Advanced communication network functions, such as resource allocation and dynamic spectrum management, heavily rely on the accurate forecasting of traffic. Data-driven solutions, e.g., Neural Network (NN) based forecasting methods, have been proven to be effective only when sufficient data is available. However, Base Stations (BSs) have limited data in the real world, since big data for communication networks could be extremely expensive to collect, store, and migrate. Therefore, most existing traffic forecasting methods have limited accuracy in reality due to the lack of big data. To tackle this problem, our key observation is that, despite the data “amount” in a BS is limited, the data “source” is rich and diverse, i.e., in addition to Internet traffic logs, there are logs of Call and SMS. More importantly, our analysis shows a high correlation between different sources, which can be utilized to improve the forecasting accuracy. Motivated by this, we introduce AdaSource, a Multi-Source Adaptive Feature Boosting approach, which utilizes data source correlations for accurate traffic forecasting even on data-limited BSs. The core idea of AdaSource is a novel two-branch NN structure that adaptively trains multiple Encoder-Decoders for refining different data sources and multiple Encoder-Predictors for utilizing data source correlations to improve the accuracy. The experiments on a real-world dataset show that AdaSource improves the forecasting accuracy by up to 30.14%, compared to the state-of-the-art methods.

https://ieeexplore.ieee.org/document/10001690