Enhancing Secure and High-Speed Wireless Communication Using Free Space Optics

Abstract

Free Space Optical (FSO) communication has been of special interest due to its capability to provide high-speed, secure, and interference-immune data transmission. Nevertheless, several constraints have hindered it from being widely implemented. The unreliable nature of FSO links stems from three principal factors which include atmospheric turbulence and alignment sensitivity and dependency on environment conditions. The combination of RF and FSO technology represents a solution to data integrity problems caused by security attacks through physical access and eavesdropping. Real-time traffic management and transition between systems proves to be a disadvantage of these hybrid systems. Machine learning techniques have been explored to mitigate the effects of turbulence, but problems like high computational complexity, data dependency, and real-time flexibility remain. Security of FSO networks is another area of highest priority, with measures taken to improve confidentiality and robustness through improvements in techniques of encryption, Quantum Key Distribution (QKD), and adaptive beam steering. Future research has to tackle such challenges by formulating adaptive machine learning algorithms, effective data-driven atmospheric modeling, and light-weight encryption methods for real-time deployment. The implementation of hybrid communication systems requires dynamic adjustment of resources with high-security measures that protect against cyber and physical attacks. Quantum communication technologies offer improved security protection to FSO networks which makes them suitable for critical applications such as space connection and military networks and metropolitan networks. The successful execution of FSO network deployment depends on closing these knowledge gaps which will build secure and high-performance FSO networks between theory and practice.

Keywords: Atmospheric turbulence mitigation, Free space optics, FSO communication, Hybrid RF/FSO systems, Machine learning in FSO.

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