WTRU Measurement Event Prioritization for Predictive Handover Decisions
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Solution Overview
Problem
In NR networks, wireless transmit/receive units (WTRUs) face challenges in efficiently managing and prioritizing between real-time and predicted measurement events, leading to suboptimal resource allocation and handover decisions.
Innovation Solution
WTRUs are enabled to prioritize future predicted events over current real events by comparing and determining the fulfillment of measurement events based on both real and predicted measurements, allowing for actions such as suspending, delaying, or canceling conditional reconfigurations, and sending measurement reports, using artificial intelligence and machine learning for prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If WTRU prioritizes real-time measurement events for immediate handover decisions, then response speed is improved, but handover reliability deteriorates due to potential suboptimal decisions
Solution Approach 1:
The system performs preliminary actions by predicting future measurement events before they actually occur. The WTRU uses machine learning models to forecast upcoming measurement events and prepares potential handover decisions in advance, allowing it to act proactively rather than reactively. This preliminary prediction and preparation enables the system to maintain fast response times while improving decision reliability through advance analysis.
2Reliability
If WTRU prioritizes predicted future measurement events over current real events, then handover optimization is improved, but decision complexity increases
Solution Approach 1:
The system introduces an intermediary layer consisting of machine learning prediction models and event prioritization logic that sits between raw measurement data and handover decisions. This intermediary processes and prioritizes measurement events, filtering and ranking them based on predicted impact and timing. By inserting this intelligent intermediary, the system manages the complexity of predicting future events while maintaining optimized handover decisions.
3Measurement precision
If WTRU implements both real and predicted measurement event evaluation, then measurement precision is improved, but processing overhead increases
Solution Approach 1:
The system applies partial action by selectively evaluating only the most relevant measurement events. Rather than processing all possible real and predicted measurement events with equal depth, the WTRU uses prioritization logic to focus computational resources on high-impact events. The machine learning model predicts which future events are most likely to influence handover decisions, allowing the system to perform detailed analysis only on those specific events while skipping or simplifying analysis of less critical ones.
Data Source
AI summary
A WTRU may receive configuration information that includes a first measurement event, a second measurement event, a first action associated with the first measurement event, a second action associated with the second measurement event, criteria associated with the first measurement event, criteria associated with the second measurement event, and/or a condition. The WTRU may predict measurements based on the measurements that are determined based on the one or more reference signals. The WTRU may determine measurements. The WTRU may determine whether the first measurement event is fulfilled. The WTRU may determine whether the second measurement event is fulfilled. The WTRU may select the first action or the second action based on the condition, a determination of whether the first measurement event is fulfilled, and a determination of whether the second measurement event is fulfilled. The WTRU may perform the selected action.