Persistent UE ID for ML Handover Feedback
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Solution Overview
Problem
In wireless communications systems, the lack of accessible feedback information for UEs transitioning between RRC modes (connected, idle, inactive) limits the effectiveness of ML models in optimizing handover procedures, particularly in dense network areas where UEs undergo multiple handovers, leading to inaccurate predictions and recommendations.
Innovation Solution
Implement signaling mechanisms that enable target nodes to provide feedback information to a predicting node via a persistent UE identifier, ensuring continuous data collection and refinement of ML models across different RRC modes, even after handovers or RRC state transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML models are used to generate predictions for UE handover, then handover optimization is improved, but prediction accuracy deteriorates due to lack of accessible feedback information across RRC modes
Solution Approach 1:
The patent implements feedback mechanisms where target nodes send feedback information about prediction outcomes back to the predicting node. This feedback includes whether the predicted target node was actually selected for handover, enabling the ML model to learn from actual handover decisions and improve future predictions. The feedback loop operates across RRC mode transitions, maintaining continuous learning capability.
Solution Approach 2:
The patent introduces persistent UE identifiers as intermediaries that bridge the gap between different RRC modes. These identifiers enable the predicting node to track UE behavior and collect feedback information even when the UE transitions between connected, idle, and inactive modes. The persistent identifier acts as a mediator that maintains continuity of data collection across state transitions where traditional connection-based identification would fail.
2Measurement precision
If feedback information is collected from multiple handover operations, then ML model accuracy is improved, but information accessibility deteriorates due to UE transitions to idle or inactive modes
Solution Approach 1:
The patent makes the predicting node universal by enabling it to collect feedback information from target nodes regardless of the UE's RRC mode. The system design allows the predicting node to receive feedback about handover outcomes even when UEs are in idle or inactive modes, not just connected modes. This multi-functionality ensures continuous information collection across all operational states of the UE.
Solution Approach 2:
The patent ensures continuous collection of feedback information by establishing mechanisms that operate uninterrupted across RRC mode transitions. Target nodes continue to provide feedback about UE handover behavior even when the UE transitions to or from idle/inactive modes. This continuity eliminates gaps in the feedback data stream, enabling the ML model to learn from a complete sequence of handover operations.
3Area of stationary object
If UEs undergo multiple handovers in dense network areas, then network coverage is improved, but feedback information availability deteriorates due to limited single-hop feedback
Solution Approach 1:
The patent merges feedback information from multiple handover hops into a unified dataset at the predicting node. Instead of treating each handover as an isolated event with limited feedback, the system combines feedback from the entire sequence of handovers a UE undergoes. This merging accumulates information across multiple target nodes and handover operations, providing comprehensive data for ML model training even in dense networks with frequent handovers.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for techniques for machine learning (ML)-based user equipment (UE) mobility. A method generally includes transmitting a handover request associated with handing over a UE from a first cell associated with an apparatus to a second cell associated with a first target network node, the handover request comprising an identifier (ID) of the apparatus and predicted information associated with the UE, the predicted information generated by a ML model; and receiving an information update message comprising: feedback information comprising feedback on the predicted information, the feedback information associated with refining the ML model, and a first persistent ID assigned to the UE, wherein the first persistent ID remains invariant across different radio resource control (RRC) modes of the UE, wherein the different RRC modes comprise an idle mode, an inactive mode, and a connected mode.