## 4 Results and Discussion
### Robustness of Deep-Learning Models under Various Communication Systems
#### 4.3.2 Performance Analysis of DL Models over AWGN Channel  
The DL model performance in terms of classification accuracy on the reconstructed images from various communication systems over AWGN channel is shown in Figure 11. The dotted lines illustrate the model accuracy achieved without applying any augmentation techniques during training, whereas the solid lines represent the accuracy achieved with augmentation techniques (WA) applied during training. The accuracy of the DL model corresponds to the mean value across all inferencing images for the specific M-QAM and E\({}_{b}\)/N\({}_{0}\). Figure 10: Illustration of EfficientNetV2-B0-based proposed deep learning model for traffic sign recognition in ITS. Performance analysis of ResNet152V2 over AWGN channel: When augmentation techniques are not applied during the training of ResNet152V2 model, the accuracy achieved on the clean (original) images is 84%, and the overall accuracy of the model is low, as shown in Figure 11a. This is due to the model not being able to learn and generalize well without augmentation techniques. From the graph, we can observe that the accuracy of the model decreases significantly with higher order M-QAM in the lower \(\rm E_{b}/N_{0}\) regions; however, for the higher \(\rm E_{b}/N_{0}\) regions the model is able to generalize well and achieves almost same accuracy as it performed on the original images across all M-QAM (except 512-QAM and 1024-QAM). We can see a clear distinction between the accuracy of the model on images from M-QAM systems with odd length of symbol and even length of symbol with a zig-zag pattern in the lower \(\rm E_{b}/N_{0}\) regions. There is a sharp drop in the accuracy of the model on images from M-QAM systems with odd length of symbol (32-QAM, 128-QAM, and 512-QAM) which is due to the increased error bits, as discussed in Section 3.1.3. The highest difference in accuracy on images across different M-QAM was observed at \(\rm E_{b}/N_{0}\) 0 dB, with 14% difference between highest accuracy (16-QAM) and lowest accuracy (512-QAM), this difference is less than 2% for \(\rm E_{b}/N_{0}\) 20 dB. For channel estimation, accuracy was better on systems using MMSE channel estimation; however, the improvement is insignificant. Overall, the lowest accuracy observed without augmentation is 63% on images from 512-QAM system using LS channel estimation at \(\rm E_{b}/N_{0}\) 0 dB. On the other hand, images from  
Figure 11: 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 AWGN channel. For order of M-QAM, \(M\in\{16,\ 32,\cdots,1024\}\) across each \(\rm E_{b}/N_{0}\). 16-QAM to 256-QAM achieved a highest accuracy of 84% (same as on the original images) for \(\text{E}_{\text{b}}/\text{N}_{0}\) 15 dB and 20 dB. When augmentation techniques were applied, the ResNet152V2 was able to learn better and the accuracy of the model on clean (original) images improved to 96%. The impact of M-QAM with odd and even length of symbol is still observed, however the overall accuracy of the model improved significantly across all M-QAM. There is no drastic drop in accuracy across higher order M-QAM and the difference in the accuracy is less than 4% for all \(\text{E}_{\text{b}}/\text{N}_{0}\). This is due to the model being able to generalize well across all M-QAM as it was trained with different augmentation techniques. Accuracy with respect to channel estimation techniques has overlapping patterns, indicating that there is no significant impact when using more complex channel estimation (such as MMSE) on the accuracy of the DL model. With augmentation, the lowest accuracy obtained was 89% for 512-QAM system at \(\text{E}_{\text{b}}/\text{N}_{0}\) 0 dB; however, beyond \(\text{E}_{\text{b}}/\text{N}_{0}\) 10 dB the accuracy of the model was greater than 93% across all M-QAM and different channel estimation techniques.