Local Oscillator Holdover Using Trained Timing Error Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network nodes with local oscillators disciplined by external timing references face performance impairment when the external reference is unavailable, requiring costly and time-consuming human intervention to maintain timing accuracy, especially in stringent standards like WiMAX and LTE where time errors must be limited to microsecond levels over extended durations.
Innovation Solution
A method involving training mathematical models of the local oscillator using external reference signals to predict correction signals, determining frequency and time errors, and selecting the model with the smallest time error to discipline the oscillator when the external reference is unavailable, allowing for autonomous holdover operation within specified error thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an external timing reference source is used to discipline the local oscillator, then timing accuracy is improved, but the system becomes vulnerable to performance impairment when the external reference is unavailable
Solution Approach 1:
The system performs preliminary training of multiple mathematical models during periods when the external reference is available. These trained models are stored and ready for use, allowing the system to switch to autonomous holdover operation immediately when the reference becomes unavailable, without requiring real-time model training or human intervention.
Solution Approach 2:
The system changes the operational mode of the local oscillator from externally disciplined to autonomously controlled by switching from using external reference signals to using pre-trained mathematical models. This parameter change enables the system to maintain timing accuracy within acceptable limits during reference unavailability.
2Measurement precision
If human intervention is used to correct timing when external reference is unavailable, then timing accuracy is restored, but operational cost and time consumption increase
Solution Approach 1:
The system implements self-service by automatically switching to pre-trained mathematical models when the external reference becomes unavailable. The built-in self-test continuously monitors timing error and validates model performance without human intervention, enabling the system to correct and maintain timing accuracy autonomously.
Solution Approach 2:
The system incorporates feedback through built-in self-test that continuously monitors the timing error of the local oscillator during holdover operation. This feedback mechanism validates whether the timing error remains within acceptable limits and triggers appropriate actions, eliminating the need for external human monitoring and intervention.
3Measurement precision
If multiple mathematical models are trained and tested, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
Multiple mathematical models are trained in advance during periods when the external reference is available. This preliminary training eliminates the need for complex real-time model training and selection when the reference becomes unavailable, reducing the computational burden and system complexity during critical holdover operation.
Solution Approach 2:
The system extracts and stores the essential characteristics of oscillator behavior by training multiple mathematical models during the locked state. These extracted models are then used during holdover, separating the complex training process from the critical prediction phase and simplifying the overall system operation.
4Measurement precision
If the local oscillator is designed with higher accuracy, then timing precision is improved, but device cost increases
Solution Approach 1:
The system changes the approach from improving hardware accuracy to improving software-based prediction accuracy. By using pre-trained mathematical models and built-in self-test, the system achieves compliant timing accuracy with a lower-cost local oscillator, avoiding the need for expensive high-precision hardware while still meeting stringent timing requirements.
Data Source
AI summary
Embodiments of the invention include a method for use in a device having a local oscillator. The method includes performing, for the local oscillator that is disciplined by an external reference signal, while locked to the external reference signal, training at least two mathematical models of the oscillator to determine a predicted correction signal for each mathematical model based at least in part on a correction signal that is a function of the external reference signal and which is used to discipline drift in the oscillator. The method also includes selecting a mathematical model of the at least two mathematical models that results in a smallest time error when disciplining the oscillator to use when the external reference signal is unavailable and an alternative correction signal is to be used to discipline drift in the oscillator. The method further includes testing the selected mathematical model using a sampled version of the correction signal such that the selected mathematical model can be used without the need for a testing duration that is in addition to a period of time used for the training.