Improved Spectrum Sensing for Cognitive Radio Based on Adaptive Threshold

Spectrum sensing is a very important process in cognitive radio to discover the spectrum holes. There are various spectrum sensing techniques like matched filter detection, energy detection, cyclostationary detection, covariance detection and wavelet detection. Out of these techniques energy detection has got the most attention by the researchers due to its simplicity and low computational requirements. However, the performance of energy detection scheme degrades at low signal to noise ratio (SNR) region. To improve the performance of the energy detection scheme at low SNR conditions, we have proposed an adaptive threshold which is a function of the detection threshold (calculated under the fixed probability of false alarm) and SNR of the primary user (SNRp) received at the sensing node. The proposed adaptive threshold based energy detection scheme provides better performance at low SNR region than the fixed threshold based energy detection scheme.