Deep learning software-based holdover for PTP IEEE 1588 synchronization in 5G networks

Abstract

This work proposes evaluates software-based algorithm mechanisms for maintaining the synchronization of a real-time clock in holdover operation when the timing reference input is unavailable. Three algorithms, Autoregressive Integrated Moving Average (ARIMA), long short term memory (LSTM), and Transformer networks, are implemented and trained using timestamps and temperature data acquired while the slave clock is locked to a master clock. When the slave clock loses its reference, the algorithm-based models take over and control the clock. The proposed method is evaluated on a testbed of IEEE 1588 Precision Time Protocol (PTP) clocks based on field-programmable gate arrays, where nanosecond-accurate timestamps are collected for offline analysis. The models are evaluated using two clocks, one cost-effective, cristal oscillator (XO), and one robust, oven controlled cristal oscillator (OCXO), in both constant and variable temperature scenarios. The results show that all algorithms can sustain clock synchronization accuracy within reasonable Time division duplex (TDD) synchronization limits over intervals of 1000 seconds in all temperature and clock scenarios, with the transformerbased holdover mechanism outperforming the statistical approach and LSTM network. This cost-effective software-based approach proves to be feasible for increasing clock accuracy during holdover operation and can be generalized to other holdover contexts, such as in a Global Navigation Satellite System (GNSS) scenario.