## 5 Emerging Trends in FSO Communication
### Artificial Intelligence-Driven FSO Systems  
The integration of AI and ML into FSO communication systems represents a significant advancement and is a rapidly growing trend, offering new possibilities for optimizing system performance and reliability. AI and ML technologies offer powerful tools to address the inherent challenges of FSO communication, such as atmospheric interference, alignment issues, and error correction. In this regard, AI-driven algorithms can be used to predict atmospheric conditions, estimate channels, modulate and demodulate signals, optimize beam alignment, monitor performance, and enhance error correction processes. For example, ML models can analyze vast amounts of meteorological data to predict weather patterns with high accuracy. By integrating these predictions into FSO systems, operators can anticipate adverse conditions and take preemptive measures [2; 404; 466; 474; 467; 468; 469; 470; 471; 472; 473; 474]. Furthermore, AI-driven systems can continuously monitor atmospheric conditions using sensors and adjust system parameters like the transmission power, modulation schemes, and other parameters accordingly in real-time to maintain optimal performance. In this regard, recent advancements in deep learning (DL) have enabled the development of sophisticated models for processing FSO signals. These models can filter out noise, enhance signal clarity, and improve data rates [467]. For example, FSO communication systems utilizing DL for various roles--including channel estimation, detection, joint constellation shaping, and as a combined channel estimator and detector--are discussed in [475]. In [476], a design of an optical feedback network employing ML techniques is presented, demonstrating its effectiveness in correcting the effects of turbulent propagation on optical modes. This artificial neural network (ANN) approach relies solely on measuring the intensity profile of the distorted modes, making it both simple and robust. Additionally,a novel joint AT detection and adaptive demodulation technique utilizing convolutional neural networks (CNNs) is proposed to enhance the reliability and flexibility of OAM-FSO communication [469]. An \(m\)-ary adaptive demodulator based on the maximum likelihood for light beams carrying OAM over FSO turbulence channels has also been proposed and demonstrated [458]. Also, in [477], a new turbo-coded 16-ary OAM-SK-FSO communication system is introduced, which incorporates a CNN-based adaptive demodulator designed for operation under strong AT. In [465], a method is proposed for demultiplexing OAM-carrying beams by capturing an image of their unique multiplexing intensity pattern and training a CNN to serve as a classifier. This CNN-based approach simplifies the demultiplexing process, as it eliminates the need for alignment, relaxes orthogonality constraints, and reduces the reliance on expensive optical hardware. AI is being used to manage and optimize entire FSO networks. This includes tasks such as traffic routing, load balancing, and resource allocation, ensuring efficient and reliable network performance. The research presented in [474; 478] utilizes the actor-critic method from reinforcement learning for power adaptation in SM FSO systems. Additionally, the study in [466] addresses the broader issue of resource allocation aimed at reducing channel fading effects in FSO communications. This resource allocation problem is framed within a constrained stochastic optimization framework, encompassing a range of FSO scenarios that involve power adaptation, relay selection, and their combined allocation. In a similar vein, ref. [479] explores optimal resource allocation in FSO fronthaul networks. This optimal allocation aims to maximize the average weighted sum-capacity while adhering to constraints related to power limitations and data congestion. The study takes into account both adaptive power assignment and node selection, leveraging the instantaneous CSI of the links.