Distributed mobile network traffic data decomposition and forecasting method and apparatus
A distributed mobile network traffic data decomposition and forecasting computer-implemented method, comprising: using a geo-location preserving mobile network representation. Locations of mobile network elements are received and converted into a graph representation. Relative distances between adjacent network elements are preserved using respective weights on graph edges; input data is received comprising aggregate network traffic data from network elements corresponding to the network elements locations. The aggregate data includes traffic data corresponding to a plurality of services operating over the network. A graph-based neural network based on the geo-location is used, preserving mobile network representation and configured to capture spatial and temporal correlations in the input data, including at least a spatio-temporal concentration block (STCB) and a parallel prediction block (PPB); loss functions 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.
1 . A distributed mobile network traffic data decomposition and forecasting computer-implemented method including the steps of:
(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 the 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.
2 . The method of claim 1 , wherein the graph-based neural network includes Geo-sequential Graph Neural Networks (GSGNN).
3 . The method of claim 2 , wherein the GSGNN include at least the Spatio-temporal Concentration Block (STCB) and the Parallel Prediction Block (PPB).
4 . The method of claim 2 , wherein the GSGNN includes two or more stacked STCB blocks.
5 . The method of claim 2 , wherein the GSGNN includes at least two layers: a temporal gated convolution (TGC) layer and a graph spatial convolution (GSC) layer.
6 . The method of claim 2 , wherein a majority of operations performed by GSGNN during training only involve addition, convolution and matrix multiplication.
7 . The method of claim 2 , wherein the GSGNN cannot produce predictions of arbitrary length.
8 . The method of any previous claim 1 , wherein a temporal gated convolution operation is used to capture temporal dynamics on a time axis of graph data.
9 . The method of claim 8 , including the step of using a one dimensional (1D)-CNN with a gating mechanism, when processing the temporal dimension of the graph data.
10 . The method of claim 1 , including the step of using predictions of per-service consumption to allocate resources to network slices.
11 . The method of claim 10 , wherein energy is saved by using optimal periodic (e.g., hourly or daily) RAN configurations that can be enacted at a corresponding time.
12 . The method of claim 10 , wherein energy is saved by using live network performance optimisations.
13 . The method of claim 1 , including the step of storing weights of the trained graph-based neural network.
14 . The method of claim 1 , wherein the geo-location preserving mobile network representation converts the locations into a graph representation based on respective coverage areas.
15 . The method of any previous claim 1 , wherein in step (i), each node in the graph represents a traffic aggregation point, and a connectivity of nodes depends on an adjacency of their coverage areas.
16 . The method of claim 1 , wherein a STCB block includes a TGC layer, followed by a GSC layer, followed by a TGC layer.
17 . The method of any previous claim 1 , wherein in a STCB, each pair of TGC and GSC layers reduces a temporal dimension of the input data and gathers spatial information from neighboring nodes.
18 . The method of any previous claim 1 , wherein a number of parallel ID-CNNs in PPB is fixed before training.
19 . The method of claim 1 , wherein the operator cost function is biased to (e.g. always) overprovision.
20 . The method of claim 1 , wherein a TGC contains two separate 1D-CNN modules.
21 . The method of any previous claim 1 , including the step of using the trained graph-based neural network to automatically allocate computational and capacity resources in the distributed mobile network.
22 . The method of any previous claim 1 , including the step of anticipating the volume of traffic flowing through the network to provision an appropriate amount of resources, e.g. number of virtual machines/containers, central processing units (CPUs), memory capacity.
23 . The method of any previous claim 1 , including the step of using the trained graph-based neural network to improve energy efficiency of infrastructure of the distributed mobile network.
24 . The method of any previous claim 1 , wherein forecast traffic are inputs to a downstream logic that adjusts capacity in Radio Access Network (RAN) and enables infrastructure owners to optimize energy savings.
25 . 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.
26 . 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.