Oscillator Holdover Modeling for Reference Loss Timing Accuracy
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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 systems.
Innovation Solution
A method that trains mathematical models of the local oscillator using external reference signals to predict correction signals, determines frequency and time errors, and selects the model with the smallest time error to maintain oscillator discipline when the external reference is unavailable, allowing for automated 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 patent trains multiple mathematical models of the local oscillator characteristics during periods when the external reference is available. This preliminary action stores learned oscillator behavior patterns that can be applied when the reference becomes unavailable, enabling the system to maintain timing accuracy without immediate human intervention.
Solution Approach 2:
The system uses its own historical correction signal data to train mathematical models that can autonomously predict future correction needs. When the external reference is unavailable, the trained models self-generate correction signals based on learned oscillator patterns, allowing the system to service itself without external assistance.
2Measurement precision
If human intervention is used to correct timing when external reference is unavailable, then timing accuracy is maintained, but operational cost and time consumption increase
Solution Approach 1:
The system autonomously maintains timing accuracy by using trained mathematical models to generate correction signals when the external reference is unavailable. This self-service capability eliminates the need for human operators to manually correct timing, thereby reducing both time loss and operational costs while maintaining the required timing precision.
3Measurement precision
If a higher accuracy local oscillator is used, then timing performance is improved, but device cost increases
Solution Approach 1:
Instead of using a more expensive high-accuracy oscillator, the patent changes the operational parameters by training multiple mathematical models that characterize the oscillator's behavior under different conditions. These models enable a lower-cost oscillator to achieve higher effective accuracy through software-based correction, thereby improving timing performance without increasing hardware cost.
Solution Approach 2:
The patent creates mathematical copies or models of the local oscillator's behavior rather than physically upgrading the oscillator itself. These virtual models capture the oscillator's characteristics and can be used to predict and correct its drift, achieving the effect of a higher-accuracy oscillator without the associated cost.
4Measurement precision
If multiple mathematical models are trained, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the oscillator characterization into multiple separate mathematical models, each trained on specific aspects of oscillator behavior. This segmentation allows the system to select and apply only the most appropriate model for given conditions, improving prediction accuracy while managing computational complexity through selective model usage rather than continuously running all models.
Data Source
AI summary
Aspects of the embodiments include a method for synchronizing a device having an oscillator to a reference signal. A correction signal can be determined based on the reference signal. A mathematical model of the oscillator can be trained based at least upon the correction signal. A predicted correction signal for the trained mathematical model can be determined. A time error using the predicted correction signal can be generated to assess suitability of the trained mathematical model for disciplining drift in the oscillator and synchronizing the device when the reference signal is not available.