Performance of Energy Detection Spectrum Sensing for Cognitive Radio Using GNU Radio

The increasing number of wireless communication applications has led to spectrum scarcity problems. On the other hand, the current system in allocating the spectrum frequency is inefficient. To mitigate this issue, a cognitive radio (CR) system is proposed. CR is a smart radio that is able to sense the environment, locate the spectrum holes, and adapt its transmission parameter to exploit the existing spectrum holes. This underlines the importance of the spectrum sensing module to enable the operation of the CR system. The objective of the spectrum sensing module is to achieve the best utility from the available spectrum frequency. CR system is implemented in the unlicensed secondary users allowed to rent the spectrum currently not used by primary users (PU). In this paper, energy-detection-based spectrum sensing is implemented on the GNU Radio platform. We first implement the power spectral density (PSD) estimation method based on the periodogram by exploiting the Embedded Python block facility on the GNU Radio. Next, we implement the spectrum sensing decision module in the GNU Radio, which compares the PSD estimate of the PU signals corrupted by noise with a threshold. The PU signal is simulated as a bandpass random process occupying a particular frequency band. The spectrum sensing decision module is developed to allow the computation of the probability of detection (PD) and the probability of false alarm (PFA), which is performed by exploiting the Embedded Python block. One indicator to evaluate the performance of the spectrum sensing module is the receiver operating characteristic curve based on the computed PD and PFA on the GNU Radio. We evaluate the performance of the spectrum sensing for different SNRs and thresholds. The result shows that the energy-detection-based spectrum sensing is able to locate the existence of the PU when the signal-to-noise ratio (SNR) is sufficiently high.