Cognition-Based Spectrum Sensing Techniques

Cognitive radio is a cutting-edge approach that aims to maximize the utilization of wireless communication network spectrum. It encompasses several crucial functions such as spectrum detection, management, mobility, and sharing. Through the application of spectrum sensing techniques, cognitive radios can optimize the usage of available spectrum resources. In contrast to inconsistency detection, which does not require prior knowledge of the primary user's signal, consistent detection necessitates knowledge of all components of the primary user's signal before detection. To address the challenge of randomly modeled Primary User (PU) signals with uncertain power, a new spectrum detection method for cognitive radio (CR) is introduced. Additionally, due to the dynamic nature of the primary user, multiple state changes in the channel are expected during detection. To tackle this, a novel method called SNR-based energy detection (S-ED) is proposed to detect randomly and dynamically modeled PUs. S-ED utilizes samples only between the Last State Change Point (LSCP) of the PU and the end of the detection period, instead of employing all data samples observed throughout the detection interval. The suggested detection method demonstrates a significant improvement in recognizing PUs with random and dynamic patterns. Furthermore, this study introduces an adaptive local spectrum detection method that allows cognitive radio to employ energy detectors for spectrum detection based on estimated, pre-computed signal-to-noise ratios (SNR) for accessible channels. Simulation results validate that this method yields reliable outcomes compared to energy detection, with the added benefit of shorter average detection time.