Coherent demodulated underwater wireless optical communication system based on convolutional neural network

The optical vortex beams carrying orbital angular momentum (OAM) is a beam with a special transverse spatial distribution, which has infinite orthogonal properties and can significantly improve the channel capacity of underwater wireless optical communication (UWOC) systems. However, for the underwater wireless optical communication system based on orbital angular momentum shift keying (UWOC-OAM-SK), turbulence in the seawater channel can distort the optical vortex beam and reduce the ability of the system to demodulate the OAM-SK signal, which can reduce the reliability of the system. To improve the performance of UWOC-OAM-SK systems in the channel with different turbulence, a coherent demodulation UWOC-OAM-SK system using an image recognizer based on convolutional neural network (CNN) as demodulator is proposed in this paper. Compared with the incoherent system, the proposed system can obtain a detection image with higher image contrast and more pattern features before the OAM mode is demodulated from the OAM-SK signal at the receiver, therefore has higher reliability. Moreover, the proposed system can recognize mutually conjugate OAM modes, which can greatly save the multiplexing resources in OAM communication. With the excellent image-data processing capability of CNN, the coherent demodulator based on CNN image recognizer has higher demodulation accuracy than the incoherent demodulator in both long distance channels or strongly turbulence channels underwater. The simulation results show that the demodulation accuracy of the proposed system is still higher than 90% in channels with strong turbulence intensity Cn2=1×10−14(K2m−2/3) or transmission distance z=80m. The work done provides some theoretical basis for the design of the UWOC-OAM-SK system.