Clock Drift Prediction Using ML for Network Time Synchronization
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Solution Overview
Problem
Current cellular communication networks face challenges in maintaining accurate clock synchronization across network nodes, particularly due to clock drift, which can lead to misalignment and loss of information during signal sampling, especially when terminal devices move to different locations, causing invalid timing advance for uplink transmissions.
Innovation Solution
An apparatus and method utilizing machine-learning models to predict clock skew and offset, accounting for temperature and hardware aging, to adjust clock drift and maintain accurate time synchronization, employing a hybrid machine-learning model for robust clock drift compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clock synchronization methods are used, then network nodes can maintain basic time alignment, but clock drift occurs leading to misalignment and loss of information during signal sampling
Solution Approach 1:
The system performs preliminary clock drift prediction using machine learning models before signal sampling occurs. By predicting clock skew and offset in advance based on historical data and temperature compensation, the system pre-adjusts timing parameters to prevent misalignment during actual transmission, thereby maintaining both high measurement precision and reliability
Solution Approach 2:
The system implements continuous feedback loops where clock skew measurements from previous transmissions are fed back into the machine learning model to improve future predictions. The system monitors timing differences between network nodes and adjusts clock synchronization parameters dynamically, creating a closed-loop control system that maintains timing accuracy despite drift
2Reliability
If clock synchronization is maintained without considering temperature effects, then system complexity remains low, but hardware aging and temperature variations cause clock drift
Solution Approach 1:
The system changes the operational parameters of the clock by introducing temperature compensation factors. The machine learning model learns the relationship between temperature variations and clock drift, adjusting timing parameters dynamically based on measured temperature conditions. This allows the system to maintain clock stability while accounting for environmental factors without requiring complete system redesign
Solution Approach 2:
The machine learning model acts as an intermediary between the physical clock hardware and the network synchronization protocol. It translates physical effects (temperature, hardware aging) into compensatory timing adjustments, shielding the upper layers from the complexity of hardware variations while maintaining reliable synchronization
3Adaptability or versatility
If terminal devices move to different locations, then network coverage and mobility are improved, but timing advance becomes invalid causing misalignment during uplink transmissions
Solution Approach 1:
The system transitions from static timing advance values to dynamic predictions. The machine learning model continuously adapts timing parameters based on the terminal device's current location, movement pattern, and historical data. This dynamic approach allows the system to maintain timing alignment accuracy even as devices move between different network cells and locations
4Measurement precision
If machine-learning models are used to predict clock skew, then clock synchronization precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The system applies partial machine learning by using pre-trained models that perform only the critical clock skew prediction function. Rather than implementing full-blown AI systems, the patent uses streamlined models that leverage historical data patterns to provide sufficient prediction accuracy with reduced computational overhead, balancing precision requirements with processing capabilities
Data Source
AI summary
Disclosed is a method comprising obtaining a plurality of previous clock skews, a reported temperature and a reported time, based on the plurality of previous clock skews, the reported temperature and the reported time, obtaining a prediction of the current clock skew, determining a current clock offset based on the predicted current clock skew, determining a clock adjustment based on the current clock offset and the reported time, and determining a corrected time based on the clock adjustment.