Deep Learning for Enhanced Spectrum Sensing in Cognitive Radio Networks with Blockchain-based Security

The growing need for wireless communication requires the optimum use of spectrum. Cognitive Radio (CR) technology enables the utilization of unused frequency channels through the identification and exploitation of these bands. Nevertheless, conventional spectrum sensing approaches are plagued by drawbacks such as instances of missed detections and false alarms. This study presents a new framework that combines deep learning and blockchain technology to improve the accuracy of spectrum sensing and ensure safe spectrum management in cognitive radio networks. Deep learning models are highly proficient at acquiring intricate signal characteristics, which allows them to attain exceptional performance in spectrum sensing. Blockchain technology, characterized by its decentralized recordkeeping system, inability to be altered, and methods for reaching agreement, promotes confidence and thwarts tampering with spectrum sensing data and user access privileges. Our platform utilizes deep learning models on CR devices to achieve precise spectrum sensing. Additionally, it leverages the secure data storage and access control features of blockchain. This integration improves the accuracy of spectrum sensing, guarantees secure and transparent management of the spectrum, and encourages efficient utilization of the spectrum in cognitive radio networks. The evaluation technique and anticipated results are well defined, setting the stage for future investigations in this field.