## 5 Conclusions  
To address the problem of dynamic noise affecting the identification effectiveness of individual satellites, we proposed an identification method based on the CC-GAN that is outlined as follows:  
1. Taking the reference signal as a benchmark, the generator network of the CC-GAN outputs the denoised signals that approximate the reference signals through adversarial learning with the discriminator network of the CC-GAN, while the ability of the  
Figure 10: The identification accuracies of the different models under different SNRs.  
discriminator to distinguish whether the dynamic noise from the output signals of the generator has been eliminated also improves over time.
2. We enhanced the discriminator by incorporating conditional control so that it has the function of auxiliary classification while realizing the control of the attributes of the generated samples, which reduces the complexity of the model to a certain extent and enhances the relevance and practicality of the generator. The experimental results on a signal dataset collected in a real environment illustrate that the proposed model outperforms traditional methods in terms of denoising, thereby enhancing the identification accuracy of the discriminator to a certain extent. Meanwhile, the complex-valued structure adopted in the proposed model can directly extract the phase-related features in the complex signals, which can better characterize the satellite individuals, and thus demonstrates superior identification performance. Furthermore, the CC-GAN does not require an evaluation in regard to noise intensity and maintains identification accuracies well under different SNRs, which effectively improves the robustness and adaptability of the SEI system for communication satellites in dynamic noisy environments. Our future work will concentrate more on the following aspects: 1. The CC-GAN demonstrates superiority in controlling the attributes of the generated denoised samples, but it faces challenges in terms of training difficulty and conditional dependence. The new training strategies and model architectures need to be developed to improve its training efficiency and stability. 2. The CVNNs can directly process complex signals and extract key individual characteristic features, but the complex structure limits their deployments and applications. Subsequent work on lightweight optimization of the CVNNs is needed. 3. We consider introducing the CC-GAN into the scenario of cross-receiver specific emitter identification, which aims to address the impact of RFF features induced by the receivers on identification performance, under the condition of eliminating dynamic noises.  
Conceptualization, J.H. and C.X.; methodology, J.H., P.L. and C.X.; validation, C.X. and C.Y.; data curation, J.H., P.L. and J.L.; writing--original draft preparation, J.H. and S.Z.; writing--review and editing, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors would like to thank the editors of Remote Sensing and the anonymous reviewers for their patience, helpful remarks, and useful feedback. The authors declare no conflict of interest.