# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Average Fade Rating Analysis of Watermark Channels and Impact on OFDM Receiver
The results show that experimental channel is close to the NOF1 and BCH1 channels. On the contrary, the experimental channel AFR is far from the NCS and KAU channel whose AFR values are quite higher and absolute errors greater. As Table 9 shows, NOF and BCH are quite good quality channels where the majority of the arriving energy is carried by stable and dominant propagation paths. NCS and KAU are poor channels with unstable signals. These findings show that the experimental channel has similar fading characteristics to what is observed in more stable underwater scenarios of the NOF and BCH channels, thereby confirming and affirming the validity of the measured data set in modeling real time situations. Deep Learning-Based OFDM Receiver, When Trained and Tested on the Same Channel (Where Each Channel is Split into Training and Testing Sets)  
This section presents an evaluation of the performance gains achieved by the proposed model within an OFDM system. A series of experiments are conducted to compare our approach with various OFDM receiver schemes for UWA communication, including both traditional methods and FC-DNN based models. Figures 13-20 offer a detailed performance comparison of the different techniques including LS, OMP, MMSE, FC-NN, and DNN in the context of UWA communication. Figure 13, which compares the training and validation accuracy and loss of DNN and FC-NN, highlights the superior performance of DNN across all channels. DNN consistently achieves higher accuracy and lower loss, particularly in complex channels like KAU1 in Figure 13b, where its validation accuracy remains around 85-90%, compared to FC-NN's 75-80%. This is attributed to DNN's ability to capture complex patterns and spatial dependencies in the data, making it robust to noise and channel variations. In contrast, FC-NN's simpler architecture struggles to model non-linear relationships, leading to higher loss and lower accuracy, especially in challenging environments. While FC-NN is faster to train and computationally less demanding, its inability to generalize well in complex channels limits its practical applicability. Figures 14 and 15 show the confusion matrices of the FC-NN and DNN when SNR = 15 dB. The FC-NN has more misclassifications, particularly in two complex channels, such as KAU1 and KAU2, where it misclassifies 12 instances in BCH1. This is because the FC-NN has failed to learn complex pattern and nonlinear relationship in high AFR channels. On the contrary, the DNN is quite robust; it only misclassifies a few instances in all channels. For instance, in BCH1, the DNN makes only four misclassifications, showing that it works efficiently on complex and time-varying channels. This is because the DNN can extract hierarchical features and model spatial dependencies, thereby performing impressively well in both stable and challenging environments. While the DNN requires higher computational powers, it performs better in different scenarios and, therefore, will be more appropriate for underwater communication systems. Figure 14: Confusion metrics of FCNN at SNR 15 (dB), when trained and tested on a same channel. Figure 13: Training and validation accuracy and loss analysis, when trained and tested on the same channel (where each of NOF1 and KAU1 channels are split into training and testing sets). Figures 16 and 17 present the ROC curves of the DNN for UWA channels (NOF1 and BCH1) across various SNRs. These curves illustrate the model's classification performance, with AUC values reflecting its overall effectiveness. In the NOF1 channel Figure 16, the DNN achieves robust performance, with an AUC approaching 1 at higher SNRs, signifying strong classification capability even in noisy environments. The model's ability to maintain high TPR and low FPR at SNRs as low as 5 dB indicates its resilience in stable, low-fade conditions. For the BCH1 channel in Figure 17, performance is more sensitive to noise. The AUC increases significantly with SNR, highlighting the DNN's reliance on cleaner signals for accurate classification.