Signal Bandwidth Impact on Maximum-Minimum Eigenvalue Detection

Spectrum sensing is critical for cognitive radio networks as it allows a secondary user to find spectrum holes for opportunistic reuse. In this paper, an improved maximum-minimum eigenvalue detection method is proposed for a cognitive user equipped with a single receiving antenna. The proposed method utilizes the temporal smoothing technique to form a virtual multiple antennas structure. At the same time, the jackknifing resampling strategy is employed to improve the detection performance. Simulation results are presented to verify the effectiveness of the proposed method.

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Spectrum Sensing Using Principal Components for Multiple Antenna Cognitive Radios

Advances in Wireless Technologies and Telecommunication - Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management ◽

10.4018/978-1-4666-6571-2.ch007 ◽

2015 ◽

pp. 179-199

Author(s):

Farrukh A. Bhatti ◽

Gerard B. Rowe ◽

Kevin W. Sowerby

Keyword(s):

Software Defined Radio ◽

Cognitive Radios ◽

Principal Component ◽

Antenna System ◽

Blind Detection ◽

Multiple Antenna ◽

Minimum Eigenvalue ◽

Pc Algorithm ◽

Correlated Signals ◽

Maximum Minimum

This chapter presents an experimental comparative analysis of the well-known Covariance-Based Detection (CBD) techniques, which include Covariance Absolute Value (CAV), Maximum-Minimum Eigenvalue (MME), Energy with Minimum Eigenvalue (EME), and Maximum Eigenvalue Detection (MED). CBD techniques overcome the noise uncertainty issue of the Energy Detector (ED) and can even outperform ED in the case of correlated signals. They can perform accurate blind detection given sufficient number of signal samples. This chapter also presents a novel CBD algorithm that is based on Principal Component (PC) analysis. A Software-Defined Radio (SDR)-based multiple antenna system is used to evaluate the detection performance of the considered algorithms. The PC algorithm significantly outperforms the MED and EME algorithms and it also outperforms MME and CAV algorithms in certain cases.

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A Modified Test Statistic for Maximum-Minimum Eigenvalue Detection Based on Asymptotic Distribution Thresholds

2018 Moratuwa Engineering Research Conference (MERCon) ◽

10.1109/mercon.2018.8421979 ◽

2018 ◽

Author(s):

Darren R. Kartchner ◽

Sudharman K. Jayaweera

Keyword(s):

Asymptotic Distribution ◽

Test Statistic ◽

Minimum Eigenvalue ◽

Maximum Minimum ◽

Modified Test

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Noise Uncertainty Effect on a Modified Two-Stage Spectrum Sensing Technique

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v1.i2.pp341-348 ◽

2016 ◽

Vol 1 (2) ◽

pp. 341 ◽

Cited By ~ 1

Author(s):

Heba A.Tag El-Dien ◽

Rokaia M. Zaki ◽

Mohsen M. Tantawy ◽

Hala M. Abdel-Kader

Keyword(s):

Spectrum Sensing ◽

Energy Detection ◽

Primary User ◽

Probability Of Detection ◽

Minimum Eigenvalue ◽

Two Stage ◽

Noise Uncertainty ◽

Second Stage ◽

Single Detector ◽

Maximum Minimum

Detecting the presence or absence of primary user is the key task of cognitive radio networks. However, relying on single detector reduces the probability of detection and increases the probability of missed detection. Combining two conventional spectrum sensing techniques by integrating their individual features improves the probability of detection especially under noise uncertainty. This paper introduces a modified two-stage detection technique that depends on the energy detection as a first stage due to its ease and speed of detection, and the proposed Modified Combinational Maximum-Minimum Eigenvalue based detection as a second stage under noise uncertainty and comperes it with the case of using Maximum-Minimum Eigenvalue and Combinational Maximum-Minimum Eigenvalue as a second stage.

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