## 1 Introduction  
Underwater acoustic (UWA) communication is used in several critical areas such as environmental monitoring, deep-sea exploration, military operations, rescue missions, commercial maritime applications, and underwater robotics [1]. Electromagnetic waves, commonly used for wireless communication on land, do not work efficiently underwater due to high absorption and scattering. Consequently, UWA communication relies on sound waves to transmit and receive information [2]. These sound waves move efficiently through water, which makes it possible to communicate over long distances, even deep under the sea [3].  
Despite its importance, UWA communication faces persistent challenges due to the complex nature of underwater channels. These challenges include multipath propagation, channel time variations, and unpredictable channel conditions [4; 5]. Multipath propagation occurs when sound waves reflect off underwater surfaces. It causes multiple delayed signal replicas that interfere at the receiver. These echoes distort signal timing and undermine the effectiveness of traditional synchronization techniques. Consequently, it often leads to symbol misalignment and degraded decoding accuracy. Similarly, environmental dynamics such as currents, temperature gradients, and object movement introduce time-varying fading and unpredictable Doppler shifts. Large Doppler spreads distort carrier frequencies and interfere with signal stability. This makes it difficult for conventional modulation schemes to adapt effectively in real time [6]. Bandwidth constraints and slow sound propagation (around 1500 m/s) further limit the achievable data rates and increase latency [7]. These physical-layer challenges necessitate intelligent signal processing approaches tailored for underwater conditions.  
To address these environmental challenges, physical-layer modules like channel estimation, adaptive modulation, and modulation recognition play an important role [1]. These components are essential for compensating multipath-induced distortion and ensuring robust synchronization. Channel estimation aims to extract channel impulse responses and delay profiles. Adaptive modulation dynamically selects optimal modulation schemes based on real-time feedback from the channel. Under severe Doppler shifts, RL-based modulation controllers offer enhanced environmental awareness and decision-making capabilities to maintain reliable throughput. Modulation recognition detects the transmitted modulation format and is essential for spectrum monitoring, interference management, and dynamic demodulation. Together, these modules form the backbone of underwater physical-layer reliability and efficiency.  
In recent years, machine learning (ML) and deep learning (DL) tools have emerged to overcome these physical-layer limitations [4; 5; 8; 9]. ML models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), facilitate accurate channel estimation by learning temporal and spatial signal patterns. This pattern recognition enables adaptive synchronization under challenging multipath conditions. For adaptive modulation, reinforcement learning (RL) algorithms continuously assess channel states, including Doppler shifts and fading variations. Based on this assessment, they select modulation schemes that improve both transmission speed and signal robustness. In modulation recognition, DL models such as CNNs and Recurrent Neural Networks (RNNs) are able to extract discriminative features from noisy acoustic signals. These models outperform traditional rule-based classifiers in accurately identifying complex modulation schemes. Collectively, ML/DL-based solutions offer data-driven adaptability, improving decoding accuracy and operational reliability in complex underwater environments.