The underwater acoustic environment is amongst the most challenging mediums for wireless communications. The three distinct challenges of underwater acoustic communication are the low and nonuniform propagation speed, frequency-dependent attenuation and time-varying multipath propagation. To cope with these challenges, physical layer communication systems allow the selection of communication parameters based on environmental conditions and constraints. This is also known as link adaptation. In this thesis, the frequency-repetition spread-spectrum (FRSS) physical layer is studied. Various channel parameters are used to classify the optimal FRSS format. Furthermore, different machine learning classifiers are implemented to solve the classification problem. It is determined that the output signal-to-noise ratio provides enough information to switch effectively between transmission formats. Among the implemented machine learning classifiers, the decision tree strikes a good balance between performance and computational complexity. It is shown that a small performance gain can be achieved when custom channel parameters are extracted from the estimated impulse response and the equalizer error sequence using a deep neural network. Optimizing the equalization process is another method to better cope with difficult environmental conditions. Various adaptive filter algorithms are implemented for the decision feedback equalizer used in the FRSS receiver. The optimal algorithm parameters are found by means of algorithm unrolling. It is shown that the standard least mean squares algorithm cannot be outperformed by various other optimization algorithms that use linear or non-linear filters. The Watermark channel simulator is used to study the performance of the link adaptation and equalization optimization solutions for a wide range of underwater channels.