Implementation of Cognitive Radio Detection and Prediction Using BP Neural Network

By employing algorithms based on Backpropagation (BP) neural networks, we have successfully implemented detection and prediction capabilities for cognitive radio systems, thereby enabling efficient spectrum allocation and management. This approach typically involves training the network with historical spectrum usage data, where the BP algorithm minimizes prediction errors through iterative weight adjustments using gradient descent. The application of this technology significantly enhances the efficiency and reliability of wireless communications, providing users with superior communication experiences. Additionally, it helps reduce spectrum wastage, improves spectrum utilization efficiency, and enables wireless communication services for more users. Therefore, cognitive radio detection and prediction technology holds substantial significance and promising application prospects in modern communication fields, particularly through automated spectrum sensing algorithms that dynamically adapt to changing radio environments.