NTN Coverage Window Prediction With Feedback-Based UE Measurements
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Solution Overview
Problem
NTN networks face challenges in providing continuous coverage, leading to inefficient energy consumption and signaling due to frequent UE predictions of coverage windows that may not be used, especially for cost-sensitive and energy-sensitive devices like NB-IoT and eMTC.
Innovation Solution
A terminal device and network device system that includes processors and memory to manage predicted coverage windows, allowing devices to perform measurements, determine accuracy, and request updates when necessary, reducing unnecessary predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the UE provides frequent coverage window predictions to the network, then the network can maintain accurate coverage information, but the UE energy consumption and signaling overhead increase
Solution Approach 1:
The network device provides feedback to the UE regarding whether the provided coverage window prediction was accurate or useful. This feedback mechanism allows the UE to adjust its prediction frequency, providing predictions only when likely to be useful, thereby reducing energy consumption while maintaining prediction accuracy where needed
Solution Approach 2:
The system dynamically adjusts the coverage window prediction frequency based on network conditions, UE mobility patterns, and prediction accuracy feedback. This dynamic approach replaces static frequent predictions with adaptive timing, reducing unnecessary energy consumption while maintaining accurate coverage information when required
2Measurement precision
If the UE provides frequent coverage window predictions to the network, then the network can maintain accurate coverage information, but the signaling overhead increases
Solution Approach 1:
The network provides feedback on prediction utility to the UE, enabling the UE to suppress redundant predictions. This feedback loop reduces signaling overhead by eliminating unnecessary prediction messages while maintaining accurate coverage information through selective transmission of only valuable predictions
3Reliability
If the UE monitors for paging frequently to maintain coverage awareness, then the network can ensure continuous communication capability, but the UE energy consumption increases
Solution Approach 1:
The system implements periodic coverage window predictions with dynamically adjusted periods based on UE mobility and network conditions. This periodic approach with adaptive timing allows the UE to monitor coverage awareness at appropriate intervals rather than continuously, maintaining communication reliability while reducing energy consumption from excessive monitoring
Data Source
AI summary
Disclosed are devices, methods, apparatuses, and computer readable media for coverage window prediction. An example terminal device may include at least one processor and at least one memory. The at least one memory may store instructions that, when executed by the at least one processor, may cause the terminal device at least to perform: receiving from a network device, information on a predicted coverage window and measurement information relating to the predicted coverage window, the measurement information comprising one or more expected measurement values; performing, according to the predicted coverage window, at least one measurement according to the measurement information; and determining whether the predicted coverage window is accurate or inaccurate based on a value of the performed at least one measurement and the corresponding at least one expected measurement value from the one or more expected measurement values.