# 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  
#### 5.5.1 Comparative Study With and Without Transfer Learning  
The results presented in Figures 24 and 25 elucidate the striking differences in performance between the proposed TL approach and the scenario without transfer learning (no fine-tuning). In this context, \"No TL\" refers to applying the pre-trained model directly to the Qingdao Lake target data without fine-tuning, whereas \"With TL\" involves adapting the pre-trained model specifically for the target environment through fine-tuning. The Figures 24 and 25 collectively demonstrate the efficacy of transfer learning in addressing the challenges posed by complex UWA environments. As depicted in Figure 24, the BER performance of the TL and without-TL models diverges significantly across varying SNR levels. For the without-TL model, the BER stagnates around 1 across all SNRs, including SNR = 15 dB, indicating an almost complete inability to generalize to the Qingdao Lake environment. The lack of fine-tuning means the pre-trained weights fail to adapt to the new channel characteristics, resulting in a model that is effectively non functional under these conditions. Conversely, the TL model demonstrates exceptional adaptability, achieving a BER of \(10^{-2.0}\) (BER \(\approx\) 0.001) at SNR = 10 dB, and BER reaches \(\approx\) 0 at SNR = 15 dB, representing an improvement over the without-TL approach. Even at moderate SNR levels such as \(SNR=0\) dB, the TL model achieves a BER of 0.08, while the without TL model remains at 0.9. This substantial reduction in BER underscores the TL model's capacity to exploit the improved signal conditions effectively, while without TL, the performance stagnates due to its inability to adjust to the nonlinear and dynamic nature of the target environment. Figure 24b further illustrates the superiority of the TL model through a comparison of ROC curves and AUC values. The without-TL model achieves an AUC of only 0.45, which is barely above random guessing and reflects its poor discriminatory power. This is a direct consequence of the model's inability to leverage the underlying structure of the target data due to the absence of fine-tuning. In stark contrast, the TL model achieves a perfect AUC of 1.00, indicating near optimal classification performance and a complete ability to separate the classes under all conditions. For reference, the FC-NN achieves a respectable AUC of 0.95, but it still falls short of the TL model. These results clearly highlight that fine-tuning the pre-trained model enables it to align its feature-extraction capabilities with the specific characteristics of the Qingdao Lake data, thereby achieving unparalleled performance. The impact of transfer learning is further quantified in Figure 25. Key classification metrics include accuracy, precision, recall, F1 score, and MCC, all of which take values very close to 1.0, hence justifying the reliability of the TL model; specifically, the accuracy of  
Figure 23: Training progress and confusion matrix of pre-trained model. (**a**) Training progress of pre-trained model; (**b**) Confusion Matrix of pre-trained model for Validation Data. the TL model approaches 1.0, while the without-TL model approaches 0.6, with evidence for frequent misclassifications. Precision and recall for the TL model are also close to perfection, showing that it can maximally increase the number of true positive predictions by allowing minimum false positives and false negatives. In contrast, the model without TL exhibited precision and recall values around 0.6, indicating a lack of confidence in its predictions. While the F1 score, representing a balanced measure of precision and recall, reaches 1.0 for the TL model but only 0.5 for the without-TL model, this underlines the notably inferior trade-offs the latter does between these metrics.