# 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
5.3 Evaluation of the Proposed Transfer Learning Model with BER Performance in an Extended Range of SNR  
Figure 28 presents a comprehensive comparison of the BER performance of the proposed TL model against conventional and deep learning-based channel-estimation techniques. At \(SNR=-10\) dB, the transfer learning model achieves a BER of 0.25, outperforming the DNN (BER = 0.3) and FC-NN (BER = 0.4). In stark contrast, traditional methods such as OMP, LS, and MMSE estimators exhibit poor performance, with BER values approaching 0.7, indicating their inability to effectively handle the highly nonlinear and time-varying nature of UWA channels. As the SNR increases, the transfer learning model maintains a significant performance advantage. At \(SNR=0\) dB, it achieves a BER of 0.12, surpassing the DNN (BER = 0.25) and FC-NN (BER = 0.3). At higher SNRs, the superiority of transfer learning becomes even more evident. At \(SNR=10\) dB, the proposed model attains a BER of 0.009, compared to 0.05 for DNN and 0.06 for FC-NN, while LS and MMSE remain above 0.07 even in high SNR scenarios. At \(SNR=15\) dB, the transfer learning model reaches an impressive BER of 0, further underscoring its robustness and adaptability to varying environmental conditions. The LS estimator, despite its simplicity, is highly sensitive to noise and fails to capture the inherent nonlinearities of the UWA channel. MMSE, while more robust, requires prior knowledge of the channel's autocorrelation function and noise variance, making it computationally prohibitive and impractical for real-time applications. Similarly, OMP, a sparsity-based method, struggles in dynamic underwater environments due to its reliance on an accurate sparsity assumption, which is often violated in real-world UWA scenarios. While deep learning based models such as FC-NN and DNN demonstrate improved performance by learning complex channel representations, they are inherently limited by their dependence on large, representative training datasets. A key drawback of these models is their performance degradation when applied to environments different from those seen during training, a challenge commonly referred to as model mismatch. This limitation severely restricts their adaptability in diverse and evolving underwater conditions. The proposed TL approach addresses these challenges by leveraging a pre-trained model, initially trained on a source dataset, and fine-tuning it with minimal new data from the target environment (e.g., Qingdao Lake). This enables the model to retain essential learned features while adapting to the unique characteristics of a new underwater communication channel, thereby reducing the need for extensive retraining. Unlike traditional deep learning models that require large-scale labeled data, TL significantly enhances generalization, ensuring robust performance across varying conditions while maintaining computational efficiency. In summary, the results highlight the transformative impact of transfer learning in UWA channel estimation. By overcoming the limitations of conventional methods and mitigating the data dependency of standard deep learning approaches, transfer learning emerges as a state-of-the-art solution, ensuring high reliability and accuracy in practical underwater communication systems.