This research explores the application of deep learning techniques for predicting and enhancing network performance across key areas like traffic forecasting, fault detection, and quality of service (QoS) management.

By using deep neural networks such as LSTM, CNN, Transformers, Autoencoders and GANs the study demonstrates how machine learning can automate network monitoring, reduce downtime, and improve resource allocation in real-time environments.

Data Inputs: Time-stamped network performance data including:

1. Signal strength (dBm)
2. Latency (ms)
3. Throughput (Mbps)
4. RF measurements
5. Locality, Network Type Models Used
6. Anomaly Detection : Autoencoder, GAN, LSTM, CNN
7. Traffic Forecasting : LSTM, CNN, Transformer, Hybrid (CNN+LSTM)
8. QoS Prediction : CNN, LSTM, Transformer, Hybrid Deep Learnin

Google Colab ( Implementation ): https://colab.research.google.com/drive/1dFot1z9rtNHWhhAZHjlEVXBd4fzCob2L?us p=sharing https://colab.research.google.com/drive/1-k8Vig0ABJ_pFGOIozgK37W6aFewkbo7?us p=sharing (for anomaly detection)