An Efficient Channel Prediction Using Artificial Neural Network Deep Learning Technique in Cognitive Radio

Cognitive Radio (CR) technology has received a lot of interest in recent years due to its potential to optimize spectrum use by dynamically adjusting to available spectrum resources. Accurate channel state prediction is a significant problem in CR systems because of its importance in efficient spectrum sensing and resource allocation. Because of their ability to learn complicated patterns from big datasets, ANNs and deep learning approaches have become useful tools for channel prediction. This paper focuses on the creation of an efficient channel prediction system for Cognitive Radio networks by utilizing ANNs and deep learning approaches. We present a new method that uses deep neural networks to describe the spatial and temporal properties of wireless channels. Predictions of future channel states are made using a combination of past data and knowledge about the surrounding environment and the network.