الفهرس 
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Abstract
Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with the primary users, by reliably detecting primary user signal. In this thesis, the primary user signal is Wireless Microphone (WM) signal. The power of the WM signal is highly concentrated in the frequency domain. Due to this property, we focus on spectrum sensing in the frequency domain. Spectrum sensing has two common methods one of them is the Energy Detection (ED) method and the other one is Cyclostationary Feature Detector (CFD). ED is considered as the simplest and the fasted sensing method, while CFD is the highest accuracy and robust sensing method. ED suffers from large degradation in performance under low Signal-to-Noise Ratio (SNR) environment. To overcome this problem Improved Periodogram-Based (IPB) detector has been introduced here, IPB detector depends on averaging the Power Spectral Density (PSD) of segmented overlapped samples with different window shapes. IPB examines the optimum segmentation number for a given sensing time and probability of false alarm (PFA) constraint. According to IEEE 802.22 Wireless Regional Area Network (WRAN) standard, the probability of false alarm (PFA) ratio is PFA=10%. PFA has been taken into consideration in the decision threshold value. IPB has used this threshold to calculate the probability of detection (PD) and the probability of missed detection (PMD=1-PD) at low sensing time. Also different window shapes effect has been tested by IBP method and the best window shape has been determined.
In addition, Noise Based Cyclostationary Feature Detector (NB-CFD) is robust against White Gaussian Noise (WGN) and large degradation in performance under low SNR environment. NB-CFD examines the windowing effect for a given sensing time and obtain the optimum thresholds in the region satisfying the probability of false alarm constraint PFA=10%. Furthermore, an analytical expression for Cyclic Spectral Density (CSD) of the WM signal is derived.
Furthermore, the USRP hardware was implemented to test our proposed detection techniques (ED and CFD) on a real WM signal. The experimental results were symmetrical to the simulated results; which identify the validation of our proposed detection techniques to be used in the real life applications.