论文标题
通过复发神经网络预测细胞交通预测
Cellular Traffic Prediction with Recurrent Neural Network
论文作者
论文摘要
交通需求的自主预测将是将来的蜂窝网络中的关键功能。过去，研究人员使用了统计方法，例如自回归综合移动平均线（ARIMA）来提供交通预测。但是，基于Arima的预测无法为动态输入数量（例如蜂窝流量）提供精确准确的预测。最近，研究人员已经开始探索深度学习技术，例如复发性神经网络（RNN）和长期 - 记忆（LSTM），以自主预测未来的细胞流量。在这项研究中，我们设计了一个基于LSTM的细胞交通预测模型。我们已经将基于LSTM的预测与基线Arima模型和Vanilla Feed-Forwand神经网络（FFNN）进行了比较。结果表明，LSTM和FFNN准确地预测了未来的细胞流量。但是，发现与FFNN相比，LSTM在短时间内训练预测模型。因此，我们得出的结论是，LSTM模型甚至可以与少量的培训数据一起有效使用，这将允许及时预测未来的细胞流量。
Autonomous prediction of traffic demand will be a key function in future cellular networks. In the past, researchers have used statistical methods such as Autoregressive integrated moving average (ARIMA) to provide traffic predictions. However, ARIMA based predictions fail to give an exact and accurate forecast for dynamic input quantities such as cellular traffic. More recently, researchers have started to explore deep learning techniques, such as, recurrent neural networks (RNN) and long-short-term-memory (LSTM) to autonomously predict future cellular traffic. In this research, we have designed a LSTM based cellular traffic prediction model. We have compared the LSTM based prediction with the base line ARIMA model and vanilla feed-forward neural network (FFNN). The results show that LSTM and FFNN accurately predicted future cellular traffic. However, it was found that LSTM train the prediction model in much shorter time as compared to FFNN. Hence, we conclude that LSTM models can be effectively even used with small amount of training data which will allow to timely predict future cellular traffic.
