Q-factor Analysis in Free Space Optical Communication and Neural Network-Based Prediction

Free Space Optics (FSO) provides a promising alternative where fiber-optic deployment is impractical due to cost or fragility. However, FSO performance is highly vulnerable to atmospheric disturbances such as fog, rain, and dust, which can significantly degrade signal quality. To optimize system performance under varying conditions, it is crucial to understand how the Q-factor responds to changes in system parameters. This study investigates the effects of bit rate, filter type, transmitter and receiver aperture diameters, and transmission range on the Q-factor in FSO systems. We developed a detailed simulation model using OptiSystem to generate data, which was then used to train a feedforward neural network via MATLAB’s Neural Network Tool (NN-Tool) using the Levenberg–Marquardt algorithm. This model effectively captures complex, nonlinear relationships between input parameters and Q-factor outcomes, allowing accurate predictions without further simulations. The hybrid approach of combining simulation data with neural network-based modeling offers a practical and user-friendly tool for performance prediction and system planning. This research contributes to the design and optimization of high-data-rate FSO systems by addressing existing limitations in modeling and parameter tuning.