US-20260128964-A1 - TRAFFIC GEOLOCATION DETERMINING WITHIN SECTIONS OF AREA OF INTEREST

US20260128964A1US 20260128964 A1US20260128964 A1US 20260128964A1US-20260128964-A1

Abstract

A method includes presenting, within a display device of a computing system, a graphical user interface (GUI) including a map of an area of interest (AOI) of the cellular network and partitioning at least a sub-area of the AOI into a plurality sections, wherein each section is formed from a particular pixel area of the GUI. The method includes retrieving a plurality of geolocations, within each section of the plurality of sections, of subscriber devices based on known geolocations. The method includes determining, using call record detail (CDR) data, a capacity metric volume for each sector of a plurality of sites covering the AOI and the capacity metric volume for each section based on the plurality of geolocations and the capacity metric volume of each sector.

Inventors

YiFei ZHAO
Ahmed Awwad Whdan

Assignees

DISH WIRELESS L.L.C.

Dates

Publication Date: 20260507
Application Date: 20241106

Claims (20)

1 . A computing system to facilitate a cellular network, the computing system comprising: one or more processing devices; and memory communicatively coupled with and readable by the one or more processing devices and having stored therein processor-readable instructions which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising: presenting, in a display device, a graphical user interface (GUI) comprising a map of an area of interest (AOI) of the cellular network; partitioning at least a sub-area of the AOI into a plurality sections, wherein each section is formed from a particular pixel area of the GUI; retrieving a plurality of geolocations, within each section of the plurality of sections, of subscriber devices based on known geolocations; determining, using call record detail (CDR) data, a capacity metric volume for each cell of a plurality of sites covering the AOI; and determining, based on the plurality of geolocations and the capacity metric volume of each cell, the capacity metric volume for each section.
2 . The computing system of claim 1 , wherein the operations further comprise: retrieving the CDR data from computing devices, of the cellular network, that are coupled to the plurality of sites; determining the known geolocations by communicating with a geolocation application running on the subscriber devices; and predicting additional geolocations for each section by also employing historical trend data of additional subscriber devices, wherein the historical trend data is associated with particular sections of the plurality of sections.
3 . The computing system of claim 1 , wherein the partitioning comprises: contiguously scanning pixels of the map from a first side to a second side of the sub-area of the AOI, wherein the second side is a farthest distance away from the first side; identifying, from each of a plurality of rows while scanning, a subset of sections within each respective row of each respective scan; and assigning a section identifier (ID) to each identified section in the AOI.
4 . The computing system of claim 1 , wherein the operations further comprise: determining, using cell IDs, an amount of capacity metric volume from each cell attributable to the plurality of geolocations within a section of the plurality of sections; and adding the amount of capacity metric volume for each cell to determine the capacity metric volume for the section.
5 . The computing system of claim 4 , wherein, to determine the amount of capacity metric volume from each cell for the section, the operations further comprise: dividing a number of the plurality of geolocations for the cell in the section by a total number of the plurality of geolocations to generate a ratio; and multiplying the ratio by the capacity metric volume for the cell.
6 . The computing system of claim 1 , wherein the operations further comprise: determining, for each section, the capacity metric volume for the section is located within a particular volume range; and shading each section of the plurality of sections based on the particular volume range determined for each respective section.
7 . The computing system of claim 1 , wherein the capacity metric volume corresponds to a capacity metric comprising one of radio resource control (RRC) connections, traffic volume, and unserved demand bandwidth.
8 . A method to facilitate a cellular network, the method comprising: presenting, within a display device of a computing system, a graphical user interface (GUI) comprising a map of an area of interest (AOI) of the cellular network; partitioning at least a sub-area of the AOI into a plurality sections, wherein each section is formed from a particular pixel area of the GUI; retrieving a plurality of geolocations, within each section of the plurality of sections, of subscriber devices based on known geolocations; determining, using call record detail (CDR) data, a capacity metric volume for each sector of a plurality of sites covering the AOI; and determining, by the computing system, based on the plurality of geolocations and the capacity metric volume of each sector, the capacity metric volume for each section.
9 . The method of claim 8 , further comprising retrieving the CDR data from computing devices, of the cellular network, that are coupled to the plurality of sites; determining the known geolocations by communicating with a geolocation application running on the subscriber devices; and predicting additional geolocations for each section by also employing historical trend data of additional subscriber devices, wherein the historical trend data is associated with particular sections of the plurality of sections.
10 . The method of claim 8 , wherein the partitioning comprises: contiguously scanning pixels of the map from a first side to a second side of the sub-area of the AOI, wherein the second side is a farthest distance away from the first side; identifying, from each of a plurality of rows while scanning, a subset of sections within each respective row of each respective scan; and assigning a section identifier (ID) to each identified section in the AOI.
11 . The method of claim 8 , further comprising: determining, using cell IDs associated with the sectors, an amount of capacity metric volume from each sector attributable to the plurality of geolocations within a section of the plurality of sections; and adding the amount of capacity metric volume for each sector to determine the capacity metric volume for the section.
12 . The method of claim 11 , wherein, to determine the amount of capacity metric volume from each sector for the section, the method further comprising: dividing a number of the plurality of geolocations for the sector in the section by a total number of the plurality of geolocations to generate a ratio; and multiplying the ratio by the capacity metric volume for the sector.
13 . The method of claim 8 , further comprising: determining, for each section, the capacity metric volume for the section is located within a particular volume range; and shading each section of the plurality of sections based on the particular volume range determined for each respective section.
14 . The method of claim 8 , wherein the capacity metric volume corresponds to a capacity metric comprising one of radio resource control (RRC) connections, traffic volume, and unserved demand bandwidth.
15 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computing system, cause the computing system to perform operations comprising: presenting, within a display device of the computing system, a graphical user interface (GUI) comprising a map of an area of interest (AOI) of a cellular network; partitioning at least a sub-area of the AOI into a plurality sections, wherein each section is formed from a particular pixel area of the GUI; retrieving a plurality of geolocations, within each section of the plurality of sections, of subscriber devices based on known geolocations; determining, using call record detail (CDR) data, a capacity metric volume for each cell of a plurality of sites covering the AOI; and determining, by the computing system, based on the plurality of geolocations and the capacity metric volume of each cell, the capacity metric volume for each section.
16 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise: retrieving the CDR data from computing devices, of the cellular network, that are coupled to the plurality of sites; determining the known geolocations by communicating with a geolocation application running on the subscriber devices; and predicting additional geolocations for each section by also employing historical trend data of additional subscriber devices, wherein the historical trend data is associated with particular sections of the plurality of sections.
17 . The non-transitory computer-readable storage medium of claim 15 , wherein the partitioning comprises: contiguously scanning pixels of the map from a first side to a second side of the sub-area of the AOI, wherein the second side is a farthest distance away from the first side; identifying, from each of a plurality of rows while scanning, a subset of sections within each respective row of each respective scan; and assigning a section identifier (ID) to each identified section in the AOI.
18 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise: determining, using cell IDs, an amount of capacity metric volume from each cell attributable to the plurality of geolocations within a section of the plurality of sections; and adding the amount of capacity metric volume for each cell to determine the capacity metric volume for the section.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein, to determine the amount of capacity metric volume from each cell for the section, the operations further comprise: dividing a number of the plurality of geolocations for the cell in the section by a total number of the plurality of geolocations to generate a ratio; and multiplying the ratio by the capacity metric volume for the cell.
20 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise: determining, for each section, the capacity metric volume for the section is located within a particular volume range; and shading each section of the plurality of sections based on the particular volume range determined for each respective section.

Description

BACKGROUND Telecommunication networks, such as cellular networks, have various resources that produce data and metadata concerning operations of the cellular network. Metadata is data that provides information about data. Metadata enriches the data with information about one or more aspects of the data. Metadata insights can facilitate efficient processing and understanding the data. Status reports, including error codes, may be generated which are indicative of deficiencies in operations of the network, including deficiencies in network capacity and connectivity. With the development of communication technologies, such as fifth generation (5G) new radio (NR) cellular networks, applications supporting a massive number of connected devices are enabled. Such applications can be based on data from myriad sources, including third party sources. Also, as the number of connected devices grows, ensuring that network capacity commensurately grows can be challenging, particularly in meeting increasing cellular traffic demands for that network capacity. Typically, engineers manually analyze alerts, errors, broken connections, and other capacity deficiencies to determine where design changes are necessary in the cellular network. This means that design changes to address capacity deficiencies are generally insufficient, overly costly, and/or untimely. BRIEF DESCRIPTION OF DRAWINGS The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. FIG. 1A is a block diagram of a cellular network system including a computing system in or associated with the cellular network for purposes of capacity planning according to at least one embodiment. FIG. 1B is a block diagram of the computing system of FIG. 1A according to some embodiments. FIG. 2A, FIG. 2B, FIG. 2C, FIG. 2D illustrate a graphical user interface (GUI), including a map of an area of interest (AOI) of the cellular network, presentable on a display device of the computing system according to at least some embodiments. FIG. 3 is an image of a menu presentable within the GUI as an overlay to the map and that provides selection options of various capacity-related aspects of the cellular network according to some embodiments. FIG. 4 is a screen capture of a GUI in which a map is presented having illustrated a plurality of partitioned sections of the AOI according to exemplary embodiments. FIG. 5 is a an image of a representative group of sections of the plurality of partitioned sections of FIG. 4, illustrating an example of determining a traffic metric volume for individual sections according to some embodiments. FIG. 6 is an image of a representative group of sections of the plurality of sections of FIG. 4, illustrating how known samples of geolocations of subscriber devices are distributed based on sector and cell according to some embodiments. FIG. 7 is a flow chart of a method for determining a capacity metric volume for each section based on a plurality of known geolocations and capacity metric volume of each cell according to some embodiments. FIG. 8A is a flow chart of an example method for partitioning the AOI according to at least one embodiment. FIG. 8B is a flow chart of an example method for predicting additional geolocations within each section based on call record detail (CDR) data according to some embodiments. FIG. 9 is a flow chart of an example method of determining capacity metric volume from each cell in order to determine a capacity metric volume for each section according to some embodiments. FIG. 10A is a screen capture of the map in which different sections are shaded differently based on a particular volume range of a capacity metric in each respective section according to an exemplary embodiment. FIG. 10B is a zoomed-in area of some of the different sections of the map of FIG. 10A to illustrate the shading with more clarity according to some embodiments. FIG. 11 illustrates a block diagram illustrating an exemplary computer device (or computing device), in accordance with implementations of the present disclosure. DETAILED DESCRIPTION As discussed above, as communication technologies advance, including the emergence of 5G new radio cellular networks, the number of connected devices grows, ensuring that cellular network capacity commensurately grows can be challenging, particularly in meeting increasing cellular traffic demands for that network capacity. Typically, engineers manually analyze alerts, errors, broken connections, and other capacity deficiencies to determine where design changes are necessary. It is not straight forward to associate capacity deficiencies with particular sites or sectors of a cellular network, even with access to subscriber data, due to not being able to geolocate subscriber devices throughout an area of interest (AOI). This can mean that design changes to address capacity deficiencies are generally insufficient, overly costly, and/or untimely. Als