As foundational infrastructure for spatiotemporal information, the Global Navigation Satellite System (GNSS) delivers high-precision positioning, navigation, and timing (PNT) services worldwide. However, satellite atomic clock drift causes satellite clock bias, degrading PNT service quality. Compared to post-processed clock bias products and real-time estimation, satellite clock bias prediction offers a key advantage: it provides high-precision real-time clock bias even in scenarios with limited real-time data or poor communication. Through analysis and summarization of error sources in prediction models, this paper proposed generalized modeling frameworks for both classical and AI-based approaches. We reviewed current research on classical mathematical models—including polynomial, grey, Kalman filter, and time series models—and AI-based models such as machine learning (ML), multilayer perceptron (MLP), recurrent neural networks (RNN), and Transformer architectures. Technical characteristics, applicability, and limitations of each model were discussed. While AI-based models demonstrate superior flexibility and adaptability in complex scenarios compared to classical approaches, they require extensive datasets and computational resources. In conclusion, we summarized the advantages, disadvantages, and future research directions, offering insights for developing next-generation real-time high-precision GNSS PNT services.

A high-precision time reference is fundamental to the positioning, navigation, and timing (PNT) of global navigation satellite systems (GNSS). The precision of clock steering determines the accuracy of practical applications that rely on the time–frequency reference. With the invention of direct digital synthesizer (DDS) technology, digital clock steering (DCS) has gradually become a mainstream technology. However, the key factor limiting DCS accuracy is the system quantization noise, which leads to a low frequency and phase adjustment accuracy. Here we propose a DCS method based on $\Sigma$-$\Delta$ modulation to address the issue of low resolution of DAC through shaping the quantization noise. A simulated GNSS time–frequency reference system experimental platform is constructed to validate the effectiveness of the proposed method. The experimental results demonstrate that this method achieves a phase adjustment accuracy of 0.48 ps and a frequency adjustment accuracy better than 0.48 pHz, which is two orders of magnitude higher than that of existing GNSS time–frequency reference systems. Thus, the proposed method offers a significant improvement in time–frequency reference systems, leading to better performance, reliability, and accuracy in a wide range of practical applications.