| dc.description.abstract | The advent of pervasive wireless systems faces several challenges due to the massive data traffic growth resulting from the interconnection of billions of new devices. This makes it essential to provide smart decision-making in identifying available spectrum resources by sensing the radio frequency environment. In this study, we aim to improve the spectrum sensing process and enhance the detection efficiency of secondary users (sensing devices) in identifying primary users (transmitting devices). We consider a scenario in which secondary users are affected by noise and fading, and employ distributed detection and data fusion to combine data from geographically distributed sensors. The results show that collaborative spectrum sensing, where multiple SUs share their sensing data, significantly enhances detection performance. By applying optimization techniques to assign optimal weight vectors to the sensors, we further increase the detection performance of the primary user, where each one is affected by different noise factors. The study reveals that detection performance improves as more users collaborate, and this improvement is validated through scenarios with varying SNR values. | es |
