FLAG: Flexible, Accurate, and Long-Time User Load Prediction in Large-Scale WiFi System Using Deep RNN

In this article, we propose FLAG for flexible, accurate, and long-time user load prediction in a large-scale WiFi system. FLAG enables prediction customization in both time granularity and prediction length. Under an operating WiFi system with more than 7000 APs, a reference implementation of FLAG is developed, which consists of three major components. For data acquisition , we process 25 074 733 association records contributed by 55 809 users, to extract the ground truth of AP-level user load. For feature extraction , we perform a comprehensive data analytics to mine vital features to label each AP, which are extracted and classified into three categories, i.e., individual features, spatial features, and temporal features. For the model design , we design a deep recurrent neural network (RNN) model, which contains two separate RNNs, i.e., the encoder RNN and decoder RNN. Particularly, the sequential feature vectors are injected into the encoder RNN to learn the “semantic” information, based on which the decoder RNN conducts sequential AP-level predictions. As the semantic vector is injected for each time step prediction, it can effectively reduce the accumulated prediction errors, which enable long period of time predictions. Real data set-based experiments corroborate the efficacy of FLAG .