## 6 Open Challenges, Future Prospects and Research Directions
### Exploring Hybrid Systems
#### 6.4.4 Spatial Diversity  
Spatial diversity techniques in FSO communication, specifically MISO, SIMO, and MIMO systems, are becoming increasingly essential in addressing the difficulties presented by AT and signal degradation. Future research in these areas is set to explore several key avenues aimed at enhancing performance, reliability, and applicability of FSO systems. Development of Advanced Adaptive Algorithms  
Adaptive approaches will greatly improve link reliability and data throughput, especially in dynamic environments, where the atmospheric conditions can vary rapidly. Real-Time Optimization of Beamforming: One of the promising research directions is the development of advanced adaptive algorithms that optimize beamforming and Rx diversity in real-time. By flexibly adjusting the transmission and reception parameters depending on current environmental conditions, these algorithms can significantly improve link reliability and quality. For instance, algorithms that can assess atmospheric conditions--such as turbulence and scatter--will allow for intelligent adjustments in beam alignment and power levels, ensuring that the optical signal remains robust even in challenging situations. Also, adaptive beamforming techniques can direct the optical beams in real-time to the most favorable paths. Enhanced Rx Diversity Techniques: In addition to beamforming, enhancing Rx diversity techniques is critical for mitigating the effects of multipath propagation caused by atmospheric disturbances. Rx diversity algorithms can combine signals from multiple Rxs to maximize signal quality. Future algorithms may incorporate feedback mechanisms that evaluate signal quality across multiple Rxs, allowing systems to select the optimal Rx in real-time. Such adaptive diversity strategies will ensure a more stable communication link and minimize the potential for signal dropouts. Integration of Machine Learning Techniques  
Integrating ML techniques into FSO communication offers another exciting research avenue for spatial diversity systems. Predictive Analytics for Turbulence Mitigation: Integrating ML techniques into spatial diversity systems presents a significant opportunity for improved performance. ML models can analyze vast amounts of historical and real-time data to predict turbulence patterns and their potential impact on signal quality. By leveraging predictive analytics, FSO systems can preemptively adjust transmission parameters, such as modulation schemes and power levels, thereby enhancing turbulence mitigation strategies and resource allocation. Optimizing Resource Allocation: ML can assist in resource allocation by determining the most efficient use of available communication channels and transceivers based on real-time and historical data. This predictive capability can greatly improve the resilience and efficiency of FSO systems. For instance, ML can play a pivotal role in optimizing resource allocation in MIMO systems. By intelligently managing the distribution of available bandwidth and power across multiple transmission paths, these algorithms can enhance throughput and efficiency, especially in dense urban environments where interference is prevalent. The ability to dynamically allocate resources based on real-time conditions will be crucial for maintaining high-quality communication links. Advancements in photonic technologies and materials will be essential for the development of more efficient, compact, and cost-effective transceivers in spatial diversity systems. This can lead to the development of FSO systems that are easier to deploy and maintain. Efficient and Compact Transceivers: Research should focus on miniaturizing components, integrating multiple functionalities into single devices, and improving energy efficiency. Innovations in photonic integration, such as the use of photonic integrated circuits (PICs), have the potential to decrease the size and power consumption of FSO devices while enhancing their performance.