DWT Based Energy Detection Spectrum Sensing Method for Cognitive Radio System

Abstract

In this paper a new blind energy detection spectrum-sensing method based on Discreet Wavelet Transform (DWT) is proposed. The method utilizes the DWT sub-band to collects the received energy. The proposed method recognizes the Primary User (PU) signal from noise only signal using the differences in the collected energy in first and last sub-bands of one level DWT. The simulation results show that the proposed method achieves improved detection probability especially at low Signal to Noise Ratio (SNR) compared to Conventional Energy Detector (CED). The results also show that the proposed method has shorter sensing time and less Energy Consumption (EC) compared to CED due to using small number of processed sample. Therefore, this method is suitable for Cognitive Radio (CR) applications where only limited energy like device battery is available.

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

“…In other words, the modulated signal's mean and autocorrelation function both display periodicity. While the autocorrelation function and the power spectral density are typically used to examine stationary signals, these functions are generalized for the analysis of cyclostationary signals and known as the Cyclic Autocorrelation Function (CAF) and Spectral-Correlation Density Function (SCD) [5].…”

“…Where X T is the Fourier transform (finite-time ) of the x(u) time-domain input signal; Fourier transform representation over time as [5]:…”

“…Spectrum sensing (SS) is performed by secondary user's access frequency spectrum dynamically. In order to establish a communication link for the Secondary User (SU) without interfering with the Primary User's (PU) transmission, SS is a crucial component of a CR network that requires the detection of an open spectrum space [4,5]. The three primary approaches for determining the signal presence each have some trade-offs.…”

Improvement of spectrum sensing performance in cognitive radio using modified hybrid sensing method

“…where x p represents the PU signal, y represents the SU received signal. The test statistics, T M F D , are then compared with a pre-defined threshold to detect the activity of PU, as shown in the following Equation (10).…”

“…However, at low signal-tonoise ratio (SNR) values, and bad channel conditions, the ED cannot differentiate between the PU signal and the noise. The matched filter (MF) maximizes the received SNR in communication systems, so it can be considered as the best detector [10]. MF has a challenge that it must know the information about the PU signal properties, i.e., packet format, pulse shaping, and the type of modulation.…”