# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 3 Proposed TL-Based UWA OFDM Receiver
### Transfer Learning and Fine-Tuning  
The proposed TL approach is shown in Figure 6. During the fine-tuning phase of TL, the convolutional layers are frozen, retaining the weights learned during pre-training on the watermark channel datasets. These layers, having already captured general feature representations, remain unchanged while the fully connected layers are fine-tuned for the new classification task. These layers are fine-tuned on the target dataset (\(D_{T}\)) so that the model becomes specialized in recognizing features of the new task. The learning rate for the frozen convolutional layers is effectively constant, as their weights are not updated during the fine-tuning phase. This prevents the disruption of the feature-extraction process learned during pre-training. In contrast, the fully connected layers have an initial learning rate of \(\gamma_{2}=0.01\). These layers are adapted to the new dataset while still utilizing the robust features extracted by the frozen convolutional layers. The learning rate for these layers decreases exponentially, following a piecewise schedule. This approach allows the model to adjust the fully connected layers for the new task while preserving the general feature representations captured by the frozen layers. This would allow for an optimal balance between the reuse and adaptation of features in the model so that it could generalize effectively on target data. The process of model adaptation and fine-tuning is shown in Tables 2 and 3. The methodology of the proposed TL-based Channel OFDM receiver is given in Algorithm 1. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer Type** & **Layer Name** & **Details** \\ \hline Input Layer & input & Input size: [1, Nt \(\times\) 2, 1] where Nt = 46. \\ Convolutional Layer 1 & _conv1_ & Kernel: 1 \(\times\) 5, Filters: 8, Stride: 10. \\ Activation Layer & relu1.1 & ReLU activation after conv1. \\ Convolutional Layer 2 & _conv2_ & Kernel: 1 \(\times\) 5, Filters: 16. \\ Activation Layer & relu2.1 & ReLU activation after conv2. \\ Convolutional Layer 3 & _conv3_ & Kernel: 1 \(\times\) 3, Filters: 16. \\ Activation Layer & relu3.1 & ReLU activation after conv3. \\ Convolutional Layer 4 & _conv4_ & Kernel: 1 \(\times\) 3, Filters: 32. \\ Activation Layer & relu4.1 & ReLU activation after conv4. \\ Convolutional Layer 5 & _conv5_ & Kernel: 1 \(\times\) 3, Filters: 32. \\ Activation Layer & relu5.1 & ReLU activation after conv5. \\ Fully Connected Layer 1 & fc1 & Units: 64, L2 regularization applied. \\ Batch Normalization Layer & bn1.1 & Batch normalization for fc1. \\ Activation Layer & relu1.2 & ReLU activation after fc1. \\ Dropout Layer & dropout1 & Dropout rate: 0.2. \\ Fully Connected Layer 2 & fc2 & Units: 32, L2 regularization applied. \\ Batch Normalization Layer & bn2.1 & Batch normalization for fc2. \\ Activation Layer & relu2.2 & ReLU activation after fc2. \\ Dropout Layer & dropout2 & Dropout rate: 0.2. \\ Fully Connected Layer 3 & fc3 & Units: 32, L2 regularization applied. \\ Activation Layer & relu3.2 & ReLU activation after fc3. \\ Dropout Layer & dropout3 & Dropout rate: 0.2. \\ Fully Connected Layer 4 & fc4 & Units: 2 (output classes). \\ Softmax Layer & softmax & Softmax activation for classification output. \\ Classification Layer & output & Categorical classification layer. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model Architecture of the pre-trained model.