## 6 Conclusions  
Using deep neural networks for AMR directly on IQ signals is currently common. However, the performance of the network in the actual scene is greatly affected by errors introduced in the signal acquisition process. Also, automatic modulation recognition for different symbol rate signals is also an urgent problem to be solved without considering resampling. Aiming at the dependence of modulation identification on parameter estimation error, such as carrier frequency and symbol rate, we proposed an integrated method of parameter estimation and modulation recognition based on the attention model. We establish a baseline CNN network to evaluate the effect of parameter estimation errors introduced during detection and isolation. With the idea of a spatial transformation network in the field of deep learning, we concatenate the SSTM before convolutional neural networks. The signal transformation network eliminates signal variations introduced in the wireless channel and receiver hardware by adaptively applying parameter transformations. This includes resampling to adjust the time offset, symbol rate, and clock recovery, mixing with the carrier to correct frequency offset. Applying this improved model to AMR significantly reduces the dependence of recognition performance on parameter estimation. The results prove that our method can bring significant performance improvement under the influence of offset. Our method realizes the integration of parameter estimation and signal captures classification, reduces the dependence on parameter estimation errors, and performs well under fading channel. However, our method exhibits greater tolerance to the offsets in symbol rate estimation than frequency offset, and this result deserves further study. Besides, no further analysis is performed on the SSTM processed signals, which require further research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Framework** & **Offset** & **SRO1** & **CFO1** \\ \hline \multirow{3}{*}{Baseline} & High & 0.17 & 0.31 \\  & Medium & 0.70 & 0.41 \\  & Low & 0.88 & 0.85 \\ \hline \multirow{3}{*}{RTN} & High & 0.29 & 0.31 \\  & Medium & 0.64 & 0.44 \\  & Low & 0.87 & 0.83 \\ \hline \multirow{3}{*}{SSTM\_CNN} & High & 0.26 & 0.35 \\  & Medium & 0.77 & 0.53 \\  & Low & 0.88 & 0.80 \\ \hline \multirow{3}{*}{SSTM\_Dense} & High & 0.17 & 0.44 \\  & Medium & 0.70 & 0.57 \\  & Low & 0.89 & 0.83 \\ \hline \multirow{3}{*}{SSTM\_BiLSTM} & High & 0.68 & 0.46 \\  & Medium & 0.85 & 0.62 \\ \cline{1-1}  & Low & 0.91 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison for methods versus typical offsets in SRO1 and CFO1 scenarios.  
In future work, we plan to explore the application of the attention model in AMR, such as channel blind equalization, synchronization, and resampling.  
All authors read and approved the manuscript. Conceptualization, M.L. and G.L.; Methodology, M.L.; Software, M.L.; Validation, C.Z.; Formal Analysis, M.L.; Investigation, O.L.; Resources, O.L.; Data Curation, G.L.; Writing-Original Draft Preparation, M.L.; Writing-Review & Editing, G.L.; Visualization, C.Z.; Supervision, O.L.; Project Administration, G.L.; Funding Acquisition, O.L.  
This research was funded by [the National Natural Science Foundation of China] grant number [61501517].  
The authors declare no conflict of interest.