Abstract：Concerning the problem that AutoRegressive Integrated Moving Average (ARIMA) model and Long Short-Term Memory (LSTM) unit do not utilize the collaboration between Base Stations (BSs) in traffic prediction, a new method called Traffic Prediction based on Space Collaboration (TPBC) which uses the collaboration between BSs produced by users was proposed. Firstly, a BS cooperative network was constructed based on the collaboration between BSs and then divided into multiple communities. Next, the cooperative BSs, which have the closest relationships with the target BS in the same community, were found via Granger causality test. Finally, a hybrid neural network was constructed by LSTM and Embedding layer, and the historial traffic of target BS and each cooperative BS was utilized for traffic prediction of target BS. The experimental results show that the Root Mean Square Error (RMSE) of TPBC is reduced by 29.19% and 27.47% compared with ARIMA and LSTM respectively. It shows that TPBC has the capability of improving the accuracy of BS traffic prediction effectively, which benefits traffic offloading and energy saving.
彭铎, 周建国, 羿舒文, 江昊. 基于空间合作关系的基站流量预测模型[J]. 计算机应用, 2019, 39(1): 154-159.
PENG Duo, ZHOU Jianguo, YI Shuwen, JIANG Hao. Base station traffic prediction model based on spatial collaboration. Journal of Computer Applications, 2019, 39(1): 154-159.
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2019, Vol. 39 
