# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 1 Introduction
The research described in [14] implemented and tested an SVM classifier on an FPGA, where a discrete wavelet transform (DWT) was used to extract the features of received modulated signal, and depending on these features, an SVM can classify binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulation techniques. The authors in [15] proposed an FPGA-based architecture to embody several modulation techniques at once, making it more versatile, cost-effective, and easy to use and test. In addition, some works have developed systems and methodologies for the modulation and/or demodulation of acoustic and optical signals in the underwater environment using machine learning techniques. For example, an asymmetric frequency shift keying (AFSK) modulation technique for visible light communication (VLC) systems was developed in [16]. AFSK provides VLC systems with a low-cost and low-complexity modulation technique that mitigates flickering and dimming for both square and rectified sine wave signals. The authors in [17] used a deep convolutional neural network (DCNN) for the demodulation of a Rayleigh-faded wireless data signal, simulating the FSK demodulation over an additive white Gaussian noise Rayleigh fading channel with average signal-to-noise ratios (SNRs) from 10 dB to 20 dB. In [18], a single deep learning (DL) model to demodulate the ASK, FSK, and PSK modulations by using a data-driven approach is presented. The research in [19] presents two DL-based demodulators called a deep belief network support vector machine (DBN-SVM) demodulator and adaptive boosting (AdaBoost)-based demodulator. The work [20] shows a modulation recognition method based on time-frequency analysis and an SVM. Ref. [21] proposes an optimization algorithm based on instantaneous statistical characteristics of modulated signals and an SVM classifier. In [22], a modulation classification method capable of classifying multiple FSK digital signals without a priori information using modified covariance method was developed. However, as far as the authors know, there does not exist a similar approach to the one presented in this work,in which the signal modulation/demodulation is made by the combination of SVM, CWT, and FSK methods and implemented in an FPGA-based architecture. Therefore, this work aimed to address this gap by developing an advanced underwater acoustic modem system that employs SVM for signal classification and the CWT for feature extraction. Unlike conventional research that primarily focuses on signal type identification, the method presented utilizes these advanced techniques to accurately decode signals and retrieve transmitted data under simulated underwater conditions. The modem employs FSK modulation for transmitting data and utilizes a combination of wavelet processing and a trained SVM model for demodulation in order to demonstrate the efficacy of an SVM-based classification system integrated with the CWT for feature extraction in a custom-designed FPGA-based underwater modem. The introduction of a new symbol named \"C\" in the decoding process allows for a more flexible interpretation of the signal transitions, enhancing the robustness and accuracy of data demodulation. This symbol acts as a transitional element within the signal structure, aiding in the mitigation of common issues, such as spectrum leakage and transition errors, that are typically encountered in underwater communications. Through experimentation and analysis, this study contributes to the theoretical understanding of machine learning applications in signal processing and also provides a practical framework for enhancing underwater communication systems. In addition, the approach proposed not only applies SVM in a novel context but also demonstrates the superiority of the CWT over traditional Fourier-based methods for handling the dynamic aspects of underwater acoustics.