## 3 Results on ML/DL-Based Channel Estimation
### Discussion on ML/DL-Based Channel Estimation in MC-UWA Communication Systems  
Multi-carrier UWA research studies in this section offer distinct innovations that collectively advance performance dynamic environments. The work in [18] presents CsiPreNet, a CNN-LSTM model for CSI (channel state information) prediction. It operates over a 4 kHz bandwidth and spans 1-5 km using 681 subcarriers. Supported modulation schemes include BPSK, QPSK, 8-QAM, and 16-QAM. The model achieves MAE of \(4\times 10^{-3}\) and BER near \(10^{-3}\) under low SNR. The work in [22] proposes a DNN-based OFDM model using QPSK modulation. It employs 64 subcarriers and 32 pilot tones with a comb-type pilot scheme. The model is trained on the WATERMARK dataset and optimized using Adam. While evaluation in realistic underwater conditions shows improved performance and efficiency, both models rely on specific modulation formats and controlled datasets, which may limit generalization and adaptability. In addition to the OFDM systems of [18; 22], the study in [58] proposes Denoising Autoencoder (DAE) with a Gated Bernoulli-Gaussian (GBG) model to remove impulsive noise in OFDM signals. The clean data is processed by a DNN trained over 10,000 epochs to predict CSI. The approach achieves BER of \(10^{-2}\) and MSE of 0.0024 at 20 dB SNR. Similarly, the work in [23] presents a CNN-based OFDM receiver with skip connections and QPSK modulation. It delivers BER gains over 0.17 and SNR improvement of 2-3 dB at \(1\times 10^{-3}\). The model runs efficiently with reduced storage, validated using the WATERMARK dataset. Despite strong performance, both models depend on specific noise assumptions and channel conditions, limiting adaptability across highly variable underwater environments. Further advancing channel estimation, a 2D BiLSTM-based estimator using water temperature data for SSP (Sound Speed Profile) estimation is presented in [24]. With an OFDM system with 462 subcarriers and 22.6 ms cyclic prefix, it achieves low MSE (0.02-0.14) and a BER gain of 2-3 dB SNR at \(1\times 10^{-3}\), with 396 bps throughput. A model-based approach of [28] presents the GM-LAMP network. It builds on approximation message passing (AMP) and Gaussian mixture priors. Another deep learning-based receiver for UWA-OFDM, known as SCABNet, is presented in [59]. It uses an attention-enhanced bi-directional LSTM (AM-BiLSTM) for signal detection and a skip connection CNN (SC-CNN) for channel estimation. The model is trained on real experimental UWA channel data from the WATERMARK dataset [26] and evaluated under various conditions. By achieving lower BER, it demonstrates resilience in time-varying and frequency-selective channels but requires substantial offline training, which may demand significant computational resources. However, its applicability to other underwater environments beyond the WATERMARK dataset remains unexplored. The CWGAN (Conditional Wasserstein Generative Adversarial Network) model for channel estimation is presented in [60] using channel attention denoising (CAD). Performance evaluation using MSE, BER, and channel prediction accuracy confirms enhanced signal robustness. Nevertheless, its reliance on specific parameter configurations, computational overhead from CAD and CWGAN-GP modules, and limited environmental diversity may restrict generalization. A Bi-directional Deep Pelican Convolutional Neural Network (BDPCNN) is presented in [27]. The system's performance is evaluated under different water conditions. The findings indicate lower BER values (0.0086-0.021) at 20 dB SNR, improved MSE-based channel estimation, and better energy efficiency compared to traditional models. However, the model is trained on simulated data, which may not fully represent real-world underwater channel variations. A Multi-Task Learning framework is presented in [32]. The framework uses a shared feature learning layer to capture multipath correlations and a task-specific head layer for refining predictions. The study compares different shared feature learning configurations.