## 6 Challenges and Future Research Directions
### Challenges in ML/DL-Based UWA Communication  
The following discussion outlines the key issues that currently limit the scalability, generalization, and fair assessment of ML/DL-driven UWA systems. Consequently, it highlights areas that warrant deeper investigation to support reliable implementation. * **Computational Complexity and Real-Time Processing:** ML/DL models such as DenseNet and transformer-based architectures [30; 31] demand intensive computational resources during both training and inference phases. This complexity restricts their deployment in underwater environments, particularly on resource-limited autonomous underwater vehicles (AUVs) and mobile nodes. The real-time execution of such models remains problematic due to constraints on onboard memory and processing throughput. Furthermore, GAN-based simulators [29] require expansive datasets and computational power to realistically emulate channel dynamics. These challenges are amplified in scenarios where real-time Doppler compensation is critical for high-speed mobile platforms. * **Limited Training Data and Generalization:** Robust ML/DL-based UWA communication models require comprehensive labeled datasets to generalize effectively across diverse environments. However, collecting underwater data is challenging due to fluctuating parameters such as temperature, salinity, and pressure [20; 27; 45]. These environmental variations often lead to poor generalization across geographic locations and seasonal conditions. Hybrid CNN-RNN models frequently struggle to transfer learned representations reliably. Physics-Informed Neural Networks offer a promising direction by incorporating environmental constraints into learning pipelines [34]. Nevertheless, these models rely on specialized datasets that accurately reflect underwater propagation dynamics, and such datasets remain scarce. * **Multipath Propagation and Doppler Effects:** Acoustic signals often encounter severe multipath effects in underwater channels. These reflections cause phase distortion and signal fading [23; 45; 59]. The rapid movement of AUVs and underwater robots introduces Doppler shifts. These shifts complicate synchronization and modulation decoding. Attention mechanisms and BiLSTM architectures improve temporal adaptation. However, they still struggle with real-time compensation under extreme Doppler conditions. CNN-LSTM models offer better multipath tracking. Yet, they face limitations in maintaining synchronization when channel conditions change rapidly. * **Energy Efficiency and Hardware Constraints:** Underwater communication nodes often rely on limited battery power. This makes energy efficiency a top priority [28]. High-complexity models, such as deep CNNs, consume substantial energy. They are not ideal for long-duration missions. Lightweight and pruned models reduce energy demands [47; 54]. However, their accuracy may decline due to reduced model capacity. Deploying DRL models on low-power acoustic modems remains challenging. These models require optimization in compression and hardware-aware training. Moreover, resource allocation strategies that balance bandwidth, communication distance, and data rate are underexplored. This area offers important opportunities for future research. * **Fragmentation in Performance Metrics:** Performance evaluation in UWA research is highly inconsistent. Different studies report metrics like BER, Accuracy, and MSE. However, they do so under varying conditions. These variations include differences in SNR ranges, acoustic channel models, datasets, and modulation schemes. There is no standard protocol for testing or reporting. As a result, comparing models becomes difficult. A technique may seem better simply due to the evaluation setup, not because it truly performs better. Many studies also lack baseline comparisons with conventional methods. This makes it hard to judge how much ML/DL methods actually improve performance. Without consistency, performance trends across tasks are unclear.