GNSS spoofing has become a significant security vulnerability threatening remote sensing systems. Hardware fingerprint-based GNSS receiver identification is one of the solutions to address this security issue. However, existing research has not provided a solution for distinguishing GNSS receivers of the same specification. This paper first theoretically proves that the CSACs (Chip-Scale Atomic Clocks) used in GNSS receivers have unique hardware noise and then proposes a fingerprinting scheme based on this hardware noise. Experiments based on the neural network method demonstrate that this fingerprint achieved an identification accuracy of 94.60% for commercial GNSS receivers of the same specification and performed excellently in anomaly detection, confirming the robustness of the fingerprinting method. This method shows a new real-time GNSS security monitoring method based on CSACs and can be easily used with any commercial GNSS receivers.

While Temperature-Compensated Crystal Oscillators (TCXOs) provide an economical solution for local timing in communication systems, their inherent holdover limitations have historically restricted deployment in mission-critical applications. We present a novel Transformer-based neural network approach that fundamentally enhances TCXO disciplining and holdover performance. Our method exploits the self-attention mechanism’s capacity to model long-term dependencies in frequency drift patterns, achieving significant improvements over conventional approaches. The proposed Software-Defined TCXO (SD-TCXO) system predicts future frequency drift trajectories and applies real-time compensation via voltage control. A key innovation lies in our Transformer architecture’s incorporation of learnable positional encoding, specifically tailored for temporal frequency characteristics. This design significantly outperforms both fixed sinusoidal encoding and no-encoding approaches. Experimental validation reveals a clear performance hierarchy: our Transformer-based method demonstrates remarkable improvements compared to both LSTM-based methods and traditional Kalman filtering techniques. Extensive testing across diverse operational scenarios, including frequency jumps and variable drift patterns, confirms the method’s robustness and adaptability. Our solution satisfies ITU-T G.8273.2 standard requirements for network boundary clock holdover performance under temperature-stable conditions, establishing TCXO viability for high-precision timing applications in 5G and beyond while maintaining computational efficiency suitable for resource-constrained environments.

IEEE 1588 Precision Time Protocol (PTP) synchronization accuracy is degraded by delay asymmetry in physical links. Accurate prediction and compensation of this asymmetry are essential for high-precision timekeeping. This paper presents a multi-horizon delay asymmetry prediction framework validated on a cesium-referenced testbed. The experimental platform integrates a cesium atomic clock (frequency stability $<5\times {10}^{-12}$), a Syncedge C10 PTP analyzer, and an RK3568 embedded TimeReceiver, providing hardware-timestamped ground-truth measurements over 24 h periods on both copper and fiber links under real operating conditions. A unified multi-output encoder architecture is compared against per-horizon recurrent models and classical baselines to predict delay asymmetry at five future horizons ($h\in \{1,2,4,8,16\}$, corresponding to $0.125$–$2.0$ s ahead). On copper links at an 8 s observation window, the unified model achieves a mean absolute error of 2.15 ns and an $R^2$ of 0.714 at the nearest horizon, statistically tied with the best per-horizon model (2.16 ns, $R^2=0.713$) while using 1.9–6.1× fewer total parameters (655 K vs. 1.2–4.0M). On fiber links, all models achieve comparable accuracy with an MAE of 2.16–2.18 ns. The method requires ground-truth labels from a calibrated 1PPS or cesium reference; both training and inference require the same physical link and environmental conditions. Generalization to other settings remains untested.