Abstract:
Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration, traffic scheduling and intrusion detection, thus potentially supporting connected intelligence of the sixth generation of mobile communications technology (6G). However, the existing studies just focus on the spatio-temporal modeling of traffic data of single network service, such as short message, call, or Internet. It is not conducive to accurate prediction of traffic data, characterised by diverse network service, spatio-temporality and supersize volume. To address this issue, a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction. Functionally, this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer (DMFS-MT). The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data, respectively, via a new combination of convolutional gated recurrent unit (ConvGRU) and 3-dimensional convolutional neural network (3D-CNN). For the latter, each task is performed for predicting service-specific traffic data based on a fully connected network. On the real-world Telecom Italia dataset, simulation results demonstrate the effectiveness of our proposal through prediction performance measure, spatial pattern comparison and statistical distribution verification.
