A Novel Wireless Propagation Model Based on Bi-LSTM Algorithm

Abstract

Establishing accurate wireless propagation models is essential for high-quality communications. Aiming to the low accuracy and complexity of the traditional wireless propagation model, a novel accurate wireless propagation model is proposed based on the bi-directional long short-term memory (Bi-LSTM) algorithm of machine learning. The model uses the machine learning technology driven by big data and can achieve a high real-time performance with a low complexity. Also, it can accurately predict the wireless signal coverage intensity in a new environment. In order to allow the model to accommodate the actual environment of target areas, the propagation model can be dynamically corrected by a deep learning and training. The Bi-LSTM is used to describe the relationship between features themselves and the relationship between features and target values of reference signal receiving power (RSRP). The Bi-LSTM is also used to represent the relationship through a full-connection layer to obtain the results so that sufficient parameter space can be provided for the model. The parameters of propagation model are searched and fitted through a full-connection optimization. After training and tuning, the predicted value of poor coverage recognition rate (PCRR) of the model can reach 0.2371 while the predicted value of root mean squared error (RMSE) can be 10.4855, which demonstrates the better accuracy of the proposed model. INDEX TERMS Bi-LSTM, deep learning, feature extraction, fully connected layer, wireless propagation.