Network Entity ML Configuration Mapping for Handover Continuity
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Solution Overview
Problem
Existing wireless communications systems face challenges in managing effective life cycle management of UE-deployed ML models due to beam switches, cell switches, and handovers, as the relationship between identifiers and network-entity specific configurations for AI-aided wireless communications is not established across multiple network entities.
Innovation Solution
Techniques for communicating network entity-specific configurations, such as data collection configurations and associated identifiers, are provided to align UE and network entities on the application of ML models for specific beams, cells, and network entities, enabling improved training and inference operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML models are deployed at UE for beam prediction and wireless communications, then communication efficiency and speed are improved, but reliability and accuracy deteriorate due to lack of alignment between UE and network entities on configuration identifiers during handovers and beam switches
Solution Approach 1:
The network entity receives feedback from the UE regarding ML model capabilities and configuration requirements, then responds with appropriate configuration identifiers. This bidirectional feedback mechanism ensures both UE and network entity have aligned understanding of which configurations apply to which ML functions, maintaining reliability during handovers and beam switches while preserving communication efficiency.
Solution Approach 2:
Configuration identifiers act as intermediaries that bridge the UE and network entity, providing a common reference framework for ML model applications. These identifiers mediate the relationship between different network entities and the UE, ensuring consistent interpretation of configurations across handovers and beam switches without sacrificing communication performance.
2Reliability
If network entity specific configurations are communicated to UE, then accuracy and reliability of ML model application are improved, but device complexity and signaling overhead increase
Solution Approach 1:
Configuration identifiers are designed to be universal across multiple network entities and ML functions. A single identifier can reference configurations that apply to multiple scenarios (beam prediction, handover, different ML models), reducing the total number of identifiers needed and simplifying UE configuration management while maintaining high accuracy in ML model application.
Solution Approach 2:
The system dynamically changes configuration parameters based on operational context. Instead of maintaining fixed, detailed configurations for every scenario, the system adjusts configuration identifiers and their associations based on current beam states, handover conditions, and ML model requirements, reducing complexity while preserving reliability through context-appropriate configuration application.
3Reliability
If configuration associations are established across multiple network entities, then reliability during handovers is improved, but loss of time for configuration synchronization increases
Solution Approach 1:
Configuration associations are established preliminarily before handovers occur. The network pre-configures identifier associations for potential target network entities and beam configurations, so that when handover occurs, the UE already has the necessary configuration information aligned with the target entity, ensuring continuity without synchronization delays during the actual handover execution.
Solution Approach 2:
Configuration information is copied and propagated across network entities through the identifier association mechanism. When a configuration is defined at one network entity, the identifier association enables automatic copying of the configuration context to other entities, ensuring consistency across handovers without requiring time-consuming synchronization protocols, thus maintaining reliability while minimizing time loss.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for communication of network entity-specific configurations for machine learning training and/or inference. An example method for wireless communications by an apparatus includes obtaining a first request for an indication of at least one configuration for use at one or more first network entities during one or more machine learning operations associated with one or more machine learning functions available for activation at a user equipment (UE); sending, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions; and communicating with the UE while using the at least one configuration.