Channel prediction for intelligent FSO transmission system

Free space optical (FSO) communication has attracted significant attention due to its high transmission rate and information security. Nevertheless, the FSO link is sensitive to various weather conditions which limit its application range. Thus, research on the FSO channel plays an important role for combatting channel fading. In the paper, we first establish a FSO transmission testbed to obtain received signal strength indication (RSSI) information of optical signal. Then we utilize an environmental chamber for indoor experiments to simulate weather changes in the real world. Finally, based on the true meteorological dataset from the official department and the RSSI dataset from the experiments, we employ expanded gated recurrent unit (GRU) neural networks to implement the FSO channel prediction. The results demonstrate that the proposed scheme can achieve the prediction of FSO channel fading with a high precision, where the absolute percentage error (APE) values lower than 6.9% account for up to ninety% of results.