The omnidirectional receiver for UWOC systems based on the diffractive deep neural network

TL;DR

This paper introduces an omnidirectional optical receiver based on a 7-layer diffractive deep neural network for underwater wireless optical communication, aiming to reduce link alignment issues and improve focusing efficiency.

Contribution

It proposes a novel D2NN-based receiver design that enables omnidirectional focusing in UWOC systems, incorporating vectorial diffraction theory for enhanced accuracy.

Findings

Achieves 90.96% average focusing efficiency.

Focuses incident light with tilt angles up to 89 degrees.

Performance plateaus with more than 7 layers.

Abstract

The link alignment requirement in underwater wireless optical communication (UWOC) systems is a knotty problem. The diffractive deep neural network (D2NN) has shown great potential in accomplishing tasks all optically these years. In this paper, an omnidirectional receiver based on 7-layer D2NN is first proposed to alleviate the link alignment difficulties in UWOC systems. In addition, the vectorial diffraction theory is introduced into the training of the D2NN to obtain more accurate diffraction calculations compared with the prevalently adopted scalar diffraction theory. Simulation results verify the validity of the vectorial diffraction theory and demonstrate that the presented method can focus incident light waves with tilt angles from 0 to 89 degrees in a 6.25% area of the detection plane with an average focusing efficiency of 90.96%, proving the feasibility of omnidirectional focusing at the receiver end. Extra simulations further reveal that more layers do not lead to a sustained performance improvement but rather reach a bottleneck, and the D2NN can achieve omnidirectional focusing with a certain range of focusing region size. With more effort, the proposed receiver design, which can be highly integrated with detectors, holds promise to realize both omnidirectional and reliable link establishment in UWOC systems in the future.