Water-waves effect on encryption and deep-learning-based recognition of speech signal transmitted over wireless optical communication channel

In many sea-exploration scenarios, a wireless optical communication link between an onshore station and a floating marine structure or between a floating buoy and an underwater vehicle is required. In order to establish high-quality wireless optical communication, the effect of sea-waves must be taken into consideration. In this work, we investigate the influence of surface water waves having various amplitudes, frequencies and waveforms on the quality of an analog speech signal transmitted over a wireless optical communication link. Such investigation is also carried out on an encrypted speech signal. The quality of the speech signals is determined by utilizing a deep learning neural network that is trained to recognize ten speech commands and produces classification probabilities. In our experiments, either the detector or the light source is non-stationary as a result of the water-waves, thus distorting the received analog signal. We also show a method of deriving the frequency of the water waves by using a remote tracking unit based on micro-electro-mechanical systems (MEMS) mirrors.