Artificial Intelligence for OWC

Artificial intelligence (AI) in Optical Wireless Communications (OWC) involves integrating machine learning (ML), deep learning (DL), and reinforcement learning (RL) to automate, optimize, and improve the performance of light-based data transmission, such as Visible Light Communication (VLC) and Free Space Optics (FSO). 

AI replaces traditional, complex, and static mathematical models with adaptive, intelligent algorithms that enhance signal detection, resource allocation, and link stability. 

The integration of AI into OWC allows for smarter, self-organizing networks that can better adapt to changing conditions and user needs compared to conventional methods.

AI-driven OWC systems enable smarter, more robust, and faster communication, crucial for IoT, 5G/6G, and autonomous vehicle technologies.

1. Key aspects of AI in OWC include:

• Adaptive Resource Allocation: AI, particularly reinforcement learning, optimizes parameters like power, time, and wavelength to maximize user data rates, especially in complex, dynamic environments.
• Intelligent Signal Detection & Estimation: Machine learning algorithms help process signals, compensating for non-linearities in light sources (like LEDs/VCSELs) and enhancing overall detection accuracy.
• Performance Optimization & Monitoring: AI is used to predictively manage network links, particularly in mitigating effects of atmospheric turbulence in FSO and enhancing reliability.
• Automated Network Management: AI handles, monitors, and configures OWC systems automatically, significantly reducing the need for manual intervention and improving operational efficiency.

2. Key Applications of AI in OWC:

• Modulation Classification: AI algorithms like CNN, SVM, and k-NN identify modulation formats (e.g., DCO-OFDM, PAM) in, for example, underwater OWC (UOWC) to enhance signal processing.
• Resource Allocation: Reinforcement learning (RL) optimizes resources in HetNet environments (Visible Light Communication), maximizing total sum rates.
• Performance Mitigation: AI predicts and mitigates atmospheric turbulence, improving stability in Free Space Optics (FSO) links.
• Intelligent Network Management: AI automates configuration, troubleshooting, and self-organization, addressing challenges like misalignment and time-varying channel conditions.
• Signal Processing: AI handles non-linearities in, for example, VCSEL-based OWC systems, managing impairments like bandwidth limitation and noise.