## 7 Responses to Formulated Research Questions  
This section addresses the research questions formulated in this article by evaluating the effectiveness of ML-based and DL-based techniques in UWA communication. RQ1: How Do ML/DL Techniques Improve Channel Estimation in UWA Communication, and What Are the Key System Characteristics and Performance Metrics of These Methods? ML and DL techniques have revolutionized channel estimation in UWA communication by improving signal prediction, reducing BER, and enhancing communication reliability. Insights drawn from Tables 4 and 5 show that various ML and DL methods, including LR, DBN, LSTM, and CNN, are applied in both SC-UWA and MC-UWA systems. SC-UWA systems primarily utilize simpler ML models like LR and LSTM, whereas MC-UWA systems employ more complex architectures such as CNN-LSTM hybrids, DNNs, GANs, indicating that multi-carrier systems require advanced learning techniques to manage signal distortions. The key characteristics of channel estimation techniques, as presented in Tables 6 and 7, highlight the differences between SC-UWA and MC-UWA systems in terms of bandwidth, transmission distance, modulation schemes, system configurations, subcarriers, and CP. SC-UWA systems typically operate within a bandwidth range of 1-25 kHz and support transmission distances from 0.2 km to 3 km, with modulation schemes mainly limited to BPSK and QPSK. In contrast, MC-UWA systems feature bandwidths extending up to 37.5 kHz, with transmission distances reaching 5 km, utilizing more robust modulation schemes like OFDM, AFDM, and QAM. Additionally, MC-UWA systems integrate subcarriers ranging from 512 to 1024, with CP values between 16 and 256 samples to combat multipath interference. Tables 8 and 9 compare training loss, computational complexity, and BER performance across different ML/DL-based channel estimation techniques. Advanced models such as LSTM-based predictors and CNN-enhanced estimators achieve MSE values as low as \(10^{-4}\), indicating superior accuracy in channel estimation. While some models, such as DenseNet-based estimators, demand high computational resources, pruned CNN architectures significantly lower complexity, making them more viable for real-time applications. BER improvements of up to 8 dB at \(10^{-3}\) BER demonstrate the effectiveness of AI-driven channel estimation in enhancing communication reliability. RQ2: How Do ML/DL Techniques Improve Adaptive Modulation in UWA Communication, and What Are the Key System Characteristics and Performance Metrics of These Methods? ML and DL techniques have significantly improved adaptive modulation in UWA communication by dynamically adjusting transmission parameters based on real-time conditions. Insights from Tables 10 and 11 show that various methods, including SVM, RL, DBN, and CNNs, are applied in both single-carrier SC-UWA and MC-UWA systems. SC-UWA systems primarily utilize classification-based ML/DL models such as SVM and CNN, whereas MC-UWA systems employ RL-based models to optimize modulation schemes in response to channel variations. The key characteristics of adaptive modulation techniques, as presented in Tables 12 and 13, highlight the differences between SC-UWA and MC-UWA systems in terms of bandwidth, transmission distance, modulation schemes, system configurations, subcarriers, and cyclic prefix. SC-UWA systems typically operate within a 5-10 kHz bandwidth and support transmission distances from 0.82 km to 3 km, with modulation schemes including PSK, QAM, and PSK. In contrast, MC-UWA systems feature bandwidths extending up to 25 kHz, with transmission distances reaching 5 km, utilizing OFDM-based modulation with subcarriers ranging from 32 to 1024 and CP values between 64 and 400 samples to enhance robustness against multipath interference. Tables 14 and 15 compare the training loss, computational complexity, throughput, and BER performance across different ML/DL-based adaptive modulation techniques.