Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression
This work addresses efficient management of future dense cellular networks for network providers, but it appears incremental as it builds on existing deep learning methods for traffic forecasting.
The authors tackled cellular traffic prediction in a metropolitan area by proposing a deep regression approach to model spatio-temporal dynamics, achieving lower prediction error compared to state-of-the-art algorithms as validated on a large public dataset.
The concept of mobility prediction represents one of the key enablers for an efficient management of future cellular networks, which tend to be progressively more elaborate and dense due to the aggregation of multiple technologies. In this letter we aim to investigate the problem of cellular traffic prediction over a metropolitan area and propose a deep regression (DR) approach to model its complex spatio-temporal dynamics. DR is instrumental in capturing multi-scale and multi-domain dependences of mobile data by solving an image-to-image regression problem. A parametric relationship between input and expected output is defined and grid search is put in place to isolate and optimize performance. Experimental results confirm that the proposed method achieves a lower prediction error against stateof-the-art algorithms. We validate forecasting performance and stability by using a large public dataset of a European Provider.