This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic(UWA)communication systems using the long short-term memory(LSTM)model with the attention mechanism.AttLstmPr...This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic(UWA)communication systems using the long short-term memory(LSTM)model with the attention mechanism.AttLstmPreNet is a deep learning model that combines an attention mechanism with LSTM-type models to capture temporal information with different scales from historical UWA channels.The attention mechanism is used to capture sparsity in the time-delay scales and coherence in the gep-time scale under the LSTM framework.The soft attention mechanism is introduced before the LSTM to support the model to focus on the features of input sequences and help improve the learning capacity of the proposed model.The performance of the proposed model is validated using different simulation time-varying UWA channels.Compared with the adaptive channel predictors and the plain LSTM model,the proposed model is better in terms of channel prediction accuracy.展开更多
基金Suppported by the National Keys Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500).
文摘This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic(UWA)communication systems using the long short-term memory(LSTM)model with the attention mechanism.AttLstmPreNet is a deep learning model that combines an attention mechanism with LSTM-type models to capture temporal information with different scales from historical UWA channels.The attention mechanism is used to capture sparsity in the time-delay scales and coherence in the gep-time scale under the LSTM framework.The soft attention mechanism is introduced before the LSTM to support the model to focus on the features of input sequences and help improve the learning capacity of the proposed model.The performance of the proposed model is validated using different simulation time-varying UWA channels.Compared with the adaptive channel predictors and the plain LSTM model,the proposed model is better in terms of channel prediction accuracy.
