Spectrum Sensing in Cognitive Radio by Use of  Volume-Based Method

Spectrum sensing is the mission of finding the licensed user signal situation, i.e. to determine the existence and deficiency of primary (licensed) user signal, the recent publications random matrix theory algorithms performs better-quality in spectrum sensing. The RMT fundamental nature is to make use of the distributed extremal eigenvalues of the arrived signal sample covariance matrix (SMC), specifically, Tracy-Widom (TW) distribution which is useful to certain extent in spectrum sensing but demanding for numerical evaluations because there is absence of closed-form expression in it. The sample covariance matrix determinant is designed for two novel volume-based detectors or signal existence and deficiency cases are differentiated by using volume. Under the Gaussian noise postulation one of the detectors theoretical decision thresholds is perfectly calculated by using Random matrix theory. The volume-based detectors efficiency is shown in simulation results.

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Spectrum sensing technique in Cognitive Radio based on sample covariance matrix

2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) ◽

10.1109/iccicct.2015.7475264 ◽

2015 ◽

Cited By ~ 2

Author(s):

Sheen Mary Jacob ◽

Nandan S.

Keyword(s):

Cognitive Radio ◽

Covariance Matrix ◽

Spectrum Sensing ◽

Sample Covariance Matrix ◽

Sample Covariance

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Enhanced energy detection based spectrum sensing in Cognitive Radio Networks using Random Matrix Theory

2014 9th International Symposium on Communication Systems, Networks & Digital Sign (CSNDSP) ◽

10.1109/csndsp.2014.6923859 ◽

2014 ◽

Cited By ~ 3

Author(s):

A. Ahmed ◽

Y. F. Hu ◽

Jim. M. Noras

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Cognitive Radio Networks ◽

Random Matrix Theory ◽

Random Matrix ◽

Matrix Theory ◽

Energy Detection ◽

Radio Networks

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Determinant of the Sample Covariance Matrix Based Spectrum Sensing Algorithm for Cognitive Radio

2011 7th International Conference on Wireless Communications, Networking and Mobile Computing ◽

10.1109/wicom.2011.6036724 ◽

2011 ◽

Author(s):

Kejun Lei ◽

Xi Yang ◽

Shengliang Peng ◽

Xiuying Cao

Keyword(s):

Cognitive Radio ◽

Covariance Matrix ◽

Spectrum Sensing ◽

Sample Covariance Matrix ◽

Sample Covariance

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A reliable spectrum sensing scheme using sample covariance matrix for cognitive radio

2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT) ◽

10.1109/icaecct.2016.7942548 ◽

2016 ◽

Author(s):

Chhagan Charan ◽

Rajoo Pandey

Keyword(s):

Cognitive Radio ◽

Covariance Matrix ◽

Spectrum Sensing ◽

Sample Covariance Matrix ◽

Sample Covariance

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On the empirical spectral distribution for certain models related to sample covariance matrices with different correlations

Random Matrices Theory and Application ◽

10.1142/s2010326322500307 ◽

2021 ◽

Author(s):

Alicja Dembczak-Kołodziejczyk ◽

Anna Lytova

Keyword(s):

Random Matrix Theory ◽

Random Matrix ◽

Spectral Distribution ◽

Matrix Theory ◽

Random Variable ◽

Covariance Matrices ◽

Convergence In Probability ◽

Stat Phys ◽

Sample Covariance ◽

Arbitrary Space

Given [Formula: see text], we study two classes of large random matrices of the form [Formula: see text] where for every [Formula: see text], [Formula: see text] are iid copies of a random variable [Formula: see text], [Formula: see text], [Formula: see text] are two (not necessarily independent) sets of independent random vectors having different covariance matrices and generating well concentrated bilinear forms. We consider two main asymptotic regimes as [Formula: see text]: a standard one, where [Formula: see text], and a slightly modified one, where [Formula: see text] and [Formula: see text] while [Formula: see text] for some [Formula: see text]. Assuming that vectors [Formula: see text] and [Formula: see text] are normalized and isotropic “in average”, we prove the convergence in probability of the empirical spectral distributions of [Formula: see text] and [Formula: see text] to a version of the Marchenko–Pastur law and the so-called effective medium spectral distribution, correspondingly. In particular, choosing normalized Rademacher random variables as [Formula: see text], in the modified regime one can get a shifted semicircle and semicircle laws. We also apply our results to the certain classes of matrices having block structures, which were studied in [G. M. Cicuta, J. Krausser, R. Milkus and A. Zaccone, Unifying model for random matrix theory in arbitrary space dimensions, Phys. Rev. E 97(3) (2018) 032113, MR3789138; M. Pernici and G. M. Cicuta, Proof of a conjecture on the infinite dimension limit of a unifying model for random matrix theory, J. Stat. Phys. 175(2) (2019) 384–401, MR3968860].

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