## 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
The gap gradually decreased for \(\rm E_{b}/N_{0}\) higher than 10 dB, and an overlapping pattern (except 1024-QAM) of accuracy on images from systems utilizing both channel estimation techniques was observed. Performance analysis of EfficientNetV2-B0 over Rayleigh Fading Channel: Like ResNet152V2, the accuracy of EfficientNetV2-B0 also decreases on images from systems using the Rayleigh Fading channel compared to simple AWGN channel, as shown in Figure 12b. Without applying augmentation techniques during training, the model was not able to generalize well, and the accuracy of the models decreases significantly with higher order M-QAM. There is a slight improvement in accuracy of the model on images from lower order M-QAM with the increase in \(\rm E_{b}/N_{0}\); however, accuracy on images from higher order M-QAM shows very little improvement. Without augmentation, the highest accuracy of 90% was obtained on images from the 16-QAM system at \(\rm E_{b}/N_{0}\) 20 dB and the lowest accuracy of 70% was obtained on images from 1024-QAM at \(\rm E_{b}/N_{0}\) 5 dB, using MMSE channel estimation. There was no significant difference in improvement by using different channel estimation techniques, except on images from the 1024-QAM system, where the model on images from system utilizing LS obtained approximately 8% greater accuracy compared to MMSE at \(\rm E_{b}/N_{0}\) greater than 5 dB. The model performed slightly better on systems utilizing MMSE for 32-QAM, 64-QAM, 128-QAM, and 512-QAM at \(\rm E_{b}/N_{0}\) 0 dB, however this improvement is insignificant considering the overall performance throughout the experiment. The EfficientNetV2-B0 model trained with augmentation techniques had drastic improvement across all systems. The model could generalize well since the difference in accuracy across M-QAM systems is less than 2% (except 1024-QAM) at all \(\rm E_{b}/N_{0}\). Throughout the experiment, the accuracy of the model was greater than 95% across all systems (except 1024-QAM). The highest accuracy of 99% was obtained on images from 16-QAM system at \(\rm E_{b}/N_{0}\) 20 dB, whereas the lowest accuracy of 88% was obtained on images from 1024-QAM system at \(\rm E_{b}/N_{0}\) 20 dB, both using MMSE channel estimation techniques. The improvement in accuracy on images from systems using different channel estimation was observed only at \(\rm E_{b}/N_{0}\) 0 dB, where accuracy was approximately 4% better on images from systems using MMSE compared to LS across all systems (except 16-QAM and 1024-QAM). However, at higher \(\rm E_{b}/N_{0}\) regions, different channel estimations did not have a significant impact on the accuracy, except for 1024-QAM, where accuracy on images from a system using LS channel estimation had approximately 5% greater accuracy compared to MMSE. Which indicates that the effect of using complex channel estimation (such as MMSE) improves the model accuracy at lower \(\rm E_{b}/N_{0}\) regions; however, it can have contrasting effect at higher \(\rm E_{b}/N_{0}\) regions and higher order M-QAM. Comparing the performance of the two models, EfficientNetV2-B0 had greater accuracy on images from systems over the Rayleigh Fading channel, compared to ResNet152V2. The accuracy of the EfficientNetV2-B0 model trained using augmentation was from 88 to 99%, whereas for ResNet152V2 it was between 75% and 90% across all systems.