## 5 Results on ML/DL-Based Modulation Recognition
### Discussion on ML/DL-Based MR Techniques in UWA Communication Systems
In contrast to real-valued pruning and ensemble techniques, the paper [55] proposes a novel adaptive modulation method for UWA communication signals utilizing deep complex networks (DCNs). Their key contribution lies in developing a DCN architecture that directly processes complex-valued UWA signals, effectively capturing amplitude and phase information, which is crucial given the complex channel impairments in UWA environments. This approach aims to improve classification accuracy and robustness compared to traditional real-valued neural networks that often discard or separately process the complex nature of the signals. A potential shortcoming, though not explicitly detailed in the abstract, could be the computational complexity associated with DCNs, especially when deployed in resource-constrained underwater environments, and the need for extensive training data specific to diverse UWA channel conditions. Exploring hardware-integrated solutions, in [56], the authors present a support vector machine (SVM)-powered underwater acoustic modem that integrates continuous wavelet transform (CWT)-based feature extraction and FPGA-based signal processing to enhance UWA communication. Key contributions include (1) a novel system architecture combining SVM for signal classification and CWT for robust feature extraction, achieving 98.28% accuracy at 5 dB SNR; (2) the introduction of a transitional \"C\" symbol to mitigate spectrum leakage and improve demodulation reliability; and (3) FPGA implementation for real-time processing, demonstrating a stable 10,000 baud rate with zero BER under controlled conditions. However, the computational complexity of CWT and SVM could pose challenges for low-power edge devices. Returning to hybrid deep learning models, the paper in [46] proposes a hybrid neural network model S&SEFM, combining SqueezeNet and SENet, for modulation recognition in UWA communication. It introduces multi-attribute features, wavelet time-frequency (WTF) spectrum, square power spectrum, and cyclic spectrum contour maps, to mitigate the limitations of single-feature methods and employs multi-scale feature fusion to enhance recognition accuracy. The model demonstrates strong generalization across different UWA channels and robustness against Doppler shift, achieving high recognition rates in both simulated and sea trial data. However, the paper does not address computational complexity in real-time applications or the model's performance in extremely low SNR conditions. Continuing the pursuit of multi-scale feature extraction, [PERSON] et al. [47] introduce several contributions. Key contributions include a data augmentation method that increases data sevenfold to address small sample size issues, a novel \"microscale\" concept for rationalizing UWA signals into time series, and the \"One2 Three block\" temporal feature extractor designed to extract features from three microscales. Additionally, they propose a \"Dual-Stream SE block\" as a spatial feature extractor to synthesize advanced spatial features. The method's effectiveness is validated on real-world datasets from the South China Sea and the Yellow Sea, demonstrating promising recognition accuracy for eight common UAC modulation modes. The computational complexity introduced by the multi-microscale feature extraction and the dual-stream architecture might be a concern for real-time deployments. Taking a step further into attention mechanisms, a two-stream transformer (TSTR)-based network is proposed in [48], aiming to overcome the challenges of complex UWA channels and severe ocean noise. Their key contributions include an input preprocessing layer that extracts I/Q and time-frequency features, a feature capture layer (FCL) for extracting high-dimensional signal features across time, frequency, and time-frequency domains, and a classification layer for modulation estimation. A notable innovation is the use of a multihead self-attention module with adaptive soft thresholding within the FCL to handle noise and varying feature characteristics.