Free-space optical (FSO) communication enables high-speed, secure, and bandwidth-efficient data transmission for terrestrial and inter-satellite networks, outperforming traditional radio frequency (RF) systems in interference immunity and directional security. Atmospheric turbulence (AT), which causes beam distortion, intensity fading, and intermodal interference, remains a significant limitation for long-distance links. Existing approaches, such as Gaussian beam transmission, static equalization, and basic convolutional models, fail to provide real-time, adaptive resilience to these challenges. To overcome these limitations, this study proposes a novel hybrid FSO framework combining resilient structured light beams (Bessel, Airy, and orbital angular momentum (OAM) modes), adaptive optics (AO), and intelligent signal processing. A Dynamic Neural Fuzzy Inference System (DNFIS) provides robust equalization, and a Deep Convolutional Neural Network with Time-domain Correlation Sequence Generation (DCNN-TCSGm) predicts and compensates for turbulence effects in real time. Furthermore, the framework models compact optical metasurface-based OAM multiplexing combined with Wavelength Division Multiplexing (WDM) in the mid-infrared range to enhance spectral and spatial throughput. Simulation results demonstrate a 55% reduction in Bit Error Rate (BER), a 22% improvement in signal voltage stability, and up to 10 dB power gain over conventional Mode Division Multiplexing (MDM)-FSO and Decision Feedback Equalizer (DFE) systems, highlighting the proposed framework’s robustness and scalability under challenging atmospheric conditions.
AHMAD et al. (Thu,) studied this question.