Multistep ahead atmospheric optical turbulence forecasting for free-space optical communication using empirical mode decomposition and LSTM-based sequence-to-sequence learning

Although free-space optical communication (FSOC) is a promising means of high data rate satellite-to-ground communication, beam distortion caused by atmospheric optical turbulence remains a major challenge for its engineering applications. Accurate prediction of atmospheric optical turbulence to optimize communication plans and equipment parameters, such as adaptive optics (AO), is an effective means to address this problem. In this research, a hybrid multi-step prediction model for atmospheric optical turbulence, EMD-Seq2Seq-LSTM, is proposed by combining empirical mode decomposition (EMD), sequence-to-sequence (Seq2Seq), and long short-term memory (LSTM) network. First, using empirical mode decomposition to decompose the non-linear and non-stationary atmospheric optical turbulence dataset into a set of stationary components for which internal feature information can be easily extracted significantly reduces the training difficulty and improves the forecast accuracy of the model. Second, sequence-to-sequence is combined with LSTM networks to build a prediction model that can eliminate time delay and make full use of long-term information and then use the model to predict each component separately. Finally, the prediction results of each component are combined to obtain the final atmospheric turbulence forecasting results. To validate the performance of the proposed method, three comparative models, including WRF, LSTM, and sequence-to-sequence-LSTM, are demonstrated in this study. The forecasting results reveal that the proposed model outperforms all other models both qualitatively and quantitatively and thus can be a powerful method for atmospheric optical turbulence forecasting.