## 5 Emerging Trends in FSO Communication
### Artificial Intelligence-Driven FSO Systems
By parameterizing the resource allocation policy, the problem is framed as an unsupervised statistical learning challenge. Furthermore, the use of graph neural networks (GNN) for policy parameterization is proposed to effectively utilize the FSO network structure with a reduced number of training parameters. Maintaining precise alignment between the Tx and Rx is crucial for FSO communication. AI algorithms can process data from various sensors to automatically adjust the alignment of the optical beam [480]. This includes compensating for environmental factors such as wind-induced vibrations or the movement of mobile platforms. ML models can predict the best alignment strategies based on historical data and real-time feedback, ensuring the beam remains accurately directed towards the Rx even in dynamic conditions [467; 481]. For instance, sensor-less AO is among the most promising techniques for compensating significant wavefront disturbances in FSO. A backpropagation ANN can be utilized in sensor-less AO systems to create a distortion correction scheme. This approach requires only one or a few online measurements to correct wavefront distortion, unlike other model-based methods, thereby enhancing the system's real-time capabilities [473]. One of the mostly used DL algorithms in optical communication applications is a deep neural network (DNN). The DNN is an extension of the ANN [482]. It is flexible and could be used for various applications, such as modulation format identification [483; 484], mitigating the fiber effects [485], optical amplifier control [486], optical performance monitoring [50; 487], along with various optical network applications such as quality of transmission (lightpaths) prediction [488] and an AI-driven routing framework for multi-domain optical networks [489]. For instance, a novel block-wise detection technique utilizing support vector machines (SVMs) to address scintillation noise in FSO links is proposed in [471]. Given that turbulence fading occurs on a millisecond scale, while signaling rates typically reach hundreds of Mbps, the SVM approach offers an exceptionally large data block proportional to the bit rates. Additionally, the SVM calculation is independent of the channel's turbulence conditions and the received signal models, requiring no prior information or heuristic assumptions. Furthermore, ref. [490] introduces a new detection method for OOK-modulated signals using various DL models across different strengths of FSO turbulent channels, without needing prior knowledge of the channel parameters. The DL decoders enhance the performance of the FSO turbulent channel and reduce power consumption. Notably, these DL models also operate faster than maximum likelihood methods with perfect channel estimation, achieving slightly better performance under turbulent conditions, thus facilitating FSO communications over turbulent atmospheric channels. In general, AI-driven FSO systems could enable autonomous vehicles, drones, and other robotic platforms to communicate seamlessly, even in challenging environments. This includes applications in disaster response, remote sensing, and smart cities. AI and ML could play a critical impact in the development of quantum FSO systems, optimizing the transmission and reception of quantum signals for ultra-secure communication. Also, AI-aided FSO systems can lead to the development of smart infrastructure, where buildings, streetlights, and other urban elements are equipped with FSO transceivers managed by AI, providing ubiquitous connectivity [2, 458, 469, 491]. In this context, AI-driven FSO communication systems are transforming the field, offering innovative solutions to long-standing challenges and paving the way for new applications. By leveraging AI-driven algorithms for predicting atmospheric conditions, optimizing beam alignment, and enhancing error correction, FSO systems can achieve higher performance and reliability.