Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications.

Orbital angular momentum (OAM) multiplexing is emerging as a critical technique for achieving high data capacity in underwater wireless optical communications (UWOC). Nonetheless, wavefront distortions induced by underwater turbulence compromise the orthogonality of OAM modes. In this paper, we introduce a physics-driven untrained learning approach for adaptive optics that operates independently of extensive amplitude datasets. Without iterative processing and pre-trained datasets, the underwater turbulence characteristics can be retrieved accurately by only relying on a one-shot distorted probe beam and a priori known amplitude of the probe beam. By leveraging a single distorted diffraction pattern and a priori known amplitude of the probe beam, the characteristics of underwater turbulence can be accurately retrieved without iterative processing or pre-trained datasets. Furthermore, by implementing a hybrid input/output alternating projection algorithm with a square constraint area, the retrieved underwater turbulence phase screen beyond the [0, 2] range aligns with the target pattern. This consistency indicates that the proposed wavefront recovery technology is validated across a broad range of turbulence strengths. As a demonstration of feasibility, numerical simulations, and optical experiments were conducted to validate the compensation of OAM beams. Furthermore, the theoretical bit error rate (BER) and channel capacity were inferred based on the purity of OAM modes and the level of crosstalk.