Aggregate Interference Prediction Based on Back-Propagation Neural Network

In dynamic spectrum access (DSA) scenarios, dense and complex deployment (e.g., in nonuniform or unknown radio propagation environment) of secondary systems (SSs) will make aggregate interference estimation highly complicated or challenging for reliable primary system (PS) protection. To tackle this problem, a back-propagation (BP) neural network based aggregate interference prediction method is proposed and evaluated via simulations. This paper also gives design guidelines of BP neural network appropriate for aggregate interference prediction via revealing the impact of several key factors on the prediction accuracy, such as the number of input parameters to the neural network, the coordinate system in use, and the number of hidden neurons.