ML-Based Network Slice Management for Real-Time Traffic

The system implements a feedback mechanism where the machine learning model continuously receives real-time traffic data, QoS data, and time data from the network, predicts radio resource usage, and dynamically adjusts network slice configurations. This closed-loop feedback system enables the network to adapt to actual traffic patterns rather than relying on static expected demand projections, thereby improving resource utilization efficiency while maintaining manageable network planning through automated decision-making.

The machine learning model autonomously performs prediction and decision-making for network slice management without requiring manual intervention. The system self-adjusts slice configurations based on its predictions of radio resource usage, enabling the network to self-optimize resource allocation in real-time. This self-service capability improves resource utilization while keeping network planning simple by automating what would otherwise require complex manual processes.

The system transitions from static network slice configurations based on expected demand to dynamic slice management driven by real-time predictions. The machine learning model continuously monitors actual traffic patterns, QoS requirements, and time-based variations, then dynamically adjusts network slice allocations to match actual user needs. This dynamic adaptation ensures service level agreements are fulfilled by aligning resource allocation with real-time demand while maintaining deployment simplicity through automated processes.

The feedback loop enables the system to continuously learn from actual network performance and user behavior patterns. By receiving real-time data on traffic volume, QoS requirements, and slice utilization, the machine learning model refines its predictions and adjusts slice configurations to ensure service level agreement fulfillment. This feedback-driven approach maintains deployment simplicity while significantly improving reliability of service delivery.

The machine learning computing system serves multiple functions: it collects and processes diverse input data (traffic data, QoS data, time data), performs radio resource usage prediction, generates slice modification decisions, and interfaces with the network management system. By consolidating these multiple functions into a single multi-functional ML system, the patent achieves optimized resource utilization while managing system complexity through functional integration rather than proliferation of separate specialized systems.

The machine learning computing system acts as an intermediary layer between the raw network data and the network slice management decisions. Rather than directly modifying complex network configurations, the ML system predicts resource usage and provides recommendation outputs that guide slice management. This intermediary role simplifies the overall system architecture by decoupling the prediction function from the execution function, making the system more manageable despite the added intelligence.

The system introduces dynamics into network slice configuration by using machine learning predictions to continuously adapt slice allocations based on real-time traffic patterns, QoS requirements, and time-based variations. Rather than maintaining static configurations, the system dynamically adjusts the number and characteristics of network slices to match actual user needs. This dynamic approach enhances adaptability while managing complexity through automated decision-making algorithms.

The machine learning model performs preliminary prediction of radio resource usage before actual traffic demands materialize. By analyzing historical patterns, time data, and current network state, the system proactively predicts future resource needs and pre-adjusts network slice configurations. This preliminary action enables the system to adapt to traffic needs in advance, improving responsiveness while maintaining manageable complexity through predictive rather than reactive management.