CP-to-UP Switch Parameter Exposure for 5G Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 3GPP communication networks lack parameters to effectively inform the network about a user equipment's (UE) intention to switch from control plane (CP) to user plane (UP) data transmission, which can lead to network congestion and negative user experience, especially for IoT devices with AI/ML applications.
Innovation Solution
Introduce expected CP to UP switch parameters, such as expected time, periodicity, duration, and volume of data transfer, to help the network anticipate and manage resource allocation for seamless transitions between CP and UP data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the network allows UE to switch from control plane to user plane without notification parameters, then the network architecture remains simple, but network congestion occurs and user experience deteriorates
Solution Approach 1:
The network function receives and processes expected CP to UP switch parameters (such as expected time, periodicity, duration, data volume) in advance before the actual switch occurs. This allows the network to proactively allocate resources and prepare the user plane infrastructure, preventing network congestion and ensuring seamless transition when the switch actually happens.
Solution Approach 2:
The patent implements a feedback mechanism where the network function receives parameters about expected CP to UP switches from the UE or external sources, processes this information, and uses it to dynamically adjust resource allocation and network configuration. This closed-loop approach enables the network to adapt to UE behavior patterns and optimize resource distribution accordingly.
2Productivity
If the network proactively allocates resources based on expected switch parameters, then service quality improves, but the complexity of parameter processing and resource management increases
Solution Approach 1:
By receiving expected switch parameters in advance, the network function can perform resource allocation and configuration准备工作 before the actual CP to UP switch occurs. This preliminary action eliminates the need for complex real-time resource negotiation and reduces processing complexity during the actual switch event.
Solution Approach 2:
The patent enables dynamic resource allocation based on received parameters such as expected time, periodicity, and duration. The network function can adaptively adjust resource allocation strategies according to different switch patterns, optimizing productivity while managing complexity through parameter-driven dynamic behavior rather than static complex configurations.
3Reliability
If the network receives detailed expected switch parameters, then congestion is reduced, but the complexity of parameter exposure and processing increases
Solution Approach 1:
The patent extracts only the essential parameters needed for congestion control (such as expected time, periodicity, duration, and data volume) from the complete set of possible UE parameters. By selecting and exposing only these critical parameters through the parameter exposure interface, the network achieves effective congestion control without the complexity of processing all possible UE parameters.
Data Source
AI summary
The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. One or more network entities, such as a network function, a user equipment of a 3GPP communication network which use one or more expected CP to UP switch parameters which are one or more new parameters to inform the network about a CP to UP switch. An expected CP to UP switch parameter may be used to inform the network about an expected time of the CP to UP switch. The one or more expected CP to UP switch parameters may be exposed. An AF may provide the one or more expected CP to UP switch parameters to the network. An AI/ML AF may provide the one or more expected CP to UP switch parameters as part of AI/ML assisted parameters.