## 3 Types of Underwater Communication Systems
### Applied Underwater Modulation Schemes
Due to the absence of interference, compared to RF, in places where RF is less implemented such as in medical fields, oil rigs and gas plants, nuclear power plants (NPPs), underwater communication, and further numerous applications, UVLC succeeds [71]. In addition, [PERSON] et al. [76] conducted tests with UVLC systems and the results showed a transmission distance of 440 m at a data rate of 50 Mbps with the average power of 100 W. However, the authors demonstrated the potential for reaching a distance of up to 500 m in pure sea water [76]. The underwater channel is in constant and unforeseeable transitions due to turbulence, changing temperature, pressure, density, turbidity, salinity, etc. Channel instability imposeslimitations on communication reliability, signal integrity, and transmission quality [5; 103]. Stationary modulation solutions are increasingly replaced by dynamic ones to address underwater channel variability and unpredictability [5; 103]. [PERSON] et al. [5] introduced three approaches to mitigate channel changes: model-based, algorithm-based, and ML approaches. Both algorithm-based and ML approaches show larger generation features because they do not require characterisation of the channels. However, ML models require large datasets of Channel State Information (CSI). Adaptive Modulation (AM) using OFDM for underwater communication has been shown to improve resiliency, as reported in [5; 103]. [PERSON] et al. [5] reported three AM approaches, namely Adaptive Modulation and Coding (AMC), Adaptive Resource Allocation (ARA), and Adaptive Transmission and Coding (ATC). These AM methods optimise resource utilisation and power efficiency and reduce transmission errors. AMC is an AM technique used in wireless communication systems to dynamically adjust the modulation scheme and coding rate to optimise performance based on the current channel conditions [5]. AMC allows for efficient and reliable data transmission, especially in wireless environments that are prone to fading and interference. ARA is an AM technique used to optimise resource utilisation in dynamic systems [5]. It involves dynamically adjusting the allocation of resources like CPU, memory, bandwidth, or power, based on real-time system conditions and performance requirements [5]. In UWSNs, ARA involves adjusting transmission power, modulation schemes, and channel allocation to optimise network performance. ATC is an AM method that adjusts the transmission scheme to the current channel conditions, allowing more efficient transmission over time-varying channels [5]. ATC offers several benefits: it (i) performs channel estimation, (ii) adjusts the transmission scheme based on the channel characteristics, (iii) adjusts to compensate for changing channel conditions, such as fading during stable or unstable channel periods, (iv) increases average throughput by taking advantage of favourable channel conditions, (v) reduces the required transmit power, and (vi) reduces the average probability of bit error [5]. In addition to adaptive techniques, [PERSON] et al. [5] also introduced predictive techniques to enhance the performance of UWAC systems, which are extensible to other UWC systems. Predictive techniques are proactive approaches that aim to predict the future state of communication channel conditions using historical observations, allowing better planning and resource allocation [5]. By anticipating future channel conditions, predictive modulation schemes can help avoid the need for retransmissions, thereby saving energy and extending the operational lifetime of underwater nodes [5]. ML-based models are increasingly implemented. [PERSON] et al. [5] reported ML-based adaptive and predictive techniques such as supervised, unsupervised and reinforcement learning. Models trained on labelled dataset are termed supervised learning, such as decision tree, linear regression, Data-Driven Sparse Learning (DDSL), SVM, Neural Networks (NNs) [5].