## 4 Results on ML/DL-Based Adaptive Modulation
### Discussion on Adaptive Modulation Techniques in SC-UWA Communication Systems  
This section discusses the research studies, targeting adaptive modulation techniques in SC-UWA communication systems. An overview of the key characteristics and performance comparison for these studies are summarized in Table 10, Table 12, and Table 14, respectively. An ML-driven link adaptation strategy is proposed in [43]. Different ML algorithms including SVM, KNN, pseudo-linear discriminant analysis, and boosted regression trees have been used to classify modulation and coding scheme by analyzing measured sea trial datasets. The boosted regression tree attains exceptional accuracy (99.97%), surpassing alternative techniques. Nonetheless, the methodology is dependent on substantial training data, and its real-time application is limited. Building on the advantages of ML-based link adaptation, the research in [36] presents an iterative learning framework for dependable link adaptation in the Internet of Underwater Things (IoUT). By utilizing multilayer regression (MLR) and multilayer perceptron (MLP) to forecast modulation and coding scheme (MCS) and BER, it employs various channel parameters (e.g., SNR, Delay Spread, and Frequency Spread) derived from authentic underwater datasets. The achieved BER is up to 25% increased throughput compared to traditional SNR-based adaptive modulation. Nonetheless, there is a need for additional optimization to minimize latency in real-time IoUT implementations. Building upon the iterative ML frameworks discussed previously, the authors in [38] present a hybrid DL model that integrates CNN with Boosted Single Feedforward Layers (BSFLs) to dynamically choose among CDMA, TDMA, and OFDM modulation methods in underwater acoustic networks. The CNN collects channel features, whereas the BSFL forecasts the ideal modulation scheme, with a high accuracy of 98.6% and a 30% enhancement in BER performance relative to traditional approaches. Significant achievements encompass illustrating the efficacy of hybrid learning in dynamic underwater settings and surpassing established models such as CNN+RF and DCNN in modulation selection. The method necessitates substantial processing resources and enormous datasets for training, while its real-time applicability is constrained by significant complexity. While the prior study employs hybrid DL, the research in [41] shifts focus to RL and present an RL-based automatic modulation switching system designed to improve UWA communication by dynamically selecting among ASK, PSK, OFDM, and BFSK schemes according to real-time channel circumstances. The achieved performance numbers are as follows: a 3.648% enhancement in RSSI, a 32% decrease in BER at 7 dB SNR, and a 5% augmentation in utility at 10 dB SNR relative to fixed FH-BFSK. Nonetheless, the system's efficacy is constrained by the confined experimental framework (0.05-0.1 m water range),and its practical scalability has yet to be validated. Expanding on RL-based adaptive strategies, a Q-learning-based adaptive modulation scheme for shallow sea UWA communication can be found in [42]. Field experiments demonstrated that the RL approach outperformed fixed threshold and random selection methods, achieving higher throughput (14,645.3 bits) and lower BER in time-varying channels. Key contributions include the practical validation of RL in real-world UWA conditions and the demonstration of its superiority over conventional adaptive modulation strategies. However, shortcomings include limited scalability to diverse environments due to site-specific training data, potential latency in real-time decision-making, and the lack of comparison with more advanced RL algorithms beyond Q-learning. The reviewed SC-UWA studies highlight multiple strategies for adaptive modulation using ML, DL, and RL techniques. Traditional models like SVM and boosted trees show high accuracy but depend on extensive datasets. Iterative learning with MLP and MLR improves throughput but needs faster decision-making for real-time use.