## 7 Responses to Formulated Research Questions
Advanced models such as RL-based adaptive modulation and CNN-enhanced classifiers achieve classification accuracies exceeding 98%, demonstrating superior adaptability to underwater channel variations. While some models, such as meta-learning-based adaptive modulation, demand higher computational resources, pruned CNN architectures significantly lower complexity, making them more viable for real-time applications. BER improvements of up to 14.8% and throughput gains of 25% indicate the effectiveness of adaptive modulation in enhancing communication reliability. RQ3: How Effective Are ML/DL-Driven Modulation Recognition Approaches in Identifying Modulation Schemes Under Complex Underwater Conditions, and What Are Their Strengths and Limitations? Insights from Tables 16-18 highlight the effectiveness of various ML and DL techniques, system configurations, and performance metrics in modulation recognition. Table 16 provides an overview of the ML and DL techniques used for modulation recognition, including CNNs, ResNet, SVM, DCN, and hybrid architectures. These models use feature extraction, sequence modeling, and contrastive learning to distinguish modulation types under challenging underwater conditions. The training examples used in these studies include both simulated and measured data, ensuring robustness across different environments. Table 17 presents the key characteristics of modulation recognition systems, including bandwidth, transmission distance, and modulation schemes. The bandwidths range from 1 kHz to 30 kHz, with transmission distances varying from 100 m to 5 km. The modulation schemes considered include PSK, QAM, FSK, DSSS, OFDM, and LFM, demonstrating the versatility of models in recognizing diverse signal types. Table 18 compares the training loss, computational complexity, classification accuracy, and precision across different ML/DL-based modulation recognition techniques. Advanced models such as CNN-RNN hybrids and transformer-based architectures achieve classification accuracies exceeding 95%, demonstrating superior performance over traditional feature-based methods. While some models, such as ensemble learning-based classifiers, demand higher computational resources, pruned CNN architectures significantly lower complexity, making them more viable for real-time applications. Precision values range  
from 40% to 100%, indicating the effectiveness of modulation recognition in minimizing false classifications. RQ4: What Innovative Approaches and Emerging Trends in Machine/Deep Learning Can Be Employed to Address Unresolved Challenges in Underwater Acoustic Communication, and How Can These Advancements Shape the Future of Intelligent, Efficient, and Scalable UWA Systems? ML and DL have significantly advanced UWA communication, yet key limitations persist as highlighted in Section 6.1. These include high computational complexity, limited training data, poor generalization across diverse environments, energy constraints on edge devices, fragmented performance metrics, and insufficient robustness under extreme Doppler and multipath conditions. To address these challenges, Section 6.2 outlines several promising directions for future research. First, lightweight and real-time models must be developed for resource-constrained platforms. Physics-Informed Neural Networks offer a pathway to improve generalization by embedding wave propagation dynamics directly into the learning process. Reinforcement learning can significantly enhance adaptive modulation in time-varying underwater channels. Beyond conventional OFDM systems, expanding AI applications to emerging modulation schemes such as index modulation, non-orthogonal multiple access, and generalized frequency division multiplexing can further optimize spectral efficiency. Visual benchmarking strategies, including radar plots and box charts, are also recommended to consolidate performance evaluations across accuracy, BER, latency, and complexity.