Clock Synchronization Filter Tuning for Accurate PTP Latency
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Solution Overview
Problem
Existing clock synchronization methods, particularly Precision Time Protocol (PTP), face challenges in efficiently optimizing filter parameters across multiple network devices due to hardware variations, link characteristics, and environmental factors, leading to inaccurate one-way latency measurements.
Innovation Solution
Implementing Bayesian Optimization to dynamically adjust filter parameters in clock circuitry, using a processing unit to optimize filter parameters device-by-device in a logical synchronization topology, allowing for parallel optimization of sub-paths and re-optimization in response to triggering events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional filter parameters are used in clock synchronization, then the system is simple to implement, but the synchronization accuracy and stability are insufficient
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting filter parameters (such as bandwidth, rise time, settling time) based on detected network conditions and device characteristics. The system transitions from using fixed, manufacturer-default parameters to adaptive parameters that are optimized for each specific deployment scenario, thereby improving synchronization accuracy without requiring complex manual configuration.
Solution Approach 2:
The patent implements self-service through automated parameter optimization where the system independently detects its own performance characteristics, evaluates different parameter sets, and selects optimal values without external intervention. The device monitors synchronization metrics, automatically adjusts parameters, and re-optimizes in response to changing conditions, eliminating the need for manual tuning while achieving high synchronization precision.
2Reliability
If filter parameters are manually configured for each device, then optimization can be achieved, but the process is time-consuming and complex
Solution Approach 1:
The patent applies preliminary action by pre-defining a comprehensive set of candidate parameter values and their associated performance characteristics during system design. The system stores these pre-characterized parameter sets and uses them as a foundation for automated selection, avoiding the need to evaluate all possible parameters from scratch during deployment and reducing optimization time while maintaining high reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors synchronization performance metrics (such as timestamp accuracy, jitter, and skew) and uses this information to evaluate and select optimal filter parameters. The automated feedback loop compares actual performance against target criteria and adjusts parameters accordingly, enabling rapid optimization without manual intervention while ensuring stable synchronization.
3Measurement precision
If extensive parameter testing is performed to find optimal values, then synchronization performance improves, but the measurement and validation process becomes lengthy
Solution Approach 1:
The patent applies partial action by testing and validating only the most promising parameter sets based on pre-established performance models and device characteristics, rather than exhaustively testing all possible parameter combinations. The system uses partial characterization data and predictive models to identify optimal parameters, achieving high measurement precision while significantly reducing validation time compared to complete brute-force testing.
Solution Approach 2:
The patent uses copying by creating virtual models or twins of the actual synchronization system that can be simulated and tested independently. These virtual representations allow extensive parameter testing and validation in a controlled environment before deployment, enabling thorough measurement precision assessment without requiring lengthy real-world validation processes.
4Adaptability or versatility
If fixed filter parameters are used across all devices, then implementation is simple, but the system cannot adapt to hardware variations and link characteristics
Solution Approach 1:
The patent applies local quality by tailoring filter parameters to specific local conditions at each device, such as hardware characteristics, link quality, and network topology. Each device independently determines its optimal parameters based on locally detected conditions rather than using uniform settings, enabling adaptation to hardware variations while keeping the overall system architecture relatively simple through decentralized decision-making.
Solution Approach 2:
The patent implements dynamics by making filter parameters adjustable and adaptive rather than fixed. The system dynamically modifies parameter values in response to changing network conditions, device state, and performance metrics, allowing each device to optimize its synchronization behavior for its specific context. This dynamic adaptability is achieved through automated mechanisms that maintain manageable system complexity.
Data Source
AI summary
In one embodiment, a device includes a processing unit to find at least one value of at least one filter parameter using Bayesian Optimization, and provide the at least one value of the at least one filter parameter to a filter to generate an adjustment to cause clock circuitry to adjust a local clock signal or local clock based on an error signal and the at least one value of the at least one filter parameter, and a memory to store data used by the processing unit.