Dataset for Proactive Resource Allocation in 5G Network Slicing

This dataset contains augmented and preprocessed network traffic and performance metrics, specifically designed to evaluate and optimize dynamic resource allocation strategies in 5G network slicing. It includes 15,760 records with key temporal and network parameters, reflecting diverse and realistic 5G traffic conditions. The data is structured to be highly versatile, making it suitable for training a wide range of Machine Learning (ML) models, evaluating optimization frameworks, and testing AI-driven network management algorithms. Researchers can utilize this dataset for various applications, including time-series forecasting of bandwidth demands, proactive resource provisioning, and intelligent network slicing optimization. Dataset Features: Timestamp: The time intervals of the recorded network activity. User_ID: Unique identifiers for network users. Application_Type: Categorical representation of different 5G service types or slices. Signal_Strength: The recorded signal strength for the connection. Latency: The communication delay experienced in the network. Required_Bandwidth: The bandwidth demanded by the user/application. Allocated_Bandwidth: The actual bandwidth assigned by the network. Resource_Allocation: The optimized metric representing the efficiency of the allocation process. This dataset provides a robust foundation for researchers and data scientists working on next-generation telecommunications, intelligent network slicing, and smart resource management systems