## 6 Conclusions and Future Work  
In this study, a novel architecture named CV-CNN-TCN is introduced for automatic modulation classification, combining Complex-Valued Convolutional Neural Networks (CV-CNNs) with Temporal Convolutional Networks (TCNs) enhanced by dilated causal convolutions (DCCs). The proposed model leverages the spatial learning capability of a CV-CNN to process complex-valued I/Q inputs and integrates the temporal modeling strength of TCN with dilated convolutions for effective long-range dependency learning. The performance of CV-CNN-TCN-DCC is comprehensively evaluated against state-of-the-art models, including CNN, ResNet, CLDNN, and SCRNN, under various SNR conditions. Experimental results demonstrate that the proposed model achieves peak classification accuracy of 75% at 10 dB, outperforming ResNet (73%) and CNN (72%). At extremely low SNR levels (e.g., \(-\)10 dB), CV-CNN-TCN-DCC achieves 40% accuracy, significantly higher than ResNet (33%) and CLDNN (28%), highlighting its robustness to noise. In terms of advanced evaluation metrics, the model also records the highest values for F1 Score (0.38), MCC (0.37), and Jaccard index (0.58) at low SNRs, confirming its superiority in balanced and unbalanced classification scenarios. Moreover, CV-CNN-TCN-DCC achieves an NPV of 93.4% and specificity of 93.5% at \(-\)10 dB, surpassing all baseline models in reliability and noise resilience. The influence of hyperparameters was also analyzed. Results show that SELU activation, a batch size of 256, and 50 training epochs offer optimal accuracy, particularly under challenging SNR conditions. These configurations enabled the model to maintain high performance while reducing overfitting. Despite its overall advantages, the proposed method exhibits limitations in classifying highly overlapping modulations such as QAM64 and complex analog schemes like WBFM. Additionally, the training time remains considerable due to the increased model depth and complex-valued processing. Future work will focus on implementing the system in real-world environments to evaluate its practical performance. This includes deploying the model on hardware platforms such as software-defined radios (SDRs) or edge devices to test its robustness, efficiency, and adaptability under realistic conditions involving dynamic noise, multipath propagation, and interference. Additionally, using real-world data with diverse modulation schemes and channel characteristics will provide deeper insights into the model's reliability and generalization capabilities. Such efforts will help bridge the gap between theoretical performance and practical applicability. Conceptualization, H.O. and A.K. ; methodology, H.O. ; software, H.O. ; validation, H.O., A.K. and N.E.-H.; formal analysis, H.O. ; investigation, H.O. and N.E.-H.; resources, Z.M. and Y.Z. ; data curation, N.E.-H.; writing--original draft preparation, H.O. ; writing--review and editing, A.K. and N.E; visualization, H.O. ; supervision, Z.M. and Y.Z. ; project administration, Z.M. and Y.Z. ; funding acquisition, Z.M. and Y.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this paper can be provided by the authors upon reasonable request. During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4, 2025) for the purposes of language refinement and editing. The authors have reviewed and edited the output and take full responsibility for the content of this publication. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: