Optimization of Spectrum Sensing in Cognitive Radio Using Energy Detection and Matched Filter

Cognitive Radio (CR) is an innovative wireless communication technology designed to intelligently and dynamically adapt to varying spectrum conditions. Unlike traditional radio systems, CR empowers devices to autonomously sense and access unused spectrum bands, optimizing spectrum utilization and enhancing overall communication efficiency. However, a main component of cognitive radio networks is sensing of spectrum, which gives devices the ability to recognize and locate open frequency bands for opportunistic spectrum access. This process plays a pivotal role in enhancing spectrum utilization and addressing the spectrum scarcity challenge. Various sensing techniques are available, including energy detection, matched filtering, which are employed to achieve reliable and efficient spectrum sensing. Hence, this work focused on optimizing spectrum sensing techniques through a synergistic combination of energy detection techniques such as: classical energy detection (CID); improved energy detection (IED); general energy detection (GED); variable threshold energy detection (VED) and matched filter detection. In order to balance computing efficiency and detection accuracy, characteristics like threshold levels and filter designs are fine-tuned during the optimization process. The performance evaluation of each technique has been evaluated by considering false alarm probability (Pfa) verses probability of detection under various scenarios for all the mentioned techniques.