## 5 Results on ML/DL-Based Modulation Recognition
### Discussion on ML/DL-Based MR Techniques in UWA Communication Systems  
The following paragraphs explore the recent advancements in ML/DL techniques applied to UWA modulation recognition. The SCNet-based schemed is presented in [49]. The main feature of this work is to employ 1D sequence convolutions with adaptive kernel sizes. It achieves higher recognition accuracy, faster training, and fewer parameters compared to traditional CNN and RNN models. Tested on real-world underwater data, SCNet outperforms models like LSTM, ResNet, and DenseNet. While primarily validated on simulated data, the results suggest strong potential for practical deployment in challenging underwater environments. Extending beyond convolutional models, the work in [50] combines optimizing autoencoder (OAE) with an evaluation-enhanced K-nearest neighbors (EEKNN) algorithm. The OAE refines noisy signal features by learning their relationship to ideal ones. Meanwhile, EEKNN improves the classification process. Together, these techniques achieve up to 99.25% accuracy and very fast recognition times (3.48 ms) on real-world data from the South China Sea. While the method shows great promise, especially for practical applications, it would benefit from further testing in more diverse and real-time underwater conditions. While the work in [50] has focused on enhancing denoising and classification, the authors in [51] have proposed R&CNN. It combines RNN and CNN. The model effectively captures both the time-dependent and spatial features of acoustic signals. Tested ontwo real-world datasets (Trestle and South China Sea), R&CNN delivers accuracy (up to 99.38%) and fast recognition times (7.164 ms). While the results are promising, further validation under more diverse underwater conditions is needed before confirming its practical deployment. Building further on hybrid architectures, the work in [52] introduces UWA communication modulation classifier-supervised contrastive learning (UMC-SCL). The method starts by using a lightweight CNN to filter out ocean noise. It then employs ResNet50 as a feature extractor. Tested on a mix of simulated, pool, and real ocean data, the model achieves strong performance, reaching 98.6% accuracy at 0 dB, and shows clear advantages over traditional methods. Although highly promising, further testing in more diverse underwater conditions would help confirm its robustness and generalization. Departing from deep neural networks, in [53], the authors present a sixth-order cumulant (\(C_{63}\)) to effectively separate OFDM signals from PSK and FSK variants, while an enhanced bispectrum approach helps differentiate between specific modulation types like BPSK, QPSK, 2 FSK, and 4 FSK. The system shows impressive performance in challenging non-cooperative environments, achieving perfect recognition at 0 dB SNR in simulations and nearly 99% accuracy in real-world lake tests using a ResNet-based classifier. While effective, the method struggles in extremely noisy conditions (below \(-8\) dB SNR), relies heavily on simulated training data, and faces computational challenges for real-time applications. While cumulant and bispectrum-based methods offer mathematical precision, an edge-enabled adaptive modulation framework for Internet of Underwater Things (IoUT), leveraging network pruning and EL to balance computational efficiency and accuracy is presented in [54]. The key contributions include (1) a novel CSI dataset for six modulation schemes, enhancing feature representation under noise; (2) a Taylor expansion-based pruning criterion to reduce redundant CNN parameters while maintaining performance; (3) an ensemble learning (EL) strategy to compensate for accuracy loss post-pruning, achieving 93.4% accuracy at 5 dB SNR; and (4) successful deployment on edge devices like NVIDIA Jetson TX2, demonstrating practical feasibility. However, the framework struggles with SNR below 0 dB, where feature extraction becomes unreliable, and its reliance on simulated data may limit real-world adaptability in highly dynamic underwater environments.