Hybrid FSO/RF Networks with Neural Prediction of RSSI and Weather

Abstract This paper investigates neural network models for predicting weather parameters and received signal strength indicator (RSSI) to enable adaptive handover in hybrid free space optics (FSO)/radio frequency (RF) systems. The most correlated parameters were visibility, temperature (measured from three independent sensors), and particle concentration. The work was exclusively focused on predicting correlated atmospheric effects and subsequently predicting RSSI parameter derived from them using a long short-term memory (LSTM) model, minute by minute over 24 hours. The predicted values can in the future serve as input for initiating handover decisions – for example, through visualization in Simulink. The threshold value of -30 dBm was taken from peer-reviewed articles and is used as a reference limit for link quality.