]- [12] are immune to noise variance uncertainty problem [17] when it is the UNP case, their performance decline in the NNP case and we will verify this for [11] [12] under the section-5 (results and analysis); for [10], it is already reported in [13].…”

Section: Related Workmentioning

confidence: 67%

“…The correlation of a SCM leads to spreading of eigenspectrum when primary user signals exist. Using this advantage, [10]- [12] proposed eigenvalues dependent test statistics. The works at [10] utilized a ratio of the minimum to the maximum eigenvalues.…”

Section: Related Workmentioning

confidence: 99%

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Scaled-Energy Based Spectrum Sensing for Multiple Antennas Cognitive Radio

2018

TIIS

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In this paper, for a spectrum sensing purpose, we heuristically established a test statistic (TS) from a sample covariance matrix (SCM) for multiple antennas based cognitive radio. The TS is formulated as a scaled-energy which is calculated as a sum of scaled diagonal entries of a SCM; each of the diagonal entries of a SCM scaled by corresponding row's Euclidean norm. On the top of that, by combining theoretical results together with simulation observations, we have approximated a decision threshold of the TS which does not need prior knowledge of noise power and primary user signal. Furthermore, simulation results-which are obtained in a fading environment and in a spatially correlating channel model-show that the proposed method stands effect of noise power mismatch (non-uniform noise power) and has significant performance improvement compared with state-of-the-art test statistics.

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“…Reference [7] proposes A multi-antenna intelligent spectrum sensing method. Reference [8] proposed multi-antenna spectrum sensing based on eigenvalue.…”

Section: Introductionmentioning

confidence: 99%

Multi-antenna Spectrum Sensing Based on Stochastic Resonance

Lu¹,

Huang²,

Yang³

2017

dtcse

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Abstract. Aiming at the problem of spectrum sensing in cognitive radio under low signal to noise ratio (SNR) and channel fading, proposed a spectrum sensing method based on Stochastic Resonance (SR) and multi-antenna. The received signal of each antenna will pass through SR, to enhance the source signal using noise energy. Then the output of multi-antenna will be combined by Equal Gain Combining (EGC), and Differential Energy Detection (DED_SR) and Periodogram Energy Detection (PED_SR) will be adopted. Simulation experiments compare the traditional Energy Detection (ED), DED_SR and PED_SR. The results show that the detection probability of PED_SR and DED_SR perform higher than ED, but the sensing time also perform higher.

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Eigenvalue-Based Multiple Antenna Spectrum Sensing: Higher Order Moments

Cited by 39 publications

References 35 publications

Performance of Blind Cooperative Spectrum Sensing under Nonuniform Signal and Noise Powers

Performance of Blind Cooperative Spectrum Sensing under Nonuniform Signal and Noise Powers

Scaled-Energy Based Spectrum Sensing for Multiple Antennas Cognitive Radio

Multi-antenna Spectrum Sensing Based on Stochastic Resonance

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