Estimation of intelligent-based quality of service in terrestrial FSO links: A case of South Africa propagation paths

Free Space Optical (FSO) communication is one of the popular technologies’ dominants in the optical communication domain. Despite all the advantages, FSO links are grossly affected by severe atmospheric conditions such as fog, rain, snow, smoke, and dust or aerosol particles suspended in the air. Fog is one of the major challenges for free-space optics to achieve carrier-class availability and causes extreme attenuation. A 10-year (2010––2019) visibility data were obtained from the South African Weather Services (SAWS) with a special focus on Cape Town (–33.925° S, 18.424° E). This extracted and processed Cape Town data was used to estimate the needed fog-induced attenuation. The intelligent algorithm is rooted in the theory of Artificial Neural Networks (ANN) systems which intelligently control the sub-tropical climate conditions of the chosen region. The adopted network-based technique is capable to interact with ever-changing weather by generating appropriate improvements to the free space optical communication link quality of service for efficient services to the customers. In this study, an Intelligent Algorithm (IA) based on an Artificial Neural Network (ANN) has been utilized to improve the reliability of QoS that can satisfy customer SLAs and improve received signal over FSO communications. The results obtained by implementing the IA system indicates that the power transmitted range from 8 to 100 dB and the adjusted Signal to Noise Ratio (SNR) between 390–––420 dB at 650 nm optical wavelength, whereas the same trend could be observed when the optical wavelength of 1550 nm was applied. Subsequently, the model effectively estimated the automatic transmit power control for enhancement in SNR and resulting in better QoS at the receiving end, regardless of the related weather changes along with the transmission link.