Wavy water-to-air optical camera communication system using rolling shutter image sensor and long short term memory neural network

We propose and experimentally demonstrate for the first time up to the authors' knowledge a wide field-of-view (FOV) water-to-air optical transmission using rolling-shutter (RS) based optical camera communication (OCC). Here, we evaluate the proposed OCC system without water ripple and with different percentage increases of water ripple. Long short term memory neural network (LSTM-NN) is utilized to mitigate the wavy water turbulence induced link outage and to decode 4-level pulse-amplitude-modulation (PAM4) RS pattern by meeting the pre-forward error correction bit-error-rate (pre-FEC BER = 3.8 × 10-3). We also evaluate the FOVs of the proposed water-to-air RS-based OCC system. This can be implemented by using different angular rotations of the camera. Experimental results show that the proposed OCC system can support ±70°, ± 30°, and ±30° rotations around the z-, y- and x-directions, respectively when operated at 6 kbit/s and decoded using LSTM-NN.