Improving OAM-Based Optical Wireless Communication in Turbulence Using Conjugate Light Field and Hybrid Neural Networks

Optical wireless communication (OWC) is in high demand due to its potential for high-speed data transmission and spectrum relief in congested radio frequencies. However, real-world implementations face significant challenges, particularly due to atmospheric turbulence, which distorts the optical signal, making reliable OWC difficult to achieve. In this study, we propose a novel methodology to mitigate the effects of turbulence by investigating the application of orbital angular momentum (OAM) and deep learning for robust OWC. In this study, we propagate information in the form of optical Laguerre-Gaussian (LG) beams through turbulent atmosphere and use the conjugate light field (CLF) method to retrieve the information. This process is further integrated into a deep learning framework to enhance the OWC systems performance. This integration reduces the computational load by minimizing the network classes to two CLF outcomes instead of using all LG beam modes as classes. The proposed method achieves faster processing time by reducing computational load, essential for real-time applications. An optical tabletop experiment also verifies the proposed technique, where we achieve a bit error rate (BER) of 2.44 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup>, demonstrating a performance that surpasses baseline algorithms by over ∼99% for a given set of parameters. To the best of the author's knowledge, this is the first time this concept has been presented for the use of OWC in turbulent atmospheric conditions.