# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Transfer Learning Model Performance on Qingdao Lake Data
#### 5.5.2 Comparative Study of Transfer Learning on Target Data  
The results presented in Figures 26-28 demonstrate the effectiveness and robustness of the proposed TL model when applied to the Qingdao Lake target data. These figures showcase the model's training progress, classification performance, and comparative evaluation against existing methods under varying SNR conditions. Figure 26a illustrates the training and validation accuracy and loss trends of the transfer learning model over 140 epochs. The training accuracy quickly approaches 100%, stabilizing within a few  
Figure 24: Comparison between BER vs. SNR and ROC curve for pre-trained and transfer learning models. (**a**) BER vs. SNR comparison of pre-trained vs. transfer learning with target data. (**b**) ROC curve comparison between a pre-trained model without fine-tuning on target data and a transfer learning approach with target data adaptation. Figure 25: Performance matrix comparison between a pre-trained model without fine-tuning on target data and a transfer learning approach with target data adaptation. epochs, while the validation accuracy also converges to 100% with negligible divergence. This rapid convergence highlights the benefits of leveraging pre-trained weights, which provide a strong initialization and allow the model to adapt efficiently to the target data. Such robustness is further reinforced in the trend of training and validation loss. Both show sharp decreases within the initial epochs and stabilize close to 0%, with minor fluctuations in the validation loss. This correspondence between training and validation metrics is indicative of good generalization. These results confirm that transfer learning allows for the  
Figure 28: BER vs. SNR of TL model on Qingdao lake/target data. Figure 26: Training progress and confusion matrix of the transfer learning model on Qingdao lake/target data. (**a**) Training and validation accuracy and loss of TL model on Qingdao lake/target data. (**b**) Confusion Matrix of TL model on Qingdao lake/target data. Figure 27: ROC curve of TL model on Qingdao lake/target data. extraction of meaningful features in UWA communication environments. The confusion matrix shown in Figure 26b depicts the performance evaluation of the model using the validation dataset. Indeed, the model predicted almost perfectly, with 231 correct samples in S1 and 249 samples correctly classified in S2 with few misclassifications. This translates into 98.3% class classification accuracy for S1 and 98.0% for S2, meaning performance was balanced between both classes and unbiased. The high accuracy of classification with low misclassification rates underlines the adaptability of the model to the Qingdao Lake environment. It tends to fine-tune the pre-trained weights and captures the distinctive characteristics of each class while canceling channel distortions. This performance underlines the adequacy of the transfer learning approach for robust underwater communication. The ROC curves of the transfer learning model on different SNRs are shown in Figure 27. At \(SNR=-10\) dB, the AUC is equal to 0.50 since noise dominates the performance and tends toward random guessing. As SNR increases, AUC increases significantly, reaching 0.86 at \(SNR=0\) dB, which is indicative of effective pattern recognition under a moderate quality of signal. For higher SNRs, the performance of the model is close to optimal: AUC = 0.95 at \(SNR=5\) dB, AUC = 0.99 at \(SNR=10\) dB, and AUC = 1.00 at \(SNR=15\) dB. These results underline how the transfer learning model could exploit an increase in signal quality and improve classification accuracy. Achieving complete discrimination for high SNRs upper bounds the performance and shows the robustness and suitability of the model for real-world UWA systems.