Application Demand Profile Adjustment During 5G Slice Fallback
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Solution Overview
Problem
When a 5G Standalone (SA) device steps down to 5G Non-Standalone (NSA) or Long Term Evolution (LTE), the network slice is dropped, leading to a lack of quality assurance for data sessions, and applications may experience a negative impact due to inefficient resource allocation and best-effort delivery.
Innovation Solution
A system and method for dynamically adjusting an application demand profile during slice fallback, utilizing an attribute quality table, RF conditions, and QoS capabilities to optimize network resource allocation, ensuring a high-quality user experience by reallocating network slices based on real-time application needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the UE falls back from 5G SA to 5G NSA or LTE, then network compatibility and coverage are improved, but network slice allocation is lost and QoS assurance deteriorates
Solution Approach 1:
The application dynamically adjusts its demand profile parameters in real-time based on network conditions and slice availability. When falling back from 5G SA to 5G NSA or LTE, the application modifies bandwidth, latency, and other QoS parameters to match the capabilities of the target network, ensuring continuous optimal performance across different network types
Solution Approach 2:
The invention changes the parameters of the demand profile (bandwidth, latency, packet loss tolerance) based on the target network type. The application receives network capability information and adjusts its resource requirements accordingly, transforming from static QoS expectations to dynamic parameter adaptation that maintains service quality across network generations
2Duration of action of stationary object
If the application downgrades demand profile parameters to maintain functionality during fallback, then service continuity is improved, but user experience quality deteriorates
Solution Approach 1:
The application implements a feedback mechanism that continuously monitors network conditions, slice availability, and performance metrics. Based on this feedback, the application intelligently adjusts its demand profile to maximize user experience within available network capabilities, rather than simply downgrading to minimum functional requirements
Solution Approach 2:
The application performs preliminary assessment of target network capabilities before falling back, and pre-adjusts its demand profile parameters accordingly. This proactive approach allows the application to maintain higher quality service by optimizing resource usage in advance, rather than reactively degrading performance after the fallback occurs
3Device complexity
If the network provides best-effort delivery during fallback, then network simplicity is improved, but service quality and resource allocation efficiency deteriorate
Solution Approach 1:
The application autonomously manages its own resource requirements and QoS parameters during network fallback. Instead of relying on complex network-side resource allocation mechanisms, the application self-adjusts its demand profile based on received network capability information, eliminating the need for intricate network control while maintaining efficient resource usage
Data Source
AI summary
Systems and methods for dynamically adjusting an application demand profile during slice fallback are provided. In some aspects, an attribute quality table for an application of a UE may be received. The attribute quality table determines a demand profile for the application on a 5G SA network. A network slice may be allocated based on the demand profile. When the UE falls back from the 5G SA network to a 5G NSA network or an LTE network, a negotiation utilizing the attribute quality table, current RF conditions, and the QoS capabilities of the 5G NSA network or the EPC of the LTE network determines an optimal combination of network resources that provides the highest quality user experience. In some aspects, the application adjusts, in real-time, the demand profile that best meets the available resources of the 5G NSA network or the EPC of the LTE network.