Timing Advance Prediction for Wireless Uplink Resources
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Solution Overview
Problem
In wireless communications, user equipment (UE) must validate timing advance (TA) before using preconfigured uplink resources (PUR), leading to fallback to legacy RACH or early data transmission if TA is invalid, undermining the benefits of PUR.
Innovation Solution
A method for predicting timing advance using a user equipment (UE) that determines a set of timing advances corresponding to different distances from the base station, measuring power metrics, and using machine learning regressors to estimate a new TA, assuming the predicted TA is valid for PUR transmission within the same cell.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE validates TA before PUR transmission, then transmission reliability is improved, but UE mobility flexibility deteriorates and fallback to legacy RACH increases
Solution Approach 1:
The system performs preliminary TA prediction using machine learning models before PUR transmission, estimating future TA values based on historical data and current channel conditions. This allows UE to prepare valid TA in advance without real-time validation, maintaining transmission reliability while enabling mobility flexibility.
Solution Approach 2:
The patent replaces the traditional mechanical TA validation mechanism with an intelligent prediction system using machine learning regressors. Instead of validating TA through explicit signaling and fallback procedures, the system uses neural networks to predict TA values, eliminating the need for legacy RACH fallback and improving both reliability and adaptability.
2Measurement precision
If UE uses legacy RACH procedure to obtain TA, then TA accuracy is improved, but transmission efficiency deteriorates due to procedure overhead
Solution Approach 1:
The system performs TA prediction in advance using machine learning models trained on historical TA data and channel characteristics. By predicting TA before transmission, the system achieves accuracy comparable to legacy RACH without the procedural overhead, significantly improving transmission efficiency while maintaining TA precision.
Solution Approach 2:
The patent creates a virtual copy of the TA measurement function through machine learning models that replicate the behavior of traditional TA measurement. The neural network learns to predict TA values by copying patterns from historical data, providing accurate TA estimates without requiring actual RACH procedures, thus improving efficiency while maintaining accuracy.
3Reliability
If UE falls back to legacy RACH when TA is invalid, then connection reliability is maintained, but PUR utilization deteriorates
Solution Approach 1:
The patent replaces the fallback mechanism with an intelligent prediction system that continuously estimates TA values using machine learning. This substitution eliminates the need for fallback to legacy RACH, maintaining connection reliability through accurate predictions while maximizing PUR utilization by keeping UEs in the optimized PUR mode rather than falling back to traditional procedures.
Data Source
AI summary
There are provided methods for predicting timing advance (TA) with respect to a base station. According to some embodiments, the method includes determining, by a user equipment (UE), a set of TAs, each TA corresponding to a particular distance from the base station and measuring, by the UE, a set of instances of a power metric, each instance of the power metric associated with a respective distance from the base station. The method further includes determining, by the UE, a set of differences between each of the instances of the power metric and determining, by the UE, a new TA at least in part using the set of TAs and the set of differences.