OAMベースの構造化光とインテリジェント適応信号処理を用いた堅牢な大容量自由空間光通信

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.