关键词: 低轨卫星, 信道预测, 注意力机制, 卷积神经和双向长短时记忆混合神经网络

Abstract:

To deal with the problem of the low earth orbit (LEO) satellite rapidly changing channels' qualities and outdated parameters, a channels prediction algorithm attention-convolutional neural network and bi-directional long-short term memory neural network(AT-CNN-BiLSTM) is proposed. The proposed method consists of the signals preprocessing, the network training and the signals prediction components. Simulating the outdoor satellite signals under the Gaussian white noise, a training set and a testing set of the satellite signals will be obtained. Then we input the training set into the training unit for features extraction, and finally input the testing set into the network for prediction analysis. The simulation results show that in the comparison of other four artificial intelligence methods, the hybrid neural network can achieve a high accuracy (91.8%) at a fast convergent speed, which effectively solves the difficulty of the LEO satellite outdated channel parameters. The proposed method can improve the satellite communication qualities and save the satellite channels' resources.

Key words: low earth orbit (LEO) satellite, channels prediction, attention mechanism, hybrid convolutional neural network and bi-directional long-short term memory neural network (CNN-BiLSTM)