Follower Clock Holdover Using ML Frequency Prediction
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Solution Overview
Problem
Existing clock synchronization systems face inaccuracies in one-way latency measurements due to unsynchronized clocks, and during clock holdover periods, relying on last adjustments leads to significant clock drift and inaccuracies.
Innovation Solution
A system that utilizes a machine learning model trained on historical data to predict frequency adjustments for a local clock based on environmental parameters and previous adjustments, enabling accurate clock synchronization during holdover periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clock synchronization methods (PTP, SyncE) are used, then clock accuracy is maintained during normal operation, but significant clock drift occurs during holdover periods when reference clock is unavailable
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing frequency adjustment data and environmental parameter measurements during normal operation (when reference clock is available). This historical data is stored and prepared in advance, so that when holdover mode is triggered, the pre-collected data can be immediately used to predict frequency adjustments without waiting for reference clock recovery.
Solution Approach 2:
The system implements feedback by continuously monitoring environmental parameters (temperature, humidity, vibration, aging) and comparing actual frequency adjustments with predictions. During holdover, the machine learning model provides predictive feedback to compensate for the absence of reference clock feedback, allowing the local clock to self-correct based on learned patterns from historical operational data.
2Measurement precision
If machine learning model is trained on historical data, then prediction accuracy for frequency adjustments is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The machine learning model acts as an intermediary between environmental parameters and frequency adjustments. Instead of directly complex hardware modifications, the system introduces a software-based ML model that processes environmental sensor data and historical frequency adjustment records to generate predictions. This intermediary layer manages the complexity by using algorithms rather than complex circuitry.
Solution Approach 2:
The system replaces traditional mechanical/electrical clock adjustment mechanisms with a computational approach. Instead of using complex hardware circuits for frequency prediction, the patent substitutes a machine learning algorithm that runs on software, utilizing environmental sensor data and historical records. This substitution reduces hardware complexity while maintaining or improving prediction accuracy through data-driven models.
Data Source
AI summary
In one embodiment, a system includes clock circuitry to generate a local clock signal, the clock circuitry including an oscillator, clock synchronization circuitry to adjust the local clock signal based on a remote clock, and a processor to train a machine learning model to predict a frequency or a frequency adjustment for applying to the local clock signal during clock holdover.