"数据融合的协作频谱感知方法研究：认知无线电新技术探索"。

Cooperative spectrum sensing based on data fusion is a crucial research topic in the field of cognitive radio (CR). Cognitive radio is a novel method proposed to improve and optimize the utilization of spectrum resources in wireless communication, making it a revolutionary intelligent spectrum sharing technology. Spectrum sensing technology plays a vital role in cognitive radio systems. This paper builds upon the single user energy detection method and conducts a simulation analysis on parameters such as detection probability, false alarm probability, missed detection probability, threshold values, and signal-to-noise ratio. The detection performance is thoroughly analyzed in this study. In the face of challenges where single user energy detection performance is compromised by factors such as channel fading, shadowing effects, and noise uncertainty, a data fusion-based cooperative spectrum sensing method is proposed. Various fusion methods including AND, OR, and K-N criteria are introduced and their characteristics, performance, and application scenarios are analyzed. This approach aims to enhance the reliability and accuracy of spectrum sensing by combining information from multiple cognitive radio nodes. Keywords: cognitive radio; spectrum sensing; data fusion; cooperative detection Overall, this research investigates the efficiency and potential of data fusion in improving cooperative spectrum sensing in cognitive radio systems. By leveraging the collaboration of multiple nodes and integrating data from various sources, the proposed method aims to overcome the limitations of individual energy detection techniques and enhance the overall spectrum sensing performance. This study provides valuable insights into the application of data fusion techniques in cognitive radio networks and offers a promising solution to optimize spectrum utilization and improve communication efficiency in wireless networks.