# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 6 Conclusions  
To transform the wireless propagation environment into a transmission medium with more desirable characteristics, an intelligent reflecting surface (IRS), as a cutting-edge technology, could potentially be implemented. In addition, IRS-enabled GEO satellite signals can be employed to extend the network capacity and improve global coverage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Method and Algorithm** & **Channel Type** & **Modulation Type** & **ACA** \\ \hline
[50] & Likelihood Function (kurtosis) & AWGN & ASK, FSK, PSK & 98.8\% \\ \hline
[51] & CNN & AWGN & LFM, BPSK & 98\% \\ \hline
[52] & NCMBFW & AWGN & ASK, FSK, PSK & 99.1\% \\ \hline
[53] & Fusion of Features & AWGN & PSK, FSK, QAM & 99\% \\ \hline
[54] & Wavelet transform & Fading + Gaussian noise & QAM, PSK, FSK & 97\% \\ \hline
[55] & GA-SVM & Rician, Rayleigh, AWGN & M-QAM & 96\% \\ \hline
[17] & Neural-network-based SVM & AWGN, Rayleigh & QAM & 98 \% \\ \hline
[20] & GFN + ABC & Rician, Rayleigh, AWGN & QAM, PSK, FSK & 97\% \\ \hline
[56] & Feature Fusion & Multi-Channel & QAM, PSK & 95\% \\ \hline
[57] & H-SOM & - & H-SOM, QAM, PSK & 97.9\% \\ \hline
**2023** & Proposed & Cascaded channel & QAM, PSK & 99.7 \% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with existing techniques.  
Based on these characteristics, this study investigated the performance of modulation classification for IRS-enabled GEO satellite signals. This GFN is optimized to improve the average classification accuracy by optimizing the Gabor parameters and weights. In general, the hybridized optimization methods perform similarly to each other. However, the GA is far superior. The primary objective of optimization was to estimate the signals of the far-field and near-field sources to determine the modulation classification. Furthermore, the simulations indicated that the classifier performs significantly better with the IRS than without the IRS. For future improvements in classification accuracy, it may be possible to refine the proposed model through genetic programming.  
Conceptualization, M.J., M.S., S.A.G. and M.A.K.; Methodology, M.J., M.S., S.A.G. and M.A.K.; Software, M.J., M.S., S.A.G. and M.A.K.; Validation, M.J., M.S., S.A.G., M.A.K., M.M. and H.M.; Formal analysis, M.J., M.S., M.A.K., M.M. and K.M.A.; Investigation, M.J., M.S., M.A.K. and M.M.M.; Resources, M.A.K., K.M.A. and H.M.; Data curation, M.J., M.S., M.A.K., M.M. and H.M.; Writing--original draft, M.J., M.S., M.A.K., M.M., K.M.A. and H.M.; Writing --review & editing, M.J., M.S., M.M., K.M.A. and H.M.; Visualization, M.J., M.S., M.M., K.M.A. and H.M.; Supervision, S.A.G. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R137), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge the support of Prince Sultan University for paying the article processing charges (APC) of this publication.  
The authors declare no conflict of interest.