Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals into time–frequency matrices as feature inputs, effectively capturing both temporal and spectral characteristics through time–frequency analysis. Extensive experimental results across an SNR range of −5 dB to 15 dB demonstrate that our method achieves a mean squared error (MSE) that closely approaches the theoretical Cramér–Rao bound (CRB), comparable to data-aided (DA) maximum likelihood methods. A quantitative analysis reveals that, even under challenging conditions, such as a low SNR of −5 dB, the model maintains superior accuracy with a mean absolute error (MAE) as low as 0.352, significantly outperforming traditional M2M4 and NDA estimators. The model’s performance was systematically evaluated in a wide range of scenarios, encompassing various signal modulation formats, upsampling factors, multipath fading channels, frequency offsets, phase shifts, and roll-off factors. The evaluation highlights its exceptional generalization capability and robustness, with high performance and stability maintained even in challenging and dynamic environments.

Distributed beamforming in UAV swarms requires fast and accurate carrier-phase alignment under sparse connectivity and propagation-induced phase bias. This paper proposes a physics-aware decentralized synchronization framework for quasi-static UAV swarm beamforming by integrating momentum-accelerated Metropolis–Hastings consensus with position-aided phase pre-compensation. To preserve phase evolution on the circular manifold, a sinusoidal coupling law is adopted, while the momentum term improves convergence in sparse random geometric graphs. A propagation model is further established to characterize how geometric separation and ranging uncertainty translate into residual phase error and coherent power loss. Under small-signal conditions, local stability is analyzed, and Monte Carlo simulations are conducted to evaluate convergence, synchronization accuracy, robustness, and beam-focusing performance. Results show that, at 2.4 GHz with low-centimeter ranging uncertainty, the proposed method achieves sub-wavelength synchronization accuracy while providing an effective balance among convergence speed, accuracy, and complexity. Compared with standard Metropolis–Hastings, fixed-weight, and other accelerated consensus methods, the proposed scheme converges faster over most sparse topologies. Although its steady-state accuracy is slightly lower than that of filter-based predictive methods such as KF-DFPC in some cases, those schemes incur higher implementation and computational overhead. Therefore, from the perspectives of decentralized realization and practical deployment, the proposed method is more suitable for lightweight phase synchronization in distributed UAV swarms.