## 6 Challenges and Future Research Directions
### Future Research Directions  
Future research directions can be categorized into the following key areas:  
* **Development of Lightweight ML/DL Models**[9; 38; 47]: As underwater communication devices operate in resource-constrained environments, optimizing models for real-time edge computing is crucial. Current architectures such as deep CNNs and RNNs often require extensive computational power, making deployment on low-power underwater sensors challenging. * Future research should focus on knowledge distillation, a technique that transfers knowledge from larger, complex models to smaller, more efficient models while retaining performance. * Quantization techniques should be explored to reduce model precision requirements, allowing ML/DL systems to run efficiently on energy-limited platforms. * Incorporating pruning methods can further reduce computational demands by eliminating redundant parameters in deep learning models. * **Integration of Physics-Informed Neural Networks (PINNs)**[33; 61]: PINNs combine traditional ML/DL approaches with domain-specific physics principles, enabling more accurate channel estimation and modulation schemes in underwater environments. * Future hybrid PINN-ML architectures can improve adaptive modulation by integrating wave propagation models into training data, reducing error rates caused by environmental distortions. * By employing PINNs, AI models can learn underwater acoustic behaviors, compensating for multipath propagation, Doppler effects, and fluctuating water conditions. * **Standardized Evaluation and Visual Benchmarking:*
* Future research must prioritize the creation of standardized evaluation platforms. These platforms should include unified protocols for SNR ranges, channel models, and benchmark datasets. Comparative baselines (such as MMSE, and shallow ML models) should also be embedded to provide clear reference points. * Performance outcomes like BER, complexity, latency, and accuracy should be visualized using box plots, radar charts, and distribution diagrams. This will enable researchers to draw meaningful comparisons and detect outliers or architectural trends more effectively. Such efforts will foster reproducibility, streamline cross-model evaluations, and guide researchers toward truly robust and scalable solutions. * To enable more intuitive insights and capture comparative strengths, visual aggregation methods such as box plots and radar charts should be considered. These can consolidate accuracy, BER, complexity, and latency across models
tested under shared conditions (e.g., same SNR, same dataset), making trends and anomalies readily visible. * **Advancements in Reinforcement Learning for Adaptive Modulation**[31; 40]:
* Adaptive modulation techniques allow communication systems to dynamically adjust transmission parameters based on real-time channel conditions. * RL-based methods such as Q-learning and Deep Q-Networks (DQN) optimize transmission power and modulation schemes to maximize throughput while minimizing BER. * Meta-learning approaches can further speed up model adaptation, enabling systems to rapidly respond to varying underwater environments without requiring extensive retraining. * **Improved Modulation Recognition Techniques**[46; 52]: Effective modulation recognition is essential for signal demodulation in UWA communication systems. * Contrastive learning-based models enhance classification accuracy in low-SNR conditions, distinguishing modulation patterns in noisy underwater environments. * Hybrid CNN-RNN architectures provide better temporal and spatial feature extraction, improving modulation classification accuracy under extreme multipath interference. * **Expansion to Novel Modulation Schemes**[36; 53]: Current ML/DL applications largely focus on conventional single-carrier systems and OFDM-based techniques. * Future research should explore index modulation, which improves spectral efficiency by encoding information in the position of active subcarriers.