基于空间合作关系的基站流量预测模型

针对传统的自回归积分移动平均(ARIMA)模型和长短时记忆(LSTM)单元在基站流量预测中没有利用基站(BS)间合作关系的问题,提出一种利用由用户群体在不同基站下访问产生的基站合作关系的流量预测(TPBC)算法。首先,通过基站之间的合作关系构建基站合作网络,并对此合作网络进行社区划分得到基站社区;然后,通过格兰杰因果关系检验方法寻找与目标基站同一社区且关系最紧密的若干基站,作为目标基站的合作基站;最后,使用LSTM和词嵌入层(Embedding)搭建混合神经网络,并根据目标基站和合作基站的流量信息进行流量预测。实验结果表明,TPBC在基站流量预测上的均方根误差(RMSE)相比ARIMA和LSTM分别减小了29. 19%和27. 47%。TPBC能有效提高基站流量预测准确率,在流量卸载和绿色节能等领域具有重要意义。

Base station traffic prediction model based on spatial collaboration

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.