Development and Implementation of a Neural Network Model for 5G Network Traffic Prediction

The exponential growth in connected devices and dynamic user behavior in 5G wireless networks have intensified the need for accurate network traffic prediction. Traditional traffic control mechanisms fall short in real-time adaptability, leading to performance bottlenecks. This paper presents the development and implementation of a deep neural network (DNN) model for traffic prediction in 5G networks. Leveraging a well-curated dataset of 5G traffic parameters, the model uses advanced feature engineering, data preprocessing, and neural network architecture to predict traffic slices effectively. Evaluation metrics, including accuracy, loss, and confusion matrix, were employed to assess the model's performance. The results show high predictive accuracy and robustness in traffic pattern recognition, demonstrating the viability of AI-enabled traffic forecasting to support autonomous traffic control, enhanced QoS, and optimized resource allocation in 5G networks.