Eigenvalue Based Blind Spectrum Sensing Techniques for Cognitive Radio Systems Under Low Sample Environment

Sreekanth, Peddinti (2018) Eigenvalue Based Blind Spectrum Sensing Techniques for Cognitive Radio Systems Under Low Sample Environment. MTech thesis.

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Cognitive Radio (CR) is an intelligent radio which can exploit the available spectrum holes by giving access to the secondary users (SU) without causing any interference to the primary user (PU). The elemental function of the cognitive radio is to sense the spectrum for the presence of primary user. In the literature, several detection techniques are explored for real time applications based on the energy, eigenvalue, waveform, cyclostationary and other characteristics of the received signal in the desired frequency band. The dependence of calculated threshold on noise causes inaccuracy in detecting the primary user. Also, the detector should not depend on any of the PU’s signal characteristics. Eigenvalue based blind detection schemes have these advantages over other detectors and are known to give promising detection performance too. Random matrix theory is used to find the theoretical expressions for threshold and also several test statistic’s distributions. Many fruitful results of RMT are deeply required to perform eigenvalue based derivations i.e., the covariance matrix of Gaussian matrix follows Wishart distribution, the extreme eigenvalues of Wishart distribution follows Tracy-Widom distribution, the limiting distribution of eigenvalues of the same follows Marchenko–Pastur distribution under the n, p → ∞ scenario are highly obligatory propositions. For practical case, where the number of sensors at the receiver is fixed, the proposed detector Corrected John’s Test (CJT) performs very well even under low sample environment. The Eigenvalue-Moment Ratio (EMR) also performs well under low sample environment but for the case where n, p → ∞, which is not very practical. The simulation results shows the dominance of proposed detector over other detectors like Scaled Largest Eigenvalue (SLE), Maximum-Minimum Eigenvalue (MME), Arithmetic to Geometric Mean (AGM) for less number of samples and fixed sensors case, under Nakagami fading channel.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Cognitive radio; Blind spectrum sensing; Random matrix theory; Low sample environment; Tracy-Widom; Chi-squared; Nakagami fading channel
Subjects:Engineering and Technology > Electronics and Communication Engineering > Wireless Communications
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:9994
Deposited By:IR Staff BPCL
Deposited On:10 May 2019 15:28
Last Modified:10 May 2019 15:28
Supervisor(s):Hiremath, Shrishail M

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