Predictive AI for PTP Backup Timing and Phase Correction
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Solution Overview
Problem
Existing networks face challenges in maintaining accurate timing due to susceptibility to jamming and environmental conditions, necessitating a reliable backup timing source that is not adequately addressed by current GPS-based systems.
Innovation Solution
Implementing a predictive artificial intelligence (AI) engine to analyze network parameters and phase offsets, enabling adaptive phase offset corrections and selecting the most suitable PTP clock reference as a backup to GPS, thereby enhancing timing accuracy and reducing the need for expensive testing and conservative engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS antennas and receivers are deployed to meet timing requirements, then timing accuracy is improved, but susceptibility to jamming and environmental conditions increases
Solution Approach 1:
The patent introduces PTP (Precision Time Protocol) as an intermediary timing source that operates over packet networks. Instead of directly relying on GPS signals that are vulnerable to jamming, the system uses PTP messages transmitted through the network infrastructure to deliver timing information, thereby mediating between the GPS source and the network elements and reducing direct exposure to harmful environmental factors
Solution Approach 2:
The patent creates a virtual copy of the GPS timing function through PTP. Rather than using the physical GPS receiver directly, the system replicates the timing function by transmitting PTP timing messages through the packet network, which carries the same timing information but is less susceptible to jamming and environmental interference
2Reliability
If PTP is used as a backup timing source, then reliability is improved, but timing accuracy deteriorates due to network variations
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors the phase offset between GPS timing and PTP timing. This phase offset information is fed back to the network element, which then applies corrective phase adjustments to the PTP timing to compensate for network variations, thereby maintaining timing accuracy while using PTP as a backup source
Solution Approach 2:
The patent dynamically changes the phase offset parameter of the PTP timing based on monitored conditions. By adjusting the phase offset in real-time according to the difference between GPS and PTP timing, the system compensates for network variations and maintains accurate timing even when using PTP as a backup source
3Reliability
If conservative engineering and expensive testing are used to ensure timing accuracy, then reliability is improved, but cost and complexity increase
Solution Approach 1:
The patent enables the network element to automatically monitor and adjust its own timing by measuring the phase offset between GPS and PTP sources and applying self-correction. This self-service capability eliminates the need for expensive external testing and conservative engineering margins, as the system automatically maintains timing accuracy through real-time phase offset compensation
Data Source
AI summary
A predictive artificial intelligence (AI) engine is trained phase offsets measured between global network satellite system (GNSS) derived clocks and precision timing protocol (PTP) derived clocks and network parameters including network impairment metrics in a packet network. The predictive AI engine may predict which PTP input source should be selected by a network element, phase offset(s) to be applied to the PTP input source, and which network element(s) should apply phase offset(s). Other embodiments are disclosed.