Sub-cell level service coverage estimation

Abstract

According to some embodiments, a method is performed by an analytics system network node. The method comprises obtaining a static service coverage map for a cell of a wireless network. The static service coverage map comprises an association of service quality and coordinates in the cell. The method further comprises: calibrating the service coverage map using one or more of drive test measurements and crowdsource data; blending the calibrated service coverage map with one or more per-flow key performance indicator (KPIs) associated with one or more wireless devices in the cell; training a machine learning (ML) model using the blended service coverage map and one or more per-flow KPIs resulting in a labeled service coverage map; and operating the ML model using actual network load, network configuration and external conditions to generate a dynamic sub-cell service coverage map.

Description

Sub-cell Level Service Coverage EstimationTECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and more specifically to sub-cell level service coverage estimation.BACKGROUND

[0002] Per-session, event-based analytics systems, like Ericsson Expert Analytics (EEA), are part of the network management domain, management data analytics function (MDAF) or core network data analytics function (NWDAF). These analytics systems are based on collecting and correlating elementary network events from different network domains, such as core, radio, and transport networks. They calculate radio and network key performance indicators (KPIs), characterizing radio / environment or network operation at the user and session level. EEA also implements service quality models to estimate end user quality of service (QoS) and quality of experience (QoE) characteristics. These types of solutions are suitable for session-based troubleshooting and analysis of network issues.

[0003] Event based analytics systems are also used in service operation centers (SOC) for monitoring the quality of the wide variety of services used in the network level, as well as for monitoring the customer experience on individual per subscriber level. These tools are widely used in customer care and other business scenarios such as assurance of value-added service for a certain user at a certain period of time.

[0004] Event based analytics requires real-time collection and correlation of characteristic node and protocol events from different radio and core nodes, probing signaling interfaces (IFs) and sampling of the user-plane traffic as well.

[0005] The NWDAF emerges as a specialized, data-centric entity designed to address the escalating complexities of modem networks. It is a standardized fifth generation (5G) core network function which was part of Third Generation Partnership Project (3GPP) Release 16. NWDAF's core function is to provide insightful analytics to other network functions and support predictive analytics through the application of machine learning (ML) models. These models require training data to deliver specific analytics as needed. NWDAF collects network-related data from varioussources, including radio access, core network, and user equipment. The data collection may occur continuously or on-demand.

[0006] Consumers of NWDAF may request analytics information on two generic interfaces. The analytics information interface provides on-demand information in a request-response manner. On the event subscription interface, consumers may subscribe to analytics data. On top of the generic interfaces, the specification defines a list of analytics identifiers that the NWDAF may implement.

[0007] According to the 3GPP specification, NWDAF implements ML models for the following types of analytics events:• Slice load level information;• Network slice instance load level information;• Service experience;• Network function (NF) load;• Network performance;• Abnormal behavior;• User equipment (UE) mobility;• UE communication;• Abnormal behavior;• User data congestion;• QoS sustainability;• Dispersion;• Session Management (SM) congestion control experience;• Redundant transmission experience;• Wireless local area network (WLAN) performance;• Data network (DN) performance; and• End-to-end (E2E) data volume transfer time.

[0008] These models return information about the actual state, make predictions about the future, detect abnormal behavior / anomalies. Some of the use cases are described in NWDAF use cases 3GPP specification at www.3gpp.org / ftp / / Specs / archive / 23_series / 23.791Z23791-g20.zip, but network operators are implementing their own, non-standard use cases as well.

[0009] The minimization of drive tests (MDT) feature provides a remote method to use for troubleshooting or verification of the radio network that is simpler and cheaper than traditional drive tests. The feature provides a tool to optimize network planning with the MDT measurement data including location information.

[0010] For operators, the traditional drive test, where vehicles with measurement equipment are used for analyzing network coverage and capacity, is costly and requires planning and coordination of resources. In the field, it is desirable to use automated drive test solutions, including involvement of UEs, for easier operation.

[0011] 3GPP specified MDT so that standard mobile terminals may be used for measurements to provide data for the operators. This includes Global Navigation Satellite System (GNSS) location information, if available in the UEs. MDT provides a simpler, cheaper, and remote method to use for troubleshooting or verification of the radio network.

[0012] Area-based immediate MDT involves measurement collection by the UE in connected mode in a specified area. Data is reported to the eNodeB and the operations support system (OSS). The feature makes it possible for the operator to enable or disable MDT measurements for each configured frequency separately.

[0013] The measurement data are observed in the specific MDT performance monitoring (PM) events. For this feature, Ml, M2, M3, M4, M5, M6, and M7 measurements are provided. The MDT PM events are stored in the cell trace reporting output period (ROP) files and sent to the destination by streaming.Ml: Reference signal receive power (RSRP) and reference signal receive quality (RSRQ) measurement by UE. This report can contain detailed location information optionally if available in the UE. For more information, see 3GPP TS 36.214, Physical Layer; Measurements.M2: Power Headroom (PH) measurement by UE. For more information, see 3GPP TS 36.213, Physical Layer Procedures.M3: Received interference power measurement by eNodeB. For more information, see 3GPP TS 36.214, Physical Layer; Measurements.M4: Data volume measurement separately for downlink and uplink, for each QoS class identifier (QCI) and UE, and by eNodeB. For more information, see 3GPP TS 36.314, Layer 2; Measurements.M5 : Scheduled Internet Protocol (IP) throughput measurement separately for downlink and uplink, for each QCI and UE, by eNodeB. For more information, see 3GPP TS 36.314, Layer2; Measurements.M6: Packet delay measurement separately for downlink and uplink. The measurement for uplink is done by the UE for QCI for each UE, and by the eNodeB for each QCI for downlink.M7 : Packet loss rate measurement separately for downlink and uplink, for each QCI and UE. The measurement is done by the eNodeB.

[0014] Traditionally, drive testing is performed in metropolitan and rural mobile / cellular networks. The vehicle is usually equipped with radio signal capturing tools and Global Positioning System (GPS) receiver and configured to collect signals / data as the vehicle is driven through various sections of the network. The evaluation of the measurement is focused on the radio coverage and uses the received signal strength and signal quality mainly. The vehicle crew includes a driver and a technician. Besides the technology evaluation, the requirements for drive test methodology have evolved. Nowadays, a drive-test vehicle is equipped - beyond the network scanner and a GPS receiver - with 10-20 mobile devices that simulate and measure various service, a high-end laptop, power supply equipment, professional data storage, and a driver and a skilled technician / engineer. To secure the proper measurement methodology of the services and to support the high-level evaluation of the measurement log fdes, it is necessary to deploy and maintain servers to secure enough throughput, enough storage, and enough evaluation processing capacity. A drive test campaign needs a very careful plan and highly skilled engineers for design and evaluation as well. It is an expensive exercise for the entire radio network of a mobile operator.

[0015] In mobile networks, crowdsourcing in QoE assessment phase involves collecting data from the user terminals or dedicated collection devices. A mobile operator or a research group may provide applications that may be run in different mobility test modes such as walk or drive tests. Crowdsourcing using users’ terminals (e.g., a smartphone) is a cheap approach for operators or researchers for addressing large scale area and may help to improve the allocated resources of a given service and / or the network provisioning in some segments.

[0016] One important use case of crowdsourcing for mobile network operators is the estimation of KPIs and relevant key QoE indicators (KQIs) to quantify the end user’s perceived quality. It is also crucial for operators that crowdsource method enables producing service coverage maps using the location-based measurement of performance indicators. Because the involved smartphones provide high spatial resolution location information (GPS latitude-longitudecoordinates) together with the measurement itself, the crowdsource data is a valuable source for sub-cell measurement and prediction application.

[0017] There currently exist certain challenges. For example, during 5G development more and more new use cases emerge where there is business need to have sub-cell level measurement and prediction of particular KPIs, performance indicator (PI) and metrics in connection with capacity and congestion, in other words - service availability. Cell level measurement and prediction typically means 2-300 meters to a 10-20 kilometers resolution, depending on the typical cell radius of the area. The sub-cell level measurement and prediction means typically 10-to-50- meter spatial resolution. However, the state-of-the-art analytic systems provide radio cell level metrics and predictions only in a native manner. Providing sub-cell level analysis and prediction is very expensive, and the available solutions’ accuracy is still not good enough.

[0018] Among the new use cases there is a repeatedly appearing requirement to predict available downlink and uplink throughput in a certain location at a certain point in time, while the available metrics to predict are only available at cell level.

[0019] Current centralized, network-wide service quality monitoring and observability functions and solutions provide only low spatial resolution: tracking area, base station or maximum cell level. Centralized, sub-cell level service quality monitoring and prediction at a reasonable cost is not solved. The following are some known methods and their issues.

[0020] Required service quality can be learnt and mapped by using extensive drive tests. However, drive tests have many disadvantages and limitations: (a) expensive, time consuming; (b) possible coverage is limited, not possible to do the test in spots, areas where later the service will be used; (c) required cell, slice configuration is not available during measurements; and / or (d) the service may not be available during test, number of services that can be tested are limited.

[0021] The MDT feature was developed to replace extensive drive tests. When it is turned on, UEs start reporting coordinates periodically, which can be correlated with any measurements done on the UE traffic. MDT would be a good solution for obtaining updated service coverage map, however, operators do not use MDT in practice for the following reasons: (a) privacy: usage needs user consent, many users do not want to be traced; (b) MDT if turned on, is limited to certain test terminals; and / or (c) UE power consumption is significantly increased, UE battery is discharged much faster than without MDT.

[0022] Although crowdsource data gives high spatial resolution measurement, it has limitations that do not facilitate directly measuring and predicting service coverage: (a) at a certainlocation it might not provide statistically sufficient number of measurement samples for a long period, or for a location there is no sample at all; (b) the measurement is coming from customers usage, it is not a controlled probe that provides accurate metrics; and / or (c) standalone crowdsource data does not reflect the cell load and other network side performance metrics.

[0023] Therefore, alternative methods are needed to address the limitations.SUMMARY

[0024] As described above, certain challenges currently exist with sub-cell level service coverage estimation. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments estimate and predict sub-cell level service quality metrics, such as traffic load, signal strength, interference, signal to noise and interference ratio (SINR) and via these metrics the available throughput and other key performance indicators (KPIs) using only cell level metric prediction methodology. Expensive high resolution sub-cell level measurement (like network-wide minimization of drive test (MDT)) is not necessary because particular embodiments convert the cell level prediction into a higher spatial resolution output. Particular embodiments are implemented in a per-flow analytics system.

[0025] The input to particular embodiments is a static service coverage map generated by, for example, a radio-cell planning tool, which is high resolution in terms of geographical location (sub-cell level). However, the static service coverage map does not reflect the factors that change in time, such as traffic load, cell load, service usage, interference, weather, network configuration, etc. Information about these factors is available to network analytics systems on cell or per-flow level only with lower spatial resolution (cell, or maximum tracking area (TA) and sector level) and used as a second input to particular embodiments.

[0026] The high-resolution service coverage KPI maps may be generated in multiple steps. First, the static service coverage map is calibrated with the available drive test, MDT and crowdsource data, where available. At this stage the data are not actualized yet and the coverage of accurately localized data is low.

[0027] In the next step, using actual TA level correlated radio measurement data from radio node events (or cell trace), the per-flow KPIs are blended with the calibrated service coverage map.

[0028] In the third step, a machine learning (ML) model is trained by a supervised learning process to consider the individual user equipment (UE) contributions to the localized KPI data byusing the UE activity changes as labels. At this step, cell and slice load, and configuration, as well as external factors like weather conditions are considered.

[0029] During execution, the calibration and blending steps are repeated to consider the latest measurement and the calibrated and enhanced service coverage KPI map is used as input to the ML model. The model is executed with the actual load, cell, slice, config, and the actual external conditions. The result is an actualized high-resolution service KPI coverage map. This process may be executed periodically and as a result, the high-resolution service KPI coverage map may be generated periodically.

[0030] Prediction with the model is made by forecasting the input load, external conditions (using weather forecast, for example), configuration and executing the model with the forecasted input.

[0031] As an example, particular embodiments may be used for predicting whether a requested service will be available in different locations at, e.g., during a mass event.

[0032] The actualized sub-cell level service coverage map may also be used for cell and slice optimization to eliminate sub-cell areas where service quality degradation or service level agreement (SLA) violation is experienced.

[0033] A goal of particular embodiments is to enhance and improve the cell-planning tool generated static service coverage map with traffic analytics and predictions, transforming it into a dynamic service coverage map that reflects a predicted load at a location within a cell.

[0034] According to particular embodiments, an ML method periodically updates and actualizes a high-resolution, sub-cell level service coverage map, obtained from radio network planning, with network-wide measurable, real and actual, but not necessarily high-resolution, cell and subscriber level metrics. Particular embodiments also consider the external (non-network) conditions, like weather data, calendar event, as well as variable cell and slice configurations (changeable comparing to radio plan), affecting service quality.

[0035] Particular embodiments include a training process. The main steps of training include: (a) calibration of the service coverage map; (b) blending KPIs and the coverage map; and (c) labeling map with per-flow UE contributions.

[0036] Particular embodiments make the connection between metrics of the radio network reported on cell level and sub-cell level based on radio events (e.g., cell trace records). Geolocation of this measurement data may be done by associating pathloss measurements and the output of the radio propagation model (blending KPIs and the coverage map), used by the cell planning tool,and further refined by incorporating drive test measurements and crowdsource data (calibration of the service coverage map), thus generating a service coverage map with high spatial resolution of metrics collected from the cell trace records (CTRs).

[0037] After aligning the radio propagation map with TA level cell trace data, particular embodiments create labels based on different UE level and cell level data (labeling map with perflow UE contribution). This enables use of supervised learning techniques to make the connection between throughput experienced by UEs localized in the cell, with help of the above map, and affected cell resources.

[0038] Particular embodiments make predictions with the model. The main input parameters that are not necessarily high-resolution data but they can be forecasted include: (a) external information: expected calendar event (like concert, football match), venue, time, spectator numbers; (b) external factors influencing the radio conditions, e.g., weather forecast; (c) expected traffic load and services; and / or (d) planned slice, cell configurations. The latest service coverage plan may be used as input to the trained ML model to generate a predicted (future) sub -cell level service coverage map.

[0039] According to some embodiments, a method is performed by an analytics system network node. The method comprises obtaining a static service coverage map for a cell of a wireless network. The static service coverage map comprises an association of service quality and coordinates in the cell. The method further comprises: calibrating the service coverage map using one or more of drive test measurements and crowdsource data; blending the calibrated service coverage map with one or more per-flow KPIs associated with one or more wireless devices in the cell; training a ML model using the blended service coverage map and one or more per-flow KPIs resulting in a labeled service coverage map; and operating the ML model using actual network load, network configuration and external conditions to generate a dynamic sub-cell service coverage map.

[0040] In particular embodiments, the method further comprises predicting a service quality at particular location in the cell based on the dynamic sub-cell service coverage map. Predicting the service quality may be based on a predicted availability of physical resource blocks (PRBs) at different positions in the cell. Predicting the service quality is based on one or more of the following external factors: weather conditions; scheduled mass events; and scheduled holidays.

[0041]

[0042] In particular embodiments, the method comprises modifying a network configuration based on the dynamic sub-cell service coverage map.

[0043] In particular embodiments, obtaining the static service coverage map further comprises obtaining radio access network configuration management data. The radio access network configuration management data may comprise slice configuration data.

[0044] In particular embodiments, the one or more per-flow KPIs are based on one or more of: cell trace records; session signaling; transport parameters; QoE metrics; and radio access network performance management counters. The transport parameters may comprise one or more of: uplink throughput; downlink throughput; bitrate; latency; jitter; and packet loss ratio. The QoE metrics may comprise one or more of: video quality; video stall time ratio; video initial buffering time; web page access time; web page download time; web page download success ratio; downloaded-uploaded bytes; and voice quality. The session signaling may comprise one or more of: session setup success or failure ratio; session setup time; registration success or failure ratio; and session failure ratio.

[0045] In particular embodiments, blending the calibrated service coverage map with one or more per-flow KPIs comprises associating the one or more per-flow KPIs with coordinates in the cell. Associating the one or more per-flow KPIs with coordinates in the cell may be based on a timing advance value. For example, associating the one or more per-flow KPIs with coordinates in the cell is further based on associating the timing advance value and coordinates associated with a matching reference signal receive power (RSRP) value.

[0046] In particular embodiments, the method further comprises periodically regenerating the dynamic sub-cell service coverage map based on updated configurations and per-flow KPIs.

[0047] In particular embodiments, the analytics system network node comprises one of a management data analytics function (MDAF) or core network data analytics function (NWDAF).

[0048] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0049] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above.

[0050] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments make possible estimating the actual service quality with highspatial resolution, which may be measured directly only with much higher spatial resolution, i.e. cell level.

[0051] The service coverage map produced by particular embodiments is more exact than the service quality plan obtained from radio propagation model. The method considers the different service usage as well as different UE distributions in the cell.

[0052] The service coverage map may be produced and updated every 5-15 minutes, which means that it adapts quickly to traffic changes.

[0053] The service quality may be estimated everywhere in the network, not only in trajectories, spots and places that are covered by drive test. Particular embodiments may also estimate service quality in locations where there is no UE activity.

[0054] Service quality in different locations may be predicted with high spatial resolution based on forecasted input data.

[0055] Particular embodiments consider external conditions, like weather, and provide service quality and resource utilization estimations for the different external conditions.

[0056] During learning, a limited number of drive test measurements, crowdsource data, and network-wide available, non-localized per-flow service quality measurements are used for reference.BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:Figure 1 is a functional block diagram illustrating the system architecture, according to particular embodiments;Figure 2 is a functional block diagram illustrating the training phase, according to particular embodiments;Figure 3 is a table illustrating an example of spatial blending, according to particular embodiments;Figure 4 is a table showing differential labelling, according to particular embodiments;Figure 5 illustrates the operation phase, according to particular embodiments;Figure 6 illustrates service coverage map estimation snapshot updates, according to particular embodiments;Figure 7 illustrates prediction with the machine learning (ML) model, according to particular embodiments;Figure 8 illustrates requesting service quality information from an analytics system, according to particular embodiments;Figure 9 shows an example of a communication system, according to certain embodiments;Figure 10 shows a user equipment (UE), according to certain embodiments;Figure 11 shows a network node, according to certain embodiments;Figure 12 is a block diagram of a host, according to certain embodiments;Figure 13 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized;Figure 14 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments; andFigure 15 is a flowchart illustrating an example method in a network node, according to certain embodiments.DETAILED DESCRIPTION

[0058] As described above, certain challenges currently exist with sub-cell level service coverage estimation. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments estimate and predict sub-cell level service quality metrics, such as traffic load, signal strength, interference, signal to noise and interference ratio (SINR) and via these metrics the available throughput and other key performance indicators (KPIs) using only cell level metric prediction methodology. Expensive high resolution sub-cell level measurement (like network-wide minimization of drive test (MDT)) is not necessary because particular embodiments convert the cell level prediction into a higher spatial resolution output.

[0059] Particular embodiments are described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0060] Figure 1 is a functional block diagram illustrating the system architecture, according to particular embodiments. Particular embodiments may be implemented in a per flow networkanalytics system 12, such as a central network data analytics function (NWDAF) or management data analytics function (MDAF) functionality.

[0061] Per-flow analytics system 12 uses network events as input real-time. Signaling events 14 are received primarily from core network 22 components, such as access and mobility management function (AMF) 16, session management function (SMF) 18, and user plane function (UPF) 20. User plane reports 22 contain high resolution user plane measurements. Analytics system 12 also receives the configured cell trace events 24 real-time.

[0062] Real-time correlator 26 correlates the core and radio events into per flow correlated records and calculates various KPIs 28 which are also added to the correlated records. Correlator 26, as state of the art function, may also implement quality of experience (QoE) models, which estimate QoE metrics 30 for dedicated services.

[0063] Calculated KPIs 28 and estimated QoE metrics 30 are used as input to sub-cell level service quality map generation model 32. Also, model 32 may be used to validate the prediction of throughput by tracing a certain set of international mobile subscriber identities (IMSIs) in the radio network at the time of usage.

[0064] Model 32 also receives non-real-time network data, such as core performance monitoring (PM) data 34 and radio network PM data 36, cell and slice configuration data 38 and as a main input radio coverage and service coverage maps 40 from radio planning tools.

[0065] External (non-network) network information 42, such as event calendar or weather condition data, may also be considered.

[0066] The output data may be written in database 44 or to an event bus 46, e.g. Kafka.

[0067] The input data includes the input service coverage map (non-real-time). This may be a mappable representation of data arranged in a table holding different possible values associated with coordinates. The source of this map may be a planning tool, based on a set of propagation models, so the values are synthetic and need refinement. These values may include the following:• Serving (dominant) cell• Downlink reference signal receive power (RSRP)• Downlink signal to interference and noise ratio (SINR)• Downlink throughput• Uplink throughputThese data are available with coordinates and the resolution is typically 10-50 m.

[0068] The input may include radio access network (RAN) configuration management (CM) data (non-real-time). RAN CM data is needed so relevant attributes of the network are known and so the different data sources may be blended. These attributes may belong to either a logical network entity or its physical representation. Examples include:• operating frequency of cell and frequencies being used on the node• available bandwidth• available baseband licenses• available physical resource blocks• configuration of cell partitions or slices• settings for MDT measurements• list of neighbor relationships• allowed maximum transmitted power• physical coordinates of cells• antenna height, azimuth• antenna / sector direction• antenna gain, horizontal and vertical beamwidth• slice configuration• radio resource sharing among slices

[0069] Input may include cell trace record (CTR) KPIs (real-time). Cell trace records represent events monitored on the air interface of the network. The events captured each have parameters, which quantify some aspect of the event in question. The event may be thought of as an occurrence or as a periodic measurement.

[0070] Measurements collected include:• downlink RSRP (coverage)• downlink RSRQ (quality)• pathloss• downlink SINR (quality)• timing advance value (distance from antenna)• uplink SINR• uplink power for physical uplink control channel (PPUCCH) (user equipment (UE) transmit power, uplink coverage)• uplink power for physical uplink shared channel (PPUSCH) (UE transmit power, uplink coverage)• traffic volume measurements on cell level, data radio bearer (DRB) level and UE level• physical resource block (PRB) utilization measurements on cell level• modulation coding scheme (MCS) utilization on cell level

[0071] Input may include per flow signaling KPIs:• Session setup success or failure ratio [0-100%]• Session setup time [0-100 s]• Registration success or failure ratio [0-100%]• Session failure ratio (drop) [0-100%]

[0072] Input may include transport KPIs• Bitrate [0-100 Mbps]• Throughput [0-1000 Mbps]• Packet loss ratio [0-100%]• Packet delay or round-trip time [0-1000 ms]• Jitter [0-1000 ms]

[0073] Input may include per-flow QoE metrics:• Video quality [1-5 MOS type of score]• Video stall time ratio [0-100%]• Video initial buffering time [0-100 s]• Web page access time [0-100 s]• Web page download time [0-100 s]• Web page download success ratio [0-100%]• Downloaded-uploaded bytes [0-1000 MB]• Voice quality [1-5 MOS type of score]

[0074] Input may include RAN PM counters:• active UE numbers• number of Radio Resource Control (RRC) active connections• number of active DRBs• RB symbol utilization• scheduler activity• received noise and interference power• measured SINR• pathloss• volume and throughput measurements on medium access control (MAC)-level and UE- level• distribution of used modulation coding schemes in uplink and downlink.

[0075] In some embodiments, input includes crowdsource data. Possible metrics include:• Latitude & Longitude• Serving cell identifier• Downlink RSRP• Downlink RSRQ• Downlink SINR• Timing advance value reported by UE• Downlink Throughput• Uplink Throughput• Latency• CQI per sub-band• Carrier aggregation state• Indoor classification• Device speed• Chipset manufacturer• In some embodiments, input includes other external data. Other data encompasses any publicly or contractually released information that can be used to better describe a geographical location tied to a cell location. This data may have descriptive power over or is considered to affect any attribute that is being monitored in the form of PM counters or cell traces. Examples might include:• Weather data (rain, fog, humidity, temperature)• Event calendar, with info e.g.• information on large gatherings of people (mass events), location, time• list, time of days marked as bank holidays, bridge days or otherwise flagged as special

[0076] Figure 2 is a functional block diagram illustrating the training phase, according to particular embodiments. Particular embodiments include enhancing the service coverage map (calibration). The starting point is having the service coverage map available, which provides an estimation of coverage and SINR associated with coordinates that fall on points of set raster size.

[0077] As this is a calculation, particular embodiments approximate reality by incorporating real network geolocated measurements. Particular embodiments fit the propagation model such that the real measurements, from crowdsource, drive test or MDT results, become part of the predicted results of the propagation model.

[0078] This can be done in a few ways. Fitting geolocated data on prediction model results works best with extensive coverage of the geolocated measurements and high accuracy location information. Other methods incorporate a three-dimensional model of the propagation environment and learn a propagation model that can be generalized to previously unseen areas (see A Deep Learning Network Planner: Propagation Modeling Using Real-World Measurements and a 3D City Model available at ieeexplore.ieee.org stamp / stamp.jsp?amumber=9954403).

[0079] The enhanced coverage map is the basis for further inference in that it helps geolocate networks-side measurements (PM counters and CTRs).

[0080] Some embodiments include spatial KPI blending. PM events (CTRs) need to undergo temporal correlation per UE identifier, so that individual radio measurements can be used. This tabularization of the event stream may be done by extracting UE identifiers from some events in the CTRs and putting them next to other metrics collected for the same UE identifier, from different events, but roughly from the same period. At this stage, particular embodiments do not need any UE identification because the embodiments are describing cell level measurements. These steps enable the construction of a joint distribution for related metrics for each timing advance (TA) value observed in each cell from which data is collected.

[0081] Because TA values represent UE distance from the antenna, particular embodiments may use the enhanced coverage map to try and predict where the samples from the radio measurement were collected. This may be done by filtering out the service area of a cell at a distance given by the TA range (steps of ~78 meters) from the enhanced service coverage map and blending the measurements based on their associated RSRP levels with the areas on the map section, now limited to a TA segment of a serving cell. This geographical association is done such that the most likely location distribution is fit to the data. If samples where no matching (i.e., close enough) RSRP values are found, particular embodiments may spread them evenly around theavailable points with closest RSRP values and aggregate to the raster size of the coverage map. An example is illustrated in Figure 3.

[0082] Figure 3 is a table illustrating an example of spatial blending, according to particular embodiments. The illustrated examples shows throughput and RSRP for various UE IDs in example cell MUC2148 with a TA value of 6.

[0083] To map time-correlated measurements from the CTRs, particular embodiments associate the RSRP values of the measurements with the RSRP values as seen on the accurate coverage map, using the RSRP values as a value to carry out the join on.

[0084] First, the TA values, together with the azimuth of the cell and its coordinates provide a fdter for the possible geographical bins where the measurements are estimated to originate from. Next, particular embodiments match up RSRP values, and corresponding other KPIs, with the RSRP values predicted by the coverage map so that a chosen statistical descriptor (e.g., mean, median, percentile points) of the distribution of measurements per bin matches those used on the coverage prediction map (e.g., RSRP). The deviation of RSRP values per bin should be as close to similar values attributed to other bins as possible.

[0085] At this step, particular embodiments now have a map of, for example, downlink throughput that may be associated with areas on a map with similar sub-cell geolocation as that of the original coverage prediction map. Radio measurements are taken with timestamps, giving these maps a temporal aspect. An entry in its tabular representation may have a structure like the following:[TIMESTAMP; LATITUDE BIN; LONGITUDE BIN; DL RSRP; DL THROUGHPUT; UL THROUGHPUT; DL PRB UTILIZATION; UL PRB UTILIZATION]

[0086] Some embodiments include training a machine learning (ML) model to infer UE contribution to cell level effects. Data sources often have different aggregation levels which can be difficult to blend. A way to blend these is to take distributions of measurements collected on cell level and measurements that represent individual users (e.g., PRB distribution and per-UE measurements that record the pathloss, timing advance and DRB throughput for the same period and for the same cell) and think of them as two different kinds of time series.

[0087] Every per-UE time series starts at a start time and comes to an end at an end time. There may be cases where, from one time step to the next, from a cell level point of view, only a single per-UE measurement will change, i.e. appear or disappear. Particular embodiments mayassess any such difference between two consecutive time steps considering the changes seen in the distributions aggregated purely on cell level.

[0088] Particular embodiments build a chain of time steps where the set of per-UE measurements and the per-cell measurements are represented as differences from the previous step, then particular embodiments obtain labels that give descriptions such as: “With a TA value of 7, a user among twelve others, experiencing a pathloss of 120dB and a DL SINR of 6.5dB, had 2.3Mbps throughput in the uplink while the average PRB distribution in the cell changed by 5% to 87% .” An example is illustrated in Figure 4.

[0089] Figure 4 is a table showing differential labelling, according to particular embodiments. The illustrated example shows pathloss and throughput for various UE IDs in example cell MUC2148 with a TA value of 7.

[0090] Having enough labels of this kind, particular embodiments begin to understand the relation between the radio conditions (pathloss and TA), user experience (traffic volume and DRB throughput) and cell resource consumption (active user numbers, change in PRB utilization). This may be done by modeling individual cell-level metrics so that a set of linear equations may be extracted from the data and solved for.

[0091] A clean label may appear for some cells and some distances (TA values). In other cases, UEs might appear in pairs or not change at all. Particular embodiments may label any change and describe it by an additional feature tracking the number of simultaneously contributing UEs, for example. A goal is to gather labels for changes in these metrics so particular embodiments may isolate individual users’ (or multiple users’, but with known usage metrics) effect on network resources or any other metric collected only at cell level.

[0092] Adding per-flow KPIs, particular embodiments may refine the model input so that certain throughput and payload measurements may be related to a particular application in use or a service provider.

[0093] The training period corresponds to the time horizon of any prediction made for a future point in time. For example, particular embodiments may use at least 5-6 weeks of data for a prediction horizon of two weeks.

[0094] Particular embodiments may formulate a machine learning problem to proceed from here. Take the following example.

[0095] The task is to predict the DL PRB UTILIZATION given a time window in the future, coordinates for an imagined UE with a set of downlink and uplink throughput criteria.

[0096] Given the above labeled data set, the above may be framed as a supervised learning task where to infer the change in PRB utilization on a cell at a given location. The cell identity is provided by the enhanced service coverage map based on the location provided.

[0097] The temporal aspect, closely tied to the traffic mix absorbed by the cell, may be covered in a variety of ways. Particular embodiments may generalize it by describing times of the week by statistical means (e.g., mean and standard deviation of the metric), by turning temporal attributes into features (e.g., day within week, hour within day, add bank holidays as extra features) or particular embodiments may opt for predicting the input features of the supervised learning problem as time series.

[0098] By plugging these inputs into the model, particular embodiments may infer the PRB utilization.

[0099] Depending on availability of external data sources, particular embodiments may incorporate weather forecasts, bank holidays or any information about special events, like number of attendees or whether these are outdoor or indoor events.

[0100] The model is retrained to accommodate any network changes that affect the distribution of labels, i.e., cause model drift and poor predictive performance, and when performance degrades on a rolling test set.

[0101] Figure 5 illustrates the operation phase, according to particular embodiments.

[0102] Figure 6 illustrates service coverage map estimation snapshot updates, according to particular embodiments.

[0103] Particular embodiments include slicing update. It is important to estimate service quality related to network slices. If the slices already exist, i.e. configured in the network, the input data to the ML model will estimate expected service quality, e.g., uplink and downlink throughput with the existing slice configuration. These configurations may be different in different cells.

[0104] If slicing is not available for the training data, the following considerations may be made, and the results may be corrected for slicing.

[0105] It is expected that the model without slicing is conservative, which means that it provides an estimate of the lower limit of the expected service quality, e.g., available bandwidth. When a new slicing is configured for, e.g. a new video service, the service quality with slice configuration may be measured in a test network (assume that this is available before the service is introduced) and compared with the estimated one. A simple correction model may be built and trained with these measurements.

[0106] The current service quality model may be retrained when enough slicing data is available. By the time slicing data is available, expected service quality (e.g., bandwidth) may be corrected by an expert model: For example, assume that the PRB resource sharing between two slices is set 50-50%. PRB utilization of slice 1 is 60%, slice 2 is empty. Predicted throughput is 10 Mbps for the video service with the non-slice model at this load. Particular embodiments may estimate that the predicted throughput will be 10*6 / 5=12 Mbps in Slice 2, because for resource contention Slice 1 traffic will be suppressed to 50% PRB utilization.

[0107] However, particular embodiments may redo the training with slice configuration in the network to get more precise output in operation phase. In general, particular embodiments may redo the training phase in every quarter of year to reflect on seasonal differences in network behavior.

[0108] Figure 7 illustrates prediction with the ML model, according to particular embodiments. An example use case is to make predictions on cell resource consumption to decide whether a known user in a provided location, with certain throughput requirements, can access these resources in a time range in the future.

[0109] Particular embodiments consult the enhanced service coverage map to understand which TA range to consult that corresponds with the location provided. A time series prediction is available for this TA range based on the training period. The product of the prediction (e.g., user numbers, expected payload and throughput requirements of users per service type) is plugged into the model to predict the resources consumed. If the predicted resource consumption does not allow for accommodation of the resources the known additional user would consume, particular embodiments may mark the situation as one where some users will likely suffer from degraded experience.

[0110] Using the trained model, particular embodiments may use the predicted time frame to fetch most likely weather forecasts (e.g., probability of rain) and use these to include possible weather patterns in the prediction.[oni] The following are some use case examples. One example is service quality prediction in different locations in a cell.

[0112] Figure 8 illustrates requesting service quality information from an analytics system, according to particular embodiments. This use illustrates answering a question if service quality, e.g. uplink and downlink throughput and / or delay, will be good enough in different parts of a cell or an area, e.g., stadium or venue of a mass event. Pure network measurement data are not enoughto answer this request because it lacks the appropriate spatial resolution. Service quality maps also provide a static estimation, which do not consider the actual load and UE distribution in the serving and neighbor cells. Weather conditions forecasted and weather forecasted traffic load are not considered either.

[0113] In this use case, during the training phase, the ML model learned to update the service coverage map at different load and external conditions.

[0114] The external user sends the time, the target KPIs (e.g., requested downlink and uplink throughput and latency) and the location information where the service is planned to be used. The analytics system uses prediction function to estimate the load and weather conditions in the location for the requested time or time interval. This uses external information such as event calendar and / or weather forecast.

[0115] The ML model uses the latest service quality map as input and updates it based on the predicted input data. The result is a predicted sub-cell level service quality map of the requested KPIs, which is used for answering the analytics request (expected service quality in different parts of one or more cells).

[0116] Note that figure 8Error! Reference source not found, shows a human to machine interface, but the request may be sent through a machine to machine interface, which uses the event bus for receiving the answer.

[0117] As described above, the ML model may be used for making predictions of future KPI maps. Particular embodiments first predict the future values of the input parameters of the ML model and then execute the ML model with the predicted input parameters to obtain the predicted map.

[0118] To make predictions on the load, particular embodiments use data that is available for long enough periods so that its seasonality, if any, may be used for predictions in a time horizon of comparable length or longer than the seasonalities observed. This data is usually present in the form of RAN counters or as aggregated CTR event metrics.

[0119] Because RAN counters are most probably available, particular embodiments pick the ones that show correlation with the CTR metrics found to be relevant during modelling of the cell level load metrics and use these for forecasting.

[0120] Once these forecasted values are found, they are translated back into their CTR pairs (e.g., modulation coding scheme (MCS) relates to noise and the expected uplink noise distribution in CTRs, with the number of concurrent users can hint at the uplink noise from counters), or directcounterparts, for which the time-correlated CTR measurements were gathered during the learning phase.

[0121] Consulting a pool of CTR data that comprises the model, values of the associated metrics will point to the resource consumption (e.g., resource block symbol utilization in a given radio environment with a specified requirement on the throughput) set out to estimate.

[0122] Another use case is cell optimization using actualized sub-cell level service coverage maps. The input radio resource plan may not reveal service quality degradation spots within a cell. Actualized service quality maps help to identify cells, and areas within the cells, where service quality degradation occurs (due to load, service utilization, UE distribution, weather conditions, etc.). A goal of the cell optimization process is to eliminate service quality degradations (or SLA violation) in these areas.

[0123] For example, it turns out that a service quality in a high-priority slice is not good enough and at the same cell the radio resource utilization is high (radio resource contention situation). In this case, the PRB resource share in the given cell is increased until the service quality degradation spot is eliminated. In the same way, if there is no issue with the premium slice, but there are resource issues related to the other slices, the resource share of the premium cell may be decreased. This process leads to more optimum (uneven) PRB resource share configuration in the network.

[0124] Service quality degradation spots may be solved by adjusting the radio parameter, e.g., changing antenna tilt, handover thresholds, turning on advanced radio features, etc., to improve the radio coverage in the problematic part of the cell, offload the affected cell, or change the serving cell of the problematic area.

[0125] Figure 9 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0126] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0127] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0128] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0129] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 mayhost a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0130] As a whole, the communication system 100 of Figure 9 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0131] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0132] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi -radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0133] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0134] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0135] Figure 10 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smartphone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0136] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0137] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 2. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0138] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware -implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0139] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0140] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0141] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0142] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external harddisk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device -readable storage medium.

[0143] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0144] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission controlprotocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0145] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0146] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0147] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0148] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0149] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0150] Figure 11 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0151] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0152] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0153] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0154] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0155] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF)transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0156] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0157] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318.The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0158] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0159] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0160] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0161] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 maycomprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0162] Embodiments of the network node 300 may include additional components beyond those shown in Figure 11 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0163] Figure 12 is a block diagram of a host 400, which may be an embodiment of the host 116 of Figure 1, in accordance with various aspects described herein. As used herein, the host 400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0164] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input / output interface 406, a network interface 408, a power source 410, and a memory 412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 10 and 3, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0165] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 may also provide for user authentication and licensing checks and mayperiodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0166] Figure 13 is a block diagram illustrating a virtualization environment 500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0167] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0168] Hardware 504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0169] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0170] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0171] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0172] Figure 14 shows a communication diagram of a host 602 communicating via a network node 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of Figure 9 and / or UE 200 of Figure 2), network node (such as network node 110a of Figure 9 and / or network node 300 of Figure 3), and host (such as host 116 of Figure 9and / or host 400 of Figure 12) discussed in the preceding paragraphs will now be described with reference to Figure 14.

[0173] Like host 400, embodiments of host 602 include hardware, such as a communication interface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host 602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 650.

[0174] The network node 604 includes hardware enabling it to communicate with the host 602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of Figure 1) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0175] The UE 606 includes hardware and software, which is stored in or accessible by UE 606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 650.

[0176] The OTT connection 650 may extend via a connection 660 between the host 602 and the network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0177] As an example of transmitting data via the OTT connection 650, in step 608, the host 602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user data that was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0178] In some examples, the UE 606 executes a client application which provides user data to the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node 604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0179] One or more of the various embodiments improve the performance of OTT services provided to the UE 606 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate and latency and thereby provide benefits such as reduced user waiting time, better responsiveness, and better QoE.

[0180] In an example scenario, factory status information may be collected and analyzed by the host 602. As another example, the host 602 may process audio and video data which may havebeen retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0181] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 650 between the host 602 and UE 606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 602 and / or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0182] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processingcircuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0183] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0184] FIGURE 15 is a flowchart illustrating an example method in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 15 may be performed by network node 300 described with respect to FIGURE 11 or an analytics system network node such as a MDAF or NWDAF.

[0185] The method begins at step 1512, where the analytics system network node obtains a static service coverage map for a cell of a wireless network. The static service coverage map comprises an association of service quality and coordinates in the cell.

[0186] In particular embodiments, obtaining the static service coverage map further comprises obtaining radio access network configuration management data. The radio access network configuration management data may comprise slice configuration data.

[0187] In particular embodiments, the analytics system network node may obtain the static service coverage map according to any of the embodiments and examples described herein.

[0188] At step 1514, the analytics system network node calibrates the service coverage map using one or more of drive test measurements and crowdsource data.

[0189] In particular embodiments, the analytics system network node may calibrate the service coverage map according to any of the embodiments and examples described herein.

[0190] At step 1516, the analytics system network node blends the calibrated service coverage map with one or more per-flow KPIs associated with one or more wireless devices in the cell.

[0191] In particular embodiments, the one or more per-flow KPIs are based on one or more of: cell trace records; session signaling; transport parameters; QoE metrics (reported by end client or service provider or estimated by the analytics system based on a QoE model); and radio access network performance management counters. The transport parameters may comprise one or more of: uplink throughput; downlink throughput; bitrate; latency; jitter; and packet loss ratio. The QoE metrics may comprise one or more of: video quality; video stall time ratio; video initial buffering time; web page access time; web page download time; web page download success ratio; downloaded-uploaded bytes; and voice quality. The session signaling may comprise one or more of: session setup success or failure ratio; session setup time; registration success or failure ratio; and session failure ratio.

[0192] In particular embodiments, blending the calibrated service coverage map with one or more per-flow KPIs comprises associating the one or more per-flow KPIs with coordinates in the cell. Associating the one or more per-flow KPIs with coordinates in the cell may be based on a timing advance value. For example, associating the one or more per-flow KPIs with coordinates in the cell is further based on associating the timing advance value and coordinates associated with a matching reference signal receive power (RSRP) value.

[0193] In particular embodiments, the analytics system network node may blend the calibrated service coverage map according to any of the embodiments and examples described herein.

[0194] At step 1518, the analytics system network node trains a ML model using the blended service coverage map and one or more per-flow KPIs resulting in a labeled service coverage map.

[0195] In particular embodiments, the analytics system network node may train the ML model according to any of the embodiments and examples described herein.

[0196] At step 1520, the analytics system network node operates the ML model using actual network load, network configuration and external conditions to generate a dynamic sub-cell service coverage map.

[0197] In particular embodiments, the analytics system network node may operate the ML model according to any of the embodiments and examples described herein.

[0198] At step 1522, the analytics system network node may predict a service quality at particular location in the cell based on the dynamic sub-cell service coverage map. Predicting the service quality may be based on a predicted availability of physical resource blocks (PRBs) at different positions in the cell. Predicting the service quality may be based on one or more of the following external factors: weather conditions; scheduled mass events; and scheduled holidays.

[0199] In particular embodiments, the analytics system network node may make predictions using the ML model according to any of the embodiments and examples described herein.

[0200] At step 1524, the analytics system network node may modify a network configuration based on the dynamic sub-cell service coverage map. For example, the analytics system network node may optimize network parameters based on the dynamic sub-cell service coverage map.

[0201] In particular embodiments, the method further comprises periodically regenerating the dynamic sub-cell service coverage map based on updated configurations and per-flow KPIs.

[0202] Modifications, additions, or omissions may be made to method 1500 of FIGURE 15. Additionally, one or more steps in the method of FIGURE 15 may be performed in parallel or in any suitable order.

[0203] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0204] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0205] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

Claims

Claims1. A method performed by an analytics system network node, the method comprising: obtaining (1512) a static service coverage map for a cell of a wireless network, the static service coverage map comprising an association of service quality and coordinates in the cell; calibrating (1514) the service coverage map using one or more of drive test measurements and crowdsource data; blending (1516) the calibrated service coverage map with one or more per-flow key performance indicator (KPIs) associated with one or more wireless devices in the cell; training (1518) a machine learning (ML) model using the blended service coverage map and one or more per-flow KPIs resulting in a labeled service coverage map; and operating (1520) the ML model using actual network load, network configuration and external conditions to generate a dynamic sub-cell service coverage map.

2. The method of claim 1, further comprising predicting (1522) a service quality at particular location in the cell based on the dynamic sub-cell service coverage map.

3. The method of claim 2, wherein predicting the service quality is based on a predicted availability of physical resource blocks (PRBs) at different positions in the cell.

4. The method of any one of claims 2-3, wherein predicting the service quality is based on one or more of the following external factors: weather conditions; scheduled mass events; and scheduled holidays.

5. The method of any one of claims 1-4, comprising modifying (1524) a network configuration based on the dynamic sub-cell service coverage map.

6. The method of any one of claims 1-5, wherein obtaining the static service coverage map further comprises obtaining radio access network configuration management data.

7. The method of claim 6, wherein the radio access network configuration management data comprises slice configuration data.

8. The method of any one of claims 1 -7, wherein the one or more per-flow KPIs are based on one or more of: cell trace records; session signaling; transport parameters; quality of experience (QoE) metrics; and radio access network performance management counters.

9. The method of claim 8, wherein the transport parameters comprise one or more of: uplink throughput; downlink throughput; bitrate; latency; jitter; and packet loss ratio.

10. The method of claim 8, wherein the QoE metrics comprise one or more of: video quality; video stall time ratio; video initial buffering time; web page access time; web page download time; web page download success ratio; downloaded-uploaded bytes; and voice quality.

11. The method of claim 8, wherein the session signaling comprises one or more of: session setup success or failure ratio; session setup time;registration success or failure ratio; and session failure ratio.

12. The method of any one of claims 1-11, wherein blending the calibrated service coverage map with one or more per-flow KPIs comprises associating the one or more per-flow KPIs with coordinates in the cell.

13. The method of claim 12, wherein associating the one or more per-flow KPIs with coordinates in the cell is based on a timing advance value.

14. The method of claim 13, wherein associating the one or more per-flow KPIs with coordinates in the cell is further based on associating the timing advance value and coordinates associated with a matching reference signal receive power (RSRP) value.

15. The method of any one of claims 1-14, further comprising periodically regenerating the dynamic sub-cell service coverage map based on updated configurations and per-flow KPIs.

16. The method of any one of claims 1-15, wherein the analytics system network node comprises one of a management data analytics function (MDAF) or core network data analytics function (NWDAF).

17. A network node (300) comprising processing circuitry (302), the processing circuitry operable to: obtain a static service coverage map for a cell of a wireless network, the static service coverage map comprising an association of service quality and coordinates in the cell; calibrate the service coverage map using one or more of drive test measurements and crowdsource data; blend the calibrated service coverage map with one or more per-flow key performance indicator (KPIs) associated with one or more wireless devices in the cell; train a machine learning (ML) model using the blended service coverage map and one or more per-flow KPIs resulting in a labeled service coverage map; and operate the ML model using actual network load, network configuration and externalconditions to generate a dynamic sub-cell service coverage map.

18. The method of claim 17, the processing circuitry further operable to predict a service quality at particular location in the cell based on the dynamic sub-cell service coverage map.

19. The method of claim 18, wherein the processing circuitry predicts the service quality based on a predicted availability of physical resource blocks (PRBs) at different positions in the cell.

20. The method of any one of claims 18-19, wherein the processing circuitry predicts the service quality based on one or more of the following external factors: weather conditions; scheduled mass events; and scheduled holidays.

21. The method of any one of claims 17-20, the processing circuitry further operable to modify a network configuration based on the dynamic sub-cell service coverage map.

22. The method of any one of claims 17-21, wherein the processing circuitry is operable to obtain the static service coverage map by obtaining radio access network configuration management data.

23. The method of claim 22, wherein the radio access network configuration management data comprises slice configuration data.

24. The method of any one of claims 17-23, wherein the one or more per-flow KPIs are based on one or more of: cell trace records; session signaling; transport parameters; quality of experience (QoE) metrics; and radio access network performance management counters.

25. The method of claim 24, wherein the transport parameters comprise one or more of: uplink throughput; downlink throughput; bitrate; latency; jitter; and packet loss ratio.

26. The method of claim 24, wherein the QoE metrics comprise one or more of: video quality; video stall time ratio; video initial buffering time; web page access time; web page download time; web page download success ratio; downloaded-uploaded bytes; and voice quality.

27. The method of claim 24, wherein the session signaling comprises one or more of: session setup success or failure ratio; session setup time; registration success or failure ratio; and session failure ratio.

28. The method of any one of claims 17-27, wherein the processing circuitry is operable to blend the calibrated service coverage map with one or more per-flow KPIs by associating the one or more per-flow KPIs with coordinates in the cell.

29. The method of claim 28, wherein associating the one or more per-flow KPIs with coordinates in the cell is based on a timing advance value.

30. The method of claim 29, wherein associating the one or more per-flow KPIs with coordinates in the cell is further based on associating the timing advance value and coordinates associated with a matching reference signal receive power (RSRP) value.

31. The method of any one of claims 17-30, further comprising periodically regenerating the dynamic sub-cell service coverage map based on updated configurations and per-flow KPIs.

32. The method of any one of claims 17-31, wherein the analytics system network node comprises one of a management data analytics function (MDAF) or core network data analytics function (NWDAF).

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