Research on error performance of underwater OAM multi-plexing optical communication assisted by deep learning

Abstract:

In this paper, a novel convolutional neural network (CNN) assisted decoding method is proposed to recover in-formation directly for underwater orbital angular momentum (OAM) multiplexing optical communication. The effect of various attenuations and ocean water types, such as absorption, scattering, turbulence fading, noise and diffraction, are considered comprehensively in our analysis. A regularly spaced continuous phase screen is used to represent ocean turbulence. And the angular diffraction function is exploited for simulating the propagation of the OAM beams. In order to minimize the BER and simplify the receiver design, a CNN assisted decoding method is used to compensate the distorted OAM light and decode the transmission data directly without channel estimation and equalization. The CNN is trained to learn the multiplexed OAM light intensity map generated under various water environment. The bit error performance of CNN OAM system is also compared with that of traditional Gerchberg-Saxton (GS) algorithm. Our numerical simulation results indicate that the CNN assisted method com-bats the impairing effects of fading and improves the underwater OAM system performance obviously. Furthermore, it outperforms GS algorithm in almost all the turbulence environment at the same water environment. And the BER of the CNN assisted system still decreases effectively by increasing signal noise ratio even in moderate and strong turbulence situations while at the same time requiring less computation complexity.

Key words:underwater OAM multiplexing optical communication; complicated vortex optical channel; CNN;Error performance;