Network Slice Controller for Dynamic Wireless Traffic Prediction
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Solution Overview
Problem
Wireless communication networks face challenges in efficiently controlling network slices in response to changing conditions and predicting when network conditions will occur that affect network slices, leading to service degradations and negative impacts on user experience.
Innovation Solution
A method and system for dynamically managing network slices by retrieving Key Performance Indicators (KPIs) that indicate traffic patterns and network parameters, generating predictions of network conditions, updating network slice parameters, and modifying the network slices based on these updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network slices are statically configured to provide specific services, then service specialization is improved, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future network conditions using machine learning models before actual congestion or service degradation occurs. This allows proactive adjustment of network slice parameters (such as bandwidth allocation, QoS settings, or resource distribution) in advance, preventing problems rather than reacting to them. The prediction capability enables the system to prepare and configure network slices ahead of time based on forecasted traffic patterns and network states.
2Speed
If network slices are dynamically adjusted in real-time, then responsiveness to network conditions is improved, but control complexity deteriorates
Solution Approach 1:
The system implements continuous feedback loops where network slice performance metrics (such as latency, throughput, packet loss, and resource utilization) are monitored in real-time and fed back to the control mechanism. This feedback enables automatic adjustments to be made based on actual network conditions, allowing the system to maintain optimal performance without requiring complex manual intervention. The feedback-driven approach automates the dynamic adjustment process, reducing control complexity while maintaining high responsiveness.
3Productivity
If network slices are manually managed, then control precision is improved, but operational efficiency deteriorates
Solution Approach 1:
The system enables network slices to self-manage through automated mechanisms including self-configuration, self-optimization, and self-healing capabilities. Machine learning models automatically analyze network conditions and adjust slice parameters without human intervention, while orchestration systems autonomously allocate resources and manage slice lifecycles. This self-service approach dramatically improves operational efficiency by eliminating manual management tasks while maintaining precise control through algorithmic decision-making.
4Reliability
If network resources are allocated to meet peak demand, then service reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system implements dynamic resource allocation where network slice parameters and resource distributions are continuously adjusted based on real-time and predicted network conditions. Instead of static over-provisioning for peak demand, the system dynamically scales resources up or down to match actual traffic patterns and service requirements. This dynamic approach maintains service reliability during peak periods while optimizing resource utilization during low-demand periods, preventing both over-provisioning waste and under-provisioning failures.
Data Source
AI summary
Various embodiments comprise a wireless communication network to dynamically manage network slices. The wireless communication network comprises a Network Slice Control Function (NSCF). The NSCF retrieves network slice Key Performance Indicators (KPIs) that indicate traffic patterns and network parameters related to a wireless network slice. The NSCF generates a prediction of network conditions for the wireless network slice based on the network slice KPIs. The NSCF updates one or more network slice parameters for the wireless network slice based on the prediction. The NSCF modifies the wireless network slice based on the one or more updated network slice parameters.