# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead
### AI/ML Techniques for FSO-Based NTNs
When determining the optimal path/trajectory, these models prioritize key factors such as real-time link quality, terrain characteristics, weather conditions, energy considerations, scalability, energy efficiency, and positioning/localization issues. By integrating sensor data and historical information, these models make intelligent decisions based on these factors, ensuring efficient and reliable data transmission. For instance, they can monitor signal quality at different locations within the network and use historical data to predict future link quality. Additionally, they can analyze topographical and geographical data to identify potential obstructions or reflective surfaces affecting the FSO link;
* **Energy Efficiency:** AI/ML models can factor in real-time data on energy generation, such as from solar panels on UAVs, and monitor energy consumption patterns based on network load. By considering these factors, AI/ML models can dynamically adjust the path and trajectory of UAVs to optimize energy harvesting and minimize energy expenditure. Additionally, energy harvesting can, not only be derived from the sun, but also from dedicated laser sources on the ground that can power low-altitude UAVs. For instance, balloons in the stratospheric layer can harvest solar power and then utilize dedicated laser sources to power fixed-wing HAPS. AI/ML technologies may facilitate the seamless integration of these energy sources into the network;
* **RIS for FSO-based NTN:** RIS can significantly enhance the performance of FSO-based NTN networks. AI/ML techniques can be employed to optimize the deployment and configuration of RIS elements within a network. These models can analyze real-time FSO link conditions, including signal quality and interference, and dynamically adjust the RIS elements' phase shifts and placements, to improve link reliability and throughput. They could also help in adjusting the beam directions based on the new locations of airborne nodes;
* **SDM/OAM for FSO-based NTN:** AI/ML models could play a crucial role in optimizing the use of SDM and OAM by dynamically selecting appropriate spatial modes and OAM states based on real-time channel conditions and traffic demands. Thesetechniques can adapt to changing link quality and interference scenarios, ensuring efficient and reliable data transmission in FSO-based NTN networks;
* **Handover:** AI/ML algorithms can facilitate seamless handover by continuously monitoring the position and movement of nodes, predicting the optimal time for handover, and selecting the best available FSO link or network segment, for uninterrupted communication. These techniques could enhance a network's ability to maintain connectivity, ensuring a seamless user experience in dynamic NTN environments. * **Scalability:** AI/ML techniques can leverage real-time data analysis to dynamically adjust network size and configuration based on evolving demands and changing environmental conditions. By continuously monitoring and predicting network traffic patterns, AI/ML could ensure the efficient allocation of resources and enhance a network's ability to accommodate varying levels of traffic. This adaptability and resource efficiency are critical for ensuring the scalability and long-term sustainability of FSO-based NTNs in the face of dynamic operational requirements and growth;
* **Positioning and Localization Issues:** AI/ML could assist in addressing these challenges by utilizing advanced positioning technologies like GNSS and combining them with AI-driven optimal placement of nodes. Through the integration of AI/ML, the system could continuously learn from real-time data, leading to enhanced accuracy and robustness in tracking and pinpointing the exact location of objects in various challenging environments, such as urban canyons or indoor settings. AI/ML techniques hold great promise for the efficient design of FSO-based NTNs.