A Location Predictive Model Based on 2D Angle Data for HAPS Using LSTM

High Altitude Platforms Station (HAPS) is considered to be an effective solution to expand the communication coverage of rural area in the fifth generation (5G) network. However, HAPS is usually in an unstable state because of space airflow. Thus, the inaccurate beamforming performed by the gateway (GW) will result in unnecessary capacity loss of HAPS communication system. To address this issue, a long short-term memory (LSTM)-based location predictive model is proposed to predict next moment location of HAPS by training the current two-dimensional (2D) angle data. Specifically, a novel preprocessing system is introduced to ensure the effectiveness of our model. Moreover, the LSTM-based model with highest predictive accuracy can be saved during the training to realize the real-time prediction. Experimental results reveal that the proposed LSTM-based model is of higher prediction accuracy compared with other two predictive models. Therefore, a more precise beamforming performed by GW can reduce the unnecessary capacity loss and improve the reliability of 5G HAPS communication system.