## 4 Results on ML/DL-Based Adaptive Modulation
### Discussion on Adaptive Modulation Techniques in MC-UWA Communication Systems  
This section discusses the research studies, targeting adaptive modulation techniques in MC-UWA communication systems. An overview of the key characteristics and performance comparison for these studies are summarized in Table 11, Table 13 and Table 15, respectively.  
A-kNN classifier in [35] employs attention mechanisms to improve selection accuracy. It enhances efficiency through principal component analysis-based dimensionality reduction and k-means clustering. The framework includes online learning for adaptability to new environments, validated using real-world lake data. Simulations employing real-world data from three lake experiments show that these approaches outperform model-based methods in throughput and dependability. However, the reliance on manually extracted features may limit performance, and the computational overhead of kNN for large datasets remains a challenge. Expanding on the attention-based classification approach, a proximal policy optimization (PPO)-based adaptive modulation system for OFDM is described in [37]. The approach attains near-optimal throughput, surpassing DQN and Double DQN (DDQN) in both convergence velocity and stability. Drawbacks encompass dependence on precise channel feedback, elevated computational complexity stemming from the PPO recurrent updates.  
Building on the use of PPO, a deep reinforcement learning framework is proposed in [39]. In this work, a DRL framework employs DQN to adaptively pick modulation schemes in OFDM systems. It maximizes system throughput while maintaining acceptable BER. However, the simulation results may not fully replicate the complexity of real-world underwater ecosystems, limiting the generalization of the findings. Extending beyond conventional OFDM-based approaches, an OTFS modulation with DL and meta-learning techniques is described in [40]. The approach addresses the challenges of rapidly varying UWA channels by using the robustness of OTFS modulation. To enhance adaptability in data-scarce environments, it integrates model-agnostic meta-learning techniques, enabling faster generalization to new and unseen underwater scenarios. Although, the strategy surpasses conventional ML-based adaptive modulation, it does not provide a discussion on computational complexity and real-time implementation problems, which may be essential for actual deployment.  
The reviewed MC-UWA adaptive modulation methods show a shift toward intelligent and data-driven solutions that improve throughput and reliability in underwater settings. Attention-based classifiers like A kNN improve adaptability and feature selection, but face challenges due to manual input and processing demands. RL frameworks such as PPO, DQN, and DDQN offer better convergence and throughput, yet require accurate feedback and high computational power. Techniques combining OTFS with meta-learning tackle channel variability and limited data but often overlook practical concerns. Many studies lack analysis on real-time performance, resource efficiency, and scalability. Future research should prioritize lightweight designs, flexible learning strategies, and broad testing for real-world deployment.