Distributed mobile network traffic data decomposition and forecasting method and apparatus

An apparatus including at least one processor, wherein the at least one processor is configured to

• (i) use a geo-location preserving mobile network representation in which locations of mobile network elements are received and converted into a graph representation, in which relative distances between adjacent network elements are preserved using respective weights on graph edges;
• (ii) receive input data comprising aggregate network traffic data from network elements corresponding to the mobile network elements locations, wherein the aggregate network traffic data includes traffic data corresponding to a plurality of services operating over a distributed mobile network;
• (iii) use a graph-based neural network based on the geo-location preserving mobile network representation, the graph-based neural network configured to capture spatial and temporal correlations in the input data, the graph-based neural network including at least a spatio-temporal concentration block (STCB) and a parallel prediction block (PPB);
• (iv) use loss functions to train the graph-based neural network using the input data, to provide network per-service traffic forecasting, the loss functions including an operator cost function configured to capture operator costs, including under-provisioning costs and/or over-provisioning costs.

A trained apparatus including at least one trained processor, the at least one trained processor's training including

• (i) using a geo-location preserving mobile network representation in which locations of mobile network elements are received and converted into a graph representation, in which relative distances between adjacent network elements are preserved using respective weights on graph edges;
• (ii) receiving input data comprising aggregate network traffic data from network elements corresponding to the mobile network elements locations, wherein the aggregate network traffic data includes traffic data corresponding to a plurality of services operating over a distributed mobile network;
• (iii) using a graph-based neural network based on the geo-location preserving mobile network representation, the graph-based neural network configured to capture spatial and temporal correlations in the input data, the graph-based neural network including at least a spatio-temporal concentration block (STCB) and a parallel prediction block (PPB);
• (iv) using loss functions to train the graph-based neural network using the input data, to provide network per-service traffic forecasting, the loss functions including an operator cost function configured to capture operator costs, including under-provisioning costs and/or over-provisioning costs, wherein the at least one trained processor is configured to perform a distributed mobile network traffic data decomposition and forecasting method.