AI/ML Positioning Model Life Cycle Management via Mobility Indicators
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G networks, face challenges in efficiently managing the life cycle of AI/ML positioning models, which are crucial for accurate user equipment (UE) location determination amidst changing mobility and handover conditions.
Innovation Solution
The proposed method involves receiving mobility capability indicators from wireless nodes, providing mobility reporting configuration information, and performing life cycle management on AI/ML positioning models based on mobility indicators. This includes configuring UEs to measure and report mobility and handover-related measurements, which are then used by location servers to apply life cycle management tasks such as activation, deactivation, selection, switching, and fallback of AI/ML positioning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/ML positioning models are used for location determination, then positioning accuracy is improved, but the system complexity and latency in model life cycle management increase
Solution Approach 1:
The patent implements feedback mechanisms where location servers receive mobility indicators from wireless nodes and use this information to dynamically manage AI/ML positioning models. The system continuously monitors mobility patterns and adjusts model activation, selection, and parameters based on real-time feedback, thereby optimizing positioning accuracy while managing system complexity through adaptive control.
Solution Approach 2:
The patent introduces dynamic life cycle management for AI/ML positioning models, where models can be activated, deactivated, selected, switched, or fallen back to based on current mobility conditions. This dynamic approach allows the system to adapt to changing network conditions and optimize performance without being locked into rigid operational modes, reducing complexity through flexible management.
2Adaptability or versatility
If mobility reporting configuration is implemented, then adaptability to changing mobility conditions is improved, but signaling overhead and processing requirements increase
Solution Approach 1:
The patent implements partial reporting mechanisms where UEs report mobility indicators selectively based on configured conditions rather than continuously reporting all mobility data. The location server configures reporting thresholds and parameters, and only requests or receives mobility indicators when specific conditions are met, thereby reducing signaling overhead while maintaining necessary adaptability to mobility changes.
Solution Approach 2:
The patent employs preliminary configuration of mobility reporting parameters where the location server pre-configures reporting thresholds, types of indicators to report, and triggering conditions before actual mobility events occur. This preliminary setup reduces the need for complex real-time decision-making and minimizes signaling overhead during actual mobility management operations.
3Reliability
If life cycle management operations are performed, then model robustness is improved, but processing time and latency increase
Solution Approach 1:
The patent implements preliminary actions by pre-configuring multiple AI/ML positioning models with different parameters and capabilities before they are needed. The location server maintains a pre-vetted library of models that can be quickly selected or switched between based on current conditions, eliminating the need for time-consuming model creation or training during active positioning operations and reducing latency while maintaining robustness.
Solution Approach 2:
The patent uses model copying or cloning mechanisms where AI/ML positioning models can be rapidly instantiated or cloned based on template models that have been pre-trained and validated. This allows the system to quickly deploy new model instances for different mobility conditions without retraining from scratch, thereby improving model robustness for various scenarios while minimizing processing time and latency through reuse of pre-computed model components.
Data Source
AI summary
Techniques are provided for reporting mobility and/or handover related measurements and events for Artificial Intelligence/Machine Learning (AI/ML) positioning. An example method for reporting mobility and handover related measurements and events for AI/ML positioning includes receiving mobility reporting configuration information from a location server, obtaining mobility indicators from one or more wireless nodes based at least in part on the mobility reporting configuration information, and providing the mobility indicators to the location server.