On the road to more accurate mobile cellular traffic predictions
This addresses the problem of network load management for cellular operators, offering a domain-specific improvement over existing approaches.
The paper tackles mobile cellular traffic forecasting by incorporating road metrics like highway flow and average speed with network data in a light learning structure, achieving substantially more accurate short-term predictions for highway segments compared to prior urban-focused methods.
The main contribution reported in the paper is a novel paradigm through which mobile cellular traffic forecasting is made substantially more accurate. Specifically, by incorporating freely available road metrics we characterise the data generation process and spatial dependencies. Therefore, this provides a means for improving the forecasting estimates. We employ highway flow and average speed variables together with a cellular network traffic metric in a light learning structure to predict the short-term future load on a cell covering a segment of a highway. This is in sharp contrast to prior art that mainly studies urban scenarios (with pedestrian and limited vehicular speeds) and develops machine learning approaches that use exclusively network metrics and meta information to make mid-term and long-term predictions. The learning structure can be used at a cell or edge level, and can find application in both federated and centralised learning.