Making phone calls, sending messages and surfing the Internet all depend on wireless communication. Too many users connect to a same base station at the same time, which would slow network speed down. To address this issue, telecom operators can tune the network capacity in advance according to predicted Maximum Connections. Therefore, predicting Maximum Connections is necessary. Traditional time series model and machine learning can be utilized to address time series prediction task. However, these methods don’t take multi-task learning into consideration, and related tasks can promote each other actually. In this paper, we propose a deep learning model based on LSTM for time series prediction in wireless communication, employing multi-task learning to improve prediction accuracy. We conducted several critical features and utilized training signal of related task as inductive bias to promote the generalization performance of main task. Through experiments on several real datasets, we found that the proposed model is effective, and it outperforms other prediction methods.

打电话，发送消息和上网都取决于无线通信。太多用户同时连接到同一基站，这会降低网络速度。为解决此问题，电信运营商可以根据预测的“最大连接数”提前调整网络容量。因此，预测最大连接数是必要的。传统的时间序列模型和机器学习可以用来解决时间序列预测任务。但是，这些方法没有考虑多任务学习，并且相关任务实际上可以相互促进。在本文中，我们提出了一种基于LSTM的深度学习模型，用于无线通信中的时间序列预测，并采用多任务学习来提高预测精度。我们进行了几个关键功能的研究，并利用相关任务的训练信号作为归纳偏差来提升主要任务的泛化性能。通过对几个真实数据集的实验，我们发现该模型是有效的，并且优于其他预测方法。