JP2024536700A - Evaluation and visualization of indoor wireless services in 3D - Google Patents

Abstract

Disclosed is a method for generating a visual representation of wireless service status within a three-dimensional display, the method including the steps of: capturing a collection of data including wireless service status from a wireless device; determining a latitude and longitude from the data and determining a reference altitude based on the latitude and longitude; determining a reporting altitude in a selected coordinate system from the data; subtracting the reference altitude from the reporting altitude in the selected coordinate system; determining an estimated ground height for the data; and displaying the wireless service status as a visual representation within a three-dimensional graphical image.
[Selected figure] Figure 3

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63/260,594, filed August 26, 2021, and U.S. Non-Provisional Patent Application No. 17/681,086, the disclosures of which are incorporated by reference in their entireties into this application.

The present invention relates to a system and method for displaying and reporting wireless service status in a vertical structure and in a map view.

Handheld mobile devices are widespread in modern society. They provide access to wireless services such as voice, SMS, and Internet through an interconnected network of transceivers. Communication between the mobile devices and the network transceivers is carried out through electromagnetic waves in the form of radio signals. To achieve and sustain good communication, these radio signals need to meet certain levels of strength and quality. Signal strength describes the amplitude of the desired signal. Signal quality is defined as the ratio between the amplitude of the desired signal and the amplitude of all other signals, the latter also called interference power.

Structures pose a challenge to wireless communications because they can block (obstruct), attenuate (reduce strength), distort (reduce quality), or reflect (bounce) the signal. These negative changes to the signal can be caused by a number of factors, including the size and location of the structure, or the location of the wireless device within the structure.

Signal interference is a key indicator of negative impacts on signal quality and therefore receives the highest level of scrutiny by parties interested in the design, deployment, and service of wireless networks. Interference can be caused by the design and operation of the wireless network itself, an example of which is lack of signal occupancy, where multiple signals from multiple network transceivers in the vicinity are received with similar strength. This effect is most commonly observed in tall buildings, where there are relatively few obstacles between the network transceiver and the mobile device. External interference (noise) sources that further exacerbate the impact on signal quality within a building include: spurious emissions from other transceivers; intermodulation products at nearby antennas; and natural sources, including but not limited to thunderstorms, electrical storms, cosmic background radiation, etc. The key performance indicator (KPI) used to quantify signal quality is the signal-to-noise ratio (SNR).

Embodiments herein relate to methods and three-dimensional visualizations related to measurements of wireless service conditions and for generating visualizations having three dimensions to include multiple measurements in the visualization, where a collated set of measurements is displayed, thereby showing trends in the wireless service conditions on a visual display. Various embodiments provide methods for obtaining the measurements, modifying the data, and generating a data set of the measurements for display. The measurements may be collected from crowd-sourced data. The final product and output results in a visual display identifying a set or multiple measurements to define one or more wireless service conditions at a given height at a given location. In this manner, wireless service conditions within a particular structure at a particular height can be determined. These wireless service conditions include, but are not limited to, signal strength and signal quality. The wireless service conditions, user density, and other characteristics can be graphically depicted on a map in the form of visual representations and the characteristics within vertically extruded polygons representing sections of the structure at a given location. Such information is useful to providers seeking to optimize service within these areas.

In a preferred embodiment, a method for generating a three-dimensional visual representation of wireless measurements includes: (a) capturing a set of data from one or more wireless devices; (b) determining a latitude and longitude from the set of data and determining a reference altitude based on the latitude and the longitude; (c) determining a reporting altitude in a selected coordinate system from the set of data; (d) subtracting the reference altitude from the reporting altitude in the selected coordinate system; (e) determining an estimated above ground height for the set of data; and (f) displaying a visual representation of the set of data in a three-dimensional graphical image.

In a further embodiment, the reporting altitude is WGS84 altitude.

In a further embodiment, the method further comprises providing an absolute threshold to the set of data by filtering the set of data with the absolute threshold. In a further embodiment, the absolute threshold is between 1 meter and 100 meters.

In a further embodiment, the method further comprises providing a relative threshold to the set of data. In a further embodiment, the relative threshold is between 80% and 99% of the total number of samples in the data set.

In a further embodiment, the method further comprises displaying the user density. In a further embodiment, the method further comprises displaying the wireless service status. In a further embodiment, the method further comprises displaying the wireless service status and the user density.

In a further embodiment, the method further comprises displaying the set of data within a predefined height segment.

In a further embodiment, the method further includes a step of displaying a plurality of radio measurements as the visual representation within a polygon segmented into a plurality of sections.

In a further embodiment, the radio service status is: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G Most Frequent Cell, 5G Strongest Cell, 5G Most Frequent Band, 5G Strongest Band, 5G Optimization Priority, LTE CQI, LTE Most Frequent Band, LTE Most Frequent Cell, LTE Most Frequent PCI, LTE Most Frequent TAC, LTE Optimization Priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE highest signal strength band, LTE highest signal strength cell, LTE highest signal strength PCI, LTE highest signal strength TAC, UMTS Ec/No, UMTS highest signal strength band, UMTS highest signal strength cell, UMTS highest signal strength LAC, UMTS highest signal strength PSC, UMTS RSSI, UMTS highest signal strength band, UMTS highest signal strength cell, UMTS highest signal strength LAC, UMTS highest signal strength PSC, GSM highest signal strength band, GSM highest signal strength BSIC, GSM highest signal strength cell, GSM highest signal strength LAC, GSM RSSI, GSM highest signal strength band, GSM highest signal strength BSIC, GSM highest signal strength cell, GSM highest signal strength LAC, CDMA Ec/Io, CDMA RSSI, EVDO Ec/Io, EVDO RSSI, User Density, Mobile Data Usage, WiFi Data Usage, Mobile + WiFi Data Usage, Downlink Throughput, Uplink Throughput, Jitter, Latency, Best Carrier 5G CSI-RSRP, Best Carrier 5G CSI-RSRQ, Best Carrier 5G CSI-SINR, Best Carrier 5G SS-RSRP, Best Carrier 5G SS-RSRQ, Best Carrier 5G SS-SINR, Best Carrier GSM RSSI, Best Carrier LTE CQI, Best Carrier LTE RSRP, Best Carrier LTE RSRQ, Best Carrier LTE SNR, Best Carrier UMTS Ec/No, Best Carrier UMTS Selected from the group consisting of RSSI, coverage improvement opportunity, multi-network coverage improvement score, optimization opportunity, sales opportunity, % low band, timing advance, and combinations thereof.

In a preferred embodiment, a method for generating a three-dimensional visual representation of wireless measurements includes: (a) capturing wireless measurements from a wireless device; (b) determining a latitude and longitude from the wireless measurements and determining a reference altitude from the latitude and longitude; (c) determining a reporting altitude in a selected coordinate system from the wireless measurements; (d) subtracting the reference altitude from the reporting altitude in the selected coordinate system; (e) determining an estimated height above ground for the wireless measurements; and (f) generating a polygon on the visual representation corresponding to the estimated height above ground so as to encompass the wireless measurements based on a predetermined threshold of a plurality of measurements.

In a further embodiment, the polygon is generated according to 90% to 99% of the measurements, each of the measurements being defined within a given range of latitude and longitude.

In a further embodiment, the given range of latitude and longitude is oriented to fall within a polygon based on a predefined threshold.

In further embodiments, the predetermined threshold is an absolute measurement of distance, or a relative measurement based on a portion of all measurements.

In a preferred embodiment, a method for generating a visual representation of wireless service status in a three-dimensional display includes: (a) capturing measurements from a wireless device, the measurements including wireless service status; (b) determining a latitude and longitude from the measurements and determining a reference altitude based on the latitude and the longitude; (c) determining a reporting altitude in a selected coordinate system from the measurements; (d) subtracting the reference altitude from the reporting altitude in the selected coordinate system; (e) determining an estimated above ground height for the measurements; and (f) displaying the wireless service status within a three-dimensional graphical image of the visual representation.

In a further embodiment, the method further comprises providing a predetermined absolute or relative threshold for the latitude and longitude.

In a further embodiment, the method further comprises providing a predetermined absolute or relative threshold for the reporting altitude in the selected coordinate system.

In a further embodiment, the method further comprises orienting the estimated ground height within a section of the three-dimensional graphical image. In a further embodiment, the height of one of the sections of the three-dimensional graphical image is between 5 meters and 50 meters. In a further embodiment, the height of one of the sections of the three-dimensional graphical image is 15 meters. In a further preferred embodiment, one of the measurements is displayed within one of the sections on a visual display, and multiple measurements are aggregated to display a trend regarding the wireless service conditions (i.e., what the wireless service conditions are like at a given height at a given location) within multiple of the sections on the visual display at a given latitude and longitude.

In a preferred embodiment, a three-dimensional representation of a wireless service condition includes: a plurality of data measurements, each of the data measurements defined by a measured latitude and longitude, each of the data measurements being provided with a reporting altitude; determining a ground elevation at the measured latitude and longitude; a final altitude is generated by comparing the reporting altitude to the ground elevation to determine a delta; each of the plurality of data measurements is displayed within the three-dimensional representation of the wireless service condition and positioned based on the measured latitude and longitude within a slice on a vertical axis based on the final altitude, the slice having a distance between 5 meters and 50 meters; each of the data measurements includes at least one wireless service condition.

In a further embodiment, with respect to said three-dimensional representation of the radio service status, said radio service status comprises: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G most heavily trafficked cell, 5G most heavily trafficked cell, 5G most heavily trafficked band, 5G most heavily trafficked band, 5G optimized priority, LTE CQI, LTE most heavily trafficked band, LTE most heavily trafficked cell, LTE most heavily trafficked PCI, LTE most heavily trafficked TAC, LTE optimized priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE most heavily trafficked band, LTE most heavily trafficked cell, LTE most heavily trafficked PCI, LTE most heavily trafficked TAC, UMTS Ec/No, UMTS highest communication band, UMTS highest communication cell, UMTS highest communication strength LAC, UMTS highest communication strength PSC, UMTS RSSI, UMTS highest communication strength band, UMTS highest communication strength cell, UMTS highest communication strength LAC, UMTS highest communication strength PSC, GSM highest communication band, GSM highest communication strength BSIC, GSM highest communication cell, GSM highest communication strength LAC, GSM RSSI, GSM highest communication strength band, GSM highest communication strength BSIC, GSM highest communication strength cell, GSM highest communication strength LAC, CDMA Ec/Io, CDMA RSSI, EVDO Ec/Io, EVDO RSSI, User Density, Mobile Data Usage, WiFi Data Usage, Mobile + WiFi Data Usage, Downlink Throughput, Uplink Throughput, Jitter, Latency, Best Carrier 5G CSI-RSRP, Best Carrier 5G CSI-RSRQ, Best Carrier 5G CSI-SINR, Best Carrier 5G SS-RSRP, Best Carrier 5G SS-RSRQ, Best Carrier 5G SS-SINR, Best Carrier GSM RSSI, Best Carrier LTE CQI, Best Carrier LTE RSRP, Best Carrier LTE RSRQ, Best Carrier LTE SNR, Best Carrier UMTS Ec/No, Best Carrier UMTS Selected from the group consisting of RSSI, coverage improvement opportunity, multi-network coverage improvement score, optimization opportunity, sales opportunity, % low band, timing advance, and combinations thereof.

In a further embodiment, an absolute or relative filter is applied to the measured latitude and longitude for the three-dimensional representation of the wireless service situation.

In a further embodiment, an absolute or relative filter is applied to the determined altitude with respect to the three-dimensional representation of the wireless service situation.

In a further embodiment, with respect to the three-dimensional representation of the wireless service situation, the method further includes indoor classification, which is necessary to utilize the data measurements in the three-dimensional representation of the wireless service situation.

FIG. 1 is an illustration of a 3D view of a network user density map. FIG. 2 is an illustration of a 3D view of network performance for a single carrier on a single platform measuring RSRP. FIG. 3 is a flow chart of a process for generating vertical measurements in a 3D view. FIG. 4 is a flow chart illustrating the process of generating 3D polygons corresponding to buildings in a visual map. FIG. 5 shows a flow chart for generating a 3D representation of user density. FIG. 6 shows a flow chart for generating a 3D display of wireless service status.

Disclosed are techniques for providing wireless service condition performance in a three-dimensional (3D) graphical representation. The 3D view allows multiple sets of data, including mobile device density and wireless service quality at a given height within a building, to be represented in one visual display. The representation can be organized by wireless service generation (GSM, UMTS, LTE, 5G), specific service provider, and metrics describing wireless service condition performance. The resulting 3D graphical representation provides a user-friendly visualization of areas with good and poor wireless service conditions, allowing service providers to quickly and efficiently prioritize their efforts towards addressing network performance issues.

Compared to typical 2D approaches to network performance design and optimization, which collapse all network condition metrics onto a single horizontal plane, the 3D representation provides a more sophisticated, layered view of the network conditions that a mobile device experiences depending on its elevation position within the building.

Wireless service status refers to data collected from a mobile device including, but not limited to, the following metrics: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G most busy cell, 5G most busy cell, 5G most busy band, 5G most busy band, 5G optimized priority, LTE CQI, LTE most busy band, LTE most busy cell, LTE most busy PCI, LTE most busy TAC, LTE optimized priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE most busy band, LTE most busy cell, LTE most busy PCI, LTE most busy TAC, UMTS. Ec/No, UMTS highest communication band, UMTS highest communication cell, UMTS highest communication strength LAC, UMTS highest communication strength PSC, UMTS RSSI, UMTS highest communication strength band, UMTS highest communication strength cell, UMTS highest communication strength LAC, UMTS highest communication strength PSC, GSM highest communication band, GSM highest communication strength BSIC, GSM highest communication cell, GSM highest communication strength LAC, GSM RSSI, GSM highest communication strength band, GSM highest communication strength BSIC, GSM highest communication strength cell, GSM highest communication strength LAC, CDMA Ec/Io, CDMA RSSI, EVDO Ec/Io, EVDO RSSI, User Density, Mobile Data Usage, WiFi Data Usage, Mobile + WiFi Data Usage, Downlink Throughput, Uplink Throughput, Jitter, Latency, Best Carrier 5G CSI-RSRP, Best Carrier 5G CSI-RSRQ, Best Carrier 5G CSI-SINR, Best Carrier 5G SS-RSRP, Best Carrier 5G SS-RSRQ, Best Carrier 5G SS-SINR, Best Carrier GSM RSSI, Best Carrier LTE CQI, Best Carrier LTE RSRP, Best Carrier LTE RSRQ, Best Carrier LTE SNR, Best Carrier UMTS Ec/No, Best Carrier UMTS RSSI, coverage improvement opportunities, multi-network coverage improvement score, optimization opportunities, sales opportunities, % low band, and timing advance. Notably, these wireless service conditions are collected simultaneously as data from a single mobile device, so that further extrapolation can be performed by using portions of the data in combination with other portions of the data. Wireless data further refers to any additional metrics that may be collected, including, but not limited to, latitude, longitude, altitude, vertical and horizontal accuracy, time, and various other metrics. Each measurement collected includes all data and all wireless service conditions, and the measurements can be interpolated into a database.

Inside a building, the dominant factors that reduce signal level and quality are: penetration loss (signal weakens while passing through dense media such as concrete walls, metal panels, etc.), reflection (signal is redirected by dense media of surrounding buildings and structures), and shadowing (signal is blocked by dense media of surrounding buildings, structures, vegetation). At ground level, these factors typically lead to suppressed coverage (i.e., signal levels from the closest network transceivers exceed those from more distant transceivers), thus resulting in higher signal occupancy, which translates into lower interference. In contrast, in a high-rise building, the higher the height, the fewer the number of obstacles in the path of the signal from the more distant network transceivers, resulting in lower signal occupancy and therefore higher interference.

To compensate for weak signals, the addition of network transceivers or changes in the directional spread along the azimuth or elevation of existing network transceiver antennas can be implemented. After the signal strength is within the desired range, further optimization is performed to reduce interference levels.

In addition to the factors outlined above, increased interference can be caused by: harmonics; frequency drift; RF leakage; and internal interference caused by the conductivity of passive devices such as connectors, antennas, cables, etc. Interference can also be caused by frequency reallocation. Operators reallocate licensed frequency spectrum between multiple technologies. For example, as usage of older services declines, they shift spectrum to newer technologies to accommodate more users and traffic. Users still using older technologies are served with less spectrum and experience more interference due to frequency reuse (multiple transceivers using the same frequency).

In some cases, frequency intermodulation can occur when two or more signals of different frequencies are mixed (multiplied) in nonlinear electronic components in a mobile device or network transceiver, leading to the generation of signals at frequencies other than the one being transmitted. If the accidental frequency at which a signal is received overlaps with a frequency already in use, interference occurs.

Identifying various possible interference issues and graphically representing user density and signal and interference levels allows providers to more easily assess problem areas as these features can be concisely represented within a 3D view. The amount of unique mobile devices and number of measurements collected within a building can also help quantify the quality of the collected data by reducing variance of metrics and presenting true averages.

Thus, after identifying wireless service conditions that require modification, changes can be made to the transceiver network to improve the performance of the wireless service conditions. Interference within a wireless network can be managed by limiting coverage and reducing overlap between adjacent transceivers. Interference is also typically reduced by adjusting various settings of the cell site's antennas and network control software. For example, antenna beams can be more focused toward target areas and structures, and transmitter power, frequency, and code settings can be modified to increase signal levels from desired network transceivers and decrease signal levels of non-desired network transceivers within the target areas and structures.

The data and wireless service status collected and utilized in the graphical representation of wireless service status can capture a representative sample of users in the wireless network. In either case, all of the data information and wireless service status is contained within a single data measurement. This allows the measurement to be positioned within the display based on its location on the horizontal x- and y-axes and the vertical z-axis. The measurement itself contains all of the relevant wireless service status associated with it, which can be effectively stored in a database. Thus, the combination of multiple measurements provides a data set, and the larger the data set, the greater the confidence in any particular trends that can be identified within the data set.

The capture of such a data set allows end users to be confident in the reliability of the data set due to the sheer number of data set points collected, where the user understands that a larger number of data set points is more reliable than a smaller number of data set points. At the same time, if the data set reveals that certain areas need changes to improve signal strength or reduce interference, or any other variety of wireless service conditions determined to be relevant, higher priority can be given to areas with higher density in order to improve wireless service conditions for a greater number of users.

Therefore, referring now to the figures, FIG. 1 details a graphical view (21) of user density within a 3D representation. This allows the generation of the chart of FIG. 1 with a vertical axis (height of the building) identifying the relevant building with respect to its physical location, and the density of users within this space according to the legend (20) on the visual display. FIG. 1 provides a simple representation of all networks with their relative density at a particular height section within the visual window. Thus, if there is a 50-storey building, with the bottom 5 floors being a parking lot, the graphical representation would include several different sections of the building and would depict the relative density of wireless networks collected at these points. Thus, since parking structures are not usually always occupied, it would depict that the parking spaces on the bottom 5 floors have a low density of network users. This is because these spaces generally do not use network services for long periods of time. In contrast, a work space floor or a residential space floor has a higher density of users and can be identified as such. In other cases, an industrial building or warehouse may have a low number of users, while residential and commercial office spaces may have a high density. The larger the number of dataset points, the more reliable the dataset will be, and optimization priorities will be dictated based on the density of users within these spaces.

In choosing the most sophisticated way to represent density, the legend (20) provides various shadings or other metrics that are easily visible to the user. However, those skilled in the art will recognize that visual graphical representations can be created using, for example, a color-themed representation where different colors represent different levels of user density, or using different shading or fill patterns. In essence, some formats of representations are similar to heat maps, and can provide a visual representation of wireless service status or data, e.g., user density, within multiple slices of the vertical axis. The result is a visual display (21) that can shade a structure (23) as a structure in the visual display (21) according to the density of users at a certain estimated height within the structure (23).

The visual display (21) further includes a search bar 30, which includes a search window (24) and various fields (e.g., 24-28) for the user to modify the display. For example, the search window (24) can enable a specific search, and a toggle field (25) allows for switching between a heat map and a binned data view, the binned data view being used only in 2D mode. The next field 26 is a field that allows for classification, for example "outdoor and indoor", or even "outdoor" only, or "indoor" only. The time window (27) can indicate, for example, "in the last 24 months", and the band window (28) allows for review of different frequency bands of wireless service. The number of fields can be modified to include any number of data sets related to wireless service conditions, or any number of points extrapolated from the data, each of which can be based on user density.

Now, FIG. 2 is a variation of FIG. 1, where the legend (40) provides a single view within the visual representation of Reference Signal Received Power (RSRP) metrics for each carrier using the LTE band. Thus, different wireless service status metrics can be easily switched between to generate a map of interest to the user.

To generate the visual displays of Figures 1 and 2, data is collected from the user, and then collected and modified in a new and unique way to capture a population data set that can be organized into a database and then displayed in a graphical representation. The data filling these views is captured by mobile devices on the network and aggregated in the database. For example, Android OS reports GPS data, which includes horizontal and vertical geographic location where measurements were collected, including latitude and longitude coordinates (decimal degrees, WGS84), altitude, horizontal accuracy, and vertical accuracy. The data may also be collected specifically from devices utilizing applications or programs on the wireless device designed to capture the data points mentioned above, or additional data points that may be related.

A key issue in displaying collected data or wireless service status is whether such information can be displayed in a format that is easily usable. The first issue is that in the orientation of the data set in the vertical axis, the mobile device reports its vertical position in a certain coordinate system. For example, one of several coordinate systems is WGS84, which is used as an example throughout, but those skilled in the art will recognize that other coordinate systems exist and are used, particularly in different areas of the world. However, each of these coordinate systems is not relative to the earth's altitude, and therefore only produces results that require correction. In fact, in WGS84, vertical position is reported in meters from the earth's geoid (an imaginary surface determined by the earth's gravity and approximated by mean sea level), rather than as height above the earth's surface (normal height). To calculate the elevation of a measurement from the earth's height, the elevation of the earth's surface relative to the geoid elevation is calculated at the reported location, and then this is subtracted from the reporting altitude of the measurement. This calculation simply takes the delta between two measurements relative to the same reference system (WGS84) to get the actual altitude (above the Earth's surface at that latitude and longitude) for purposes of representation on the display.

Horizontal and vertical position accuracy readings are important to capture the true service conditions at a location. Thus, if multiple location data have variances that exceed a predetermined amount, these data may be removed from the data set. This predetermined variance may depend on the measurement conditions and the total number of measurements. For example, if the number of measurements is relatively large, it may be more appropriate to use a stricter threshold of variance, such as a measurement of only 10 meters, whereas if there are only 10 measurements available, a larger variance, such as 50 meters, may be acceptable. Furthermore, relative calculations can be used instead of absolute measurements in meters to obtain the best data, such as the middle 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 99% of all data measurements and radio service conditions sorted in ascending order of altitude. Thus, in a simple data set of 10 measurements, the lowest and highest data points would be removed using the 80% metric. Similarly, with a data set of 1000 measurements, a sample data set is obtained by removing a total of 50 measurements from the top and 50 measurements from the bottom by 90% of the metrics. These variables can be set and modified as needed by the user.

After filtering out measurements that do not meet the vertical and horizontal accuracy variance thresholds for a given set of data, the measurements are grouped into segments that represent a range of vertical heights (storeys) within the building. These segments can be as short as 1 meter, but are preferably 15 meters. Alternatively, measurements can be grouped according to the height of a particular building to provide the maximum number of segments. For example, a building with a total number of segments of 5 and a height of 100 meters would result in a segment that is 20 meters high. However, more than 70% of all buildings are less than 15 meters high. By setting the segment height to 15 meters, many buildings can be grouped into a single segment, eliminating erroneous data that may exist if one were to create 5 or 10 meter segments and group the data into these smaller sections. The average of the grouped measurements is presented in the visual portal and display as shown in Figures 1 and 2.

Figure 3 provides an overview of one method of utilizing captured data regarding wireless service conditions to modify and utilize the data for presentation. Step (1) provides for the capture of data from a wireless device. As previously detailed, such data includes, but is not limited to: latitude, longitude, horizontal accuracy of location, vertical accuracy of location, and wireless service conditions.

Step (2) then utilizes the collected location data to determine the ground height for each measurement. The data, including the defined latitude and longitude of the measurement, provides an accurate location relative to the earth's surface. A database is provided that identifies the ground height at each given latitude and longitude. Given the latitude and longitude, the horizontal accuracy of these measurements is provided. If the horizontal accuracy is within the distance of the structure, the data can be assumed to be accurate. If the horizontal accuracy is greater than the distance/footprint of the structure, a specific filtering protocol can be used to filter out data for distances greater than x meters (i.e., absolute threshold), or a relative threshold can be applied as detailed herein. In certain cases, the horizontal accuracy is not as important since the variance is negligible because the earth's surface is horizontal. Data of one structure adjacent to another structure may be very well preserved. Thus, such variance may not have a significant effect on the data. However, in hilly areas (e.g., San Francisco), even a distance of 15 meters in either horizontal direction can result in a significant change in ground height. In such instances, it may be necessary to modify the data cutoff at a given threshold to ensure accuracy of the data in such circumstances.

Step (3) then takes the known measurements with determined latitude and longitude and estimates the height based on the measured data. Thus, a ground elevation transformed into a relevant coordinate system, e.g., WGS84 vertical elevation (height relative to the Earth's ellipsoid), is determined for each structure using data from a third-party DEM (Digital Elevation Model) or DSM (Digital Surface Model). Then, since the location data collected by the wireless device is already in the WGS84 coordinate system (as reported by the device's GPS), the height above ground of the measurement is calculated as the arithmetic difference between the elevation of the measurement and the ground elevation. To the extent that another elevation measurement is utilized, an appropriate correction is made as necessary based on that measurement. The resulting data is the corrected elevation of the measurement to accurately place the measurement within the polygon of the visual display. This results in a measurement of each data point at a given elevation.

Then, in step (4), the measurements within the structure can be estimated based on the height above ground of the measurements calculated from step (3). This can be easily done if the height of the structure is known. In certain cases, the height of a structure whose height is unknown can be estimated from the collected measurements/data, as shown in more detail in FIG. 4. Regardless of how the height of the structure is determined or estimated, data from multiple measurements is stored in a database, which aggregates these data for 3D mapping in step (5).

Along these lines, certain measurements are provided with known accuracy. In practice, data is often provided with vertical and horizontal accuracy measurements. These measurements are often provided in meters (distance) and/or include an associated confidence level. Certain measurements will have low or high accuracy readings, and therefore shorter distances will have a higher confidence in the actual location. In step (5), the data is grouped according to absolute measurements. This means that data is only used if it has an accuracy measurement less than a certain distance. In various embodiments, this distance is between 1,000 meters and 0.01 meters, with typical distances being less than 100 meters, less than 50 meters, less than 25 meters, less than 15 meters, less than 10 meters (including any distance ranges in between). However, absolute variance is not always used, and often a relative threshold is used, where the entire data set is examined and a portion of the data set is used to ensure accuracy. In these cases, the relative thresholds are 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, and 99% of the data set, which thresholds represent the central portion of the data set. For example, a threshold of 80% would exclude the top 10% and bottom 10% of the data set. The entire data set remains in the database, but the data captured and displayed is the portion determined under the absolute or relative thresholds as defined herein. Thus, the data presented in the visual display is specific to the exact measurement, allowing for precise identification of individual measurements.

Step (6) involves generating groupings of data within similar latitude and longitude at similar heights. Again, data for the entire data set is taken and a predefined threshold is used to determine which data to display. There may be some variance, particularly since not all measurements have exactly the same latitude and longitude, and not all have the same error variance in the vertical axis. The grouping will therefore best match the measurements to best determine whether they were taken within the same structure or adjacent structures. The variance here may depend on a number of factors, including the proximity of adjacent structures and the error variance of the measurements.

Finally, step (7) involves displaying a visual representation of the data, examples of which are shown in both Figures 1 and 2, where Figure 1 shows user density and Figure 2 shows RSRP for a single wireless carrier. Each of these representations has visual or display elements that are defined in the legend. The end user can retrieve the display and modify the wireless service status to modify the display based on their particular wireless service status, thereby allowing for the representation of these different wireless service statuses. User density may be displayed in all cases or may be visually presented in an underlying pop-up or other visual cue as the user evaluates the data in the visual representation.

In certain instances, the data provides measurements of structures with unknown height or dimensions. This occurs when new construction is completed, or where data is simply not publicly provided. In certain embodiments, structures with unknown height are extruded from ground level based on the reporting measurement elevation if the total number of users of the structure is 10 or more. If less than 10, only the base segment (0m-15m) is displayed. Extrusion continues until a segment contains x% of the total number of samples of the structure within itself and the segments below it, e.g. 97%. This helps prevent a small number of samples at very high elevations from displaying an unrealistic structure height. In some embodiments, if there are more than a predefined number (e.g. 8) of consecutive segments without measurements, extrusion stops regardless of whether the structure height is known. If the height is known, multiple segments are displayed until the structure height is reached. If a measurement is present, the segment is colored, otherwise the segment is gray or depicted with some other shading or visual cue.

Continuing with this logic, FIG. 4 shows a process flow for determining the height of a polygon to be represented in a display window such as that seen in FIGS. 1 and 2. Following FIG. 3, the first step is to collect data from the wireless device (1). The data from this first step is then used to determine the latitude and longitude (10). Once the first two steps are completed, the next process defines the height of the polygon. In step (11), the polygon is extruded based on known structure and height, or in step (12), the polygon is extruded based on structure inferred from data within the boundary of the building polygon. In practice, these steps can work together to ensure accurate display of the building, although it is in this particular case where the height is not known that it is important.

In reality, even if a known structure exists, its specific height and dimensions may not be specifically known. In other cases, such as recently developed structures, the existence of a structure may not be known, and thus the collection of data implies the presence of a structure to be displayed. Finally, a particular structure may have errors or unused space that, if present, may introduce uncertainty into the visual display.

Thus, step (13) takes the collective data and refines it to remove outlier data. This is done by removing data with less accurate parameters, whether in vertical position accuracy or latitude and longitude accuracy. This data is typically captured in step (1), i.e. the data points literally define the accuracy estimate of the captured data points. Preferably, the complete data set of measurements is combined and a specific process is used to filter this data to generate the best data set. The various accuracy metrics are the same as those detailed in FIG. 3, i.e. absolute measurements of distance, or relative measurements taking a portion of the data set to remove outlier data.

Finally, using the refined data, in step (14), the polygon from step (11) or (12) can be modified, particularly in height, to include x% of the samples. This allows any of the polygon heights to be corrected based on the data. In particular, the percentage of samples to be included in this and other steps will vary based on several factors, including the total coverage, the total number of samples, the confidence of the data, and other factors. Typically, the percentage should be more than 80% of the samples, more preferably more than 90%, 95%, 97%, or 99% of the samples.

To provide context for this determination, the set of samples contains 1000 data points and there is one structure with an unknown height. The data set is set to 97% of the samples. The structure height begins with the lowest elevation measurement and a total of 970 measurements are captured (reaching 97% of all samples). After collecting 970 samples, the structure height is determined to be the highest measurement in this set of samples. The remaining 30 higher measurements are excluded from the determined structure height, thereby eliminating measurements that may be inaccurate. This percentage can be modified according to the total number of measurements, the accuracy of these measurements, and other parameters determined in each scenario.

In certain instances, especially in large cities, there are often underground measurements. For example, the New York City subway system or subway stations may have thousands of underground measurements. In such cases, measurements to determine height begin with measurements that are determined to be above ground level based on latitude and longitude. Thus, if there are 10,000 measurements, and 1,000 of them are determined to be underground, then 9,000 data points are considered for the structure height. If 97% of the samples are used for height, then a total of 8,730 samples are used to calculate height, excluding the remaining 270 samples that are at higher elevations.

Thus, as shown in Figures 1 and 2, the visual display includes a number of buildings, each of which is represented by a polygon with a vertical direction and by x and y coordinates. In certain embodiments, the size of the buildings is provided or already known, specifically including the height, but in some embodiments also including the area in square feet in x and y coordinates. For example, the building polygon and height from a third party source are used if available, but may be inaccurate. Buildings with unknown heights are extruded upward from ground level based on the reporting altitude of the measurements. Extrusions are added upward from ground level until x% of the total number of building samples are contained within the 3D representation of the building. Using these cutoffs on the data sample set helps prevent unrealistic building heights from being displayed due to imprecision and low numbers of samples at very high altitudes. For buildings that still appear taller than they are, typically the topmost segment or segments have very low user populations (1 or 2) and can be ignored using these cutoff metrics. The objective here is simply to provide a data set that provides a representative sample of data for evaluating wireless service metrics and the reliability of those metrics based on the density of users and the aggregate measurements of those wireless service metrics.

The data collected from the wireless devices in each method and the wireless service status (step 1) provide a collective approach towards identifying wireless service status in parallel with identifying user density and providing a visual approach in the z-axis (vertical). The results of this approach are verified by comparing the data with real-world examples. For example, signal levels and quality are significantly higher in buildings known to have in-building cell site systems installed, which are known to improve signal levels and reduce interference. Furthermore, the data shows that signal levels increase with height, while at the same time, higher elevations show increasing levels of interference, as would be expected from more interference at higher floors. Finally, the total number of users is available in the visual display, which helps the user judge the reliability of the presented information. Thus, the user can independently determine the displayed wireless service status according to the user's choice, by determining whether the data has a large or small number of users, and other metrics that may affect the reliability of the data.

In certain embodiments, it may be further useful to utilize indoor classification techniques, especially in relatively low-level locations within a building. This allows certain embodiments to classify indoor or outdoor measurements in areas with large square footage of both indoors and outdoors. This can be done by using the collected data, comparing the footprint of the building with latitude and longitude measurements, and evaluating it with respect to horizontal accuracy measurements. In practice, for any given measurement, horizontal accuracy is required to ensure that a given measurement is within one building and not another. This indoor classification can further be useful in cases where horizontal accuracy is low, or where additional data points may simply be needed to improve the accuracy of the data. A specific indoor classification protocol is defined in U.S. Patent Application Publication No. 16/381,961 and can be utilized in conjunction with the methods and processes detailed herein.

Determining the density of users at a given location can improve the reliability of the data, as described in detail herein. Figure 5 provides a simplified diagram of an embodiment for generating this information. Step (1) involves capturing data from a wireless device. Step (2) determines the latitude and longitude direction from the data and determines the altitude at that point via a database. Step (51) then applies an initial filter to the data based on horizontal accuracy, for example filtering out data with an accuracy rating that exceeds a predefined acceptable distance. Step (3) (following step (51)) estimates the height of the measurement based on the delta between the determined altitude and the measurement data from the coordinate system (i.e., WGS84). Step (52) optionally applies further horizontal accuracy processes, such as a relative process to ensure the middle 90%, 95%, or 97% of the measurements, or other suitable processes. Step (4) (following step (52)) then estimates the location within the structure based on the estimated height. Then step (5) aggregates the data into a database for 3D mapping. Step (6) generates groupings of data within similar latitude and longitude and similar elevation based on the previous steps and the applied threshold step. Finally, step (53) provides an indication of user density on a visual display.

6 applies a similar process to the evaluation of a particular wireless service condition, in accordance with the disclosure herein. Step (61) captures data including wireless service conditions from a wireless device (from multiple wireless devices). Step (2) determines the direction of a single data measurement based on the latitude and longitude of the data, and also determines the altitude at that point. Step (3) estimates the actual ground height based on the delta between the WGS84 measurement and the altitude determined from the latitude and longitude. In each of steps (2) and (3), specific filters and thresholds can be applied, if appropriate, to filter out data with large variance based on absolute or relative thresholds. Step (4) then estimates the location within the structure based on the estimated height. Step (5) then aggregates the data into a database for 3D mapping. Step (62) (following step (5)) then applies filters to all data, if necessary to ensure accuracy of the data, which may be performed separately or in addition to the optional application of filters and thresholds to steps (2) and (3) beforehand. Step (6) produces groupings of data within similar latitude and longitude and similar elevations, and step (63) concludes with a visual display rendering the 3D data from measurements from the wireless devices to display wireless service status metrics for the selected objects of interest.

Those skilled in the art will recognize that the various methods and processes described above may be combined in whole or in part to modify a particular process. Furthermore, certain steps may be included as options in various embodiments. Those skilled in the art will recognize that the embodiments detailed herein are not limited to the manner in which data may be manipulated or displayed.

Claims (27)

1. A method for generating a three-dimensional visual representation of wireless measurements, comprising:
a. capturing a set of data representative of a plurality of said wireless measurements from one or more wireless devices, each said wireless measurement including location information indicative of a location where the corresponding said wireless measurement was made;
b. determining a latitude and longitude corresponding to each said radio measurement and determining a reference altitude based on said latitude and said longitude;
c. determining a reporting altitude in a selected coordinate system corresponding to each said wireless measurement;
d. subtracting the reference altitude from the reporting altitude in the selected coordinate system;
e) determining an estimated height above ground corresponding to each said wireless measurement; and f) displaying a visual representation of said collection of data within a three dimensional graphical image based on said estimated height above ground for said wireless measurements. The method of claim 1, wherein the reporting altitude is a WGS84 altitude. The method of claim 1, wherein each of the wireless measurements includes an accuracy associated with the corresponding location information, and the step of displaying the visual representation of the collection of data further includes providing an absolute threshold to the collection of data, and filtering the collection of data by comparing the accuracy for the corresponding wireless measurement to the absolute threshold to determine a subset of collective data used to display the visual representation. The method of claim 3, wherein the absolute threshold is between 1 meter and 100 meters. The method of claim 1, wherein each of the wireless measurements includes an accuracy associated with the corresponding location information, and the step of displaying the visual representation of the collection of data further includes providing a relative threshold to the collection of data, and filtering the accuracy for the corresponding wireless measurement by the relative threshold to determine a subset of collective data used to display the visual representation. The method of claim 5, wherein the relative threshold is between 80% and 99% of the total number of samples in the dataset. The method of claim 1, wherein the step of displaying the visual representation includes a step of displaying user density. The method of claim 1, wherein the step of displaying the visual representation includes a step of displaying wireless service status. The method of claim 1, wherein the step of displaying the visual representation includes a step of displaying wireless service status and user density. The method of claim 1, wherein the step of displaying the visual representation includes displaying the collection of data within a predetermined height segment. The method of claim 1, wherein the step of displaying the visual representation includes displaying the wireless measurements as the visual representation within a polygon segmented into a plurality of sections. The wireless service status is: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G Most Frequent Cell, 5G Strongest Cell, 5G Most Frequent Band, 5G Strongest Band, 5G Optimization Priority, LTE CQI, LTE Most Frequent Band, LTE Most Frequent Cell, LTE Most Frequent PCI, LTE Most Frequent TAC, LTE Optimization Priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE highest signal strength band, LTE highest signal strength cell, LTE highest signal strength PCI, LTE highest signal strength TAC, UMTS Ec/No, UMTS highest signal strength band, UMTS highest signal strength cell, UMTS highest signal strength LAC, UMTS highest signal strength PSC, UMTS RSSI, UMTS highest signal strength band, UMTS highest signal strength cell, UMTS highest signal strength LAC, UMTS highest signal strength PSC, GSM highest signal strength band, GSM highest signal strength BSIC, GSM highest signal strength cell, GSM highest signal strength LAC, GSM RSSI, GSM highest signal strength band, GSM highest signal strength BSIC, GSM highest signal strength cell, GSM highest signal strength LAC, CDMA Ec/Io, CDMA RSSI, EVDO Ec/Io, EVDO RSSI, User Density, Mobile Data Usage, WiFi Data Usage, Mobile + WiFi Data Usage, Downlink Throughput, Uplink Throughput, Jitter, Latency, Best Carrier 5G CSI-RSRP, Best Carrier 5G CSI-RSRQ, Best Carrier 5G CSI-SINR, Best Carrier 5G SS-RSRP, Best Carrier 5G SS-RSRQ, Best Carrier 5G SS-SINR, Best Carrier GSM RSSI, Best Carrier LTE CQI, Best Carrier LTE RSRP, Best Carrier LTE RSRQ, Best Carrier LTE SNR, Best Carrier UMTS Ec/No, Best Carrier UMTS 9. The method of claim 8, wherein the selected from the group consisting of RSSI, coverage improvement opportunity, multi-network coverage improvement score, optimization opportunity, sales opportunity, % low band, timing advance, and combinations thereof. 1. A method for generating a three-dimensional visual representation of wireless measurements, comprising:
a. capturing a plurality of said wireless measurements from one or more wireless devices, each said wireless measurement including location information indicative of a location where the corresponding said wireless measurement was made;
b. determining a latitude and longitude corresponding to each said radio measurement and determining a reference altitude from said latitude and said longitude;
c. determining a reporting altitude in a selected coordinate system corresponding to each said wireless measurement;
d. subtracting the reference altitude from the reporting altitude in the selected coordinate system;
e) determining an estimated height above ground corresponding to each of said wireless measurements; and f) generating a polygon on said visual representation corresponding to said estimated height above ground for said plurality of wireless measurements so as to encompass said wireless measurements based on a predetermined threshold. The method of claim 13, wherein the polygon is generated according to 90% to 99% of the measurements, each of the measurements being defined within a given range of latitude and longitude. The method of claim 14, wherein the given range of latitude and longitude is oriented to lie within one of the polygons based on a predefined threshold. The method of claim 15, wherein the predetermined threshold is an absolute distance measurement or a relative measurement based on a portion of the plurality of measurements. 1. A method for generating a visual representation of a wireless service situation on a three-dimensional display, comprising:
a. capturing measurements from a wireless device that include the wireless service conditions, the measurements including location information indicative of a location where the wireless measurements are made;
b. determining a latitude and longitude from the measurements and determining a reference altitude based on the latitude and longitude;
c. determining a reporting altitude in a selected coordinate system from said measurements;
d. subtracting the reference altitude from the reporting altitude in the selected coordinate system;
e) determining an estimated height above ground of said measurements; and f) displaying said wireless service conditions within said three-dimensional graphical image of said visual representation based on said estimated height above ground of said measurements. The method of claim 17, wherein the measurements include an accuracy associated with the location information, and the step of displaying the wireless service status further includes providing a predetermined absolute or relative threshold for the latitude and longitude that is compared with the accuracy associated with the location information to determine whether to use the estimated height above ground of the measurements in the three-dimensional graphical image. The method of claim 17, wherein the measurements include an accuracy associated with the location information, and the step of displaying the wireless service status further includes providing a predetermined absolute or relative threshold for the reporting altitude in the selected coordinate system against which the accuracy associated with the location information is compared to determine whether the estimated height above ground of the measurements is to be used for the three-dimensional graphical image. The method of claim 17, further comprising orienting the estimated ground height within a section of the three-dimensional graphical image. The method of claim 20, wherein the section of the three-dimensional graphical image has a height between 5 meters and 50 meters. 22. The method of claim 21, wherein the section of the three-dimensional graphical image is 15 meters high. 1. A method for generating a three-dimensional representation of a wireless service situation, comprising:
capturing a plurality of data measurements, each of said data measurements including a measured latitude and longitude and a reporting altitude;
determining the ground elevation at the measured latitude and longitude corresponding to each of said data measurements;
determining an altitude corresponding to each of the data measurements by determining a delta between the reporting altitude and the ground height; and displaying the plurality of data measurements within the three-dimensional representation of the wireless service situation,
The method of claim 1, wherein the plurality of data measurements are arranged based on the measured latitude and longitude within slices on a vertical axis based on the determined altitude, the slices having a distance of 5 meters to 50 meters, and each of the data measurements includes at least one of the wireless service conditions. The wireless service status is: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G most heavily used cell, 5G most heavily used cell, 5G most heavily used band, 5G most heavily used band, 5G optimized priority, LTE CQI, LTE most heavily used band, LTE most heavily used cell, LTE most heavily used PCI, LTE most heavily used TAC, LTE optimized priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE most heavily used band, LTE most heavily used cell, LTE most heavily used PCI, LTE most heavily used TAC, UMTS Ec/No, UMTS highest traffic band, UMTS highest traffic cell, UMTS highest traffic strength LAC, UMTS highest traffic strength PSC, UMTS RSSI, UMTS highest traffic band, UMTS highest traffic strength cell, UMTS highest traffic strength LAC, UMTS highest traffic strength PSC, GSM highest traffic band, GSM highest traffic strength BSIC, GSM highest traffic cell, GSM highest traffic strength LAC, GSM RSSI, GSM highest traffic band, GSM highest traffic strength BSIC, GSM highest traffic strength cell, GSM highest traffic strength LAC, CDMA Ec/Io, CDMA RSSI, EVDO Ec/Io, EVDO RSSI, User Density, Mobile Data Usage, WiFi Data Usage, Mobile + WiFi Data Usage, Downlink Throughput, Uplink Throughput, Jitter, Latency, Best Carrier 5G CSI-RSRP, Best Carrier 5G CSI-RSRQ, Best Carrier 5G CSI-SINR, Best Carrier 5G SS-RSRP, Best Carrier 5G SS-RSRQ, Best Carrier 5G SS-SINR, Best Carrier GSM RSSI, Best Carrier LTE CQI, Best Carrier LTE RSRP, Best Carrier LTE RSRQ, Best Carrier LTE SNR, Best Carrier UMTS Ec/No, Best Carrier UMTS 24. The method of claim 23, wherein the selected from the group consisting of RSSI, coverage improvement opportunity, multi-network coverage improvement score, optimization opportunity, sales opportunity, % low band, timing advance, and combinations thereof. 24. The method of claim 23, wherein applying an absolute or relative filter to the measured latitude and longitude determines whether the corresponding data measurements are utilized in the three-dimensional representation of the wireless service conditions. 24. The method of claim 23, wherein an absolute or relative filter is applied to the determined altitude to determine whether the corresponding data measurements are to be utilized in the three-dimensional representation of the wireless service conditions. 24. The method of claim 23, further comprising: an indoor classification, the indoor classification being used to determine whether to utilize the data measurements in the three-dimensional representation of the wireless service conditions.