# 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  
Artificial Intelligence/Machine Learning (AI/ML) techniques have gained significant attention in the context of designing wireless communication systems, as evidenced by recent research studies [198; 199; 200]. Similarly, they hold great promise for the optimal design of FSO-based NTNs. In the following, we list some of the open research problems that could benefit from the use of AI/ML techniques. * **Real-time Adaptive Transmission:** One notable application of AI/ML in this domain is the real-time optimization of airborne FSO link parameters. By continuously monitoring atmospheric conditions and system performance, AI/ML models could dynamically adjust key parameters like transmit power, modulation schemes, and beam steering angles, to maximize link quality. This adaptive optimization ensures efficient data transmission, even in challenging environments, such as those characterized by strong turbulence or fluctuating weather conditions. Furthermore, AI/ML algorithms could play an important role in optimizing path and trajectory planning within FSO-based NTNs. These optimization tasks must consider real-time factors such as link quality, terrain characteristics, weather conditions, and energy harvesting and consumption considerations. In fact, the application of AI/ML in FSO communication systems holds significant promise for improving various aspects of performance;
* **Pointing Errors:** Pointing errors in NTN communication systems can result from the dynamic movement of UAVs and HAPSs, leading to misalignment of communication beams. These errors can significantly degrade link performance, causing signal loss and interference. To address this challenge, adaptive tracking mechanisms, combined with AI/ML algorithms, can be employed. AI/ML can continuously analyze sensor data, predict movement patterns, and make real-time adjustments to the beam direction. Advanced sensors, such as Global Positioning System (GPS)-/GNSS-basedtracking and inertial navigation systems, enhance the accuracy of AI-driven pointing error mitigation strategies;
* **Atmospheric Turbulence and Other Disturbances:** Atmospheric turbulence leads to fluctuations in the refractive index of the atmosphere, resulting in beam wander and beam broadening effects. To mitigate the impact of atmospheric turbulence, AI/ML-powered AOs techniques can be potentially used. These techniques involve the use of deformable mirrors and wavefront sensors, guided by AI/ML algorithms, to dynamically correct phase distortions introduced by atmospheric turbulence. AI/ML can analyze turbulence patterns, adjust optical elements, and optimize beam quality in real time, ensuring a stable communication link, even under turbulent conditions. To combat scintillation index effects, AI/ML can be applied to analyze historical data on scintillation index patterns, allowing for predictive scintillation index mitigation strategies. Apart from turbulence, atmospheric disturbances like cloud, rain, and snow can challenge NTN communication. AI/ML can assist in managing these disturbances, by analyzing weather data and predicting atmospheric conditions. AI/ML models can provide advanced weather forecasting, enabling dynamic routing algorithms to make proactive decisions about traffic rerouting during adverse weather conditions. In vertical/slant FSO links between HAPSs and lower-altitude UAVs or ground base stations, cloud effects can disrupt communication. AI/ML algorithms can incorporate real-time data on cloud distribution and wind direction to predict when clouds might obstruct these vertical/slant FSO links. Additionally, in horizontal FSO links where communication between low-altitude UAVs is susceptible to blockages by high buildings, advanced routing algorithms, and obstacle-aware protocols could be employed to optimize data transmission paths and mitigate potential disruptions caused by urban environments;
* **Trajectory Planning:** AI/ML algorithms can be used for trajectory planning within FSO-based NTNs.