Network expansion optimization
Systems and methods are provided herein to dynamically and efficiently optimize network expansion of a network carrier based on UE data collected from a plurality of user equipment (UEs). The UE data is processed based on different computerized algorithms to locate optimal locations for adding a new access point to expand the network. Aspects herein are also directed to systems and methods for generating and displaying dynamic maps on graphical user interfaces (GUI) in accordance with aspects herein.
1. A computer-implemented method for determining an optimal location for a new access point, the method comprising:
processing UE data stored in a database to identify a plurality of geographic areas having a predetermined size at a geographic location;
determining a coverage status for each geographic area of the plurality of geographic areas using the UE data stored in the database of the network carrier;
determining one or more grids within the geographic location based on the coverage status of the plurality of geographic areas having no network coverage or poor network coverage, wherein each grid in the one or more of grids comprises a plurality of rectangles;
clustering the plurality of rectangles into one or more clusters, wherein each cluster of the one or more clusters comprises at least a first threshold number of rectangles, wherein each rectangle in the plurality of rectangles comprises at least a second threshold number of geographic areas enclosed therein; and
determining a centroid for the each cluster of the one or more clusters, wherein the centroid represents a potential site for the new access point.
2. The computer-implemented method of claim 1 , wherein the no network coverage or poor network coverage is based on the UE data indicating an emergency only service state, a roaming service state, and/or a no service state.
3. The computer-implemented method of claim 1 , wherein the centroid is determined by adding a total number of coordinates defining a cluster boundary for the each cluster to calculate a coordinate sum and dividing the coordinate sum by the total number of coordinates to determine centroid coordinates of the centroid.
4. The computer-implemented method of claim 3 , wherein the centroid coordinates of the centroid are within a third threshold distance away from each of the total number of coordinates defining the cluster boundary.
5. The computer-implemented method of claim 3 , wherein the centroid coordinates of the centroid are not within a third threshold distance away from each of the total number of coordinates defining the cluster boundary and determining a second centroid having second centroid coordinates located within the third threshold distance away from the centroid coordinates of the centroid and from each at least a portion of the total number of coordinates defining the cluster boundary.
6. The computer-implemented method of claim 5 , wherein the second centroid represents a potential site for an additional new access point.
7. The computer-implemented method of claim 1 , wherein each rectangle of the plurality of rectangles is of a predetermined size.
8. The computer-implemented method of claim 7 , wherein the predetermined size of the each rectangle is between 0.05 to 2.0 miles.
9. The computer-implemented method of claim 7 , wherein the predetermined size the each rectangle is 0.1 miles.
10. The computer-implemented method of claim 1 , wherein the first threshold number of rectangles is ≥5.
11. The computer-implemented method of claim 1 , wherein the second threshold number of geographical areas is ≥2.
12. The computer-implemented method of claim 1 , wherein the first threshold number of rectangles within each cluster is ≥10.
13. The computer-implemented method of claim 1 , wherein the first threshold number of rectangles within each cluster varies according to a type of the geographic location.
14. The computer-implemented method of claim 13 , wherein the type of the geographic location can be either urban or rural, and wherein the first threshold number of rectangles is greater in a rural cluster compared to an urban cluster.
15. The computer-implemented of claim 1 , further comprising using satellite data to determine whether the potential site for the new access point is located at a viable location.
16. A computer-implemented method for generating an interactive graphical user interface (GUI) configured to display network coverage information of a home network carrier, the method comprising:
processing the UE data stored in a database to identify a plurality of geographic areas;
determining a coverage status for each geographic area of the plurality of geographic areas using the UE data stored in the database of the network carrier;
determining one or more grids within a geographic location based on the coverage status of the plurality of geographic areas having no network coverage or poor network coverage, wherein each grid in the one or more of grids comprises a plurality of rectangles;
clustering the plurality of rectangles into one or more clusters, wherein each cluster of the one or more clusters comprises at least a first threshold number of rectangles, wherein each rectangle in the plurality of rectangles comprises at least a second threshold number of geographic areas enclosed therein;
determining a centroid for the each cluster of the one or more clusters, wherein the centroid represents a potential site for a new access point; and
displaying on the GUI a dynamic map, wherein the centroid is displayed with a first visual characteristic that is visually distinct from a second visual characteristic of the plurality of geographic areas.
17. The computer-implemented method of claim 16 , wherein the no network coverage or poor network coverage is based on the UE data indicating an emergency only service state, a roaming service state, and/or a no service state.
18. The computer implemented method of claim 16 , wherein the centroid is determined by adding a total number of coordinates defining a cluster boundary for the each cluster to calculate a coordinate sum and dividing the coordinate sum by the total number of coordinates to determine centroid coordinates of the centroid.
19. The computer-implemented method of claim 18 , further comprising displaying the cluster boundary for the each cluster in the one or more clusters.
20. The computer-implemented method of claim 16 , wherein each rectangle of the plurality of rectangles is of a predetermined size.