Dynamic LTE Layer Assignment for EPS Fallback UEs
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Solution Overview
Problem
Existing EPS fallback procedures in wireless networks blindly assign all UEs to the same LTE layer without considering UE capabilities or historical data, leading to non-optimal resource utilization and user experience.
Innovation Solution
A network component monitors UE load and analyzes layer capability and historical data to dynamically assign an optimal LTE layer to each UE during EPS fallback, based on compatibility and usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all UEs are assigned to the same LTE layer during EPS fallback, then the assignment process is simple and fast, but resource utilization is non-optimal and user experience deteriorates
Solution Approach 1:
The network component pre-collects and stores capability information and historical data for each UE before EPS fallback occurs. This preliminary data collection enables optimized layer assignment during fallback without adding complexity to the critical handover process, as the analysis work is已完成 in advance.
Solution Approach 2:
The system automatically monitors UE load, analyzes capability information and historical data, and dynamically determines optimal layer assignments without manual intervention. This self-service approach optimizes resource utilization while keeping the assignment process autonomous and efficient.
2Reliability
If dynamic layer assignment based on UE capability and historical data is implemented, then coverage and throughput improve, but the complexity of the assignment process increases
Solution Approach 1:
Capability information and historical data are collected and analyzed before EPS fallback or voice session establishment. This preliminary analysis ensures that when assignment is needed, the network component can quickly determine the optimal layer based on pre-processed information, maintaining reliability while managing complexity.
Solution Approach 2:
The system continuously monitors UE load on various layers and uses this feedback to dynamically adjust layer assignments. Historical data tracks the performance and behavior of UEs over time, enabling the system to learn and optimize assignments, improving user experience while keeping the process automated.
3Speed
If blind layer assignment is used during EPS fallback, then the process is fast and simple, but coverage and throughput are suboptimal
Solution Approach 1:
The network component pre-analyzes UE capability information and historical data before EPS fallback occurs. This preliminary action enables fast assignment during the actual fallback event, as the optimal layer can be determined quickly based on pre-processed information rather than analyzing everything in real-time.
Solution Approach 2:
The system autonomously performs monitoring, analysis, and assignment without manual intervention. This self-service capability maintains fast assignment speeds while improving reliability through data-driven decisions, as the automated system can process information efficiently without human delay.
4Ease of operation
If the same layer is assigned to every UE, then the assignment process is uniform and simple, but congestion occurs and user experience deteriorates
Solution Approach 1:
Instead of uniform assignment, the system applies different layer assignments to different UEs based on their specific capability information and historical data. This local quality approach allows each UE to be assigned to the most appropriate layer, preventing congestion on any single layer while maintaining simple automated operation through data-driven decision-making.
Solution Approach 2:
The layer assignment becomes dynamic rather than static, adjusting based on current UE load conditions and individual UE characteristics. This dynamic approach distributes UEs across multiple layers according to network conditions and UE capabilities, improving throughput while keeping the assignment process automated and adaptive.
Data Source
AI summary
Systems and methods are provided for optimizing layer assignment to UEs that are currently on an LTE network, or that are experiencing EPS fallback and are moving from 5G to LTE. Instead of blindly assigning a UE an LTE layer or assigning the same layer to all UEs, aspects provided for an intelligent and dynamic selection of an LTE layer for each UE. The network may analyze one or both of layer compatibility information and historical data associated with the UEs. With this information, the network assigns an LTE layer to each UE.