Evaluation and visualization of indoor wireless services in 3D

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.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 260,594, filed Aug. 26, 2021, and U.S. Non - Provisional Patent Application No. 17 / 681,086, the entire disclosures of which are incorporated herein by reference.

[0002] The present invention relates to systems and methods for displaying and reporting wireless service status within a vertical structure and within a map view.

Background Art

[0003] Handheld mobile devices are widely popular in modern society. They provide access to wireless services such as voice, SMS, and the Internet via an interconnected network of transceivers. Communication between a mobile device and a network transceiver is carried out via electromagnetic waves in the form of wireless signals. To achieve and sustain good communication, these wireless signals need to meet a certain level 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 amplitudes of all other signals, which is also called interference power.

[0004] Buildings can pose problems for wireless communication because they can block (interfere with), attenuate (reduce the strength of), distort (reduce the quality of), or reflect (bounce back) the propagation of signals. Such negative changes to signals can be caused by a number of factors, including the size and location of the building or the location of the wireless device within the building.

[0005] Signal interference is a critical indicator of its negative impact on signal quality and is therefore subject to 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, one example being a lack of signal occupancy, in which multiple signals from several surrounding network transceivers are received at similar intensity. This effect is most commonly observed in high-rise buildings, where there are relatively few obstacles between network transceivers and mobile devices. External interference (noise) sources that further exacerbate the impact on signal quality within buildings include: spurious emissions from other transceivers; intermodulation products in nearby antennas; and natural sources including, but not limited to, thunderstorms, electrical storms, and cosmic microwave background radiation. The key performance indicator (KPI) used to quantify signal quality is the signal-to-noise ratio (SNR). [Overview of the project] [Problems that the invention aims to solve]

[0006] Embodiments herein relate to methods and three-dimensional visualizations relating to measurements of wireless service conditions and to generating visualizations having three dimensions to include multiple measurements in the visualization, the visualization displaying a matched set of measurements, thereby showing trends within the wireless service conditions on a visual display. Various embodiments provide methods for acquiring the measurements, correcting the data, and generating a dataset of the measurements for display. The measurements may be collected from crowdsourced data. The final product and output result in a visual display that identifies a set or multiple measurements to define one or more wireless service conditions at a given height at a given location. In this way, wireless service conditions can be determined within a particular building at a particular height. 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 characteristics within vertically extruded polygons representing sections of a building at a given location. Such information is useful for providers seeking to optimize their services within these areas. [Means for solving the problem]

[0007] In one 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 latitude and longitude from the set of data and determining a reference altitude based on the latitude and 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 ground clearance of the set of data; and (f) displaying a visual representation of the set of data in a three-dimensional graphical image.

[0008] In a further embodiment, the reporting altitude is the WGS84 altitude.

[0009] In a further embodiment, the method further includes the step of providing an absolute threshold to the data set by filtering the data set by the absolute threshold. In a further embodiment, the absolute threshold is between 1 meter and 100 meters.

[0010] In a further embodiment, the method further includes the step of providing a relative threshold to the data set. In a further embodiment, the relative threshold is 80% to 99% of the total number of samples in the dataset.

[0011] In a further embodiment, the method further includes the step of displaying user density. In a further embodiment, the method further includes the step of displaying wireless service status. In a further embodiment, the method further includes the step of displaying wireless service status and user density.

[0012] In a further embodiment, the method further includes the step of displaying the set of data within a predetermined height segment.

[0013] In a further embodiment, the method further includes the steps of: a plurality of wireless measurement values; and displaying the wireless measurement values ​​as a visual representation within a polygon segmented into a plurality of sections.

[0014] In further embodiments, the above 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 bandwidth, 5G Strongest bandwidth, 5G Optimization Priority, LTE CQI, LTE Most Frequent bandwidth, LTE Most Frequent cell, LTE Most Frequent PCI, LTE Most Frequent TAC, LTE Optimization Priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE Strongest bandwidth, LTE Strongest cell, LTE Strongest PCI, LTE Strongest TAC, UMTS Ec / No, UMTS Most Frequent bandwidth, UMTS Strongest cell, UMTS Strongest Frequently degree LAC, UMTS best communication Frequently GSM Maximum Communication Strength Score (PSC), UMTS RSSI, UMTS Maximum Communication Strength Bandwidth, UMTS Maximum Communication Strength Cell, UMTS Maximum Communication Strength LAC, UMTS Maximum Communication Strength PSC, GSM Maximum Communication Frequency Bandwidth, GSM Maximum Communication Frequently BSIC, GSM highest communication frequency cell, GSM highest communication FrequentlyLAC, GSM RSSI, GSM Maximum Bandwidth, GSM Maximum BSIC, GSM Maximum Cell, GSM Maximum 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 The selection is made from a group consisting of RSSI, coverage improvement opportunities, multi-network coverage improvement scores, optimization opportunities, sales opportunities, % low bandwidth, timing advance, and combinations thereof.

[0015] In one 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 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 ground clearance of the wireless measurements; and (f) generating a polygon on the visual representation that encloses the wireless measurements, based on predetermined thresholds of a plurality of measurements.

[0016] In a further embodiment, the polygon is generated according to 90% to 99% of the above measurements, and each of the above measurements is defined within a given range of latitude and longitude.

[0017] In a further embodiment, the given range of latitude and longitude is oriented to fit within a polygon based on a predetermined threshold.

[0018] In further embodiments, the predetermined threshold is either an absolute measurement of distance or a relative measurement based on a portion of all measurements.

[0019] In one preferred embodiment, a method for generating a visual representation of wireless service status on a three-dimensional display includes: (a) capturing measurements from a wireless device, including the wireless service status; (b) determining 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 the measurements; (d) subtracting the reference altitude from the reporting altitude in the selected coordinate system; (e) determining an estimated ground clearance from the measurements; and (f) displaying the wireless service status in a three-dimensional graphical image of the visual representation.

[0020] In a further embodiment, the method further includes the step of providing a predetermined absolute or relative threshold for the latitude and longitude.

[0021] In a further embodiment, the method further includes the step of providing a predetermined absolute or relative threshold for the reporting altitude in the selected coordinate system.

[0022] In a further embodiment, the method further includes the step of oriented the estimated ground clearance within a section of the three-dimensional graphical image. In a further embodiment, the height of one section of the three-dimensional graphical image is between 5 meters and 50 meters. In a further embodiment, the height of one section of the three-dimensional graphical image is 15 meters. In a further preferred embodiment, one measurement is displayed within one section on a visual display, and multiple measurements are aggregated to show a trend regarding the radio service status within multiple sections on the visual display at a given latitude and longitude (i.e., what the radio service status is like at a given height at a given location).

[0023] In one preferred embodiment, a three-dimensional representation of a radio service situation includes: a plurality of data measurements, each of which is defined by measured latitude and longitude, and each of which is provided with a reported altitude; a step of determining the ground elevation at the measured latitude and longitude; a final altitude being generated by determining a delta by comparing the reported altitude with the ground elevation; each of which is displayed in the three-dimensional representation of the radio service situation, positioned on a vertical axis slice based on the final altitude, with the slice having a distance of 5 meters to 50 meters; and each of which is included in at least one radio service situation.

[0024] In a further embodiment, regarding the three-dimensional representation of the wireless service situation, the wireless service situation includes: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G highest communication frequency cell, 5G highest communication intensity cell, 5G highest communication frequency band, 5G highest communication intensity band, 5G optimization priority, LTE CQI, LTE highest communication frequency band, LTE highest communication frequency cell, LTE highest communication frequency PCI, LTE highest communication frequency TAC, LTE optimization priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE highest communication intensity band, LTE highest communication intensity cell, LTE highest communication intensity PCI, LTE highest communication intensity TAC, UMTS Ec / No, UMTS highest communication frequency band, UMTS highest communication frequency cell, UMTS highest communication Frequently degree LAC, UMTS highest communication Frequently degree PSC, UMTS RSSI, UMTS highest communication intensity band, UMTS highest communication intensity cell, UMTS highest communication intensity LAC, UMTS highest communication intensity PSC, GSM highest communication frequency band, GSM highest communication Frequently degree BSIC, GSM highest communication frequency cell, GSM highest communication FrequentlyDegree LAC, GSM RSSI, GSM highest communication intensity band, GSM highest communication intensity BSIC, GSM highest communication intensity cell, GSM highest communication intensity 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 opportunity, multi-network coverage improvement score, optimization opportunity, sales opportunity, % low band, timing advance, and combinations thereof.

[0025] In a further embodiment, with respect to the three-dimensional representation of the wireless service situation, an absolute filter or a relative filter is applied to the measured latitude and longitude.

[0026] In a further embodiment, with respect to the three-dimensional representation of the wireless service situation, an absolute filter or a relative filter is applied to the determined altitude.

[0027] In a further embodiment, with respect to the three-dimensional representation of the wireless service situation, the method further includes indoor classification, and the indoor classification is necessary for utilizing the data measurement values in the three-dimensional representation of the wireless service situation.

Brief Description of the Drawings

[0028] as it seems to be an incomplete or placeholder-like tag in the original. If you have any specific requirements regarding handling such tags, please let me know.Figure 2 is a 3D view of network performance for a single carrier on a single platform measuring RSRP. [Figure 3] Figure 3 is a flowchart of the process for generating vertical measurements within a 3D view. [Figure 4] Figure 4 is a flowchart showing the process of generating 3D polygons corresponding to buildings in a visual map. [Figure 5] Figure 5 shows a flowchart for generating a 3D representation of user density. [Figure 6] Figure 6 shows a flowchart for generating a 3D display of the wireless service status. [Modes for carrying out the invention]

[0029] Disclosed is a technique for providing wireless service status performance within a three-dimensional (3D) graphical representation. The 3D view allows for the representation of multiple sets of data, including mobile device density and wireless service quality at a given height within a building, within a single visual display. This representation can be organized by wireless service generation (GSM, UMTS, LTE, 5G), specific service provider, and metrics describing wireless service status performance. The resulting 3D graphical representation provides a user-friendly visualization of areas with good and poor wireless service status, enabling service providers to quickly and efficiently prioritize their efforts towards addressing network performance issues.

[0030] Compared to the typical 2D approach for designing and optimizing network performance, which pushes all network state metrics onto a single horizontal plane, a 3D representation provides a more sophisticated, layered view of the network state experienced by a mobile device depending on its height location within a building.

[0031] Wireless service status refers to data collected from mobile devices, 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 Highest Frequency Cell, 5G Highest Signal Strength Cell, 5G Highest Frequency Bandwidth, 5G Highest Signal Strength Bandwidth, 5G Optimization Priority, LTE CQI, LTE Highest Frequency Bandwidth, LTE Highest Frequency Cell, LTE Highest Frequency PCI, LTE Highest Frequency TAC, LTE Optimization Priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE Highest Signal Strength Bandwidth, LTE Highest Signal Strength Cell, LTE Highest Signal Strength PCI, LTE Highest Signal Strength TAC, UMTS Ec / No, UMTS Highest Frequency Bandwidth, UMTS Highest Frequency Cell, UMTS Highest Frequently degree LAC, UMTS best communication Frequently GSM Maximum Communication Strength Score (PSC), UMTS RSSI, UMTS Maximum Communication Strength Bandwidth, UMTS Maximum Communication Strength Cell, UMTS Maximum Communication Strength LAC, UMTS Maximum Communication Strength PSC, GSM Maximum Communication Frequency Bandwidth, GSM Maximum Communication Frequently BSIC, GSM highest communication frequency cell, GSM highest communication FrequentlyLAC, GSM RSSI, GSM Maximum Bandwidth, GSM Maximum BSIC, GSM Maximum Cell, GSM Maximum 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 scores, optimization opportunities, sales opportunities, % low bandwidth, and timing advance. In particular, since these radio service conditions are collected simultaneously as data from a single mobile device, further extrapolation can be performed by combining the use of a portion of the above data with other portions of the above data. Radio data further refers to any further metrics that may be collected, including but not limited to latitude, longitude, altitude, vertical and horizontal accuracy, time, and various other metrics. Each collected measurement includes all data and all radio service conditions, and the above measurements can be interpolated in the database.

[0032] Within buildings, the dominant factors for signal level and quality degradation are transmission loss (signals weaken as they pass through high-density media such as concrete walls and metal panels), reflection (signals are redirected by high-density media of surrounding buildings and structures), and shadowing (signals are blocked by high-density media of surrounding buildings, structures, and vegetation). At ground level, these factors typically lead to reduced coverage (i.e., the signal level from the nearest network transceiver exceeds the signal level from more distant transceivers), resulting in higher signal occupancy, which leads to lower interference. In contrast, within high-rise buildings, as height increases, the number of obstacles in the path of signals from distant network transceivers decreases, resulting in lower signal occupancy and consequently higher interference.

[0033] To compensate for weak signals, additional network transceivers can be added, or the directivity spread of existing network transceiver antennas can be altered along their azimuth or elevation angles. After the signal strength reaches the desired range, further optimizations can be performed to reduce interference levels.

[0034] 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, and cables. Interference can also be caused by frequency reallocation. Operators reallocate licensed frequency spectrum across multiple technologies. For example, as usage of older generation services decreases, the spectrum is shifted to newer technologies to accommodate more users and traffic. Users still using older technologies are served with less spectrum and experience greater interference due to frequency reuse (multiple transceivers using the same frequency).

[0035] In some cases, when two or more signals of different frequencies are mixed (multiplied) within a nonlinear electronic component in a mobile device or network transceiver, frequency intermodulation can occur, which can lead to the generation of signals at frequencies other than the one being transmitted. Interference occurs when the accidental frequency at which a signal is received overlaps with a frequency already in use.

[0036] By identifying various potential interference problems and graphically representing user density, as well as signal and interference levels, these features can be concisely represented within a 3D view, making it easier for providers to assess problematic areas. The quantity of unique mobile devices and the number of measurements collected within the building can also help quantify the quality of the collected data by reducing metric variance and presenting the true mean.

[0037] Therefore, after identifying the radio service conditions that require correction, the performance of the radio service conditions can be improved by making changes to the transceiver network. Interference within the radio network can be managed by suppressing coverage and reducing overlap between adjacent transceivers. Interference is also typically reduced by adjusting various settings of the cell site antennas and network control software. For example, the antenna beam can be further concentrated toward the target area and buildings, and the transmitter power, frequency, and coding settings can be modified to increase the signal level from desired network transceivers within the target area and buildings, and decrease the signal level of undesirable network transceivers.

[0038] The collected data and wireless service status, used in a graphical representation of wireless service status, can capture a representative sample of users within the wireless network. In all cases, a single data measurement contains all the data information and wireless service status. This allows the measurement to be positioned on the display based on its location on the horizontal x and y axes and the vertical z axis. The measurement itself contains all the associated wireless service status, which can be effectively stored in a database. Therefore, a dataset is provided by combining multiple measurements, and the larger the dataset, the more reliable the specific trends that can be observed within that dataset.

[0039] By capturing such datasets, end users can be confident in the reliability of the dataset due to the vast number of collected dataset points, understanding that a larger number of dataset points generally indicates higher reliability than a smaller number. Simultaneously, if the dataset reveals that certain areas require modifications to improve signal strength or reduce interference, or any other relevant wireless service conditions, higher priority can be given to areas with higher density in order to improve wireless service conditions for a larger number of users.

[0040] Therefore, referring to the figure here, Figure 1 provides a detailed graphical view (21) of user density in a 3D representation. This allows for the generation of a chart in Figure 1 with a vertical axis (building height) that identifies the relevant buildings in terms of their physical location, and the density of users in this space follows the legend (20) on the visual display. Figure 1 provides a simple representation of all networks with their relative density in sections of a particular height within the visual window. Thus, if there is a 50-story building and the bottom 5 floors are a parking lot, the graphical representation will show the building of This will include multiple different sections, and the wireless networks collected at these points dataThis will depict the relative density. Therefore, since parking structures are not typically places where people are always present, the lower 5th floor parking spaces will be depicted as having a low density of network users. This is because network services are generally not used for extended periods in these spaces. In contrast, workspace or residential floors have a higher user density and can be identified as such. In other cases, industrial buildings or warehouses may have a low number of users, while residential and commercial office spaces may have a high density. A larger number of dataset points improves the reliability of the dataset, and optimization priorities are indicated based on the user density within these spaces.

[0041] In selecting the most sophisticated method for representing density, the legend (20) provides various shadings or other metrics that are easily visible to the user. However, it will be recognized by those skilled in the art that visual graphical representations can be created, for example, using color-themed representations where different colors represent different levels of user density, or using different shading or fill patterns. Essentially, some formats of representation are similar to heatmaps and can provide a visual representation of wireless service conditions or data, such as user density, within multiple slices of a vertical axis. The result is a visual display (21) in which a structure (23) as one of the structures within the visual display (21) can be shaded according to the density of users at a certain estimated height within the structure (23).

[0042] 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 specific searches, the toggle field (25) allows switching between a heatmap and a binning data view, the binning data view is used only in 2D mode. The next field 26 is a field that enables classification, for example, representing "outdoor and indoor," or sometimes representing only "outdoor" or only "indoor." The time window (27) shows, for example, "over the past 24 months," and the bandwidth window (28) allows review of multiple different frequency bands of wireless services. The number of fields can be modified to include any number of datasets related to wireless service conditions, or any number of points extrapolated from the data, and each of these can further be based on user density.

[0043] Next, Figure 2 is a variation of Figure 1, and the legend (40) provides a single view that displays the Reference Signal Received Power (RSRP) metric for individual carriers using the LTE band within a visual representation. Thus, multiple different radio service status metrics can be easily switched to generate a map of the user's interest.

[0044] To generate the visual displays shown in Figures 1 and 2, after collecting data from users, a population dataset is captured by collecting and modifying this data in new and unique ways, organizing it into a database, and then displaying it graphically. Data that satisfies these views is captured by mobile devices on the network and aggregated in the database. For example, the Android OS reports GPS data, which includes horizontal and vertical geographical location measurements, including latitude and longitude coordinates (decimal angle, WGS84), altitude, horizontal accuracy, and vertical accuracy. The above data may also be specifically collected from devices that utilize applications or programs on wireless devices designed to capture the aforementioned data points, or additional data points that may be relevant.

[0045] A key issue in displaying collected data or wireless service status is whether such information can be displayed in an easily usable format. The first issue is the orientation of the dataset within the vertical axis, as mobile devices report their vertical position in a specific coordinate system. For example, one of several coordinate systems, WGS84, is used as an example throughout, but other coordinate systems exist and are used in different areas of the world, as will be recognized by those skilled in the art. However, since these coordinate systems do not relate to elevation from the Earth's surface, they only yield results that require correction. Indeed, in WGS84, the vertical position is reported in meters from the Earth's geoid (a virtual surface determined by Earth's gravity and approximated by mean sea level), not from the Earth's surface (positive elevation). To calculate the elevation of a measurement from the Earth's surface, the elevation of the Earth's surface relative to the geoid elevation at the reported location is calculated, and this is then subtracted from the reported elevation of the measurement. This calculation simply obtains the delta between two measurements on the same reference frame (WGS84) to obtain the actual elevation (from the ground at that latitude and longitude) for display purposes.

[0046] The accuracy of horizontal and vertical position readings is crucial for capturing the true service status at a given location. Therefore, if multiple location data points have a variance exceeding a predetermined amount, these data may be excluded from the dataset. 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, a stricter variance threshold, such as using measurements of only 10 meters, may be more appropriate. However, if only 10 measurements are 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 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 99% median of all data measurements and radio service status sorted in ascending order of elevation. Thus, in a simple dataset of 10 measurements, the lowest and highest data points would be removed, using the 80% metric. Similarly, when using a dataset of 1000 measurements, a sample dataset can be obtained by removing the top 50 and bottom 50 measurements for 90% of the metrics. These variables can be set and modified as needed by the user.

[0047] With respect to the given data, after filtering out measurements that do not meet the thresholds for vertical and horizontal accuracy variance, the measurements are grouped into multiple segments representing the range of vertical height (floors) within the building. These segments can be as short as 1 meter, but are preferably 15 meters long. Alternatively, the maximum number of segments can be provided by grouping the measurements according to the height of a particular building. For example, if the total number of segments is 5, a building with a height of 100 meters will result in a segment of 20 meters. However, more than 70% of all buildings are less than 15 meters tall. By setting the segment height to 15 meters, many buildings can be grouped into a single segment, thereby eliminating erroneous data that may exist if we were to create 5 or 10-meter segments and group the data into these relatively small sections. The average of the grouped measurements is presented in the visual portal and display as shown in Figures 1 and 2.

[0048] Figure 3 provides an overview of one method for utilizing captured data regarding wireless service status to refine and utilize the data for presentation. Step (1) provides data capture from wireless devices. As already detailed, the above data includes, but is not limited to: latitude, longitude, horizontal accuracy of position, vertical accuracy of position, and wireless service status.

[0049] Next, step (2) uses the collected location data to determine the ground height for each measurement. The data, including the defined latitude and longitude of the measurement, provides the precise location relative to the ground. A database is provided that identifies the ground height at each given latitude and longitude. The horizontal accuracy of these measurements is provided, taking latitude and longitude into account. If the horizontal accuracy is within the distance of the building, the data can be assumed to be accurate. If the horizontal accuracy is greater than the distance / footprint of the building, data at distances greater than, for example, x meters (i.e., an absolute threshold) can be excluded using a specific filtering protocol, or a relative threshold, as detailed herein, can be applied. In certain cases, horizontal accuracy is not very important because the dispersion is negligible due to the horizontal nature of the ground. Data for a building adjacent to another building may be preserved very well. Therefore, such dispersion may not have a significant impact on the data. However, in hilly areas (e.g., San Francisco), even a distance of 15 meters in any horizontal direction can result in a significant change in ground height. In such cases, it may be necessary to adjust the data cutoff at a predetermined threshold to ensure the accuracy of the data under these circumstances.

[0050] Step (3) then acquires known measurements with determined latitude and longitude, and estimates the height based on the measured data. Thus, the ground elevation, e.g., WGS84 vertical elevation (altitude relative to the Earth's ellipsoid), transformed into the relevant coordinate system, is determined for each structure using data from a third-party DEM (Digital Elevation Model) or DSM (Digital Surface Model). Subsequently, since the position data collected by the wireless device is already in the WGS84 coordinate system (reported by the device's GPS), the ground elevation of the measurement is calculated as the arithmetic difference between the elevation of the measurement and the ground elevation. Wherever another elevation measurement is available, appropriate corrections are made as needed based on that measurement. The resulting data is the corrected elevation of the measurement, which is used to accurately position the measurement within the polygon of the visual display. This provides the measurement for each data point at a given elevation.

[0051] Then, in step (4), it becomes possible to estimate the measurements within the building based on the ground height measurements calculated from step (3). This can be easily done if the height of the building is known. In certain cases, the height of a building whose height is unknown can be estimated from the collected measurements / data, as shown in more detail in Figure 4. Regardless of how the height of the building is determined or estimated, data from multiple measurements is stored in a database, and the database aggregates this data for 3D mapping in step (5).

[0052] Following these processes, specific measurements with known accuracy are provided. In practice, data with vertical and horizontal accuracy measurements are often provided. These measurements are often provided in meters (distance) and / or include an associated confidence level. A particular measurement will have either a low accuracy reading or a high accuracy reading, and thus shorter distances provide greater confidence in the actual location. In step (5), the data are grouped according to absolute measurements. This means that data is only used if it has an accuracy measurement shorter than a given 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, and less than 10 meters (including any distance range in between). However, absolute variance is not always used; relative thresholds are often used, in which case the entire dataset is examined and a portion of the dataset 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 dataset, and these thresholds represent the central portion of the dataset. For example, the 80% threshold excludes the top 10% and bottom 10% of the dataset. The entire dataset remains in the database, but the data captured and displayed is determined under the absolute or relative thresholds as defined herein. Thus, the data presented on the visual display is specific to the exact measurement, enabling precise identification of individual measurements.

[0053] Step (6) involves generating groupings of data within similar heights at similar latitudes and longitudes. Again, data is acquired for the entire dataset, and a predetermined threshold is used to determine which data should be displayed. In particular, some degree of variance may exist, as not all measurements have exactly the same latitude and longitude, nor do they have the same error variance on the vertical axis. The groupings thus best fit these measurements so that it can best determine whether the measurements were taken in the same building or in adjacent buildings. The variance here may depend on a number of factors, including the proximity of adjacent buildings and the error variance of the measurements.

[0054] 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 radio carrier. Each of these representations has a visual or display element, which is defined in the legend. End users can modify the display based on this specific radio service situation by obtaining the display and modifying the radio service situation. This makes it possible to represent these various radio service situations. User density can be displayed in all cases, or it can be visually presented in a lower-level pop-up or other visual cue when the user evaluates the data in the visual representation.

[0055] In certain cases, the data yields measurements for structures whose height or dimensions are unknown. This occurs when new construction is completed or simply in locations where data is not publicly available. In certain embodiments, structures with unknown height are extruded from ground level based on the reported measurement altitude if the total number of users of the structure is 10 or more. If there are fewer than 10 users, only the base segments (0m to 15m) are displayed. The extrusion continues until a segment contains x% of the total number of samples of the structure, e.g., 97%, within itself and the segments below it. This helps prevent unrealistic building heights from being displayed due to a small number of samples at extremely high altitudes. In some embodiments, if there are more than a predetermined number (e.g., 8) of consecutive segments without measurements, the extrusion stops, regardless of whether the height of the structure is known. If the height is known, multiple segments are displayed until the height of the structure is reached. If measurements are present, the segments are colored; otherwise, the segments are grayed out or depicted by some other shading or visual cue.

[0056] Continuing this logic, Figure 4 shows the flow of the process for determining the height of a polygon that will be represented within a display window, such as those seen in Figures 1 and 2. According to Figure 3, the first step is to collect data from a wireless device (1). Subsequently, the data from this first step is used to determine the latitude and longitude (10). Once the first two steps are complete, 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 estimated from data within the boundaries of the building polygon. In practice, the important point is this particular case where the height is unknown, but these steps can also work together to ensure an accurate representation of the building.

[0057] In practice, even if a known structure exists, its specific height and dimensions may not be known. In other cases, the existence of a structure may not be known at all, such as a recently developed structure, and therefore the set of data implies the existence of a structure to be displayed. Finally, a particular structure may have errors or unused space, which, if present, can introduce uncertainty into the visual display.

[0058] Therefore, step (13) acquires aggregate data and refines this data to exclude outlier data. This is done by excluding data with parameters that have low accuracy, whether in terms of vertical position accuracy or latitude and longitude accuracy. This data is typically captured in step (1), i.e., the data points literally define estimates of the accuracy of the captured data points. Preferably, the best dataset is generated by combining complete datasets of multiple measurements and filtering this data using a specific process. The various accuracy metrics are the same as those shown in detail in Figure 3, i.e., absolute measurements of distance, or relative measurements from which a portion of the dataset is taken to exclude outlier data.

[0059] Finally, using the refined data, step (14) can be used to modify the polygons from step (11) or (12), particularly in terms of height, so that they encompass x% of the sample. This allows for correction of any of the polygon heights based on the data. In particular, the percentage of sample to include in this step and other steps will vary based on several factors, including the total range, the total number of samples, the confidence level of the data, and other factors. Typically, the above percentages should be greater than 80% of the sample, more preferably greater than 90%, 95%, 97%, or 99% of the sample.

[0060] To provide context for this decision, the sample set includes 1000 data points and one structure with an unknown height. The dataset is set to 97% of the sample. The structure height is determined starting with the lowest height measurement, with a total of 970 measurements captured (reaching 97% of the total sample). After collecting 970 samples, the structure height is determined to be the highest measurement in this sample set. The remaining 30 higher measurements are excluded from the determined structure height, thereby excluding potentially inaccurate measurements. This percentage can be adjusted according to the total number of measurements, the accuracy of these measurements, and other parameters determined in each scenario.

[0061] In certain cases, especially in large cities, underground measurements are often available. For example, the New York City subway network or subway stations may have thousands of underground measurements. In such cases, measurements for determining height are started with those determined to be above ground level, based on latitude and longitude. Therefore, if there are 10,000 measurements and 1,000 of them are determined to be underground, 9,000 data points are considered for the height of the structure. If 97% of the samples are used for height, a total of 8,730 samples are used in the height calculation, and the remaining 270 samples at higher elevations are excluded.

[0062] Accordingly, as shown in Figures 1 and 2, the visual display includes multiple structures, each structure represented by a polygon with a vertical orientation and x and y coordinates. In certain embodiments, the size of the structures is provided or already known, specifically including height, but in some embodiments, it also includes area in square feet in x and y coordinates. For example, if polygons and heights of structures from a third-party source are available, these are used, although they may be inaccurate. Structures with unknown heights are pushed upward from ground level based on the reported altitude of the measurements. The push-out is added upward from ground level and continues until x% of the total number of structure samples are contained within the 3D representation of the structures. Using these cutoffs for the data sample set helps prevent unrealistic structure heights from being displayed due to inaccuracies and small numbers of samples at extremely high altitudes. For structures that still appear taller than they actually are, typically the top one or more segments have very few users (1 or 2) and can be ignored using these cutoff metrics. The objective here is simply to provide a dataset that offers a representative sample of data for evaluating wireless service status metrics and the reliability of these metrics based on user density and the total measurements of these wireless service statuses.

[0063] The data and wireless service status collected from wireless devices in each method (Step 1) provide a collective approach toward identifying wireless service status in parallel with user density identification and providing a visual approach on the z-axis (vertical direction). The results of this approach are validated by comparing the data with real-world examples. For example, signal levels and quality are extremely high in buildings where an in-building cell site system, known to improve signal levels and reduce interference, is installed. Furthermore, the data shows that signal levels increase with height, and at the same time, interference levels increase with altitude, as expected from more interference at higher floors. Finally, the total number of users is available in the visual display, which helps users judge the reliability of the presented information. Thus, users can independently determine the displayed wireless service status at their own discretion by the data, by judging the number of users in the data and other metrics that may affect its reliability.

[0064] In certain embodiments, the use of indoor classification techniques may be even more useful, particularly in relatively lower-level locations within buildings. This allows certain embodiments to enable the classification of indoor or outdoor measurements in areas where both indoor and outdoor square-foot areas are large. This can be done by using the collected data, comparing the building footprint with latitude and longitude measurements, and evaluating it with regard to horizontal accuracy measurements. In practice, for any given measurement, horizontal accuracy is necessary to ensure that a given measurement is within one building and not another. This indoor classification can be further useful when horizontal accuracy is low, or when additional data points may be needed simply 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 used in conjunction with the methods and processes detailed herein.

[0065] Determining the user density at a given location can improve the reliability of the data, as detailed herein. Figure 5 provides a simplified diagram of an embodiment for generating this information. Step (1) includes capturing data from a wireless device. Step (2) determines the latitude and longitude direction from the data and determines the altitude at the point using a database. Next, step (51) applies an initial filter to the data based on horizontal accuracy, which excludes, for example, data with accuracy ratings exceeding a predetermined 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 a further horizontal accuracy process, such as a relative process or other appropriate process to ensure that the center of the measurement is 90%, 95%, or 97%. Next, step (4) (following step (52)) estimates the position within the building based on the estimated height. Finally, step (5) aggregates the data into a database for 3D mapping. Step (6) generates groupings of data within similar heights of similar latitudes and longitudes based on the previous steps and the applied threshold step. Finally, step (53) provides a display of user density on a visual display.

[0066] Figure 6 shows the application of a similar process to the evaluation of a specific wireless service condition, based on the disclosures herein. Step (61) captures data including the wireless service condition from a wireless device (or multiple wireless devices). Step (2) determines the orientation of a single data measurement based on the latitude and longitude of the above 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. Where appropriate in each step (2) and (3), specific filters and thresholds may be applied to exclude data with large variances based on absolute or relative thresholds. Next, step (4) estimates the location within a building based on the estimated height. Subsequently, step (5) aggregates the data into a database for 3D mapping. Then, step (62) (following step (5)) applies filters to all data if necessary to ensure data accuracy, which may be done individually or in addition to optionally applying filters and thresholds earlier in steps (2) and (3). Step (6) generates groupings of data within similar altitudes of similar latitude and longitude. Step (63) then ends with a visual display representing 3D data from measurements from wireless devices to display selected wireless service status metrics of interest.

[0067] Those skilled in the art will recognize that a particular process can be modified by combining all or part of the various methods and processes described above. Furthermore, specific steps can be optionally included in various embodiments. Those skilled in the art will recognize that the embodiments detailed herein are not limited to methods for manipulating or displaying data.

Claims

1. A method for generating a three-dimensional visual representation of wireless measurements, performed by a system, the following: a. The system captures a set of data representing a plurality of wireless measurement values ​​from a plurality of wireless devices, wherein each of the plurality of wireless measurement values ​​includes location information indicating the location where the corresponding wireless measurement value is generated; b. The system determines the latitude and longitude corresponding to each of the plurality of radio measurement values, and determines a reference altitude based on the latitude and longitude, wherein the reference altitude is determined relative to the geoid elevation; c. The system determines a reporting altitude in a selected coordinate system corresponding to each of the plurality of wireless measurement values; d. The system subtracts the reference altitude from the reporting altitude in the selected coordinate system; e. The system determines an estimated ground height corresponding to each of the plurality of wireless measurement values ​​based on the result of the subtraction; and f. The system displays a visual representation of the data set in a three-dimensional graphical image based on the estimated ground height relating to the plurality of wireless measurement values. A method that includes this.

2. The method according to claim 1, wherein the reporting altitude is the WGS84 altitude.

3. The method according to claim 1, wherein each of the plurality of wireless measurements includes an accuracy associated with the corresponding location information, and the step of displaying the visual representation of the set of data further includes providing an absolute threshold to the set of data, and filtering the set of data by comparing the accuracy with respect to the corresponding wireless measurement and the absolute threshold to determine a subset of the collective data to be used for displaying the visual representation.

4. The method according to claim 3, wherein the absolute threshold is 1 meter to 100 meters.

5. The method according to claim 1, wherein each of the plurality of wireless measurements includes an accuracy associated with the corresponding location information, and the step of displaying the visual representation of the set of data further includes providing a relative threshold to the set of data, and filtering the accuracy relating to the corresponding wireless measurement by the relative threshold to determine a subset of the collective data used to display the visual representation.

6. The method according to claim 5, wherein the relative threshold is 80% to 99% of the total number of samples in the dataset.

7. The method according to claim 1, wherein the step of displaying the visual representation includes the step of displaying user density.

8. The method according to claim 1, wherein the step of displaying the visual representation includes the step of displaying the wireless service status.

9. The method according to claim 1, wherein the step of displaying the visual representation includes the step of displaying wireless service status and user density.

10. The method according to claim 1, wherein the step of displaying the visual representation includes the step of displaying the set of data within a predetermined height segment.

11. The method according to claim 1, wherein the step of displaying the visual representation includes the step of displaying the plurality of wireless measurement values ​​as the visual representation in a polygon segmented into a plurality of sections.

12. The aforementioned 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 bandwidth, 5G Strongest bandwidth, 5G Optimization Priority, LTE CQI, LTE Most Frequent bandwidth, LTE Most Frequent cell, LTE Most Frequent PCI, LTE Most Frequent TAC, LTE Optimization Priority, LTE RSRP, LTE RSRQ, LTE SNR, LTE Maximum Communication Strength Bandwidth, LTE Maximum Communication Strength Cell, LTE Maximum Communication Strength PCI, LTE Maximum Communication Strength TAC, UMTS Ec / No, UMTS Maximum Communication Frequency Bandwidth, UMTS Maximum Communication Frequency Cell, UMTS Maximum Communication Frequency LAC, UMTS Maximum Communication Frequency PSC, UMTS RSSI, UMTS Maximum Communication Strength Bandwidth, UMTS Maximum Communication Strength Cell, UMTS Maximum Communication Strength LAC, UMTS Maximum Communication Strength PSC, GSM Maximum Communication Frequency Bandwidth, GSM Maximum Communication Frequency BSIC, GSM Maximum Communication Frequency Cell, GSM Maximum Communication Frequency LAC, GSM RSSI, GSM Maximum Communication Strength Bandwidth, GSM Maximum Communication Strength BSIC, GSM Maximum Communication Strength Cell, GSM Maximum Communication Strength LAC, CDMA Ec / Io, CDMA RSSI, EVDO Ec / Io, EVDO RSSI, User Density, Mobile Data Usage, Wi-Fi Data Usage, Mobile + Wi-Fi 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 UMTSThe method according to claim 8, selected from the group consisting of RSSI, coverage improvement opportunities, multi-network coverage improvement scores, optimization opportunities, sales opportunities, % low bandwidth, timing advance, and combinations thereof.

13. A method for generating a three-dimensional visual representation of wireless measurements, performed by a system, the following: a. The system captures a plurality of wireless measurement values ​​from a plurality of wireless devices, wherein each of the plurality of wireless measurement values ​​includes location information indicating the location where the corresponding wireless measurement value is generated; b. The system determines the latitude and longitude corresponding to each of the plurality of radio measurement values, and determines a reference altitude from the latitude and longitude, wherein the reference altitude is determined relative to the geoid elevation; c. The system determines a reporting altitude in a selected coordinate system corresponding to each of the plurality of wireless measurement values; d. The system subtracts the reference altitude from the reporting altitude in the selected coordinate system; e. The system determines an estimated ground height corresponding to each of the plurality of wireless measurement values ​​based on the result of the subtraction; and f. The system generates a polygon on the visual representation that corresponds to the estimated ground height for the plurality of wireless measurement values, such that it includes the plurality of wireless measurement values, based on a predetermined threshold. Methods that include...

14. The method according to claim 13, wherein the polygon is generated according to 90% to 99% of the plurality of radio measurements, and each of the plurality of radio measurements is defined within a given range of latitude and longitude.

15. The method according to claim 14, wherein the given range of latitude and longitude is oriented to fall within one of the polygons based on the predetermined threshold.

16. The method according to claim 15, wherein the predetermined threshold is an absolute measurement of distance, or a relative measurement based on a portion of the plurality of wireless measurement values.

17. A method for generating a visual representation of wireless service status on a three-dimensional display, performed by a system, the following: a. The system captures a radio measurement from a wireless device, the radio measurement including the radio service status, the radio measurement including location information indicating the location where the radio measurement is made; b. The system determines latitude and longitude from the radio measurement values, and determines a reference altitude based on the latitude and longitude, wherein the reference altitude is determined relative to the geoid elevation; c. The system determines a reporting altitude in a selected coordinate system from the wireless measurement values; d. The system subtracts the reference altitude from the reporting altitude in the selected coordinate system; e. The system determines the estimated ground height of the wireless measurement based on the result of the subtraction; and f. The system displays the wireless service status in a three-dimensional graphical image of the visual representation, based on the estimated ground height of the wireless measurement. Methods that include...

18. The method according to claim 17, wherein the wireless measurement includes an accuracy associated with the location information, and the step of displaying the wireless service status further includes providing the latitude and longitude with predetermined absolute or relative thresholds which will be compared with the accuracy associated with the location information to determine whether to use the estimated ground clearance of the wireless measurement for the three-dimensional graphical image.

19. The method according to claim 17, wherein the wireless measurement includes accuracy associated with the location information, and the step of displaying the wireless service status further includes providing a predetermined absolute or relative threshold to the reporting altitude in the selected coordinate system, which will be compared with the accuracy associated with the location information to determine whether to use the estimated ground height of the wireless measurement for the three-dimensional graphical image.

20. The method according to claim 17, further comprising the step of oriented the estimated ground clearance within one section of the three-dimensional graphical image.

21. The method according to claim 20, wherein the height of the section of the three-dimensional graphical image is 5 meters to 50 meters.

22. The method according to claim 21, wherein the height of the section of the three-dimensional graphical image is 15 meters.

23. A method for generating a three-dimensional representation of wireless service status, performed by a system, comprising: The system comprises the step of capturing a plurality of data measurements from a plurality of wireless devices, wherein each of the plurality of data measurements includes a measured latitude and longitude and a reporting altitude; The system determines the ground elevation at the measured latitude and longitude corresponding to each of the data measurements; The system determines the altitude corresponding to each of the plurality of data measurements by determining the delta between the reporting altitude and the ground height; and The system includes the step of displaying the plurality of data measurements within the three-dimensional representation of the wireless service status. Includes, The method wherein the plurality of data measurements are arranged based on the measured latitude and longitude within a slice on a vertical axis based on the determined altitude, the slice having a distance of 5 meters to 50 meters, and each of the plurality of data measurements includes at least one of the radio service conditions.

24. The aforementioned wireless service statuses are: 5G CSI-RSRP, 5G CSI-RSRQ, 5G CSI-SINR, 5G SS-RSRP, 5G SS-RSRQ, 5G SS-SINR, 5G PCI, 5G highest frequency cell, 5G highest signal strength cell, 5G highest frequency band, 5G highest signal strength band, 5G optimization priority, LTE CQI, LTE highest frequency band, LTE highest frequency cell, LTE highest frequency PCI, LTE highest frequency 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 Maximum Frequency Bandwidth, UMTS Maximum Frequency Cell, UMTS Maximum Frequency LAC, UMTS Maximum Frequency PSC, UMTS RSSI, UMTS Maximum Intensity Bandwidth, UMTS Maximum Intensity Cell, UMTS Maximum Intensity LAC, UMTS Maximum Intensity PSC, GSM Maximum Frequency Bandwidth, GSM Maximum Frequency BSIC, GSM Maximum Frequency Cell, GSM Maximum Frequency LAC, GSM RSSI, GSM Maximum Intensity Bandwidth, GSM Maximum Intensity BSIC, GSM Maximum Intensity Cell, GSM Maximum Intensity LAC, CDMA Ec / Io, CDMA RSSI, EVDO Ec / Io, EVDO RSSI, User Density, Mobile Data Usage, Wi-Fi Data Usage, Mobile + Wi-Fi 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 The method according to claim 23, selected from the group consisting of RSSI, coverage improvement opportunities, multi-network coverage improvement score, optimization opportunities, sales opportunities, % low bandwidth, timing advance, and combinations thereof.

25. The method according to claim 23, wherein an absolute filter or a relative filter is applied to the measured latitude and longitude to determine whether the corresponding data measurements are to be used in the three-dimensional representation of the wireless service status.

26. The method according to claim 23, wherein an absolute filter or a relative filter is applied to the determined degree to determine whether the corresponding data measurement is to be used in the three-dimensional representation of the wireless service status.

27. The method according to claim 23, further comprising an indoor classification, the indoor classification being used to determine whether to use the plurality of data measurements in the three-dimensional representation of the wireless service status.

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