## 2 Literature Review Methodology
### Development of the Review Protocol
#### 2.2.3 Systematic Approach Used in Extracting and Analyzing Studies

Table 6: Characteristics of ML/DL-based channel estimation in SC-UWA communication. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **ML/DL Technique** & **Optimizer** & **Training Examples** \\ \hline
[18] & CsiPreNet & Adam & Measured Data \\ \hline
[22] & DNN & Adam & Measured Data (From [26]) \\ \hline
[58] & DAE and DNN & — & Simulated Data (GBG) \\ \hline
[23] & CNN & Adam & Measured and Simulated \\ \hline
[24] & 2D BiLSTM & Adam & Measured and Simulated \\ \hline
[28] & UDNet & Adam & Measured and Simulated \\ \hline
[59] & SC-CNN, AM-BiLSTM & RMSprop & Measured Data (From [26]) \\ \hline
[60] & CWGAN-GP, CNN, CAD & — & Simulated Data (From [25]) \\ \hline
[27] & BDPCNN & Pelican & Unclear \\ \hline
[29] & UACC-GAN & Adam & Measured Data (From [26]) \\ \hline
[32] & LSTM and Transformer & Adam & Measured Data \\ \hline
[30] & DenseNet & Adam & Measured Data (From [26]) \\ \hline
[31] & CNN and LSTM & Adam & Measured Data (From [26]) \\ \hline
[33] & DNN & Adam & Simulated Data \\ \hline
[34] & S-CNN-ResNet & Adam & Simulated Data \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of ML/DL-based channel estimation in MC-UWA communication. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Bandwidth (KHz)** & **Tx-Rx Distance (km)** & **Modulation** \\ \hline
[17] & 2 & 0.205 & BPSK \\ \hline
[20] & 4 & 2 to 3 & BPSK, DS-SS \\ \hline
[21] & 14 to 18 & 1 & QPSK \\ \hline
[57] & 10 \& 5 & 1 & — \\ \hline
[29] & 10 to 18 & — & FH-SS \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Characteristics of ML/DL-based channel estimation in MC-UWA communication. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Bandwidth (KHz)** & **Tx-Rx Distance (km)** & **System** & **Subcarr.** & **Modulation** & **CP** \\ \hline
[18] & 4 & 1 to 5 & OFDMA & 681 & Various & 25 \\ \hline
[22] & — & 0.54 to 3.16 & OFDM & 64 & QPSK & 16 \\ \hline
[58] & — & — & OFDM & 512 & 16 QAM & 64 \\ \hline
[23] & — & 0.75, 3 & OFDM & — & QPSK & — \\ \hline
[24] & 5 & 1 & OFDM & 462 & — & 22.6 ms \\ \hline
[28] & 5 & 1.5, 3 & OFDM & 1024 & Various & 256 \\ \hline
[59] & 6 & 0.54, 0.75, 3.16 & OFDM & 512 & BPSK & 128 \\ \hline
[60] & 6–10 & 0.75, 1.08 & OFDM & 512 & QPSK & 128 \\ \hline
[27] & 1000 & — & MIMO-OFDM & 512 & QPSK & 128 \\ \hline
[29] & 10–18 & — & OFDM & 1024 & QPSK & 256 \\ \hline
[32] & — & 0.883, 0.967 & OFDM & — & — & — \\ \hline
[30] & 32.5–37.5 & 0.8, 1.08, 3.16 & OFDM & 1024 & BPSK, QPSK & 256 \\ \hline
[31] & 2 & — & OFDM & 64 & QPSK & 16 \\ \hline
[33] & — & — & OFDM & 32, 128 & BPSK, QPSK & 0 \\ \hline
[34] & 4 & — & OTFS & — & BPSK & — \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of ML/DL-based channel estimation in SC-UWA communication. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **ML/DL Technique** & **Optimizer** & **Training Examples** \\ \hline
[43] & SVM, KNN, LDA, BRT & — & Measured Data \\ \hline
[36] & MLR, MLP & — & Measured Data \\ \hline
[38] & CNN & Adam & Simulated Data \\ \hline
[41] & RL & Not Applicable & No Dataset Used \\ \hline
[42] & RL & Not Applicable & Measured Data \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Overview of ML/DL-based adaptive modulation in SC-UWA communication. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Training Loss** & **Complexity** & **Channel Prediction** & **Gain (BER)** \\ \hline
[17] & CE & — & 66\% Acc., 88\% Prec. & — \\ \hline
[20] & CE & — & — & \(1.2\times 10^{-3}\) \\ \hline
[57] & \(4\times 10^{-3}\) (MAE) & — & 7\% better & — \\ \hline
[29] & WGAN-GP & — & TVIR, CDF, JS Divergence, Entropy & — \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison for ML/DL-based channel estimation in SC-UWA communication.