UE AI Signaling for Radio Link Failure Prediction
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Solution Overview
Problem
Wireless communications systems face challenges in predicting and managing radio link failures (RLF) due to UE mobility, blockage, or RF signal attenuation, which can lead to communication disruptions without effective anticipation or proactive measures.
Innovation Solution
Implementing an AI model at the UE for predicting RLF by configuring it to generate predictions based on beam or cell-level measurements, using thresholds and conditions to trigger reports, enabling flexible resource allocation and proactive communication adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI model is implemented at UE for RLF prediction, then communication reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting RLF before it actually occurs. The AI model analyzes current signal conditions and predicts future RLF events, allowing the network to take proactive measures such as initiating handover procedures in advance. This preliminary prediction and action prevents communication disruptions rather than reacting after failures occur.
Solution Approach 2:
The AI model acts as an intermediary between the UE's measurement capabilities and the network's control functions. It processes measurement reports from the UE, generates predictions about future RLF events, and provides this information to the network entity which then makes informed decisions about resource allocation and handover management. This intermediary layer translates raw measurements into actionable predictions.
2Reliability
If proactive measures are taken for RLF management, then communication disruptions are reduced, but time for prediction and response is required
Solution Approach 1:
The system performs preliminary actions by predicting RLF before it actually occurs. The AI model analyzes current signal conditions and predicts future RLF events, allowing the network to take proactive measures such as initiating handover procedures in advance. This preliminary prediction and action prevents communication disruptions rather than reacting after failures occur.
Solution Approach 2:
The system establishes a feedback loop where the AI model continuously receives measurement reports from the UE, generates predictions, and the network entity adjusts its decisions based on these predictions. Measurement reports are periodically collected, processed through the AI model, and used to update handover decisions, creating an adaptive feedback mechanism that responds to changing signal conditions in real-time.
Data Source
AI summary
In some examples of the techniques described herein, a user equipment (UE) may utilize an artificial intelligence (AI) model to predict whether radio link failure (RLF) will occur. For instance, a network entity may configure the UE to report a prediction of the occurrence of a RLF based on beam or cell-level measurements performed on the cell. In some approaches, the configuration may include a time of prediction for RLF and one or more thresholds or conditions for triggering the report. The UE may utilize the configuration(s) to predict a probability of RLF and to report the prediction if the one or more thresholds or conditions are satisfied. The prediction may be utilized to trigger the measurement or configuration of one or more candidate cells for a potential handover. Configuring the UE to predict whether RLF will occur may enable flexibility in how an AI model is utilized.