Compressed wavelet packet-based spectrum sensing with adaptive thresholding for cognitive radio

Abstract

Cognitive radio is a system to utilize spectrum holes efficiently as a solution of spectrum scarcity. The availability of channels for secondary users is determined in the spectrum sensing phase by energy detection. Energy levels of sampled primary user's (PU's) signal can be measured by wavelet transform with more accuracy compared with Fourier-based methods. Wavelet packet-based spectrum sensing measures the energy level at each subcarrier and sets the decision threshold. However, at the first step of energy detection for wideband spectrum sensing, high-rate analog-to-digital converter (ADC) sampling requires a large dynamic range and high-speed signal processors. In this paper, compressed sampling for PU's signal acquisition is proposed to reduce the rate of sampling and solve the implementation complexity of ADC. The simulation results verify that this mechanism is promising to estimate the power spectrum density (PSD) of PU's signals. The graphs prove low side-lobes of the detected PSD and acceptable probability of detection and false alarm due to the target values and certain compression ratio.