## 3 Results on ML/DL-Based Channel Estimation
### Comparative Analysis of Channel Estimation Techniques  
It evaluates ML/DL-based channel estimation techniques in UWA communication using key performance metrics. These metrics reflect efficiency, accuracy, and reliability under varying underwater conditions, offering insight into each model's practical effectiveness. Tables 8 and 9 present the performance evaluation of SC-UWA and MC-UWA systems, respectively.  
One of the key metrics for performance evaluation is training loss, which measures the training effectiveness of an algorithm. It is assessed by plotting MSE or mean absolute error (MAE) against the number of epochs during the training, testing, and validation phases. It calculates the average squared difference between predicted and actual values, providing a measure of prediction errors. The mathematical formula for MSE is [61]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{1}\]  
On the other hand, MAE quantifies the sum of absolute errors divided by the sample size. Its formula is [62]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{2}\]  
Another critical parameter is complexity, which evaluates an algorithm's efficiency compared to existing methods. A lower complexity is represented by a positive value indicating the gain in efficiency, while a higher complexity is denoted by a negative value, highlighting the algorithm's inferiority compared to existing techniques. The computational efficiency is evaluated in terms of theoretical time complexity (expressed using Big-O notation), observed runtime, FLOPs (Floating Point Operations Per Second), the number of trainable network parameters (denoted as P in the table), and associated storage requirements. Similarly, channel prediction performance compares the predicted parameters with actual measurements using MSE, Normalized Mean Square Error (NMSE), Time-varying Impulse Response (TVIR), Cumulative Density Function (CDF), Jensen-Shannon (JS) divergence, and information entropy. Among these, TVIR is typically evaluated visually due to its graphical nature.  
Lastly, BER is a widely used for assessing communication system performance. It evaluates the accuracy of receiver using channel estimates obtained through the proposed technique. The performance is presented through BER versus SNR plots, which compare the proposed method against existing techniques. Tables 8 and 9 highlight the BER gain at a fixed SNR value relative to baseline techniques. When multiple techniques are compared, the BER gain is computed against the best-performing technique. If the authors provide multiple BER plots, the highest performance is taken, with the comparison technique's name indicated in parentheses alongside the reported BER gain.  
It can be observed from Tables 8 and 9 that the training loss in SC-UWA systems values vary, with some studies using MAE and others using MSE. Complexity is not explicitly provided for most studies. The BER improvements range from \(10^{-3}\) to \(10^{-5}\), indicating varying levels of performance enhancement. In MC-UWA systems, training loss values are mostly MSE, with some studies combining MSE and BER for optimization. Complexity varies, with some models requiring high memory usage (e.g., 20.7 MB) and millions of parameters, and others focusing on execution time (e.g., 47.8 ms per OFDM block). BER improvements range from \(10^{-3}\) to \(10^{-6}\), with some studies reporting over 40% improvement in performance. To summarize, the SC-UWA and MC-UWA systems exhibit diverse training loss functions and performance gains, with BER improvements ranging from \(10^{-3}\) to \(10^{-6}\), while computational complexity is inconsistently reported, spanning high memory usage, millions of parameters, and execution times such as 47.8 ms per OFDM block.