In the rapidly developing field of wireless communications, the precise classification of modulated signals is essential for optimizing spectrum utilization and improving communication quality. However, existing networks face challenges in robustness against signals containing phase shift keying and computational efficiency. This paper introduces TCN-GRU, a lightweight model that combines the advantages of multiscale feature extraction of the temporal convolutional network (TCN) and global sequence modeling of gated recurrent unit (GRU). Compared to the state-of-the-art MCLDNN, TCN-GRU reduces parameters by 37.6%, achieving an accuracy of 0.6156 and 0.6466 on the RadioML2016.10a and RadioML2016.10b, respectively (versus MCLDNN’s 0.6101 and 0.6462). Furthermore, TCN-GRU demonstrates superior ability in distinguishing challenging modulations such as QAM16 and QAM64, and it improves classification accuracy by about 10.5% compared to MCLDNN. These results suggest that TCN-GRU is a robust and efficient solution for enhancing AMC in complex and noisy environments.

The successful launching of more satellites in recent years has made data fusion an important and promising task because it can significantly increase the temporal frequency of the resulting time series data. To improve the usability of Gaofen-1 analysis ready data (GF1-ARD), Sentinel-2 (S2) is selected to enhance the temporal resolution of GF1-ARD due to their similar characteristics and short revisit period. Before constructing a denser time series from different platforms, bandpass alignment is required. Most researchers implement bandpass alignment using the linear model. However, the transformed bands of S2 by the linear model cannot match GF1-ARD well due to the limited globally shared parameters. In contrast, local-derived lookup tables (LUTs) can better address this problem. Inspired by the powerful capability of deep learning, we develop a model based on the U-shaped network (UNet) to learn tile-adaptive LUTs. Specifically, the LUTs are adaptively learned from the histogram of the S2 tile. Given that the bandpass alignment can be viewed as a histogram matching process, the expected LUTs are believed to be highly correlated with the input histogram. In addition, a simple convolutional module is further introduced to address the pixel-level misregistration. We have created a large-scale dataset and conducted extensive experiments on it to evaluate the competitive performance of the proposed model. Meanwhile, extensive visualizations are generated to illustrate the mechanism of our model. Furthermore, the temporal frequency of S2 and GF1-ARD is thoroughly assessed to demonstrate that bandpass alignment can significantly improve the temporal resolution of GF1-ARD.