# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 1 Introduction  
Underwater acoustic (UWA) communication is a pivotal area of research, driven by its critical applications in underwater exploration, surveillance, and data transmission between submerged devices. Despite its importance, the field faces significant challenges, including signal attenuation, multipath propagation, and noise interferences, which severely impact the reliability and clarity of underwater transmissions [1]. These unique challenges are due to the inherent properties of the aquatic environment, such as significant signal distortion and persistent noise from various natural and anthropogenic sources, such as ocean currents, marine life, vessel propellers, and the relative movement of communicating parties, each introducing their own complexities to signal transmission and reception [2]. Addressing these challenges, this work introduced a new approach that leverages a combination of the continuous wavelet transform (CWT) and support vector machines (SVMs) for the effective demodulation of known frequency shift keying (FSK) signals. The current state of research in UWA communication reveals a spectrum of approaches, from traditional signal processing methods to emerging machine learning algorithms. While traditional techniques have provided a foundation for the field, they often fall short in complex scenarios characterized by variable noise conditions and dynamic aquaticenvironments. In order to mitigate these harsh conditions, several approaches have been adopted. In [3], a compressed sensing-based technique for time-domain synchronization is proposed over a real-time underwater channel. In [4], a new estimation scheme, which does not suffer from intercarrier interference (ICI), is proposed to estimate the channel variations. In [5], a precoded index modulation is proposed to increase the transmission efficiency and exploit the spreading and multipath diversities. These complex underwater conditions have led to a growing interest in data-driven approaches, which offer adaptability and improved performance. In this sense, recent advancements have leveraged sophisticated signal-processing techniques and machine learning to enhance communication capabilities in these challenging environments. For example, the work described in [6] employed a neural network with feature extraction and automatic learning ability to replace the demodulation modules to recover transmitted signals without explicit channel estimation and equalization. A UWA communication modulation classifier is proposed in [7], which uses a convolutional neural network (CNN) to identify the presence of the UWA signals; then, the classifier uses the feature vector output by the encoder to distinguish the final modulation categories. Other interesting examples are found in [8; 9; 10]. In [8] an SVM classifier is employed, along with a CNN in a camera receiver. A CNN is used to detect every LED region from the image frame, and then essential features are extracted to feed into an SVM classifier for further accurate classification. In [9], a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels was developed. In [10], neural network architectures and training methods suitable for demodulation in power-limited communication systems were examined. Among the different approaches, the SVM and CWT have emerged as promising tools for improving signal classification and feature extraction, respectively [11; 12; 13]. However, the integration of these techniques in underwater communication systems, particularly in conjunction with FPGA-based architectures for real-time processing, remains underexplored. Some exceptions that employ this FPGA-based architecture are found in [14; 15].