## 4 Results and Discussion
### Robustness of Deep-Learning Models under Various Communication Systems
#### 4.3.3 Performance Analysis of DL Models over Rayleigh Fading Channel  
In the evaluation of communication systems and image quality discussed in Section 4.2, we have observed severe degradation in performance when the Rayleigh Fading channel is utilized, as compared to employing only a simple AWGN channel model. We can see a similar trend in the accuracy of the DL models; however, when the DL models utilized augmentation techniques during training, they have achieved favorable results even on the Rayleigh Fading channel, as shown in Figure 12.  
Performance analysis of ResNet152V2 over Rayleigh Fading Channel: When using ResNet152V2 without applying augmentation techniques during training, the accuracy on the images from the Rayleigh Fading channel achieved very low accuracy, as shown in Figure 12a. The accuracy of the DL model dropped on images from higher order M-QAM systems and there was no significant improvement with higher \(\rm E_{b}/N_{0}\), in fact the difference in accuracy on the images from lower order M-QAM and higher order M-QAM was more significant in the higher \(\rm E_{b}/N_{0}\) region. The difference in accuracy on images from 16-QAM (highest accuracy) to 1024-QAM (lowest accuracy) was 8% at \(\rm E_{b}/N_{0}\) 0 dB; however, the difference was 21% at \(\rm E_{b}/N_{0}\) 20 dB. This is because the image quality in lower order M-QAM improved more significantly compared to those in higher order M-QAM with the increase in \(\rm E_{b}/N_{0}\). Additionally, it also indicates that the model was not able to generalize well on the images from different M-QAM systems. The highest accuracy obtained without augmentation was 68% at \(\rm E_{b}/N_{0}\) 20 dB for 16-QAM system with MMSE channel estimation and the lowest accuracy of 46% was obtained at \(\rm E_{b}/N_{0}\) 10 dB on images from the 1024-QAM system with MMSE channel estimation. For analysis on different channel estimations, there was no significant difference in the improvement in accuracy (except for 16-QAM and 1024-QAM) due to different channel estimation, as it had overlapping trends throughout the experiment. Images from 16-QAM system using MMSE channel estimation achieved almost 5% better accuracy compared to LS at \(\rm E_{b}/N_{0}\) 15 dB and 20 dB; however, images from  
Figure 12: DL model performance of (**a**) ResNet152V2 and (**b**) EfficientNetV2-B0 on recovered images with respect to \(\rm E_{b}/N_{0}\) for various OFDM systems under the Rayleigh Fading channel. For order of M-QAM, \(M\in\{16,\ 32,\cdots,1024\}\) across each \(\rm E_{b}/N_{0}\). 1024-QAM achieved almost same improvement in accuracy using LS channel estimation compared to MMSE for \(\rm E_{b}/N_{0}\) higher than 5 dB. The ResNet152V2 model trained using augmentation techniques performed much better even on the multipath Rayleigh Fading channel. Overall, the model accuracy improved, and after \(\rm E_{b}/N_{0}\) 10 dB the model accuracy was greater than 86% across all systems (except 1024-QAM). The highest accuracy of 90% was obtained by 64-QAM at \(\rm E_{b}/N_{0}\) 15 dB and 20 dB and the lowest accuracy of 75% was obtained at \(\rm E_{b}/N_{0}\) 10 dB on images from 1024-QAM system, both using MMSE channel estimation. Overall, the difference in accuracy on images across higher order M-QAM was less than 4% (except 1024-QAM) which indicates the model was able to generalize well on images even from higher order M-QAM system. In terms of the effect of channel estimation techniques, there were distinct differences in the accuracy of the model on images from systems utilizing different channel estimation techniques in the lower \(\rm E_{b}/N_{0}\) regions. From \(\rm E_{b}/N_{0}\) 0-10 dB the accuracy on images from systems using MMSE channel estimation was greater compared to LS channel estimation, and at \(\rm E_{b}/N_{0}\) 0 dB this difference was the highest with almost 5% improvement in accuracy for 64-QAM, 128-QAM, 256-QAM and 512-QAM.