Authors - Shreepreet Sahu, Prasant Kumar Sahu Abstract - Free-space optical (FSO) communication is a promising technology for B5G and 6G communication systems due to its security, reliability, high data rates, low latency and electromagnetic immunity. However, its performance is limited by atmospheric turbulence, weather conditions, beam divergence, misalignment errors and link range variations. Existing analytical or simulation-based methods become too complex or computationally expansive as number of impairments considered simultaneously increases introducing a gap in fast and precise system-level performance estimation. This limitation motivates the use of intelligent data-driven approaches capable of capturing highly nonlinear interrelations. This paper proposes an artificial neural network (ANN) for predicting Q-factor values of the modelled system. The ANN-based model is trained by an extensive dataset generated under varying FSO link ranges and other scenarios. Model legitimacy specification starts with error histograms proceeding through mean squared error (MSE) convergence finding concluding regression analysis before eye pattern evaluation takes place. As shown by the results the high prediction accuracy, generalization capability and closeness of forecasted Q-value to the actual one ensures noticeable improvement over existing framework satisfactorily addressing the above issues. The proposed approach provides an efficient alternative to conventional analytical methods, making it suitable for real-time performance evaluation and optimization practical FSO systems.
