# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 6 Transfer Learning between Datasets
### Conventional Regression Models  
For completeness and to provide a reference, the conventional models are transferred first. They are trained on the training split of one dataset's and evaluated on the test split of the other dataset. The results are shown in Table 5. Interestingly, they perform about as well as the original models (cf. Table 2).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**KWAUG14**} & \multicolumn{2}{c}{**SPACE08**} \\ \hline
**Method** & **MAE** & **Pearson Corr.** & **MAE** & **Pearson Corr.** \\  & & **Coeff.** & & **Coeff.** \\ \hline Linear Regression & 0.0097 & 0.88 & **0.0097** & **0.89** \\  & (\(2.0\times 10^{-4}\)) & (\(4.1\times 10^{-3}\)) & (\(8.6\times 10^{-4}\)) & (\(2.1\times 10^{-2}\)) \\ \hline Two Layer NN & 0.010 & 0.88 & **0.010** & **0.88** \\  & (\(1.3\times 10^{-4}\)) & (\(6.5\times 10^{-3}\)) & (\(5.8\times 10^{-4}\)) & (\(1.5\times 10^{-2}\)) \\ \hline KNN & **0.0090** & **0.91** & **0.0099** & **0.88** \\  & **(\(1.6\times 10^{-4}\))** & (\(7.4\times 10^{-3}\)) & (\(8.1\times 10^{-4}\)) & (\(2.3\times 10^{-2}\)) \\ \hline Dummy & 0.024 & NaN & 0.021 & NaN \\  & (\(1.5\times 10^{-4}\)) & & (\(2.6\times 10^{-4}\)) & \\ \hline Decision Tree & 0.012 & 0.85 & 0.012 & 0.83 \\  & (\(2.7\times 10^{-4}\)) & (\(1.3\times 10^{-2}\)) & (\(1.2\times 10^{-3}\)) & (\(3.5\times 10^{-2}\)) \\ \hline \hline \end{tabular} Note: Each cell shows the relevant metric—mean (std-dev) based on five fold cross validation. Metrics marked in bold indicate best performing method on each data set based on a one-tailed \(t\)-test using a 5% significance level [29].  
\end{table}
Table 4: Conventional regression results using CNN-derived features.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Train-KW/Test-SP**} & \multicolumn{2}{c}{**Train-SP/Test-KW**} \\ \hline
**Method** & **MAE** & **Pearson Corr.** & **MAE** & **Pearson Corr.** \\  & & **Coeff.** & & **Coeff.** \\ \hline Linear Regression & **0.021** & **0.35** & **0.022** & **0.31** \\  & **(\(3.8\times 10^{-4}\))** & (\(8.9\times 10^{-3}\)) & (\(1.0\times 10^{-4}\)) & (\(1.1\times 10^{-2}\)) \\ \hline Two Layer NN & 0.023 & 0.14 & **0.027** & **0.23** \\  & (\(1.1\times 10^{-3}\)) & (\(2.7\times 10^{-2}\)) & (\(5.5\times 10^{-3}\)) & (\(3.7\times 10^{-2}\)) \\ \hline KNN & 0.037 & 0.26 & 0.028 & 0.25 \\  & (\(7.6\times 10^{-4}\)) & (\(2.0\times 10^{-2}\)) & (\(2.0\times 10^{-4}\)) & (\(8.1\times 10^{-3}\)) \\ \hline Dummy & 0.029 & NaN & 0.029 & NaN \\  & (\(1.9\times 10^{-4}\)) & & (\(1.46\times 10^{-4}\)) & \\ \hline Decision Tree & 0.047 & 0.24 & 0.032 & 0.24 \\  & (\(3.7\times 10^{-3}\)) & (\(1.9\times 10^{-2}\)) & (\(8.3\times 10^{-4}\)) & (\(2.9\times 10^{-2}\)) \\ \hline \hline \end{tabular} Note: Each cell shows the relevant metric—mean (std-dev) based on five fold cross validation. Metrics marked in bold indicate best performing method on each data set based on a one-tailed \(t\)-test using a 5% significance level [29].  
\end{table}
Table 5: Conventional regression results with transfer learning using CIR-derived features.