## 4 Results and Discussion
### Discussion  
The generalizability of the deep learning model across the M-QAM system using different channel estimation can be further observed using the t-distributed stochastic neighbor embedding (t-SNE) [85] visualization algorithm, as shown in Figure 13. It is a probabilistic dimensionality reduction algorithm which maps high-dimensional feature points into low-dimensional feature space (typically 2D or 3D) while preserving the similarities. Features close to each other in the high-dimensional space are mapped closer to each other in the lower-dimensional space with high probability. Points that are close together in the t-SNE plot are likely to have similar features or attributes (in terms of color, texture, or shape), whereas points that are far apart are likely to be dissimilar. From Figure 13, it can be observed that the same class points from the DL model trained with augmentation are close together and do not have many overlapping points of different class (color) across E\({}_{\text{b}}\)/N\({}_{0}\) 0 dB, 10 dB, and 20 dB for the M-QAM systems. Whereas points from the DL model trained without augmentation are more scattered and overlap with other classes. There are no significant differences in class separability for different channel estimation used across the M-QAM systems. Overall, DL model trained with augmentation provides better separability between the seven classes and their features, resulting in better classification performance across all communication systems compared to DL model trained without augmentation.  
Gradient-weighted Class Activation Mapping (Grad-CAM) heat map is another visualization algorithm that is used to visualize the attention region for a CNN while making  
Figure 13: Two-dimensional t-SNE plot showing feature space analysis of EfficientNetV2-B0 model on recovered images across different OFDM-based image communication systems under the Rayleigh Fading channel. Each color of the dots represents one of the seven classes chosen from the GTSRB dataset.  
predictions [86; 87]. It uses the gradient of the final convolutional layer with respect to the predicted class to weigh the layer activations, and then averages these weighted values over the spatial dimensions to obtain a class activation map. The map shows the crucial region of the image that is used to make the prediction by the CNN model. Figure 14 shows the Grad-CAM visualization of EfficientNetV2-B0 model prediction on images recovered from different communication systems. The images that were correctly predicted has Grad-CAM heat map concentrated in the ROI region of the traffic sign, whereas the two images that were wrongly predicted has the Grad-CAM heat map outside the ROI, therefore it could not extract the important features from the ROI to make the accurate prediction for the traffic sign.  
For the t-SNE and Grad-CAM analysis, we visualize the performance of EfficientnetV2-B0 on noisy images recovered from OFDM communication system using the Rayleigh Fading channel only. The reason for this is that we have observed from Section 4.3 that EfficientnetV2-B0 is superior and more robust compared to ResNetV152, and the Rayleigh Fading channel replicates the real-world environment inducing more noise on the images.  
For the experiment, the simulation of the various image communication systems and the BER analysis was conducted using Python 3.9.7, and the image quality analysis (PSNR and MS-SSIM) was performed using MATLAB R2021b. The performance analysis of the DL models was carried out using Python 3.9.7 and TensorFlow 2.2.0. The experiments were carried out jointly on Intel Core i5-4590 CPU and NVIDIA Tesla V100 (32 GB Memory) GPU. The GPU resource provider is mentioned in the acknowledgement.