Cell congestion detection method

Abstract

A method performed by a network management node, or network function, to detect congestion in a first cell. The method comprises a learning phase (31), where cells in a mobile network, having similar traffic profiles and usage patterns as the first cell, are classified (312) into a cell group, and where a tipping point indicating a congestion threshold value for the cells in the cell group is identified (313). The method further comprises an operational phase, where a measurement sample, represented by a momentary cell load, and a momentary throughput value, of the first cell, is determined. A cell congestion level for the first cell is determined based on a distance between the measurement sample and the threshold value.

Description

[0001] CELL CONGESTION DETECTION METHOD

[0002] TECHNICAL FIELD

[0003] Embodiments herein relate to a method performed by a network management node, or network function, to detect congestion in a first cell.

[0004] BACKGROUND

[0005] In a typical wireless communication network, wireless devices, also known as wireless communication devices, mobile stations, stations (STA) and / or User Equipment (UE), communicate via a Wide Area Network or a Local Area Network such as a Wi-Fi network or a cellular network comprising a Radio Access Network (RAN) part and a Core Network (CN) part. The RAN covers a geographical area which is divided into service areas or cell areas, which may also be referred to as a beam or a beam group, with each service area or cell area being served by a radio network node such as a radio access node e.g., a Wi-Fi access point, a Base Station (BS) or a radio base station (RBS), which in some networks may also be denoted, for example, a Base Station (BS), a NodeB, eNodeB (eNB), or gNodeB (gNB) as denoted in Fifth Generation (5G) telecommunications. A service area or cell area is a geographical area where radio coverage is provided by the radio network node. The radio network node communicates over an air interface operating on a radio frequency with the wireless devices within the range of the radio network node.

[0006] 3rd Generation Partnership Project (3GPP) is the standardization body for specifying the standards for the cellular system evolution, e.g., including 3G, 4G, 5G and the future evolutions. Specifications for Evolved Universal Terrestrial Radio Access (E- UTRA) and Evolved Packet System (EPS) have been completed within the 3GPP. In 4G also called a Fourth Generation (4G) network, EPS is core network and E-UTRA is radio access network. In 5G, 5G Core (5GC) is core network, NR is radio access network. As a continued network evolution, the new release of 3GPP specifies a 5G network also referred to as 5G New Radio (NR) and 5GC.

[0007] Frequency bands for 5G NR are being separated into two different frequency ranges, Frequency Range 1 (FR1) and Frequency Range 2 (FR2). FR1 comprises sub-6 GHz frequency bands. Some of these bands are bands traditionally used by legacy standards but have been extended to cover potential new spectrum offerings from 410 MHz to 7125 MHz. FR2 comprises frequency bands from 24.25 GHz to 52.6 GHz. Bands in this millimeter wave range have shorter range but higher available bandwidth than bands in the FR1.

[0008] Multi-antenna techniques may significantly increase the data rates and reliability of a wireless communication system. For a wireless connection between a single user, such as UE, and a base station (BS), the performance is in particular improved if both the transmitter and the receiver are equipped with multiple antennas, which results in a Multiple-Input Multiple-Output (MIMO) communication channel. This may be referred to as Single-User (SU)-MIMO. In the scenario where MIMO techniques is used for the wireless connection between multiple users and the base station, MIMO enables the users to communicate with the base station simultaneously using the same time-frequency resources by spatially separating the users, which increases further the cell capacity. This may be referred to as Multi-User (MU)-MIMO. Note that MU-MIMO may benefit when each UE only has one antenna. The cell capacity can be increased linearly with respect to the number of antennas at the BS side. Due to that, more and more antennas are employed in BS. Such systems and / or related techniques are commonly referred to as massive MIMO.

[0009] Congestion in telecommunication networks in itself is a topic of fundamental importance, being a key reason for user experience degradation and decreased resource efficiency.

[0010] With the introduction of Network Data Analytics Function (NWDAF) as a standard 5G Core (5GC) network element, 3GPP has defined a set of so-called Analytic IDs (Al Ds) as can be seen in publication “5G System; Network Data Analytics Services; Stage 3, 3GPP TS 29.520, Release 18, 18.7.0, 2024-09-16”. These AIDs identify insights to be provided by NWDAF on its standard Service Based Interface, SBI, to potential consumers of the analytics information, i.e. in the form of one-time requests and / or subscriptions.

[0011] One of these analytic IDs is User Data Congestion (UDC), as shown in publication 3GPP TR 23.791 V16.2.0 (2019-06). The requirement towards the NWDAF is the ability to report past statistics and future predictions of “congestion level” for the requested Area of Interest, i.e. a cell or a Tracking Area, or for selected UEs e.g. based on their location. However, the exact definition of congestion level is not specified by the standard, therefore up to the NWDAF vendor to specify.

[0012] The detection of congestion is far from being straightforward. The pure fact of high, -100%, utilization of network resources is not an unwanted scenario: the resources are deployed with the purpose of being used. Increased network load due to UEs competing for network resources leads to a “linear” degradation of observed experience due to shared resources; however, at a certain level of high load, or overload, resource efficiency suddenly drops below the expected “linear” trend, which is the phenomenon of congestion.

[0013] Identifying the moment when congestion kicks in, or on a non-binary scale to capture congestion levels is the key for the User Data Congestion analytic ID by NWDAF. The 5GC Networks Functions (NFs) being consumers of NWDAF analytics can use the congestion level statistics and predictions to implement congestion avoidance mechanisms, or congestion aware use cases.

[0014] SUMMARY

[0015] The embodiments herein targets a use case driven congestion level definition, as well as the methodology to calculate based on available Radio Access Network (RAN) performance management (PM) counters or cell trace records (CTRs). It can be noted that the event-based solution allows more real-time solution and allows shorter aggregations, but the solution described below is basically the same.

[0016] As part of developing embodiments herein, some problems have been identified that first will be described.

[0017] Comparison to prior Art:

[0018] The 3GPP standard defines the interfaces and describes use cases for user data congestion analytics ID but it does not specify, describe a method on how to detect cell congestion. It leaves implementation for the vendors.

[0019] Existing solutions, if any, are rule-based solutions, which have the following drawbacks.

[0020] The level of congestion is not quantified, so it cannot be communicated to implement counter measures.

[0021] Expert knowledge is needed to work out and maintain the rules, resulting in high operating expenses.

[0022] Rule-based solutions do not adapt to network and traffic changes automatically, they have to be changed manually, they can be inaccurate leading to false indication or that congestion is not detected, fixed, which negatively affects user satisfaction.

[0023] Due to the large number and vide variety of cell types, traffic and radio conditions in mobile networks today, a rule-based solution requires a huge number of rules and maintenance of rule parameters. A reduction of the number of rules results in that the same rules are applied to different kinds of cells resulting in that the detection will be inaccurate or suboptimal.

[0024] In existing solutions there are uncertain or inaccurate rules based on multiple parameters, where metrics need human involvement and evaluation before fixing the issue, these methods cannot be applied in closed loop corrective algorithms.

[0025] An object of embodiments herein is to provide a possibility to predict potential cell congestion and act in advance to avoid congestion through an accurate and decisive congestion indication, thus enabling its application in automatic, corrective closed loop algorithms in a mobile network consisting of high number of cells.

[0026] According to an aspect of embodiments herein, the object is achieved by a method performed by a network management node, or network function to detect congestion in a first cell, wherein the method comprises a learning phase, where cells in a mobile network, having similar traffic profiles and usage patterns as the first cell, are classified into a cell group, and where a tipping point indicating a congestion threshold value for the cells in the cell group is identified.

[0027] The method also comprises an operational phase, where a measurement sample, represented by a momentary cell load and a momentary throughput value of the first cell, is determined, and where a cell congestion level for the first cell is determined based on a distance between the measurement sample and the threshold value.

[0028] It is proposed that the learning phase may comprise collecting cell performance management counters or events for the cells within a cell group, classifying cells into cell groups based on data from a data driven algorithm, and identifying the tipping point indicating the determined cell congestion threshold value for the cell group based on the collected cell performance management counters or events.

[0029] Embodiments herein teaches that the operational phase may comprise determining a current measurement sample for the first cell, the measurement sample being based on a momentary cell load and a momentary throughput value for the first cell, comparing the determined measurement sample with an indicated congestion threshold value of the said cell group, determining a cell congestion level for the cell based on a distance between the determined measurement sample and the indicated threshold value, and deciding that the first cell is exposed to congestion when the threshold value is exceeded.

[0030] It is further proposed that collecting in the learning phase, or determining in the operational phase, comprises collecting and using cell Radio Frequency, RF, planning data and per User Equipment, UE, data to increase the accuracy of the model, where UE data are aggregated per cell, and the per cell aggregated UE data and the per cell RF planning data are blended with the per cell performance counters or cell performance events, i.e. performance management, PM, counters or cell trace records, CTR, events.

[0031] According to another aspect of embodiments herein, the object is achieved by a network management node, or network function, adapted to detect congestion in a first cell, wherein the network management node, or network function, is adapted to perform a learning phase, where the network management node, or network function, is adapted to classify cells in a mobile network, having similar traffic profiles and usage patterns as the first cell, into a cell group, and where the network management node, or network function, is adapted to identify a tipping point indicating a congestion threshold value for the cells in the cell group.

[0032] The network management node, or network function, is also adapted to perform an operational phase, where the network management node, or network function, is adapted to determine a measurement sample, represented by a momentary cell load, and a throughput value, of the first cell, and where the network management node, or network function, is adapted to determine a cell congestion level for the first cell based on a distance between the measurement sample and the threshold value.

[0033] It is proposed that the network management node, or network function, is adapted to, in the learning phase collect cell performance management counters or events for the cells within a cell group, classify cells into cell groups based on data from a data driven algorithm, and identify the tipping point indicating the determined cell congestion threshold value for the cell group based on the collected cell performance management counters or events.

[0034] Embodiments teaches that the operational phase comprises that the network management node, or network function, may be adapted to determine a current measurement sample for the first cell, the measurement sample being based on a momentary cell load and a momentary throughput value for the first cell, compare the determined measurement sample with an indicated congestion threshold value of the said cell group, determine a cell congestion level for the cell based on a distance between the determined measurement sample and the indicated threshold value, and decide that the first cell is exposed to congestion when the threshold value is exceeded.

[0035] It is proposed that the network management node, or network function, may be adapted to collect and use cell RF planning data and per UE data, where UE data are aggregated per cell, and the per cell aggregated UE data and the per cell RF planning data are blended with the per cell performance counters or cell performance events in the process of collecting in the learning phase, or determining in the operational phase. Thanks to that the proposed method and network management node enables and ensures accurate and decisive congestion indication, thus enabling its application in automatic, corrective closed loop algorithms

[0036] Embodiments herein may provide one or more of the following advantages:

[0037] With this method it is possible to provide a data driven solution which automatically detects congestion level per cell, independently of cell types and traffic mix. No separate rules or thresholds need to be determined for each cell and / or actual traffic mix.

[0038] Embodiments herein provides a counter-based, inexpensive, low footprint solution, which counters are usually available and activated at the networks. The possibility to use cell level measurements only bypasses the need to collect data from other sources, such as core network elements, to match them so that usage patterns are identifiable.

[0039] Cell level metrics on available radio resources are provided, that are implicitly indicating the bandwidth available in the cell, and that are directly tied to the throughput attainable by the user.

[0040] Cell traffic and radio conditions, for which the same thresholds can be applied, are classified, thus providing a reduction of the variety of parameters that need to be maintained. The provided clustering of cells based on their available throughput will inherently result in groupings of similar cells. Describing a group of cells with a single tipping point enables an interpretation of a congestion level metric. An example metric to be used can be the number of available resource block symbols.

[0041] Additional use of RF planning data opens possibilities related to clustering cells so that their similarity based on their location and others, e.g. inter-site distance, number of carriers on site, theoretical overlap between cells, can be measured, further improving relevance of the grouping of cells with a similar traffic demand.

[0042] The resulting congestion level metric is a time-series and thus allows use of various prediction techniques to warn users about imminent heightened congestion levels. For example, in the context of NWDAF, the method defines a metric, which can be monitored during the operation by means of the learning algorithm determining a threshold value for cell load and throughput metrics. Monitoring the trend of the metrics and the distance from the threshold provides the possibility to predict potential cell congestion and act in advance to avoid congestion.

[0043] BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Examples of embodiments herein are described in more detail with reference to attached drawings in which: Figure 1 is a schematic block diagram illustrating embodiments of a communications network.

[0045] Figure 2 is a schematic block diagram of a system architecture.

[0046] Figure 3 is a flowchart illustrating the learning phase.

[0047] Figure 4 is a flowchart illustrating the operational phase.

[0048] Figure 5 is a flowchart illustrating the clustering of cells.

[0049] Figure 6a is a graph illustrating throughput as a function of congestion metric without cell classification, for all available cell groups.

[0050] Figure 6b is a graph illustrating throughput as a function of congestion metric with cell classification, for all available cell groups.

[0051] Figure 7 is a graph illustrating the determination of the tipping point.

[0052] Figure 8 is a graph showing the congestion metric and throughput over time for a few cells during operation phase.

[0053] Figure 9 is a schematic block diagram illustrating embodiments of network management node.

[0054] Figure 10 schematically illustrates embodiments of a communication system.

[0055] Figure 11 is a generalized block diagram of embodiments of a UE.

[0056] Figure 12 is a generalized block diagram of embodiments of a network node.

[0057] Figure 13 is a generalized block diagram of embodiments of a virtualization environment.

[0058] DETAILED DESCRIPTION

[0059] Embodiments herein relate to an efficient cell congestion detection method. The method is based on blocking probability and throughput, which may be represented by reported radio resource utilization via parameters such as radio PM counters and CTRs. The method is applied for UL and DL traffic separately.

[0060] The method is applied for cell groups, which may be determined by a clustering method. The clustering method identifies cell groups which have similar traffic profiles and usage patterns. Clustering may be done during data ingestion.

[0061] The process of cell congestion detection includes two phases: a learning phase and an operational phase.

[0062] In the learning phase, cell counters may be collected for a longer period. Optionally, per cell RF planning data and per UE data may also be used to increase the accuracy of the model. UE data may be aggregated per cell. The per cell aggregated UE data and the per cell RF planning data may be blended with the per cell counter, i.e.PM counters, or CTR data. During the learning phase a tipping point indicating the congestion threshold is identified per cell group, based on the method described below.

[0063] In the operation phase, the congestion level is determined based on momentary cell load, i.e. cell counter or event data, and momentary throughput value, which is obtained for shorter time periods. Optionally, per cell RF data and per UE event data may also be used, which may be aggregated per cell and blended with the cell level data. The actual cell load is determined per cell and compared with the threshold value, or tipping point, of the corresponding cell group as determined in the learning phase. The distance from this point indicates the cell congestion level. If the threshold is exceeded, congestion is indicated, and based on the forecasted traffic profile, congestion can be predicted.

[0064] In case of congestion detected or predicted, closed loop corrective actions may be executed.

[0065] Figure 1 is a schematic overview depicting a wireless communications network 100 wherein embodiments herein may be implemented. The wireless communications network 100 comprises one or more RANs, one or more CNs and a conversation AR network. The communications network 100 may use 5G NR but may further use a number of other different technologies, such as, 6G, Wi-Fi, Long Term Evolution (LTE), LTE- Advanced, Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications / enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.

[0066] Base stations, such as a first base station 111 and a second base station 112, operate in the RAN the communications network 100. The base stations 111 , 112, may each be a transmission and reception point e.g. a radio access network node such as a base station, e.g. a radio base station such as a NodeB, an evolved Node B (eNB, eNode B), an NR Node B (gNB), a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a transmission arrangement of a radio base station, a stand-alone access point, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), an access controller, or any other network unit capable of communicating with UEs, such as a UE 121 , within a cell, served by the respective base station 111 , 112. The respective base station 111 , 112 may be referred to as a serving radio network node and may communicate with the UE 121 with Downlink (DL) transmissions to the UE 121 and Uplink (UL) transmissions from the UE 121. The network 100 comprises a network function, or network management node 1, i.e. a NWDAF. It should be understood that this network management node 1 or network function may be distributed over, or performed by, several nodes.

[0067] It should be understood by the skilled in the art that “UE” is a non-limiting term which means any terminal, client, mobile client, IMS client, wireless communication terminal, user equipment, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a car or any small base station communicating within a cell.

[0068] Proposed embodiments relate to a cell congestion detection method, which:

[0069] - yields a congestion level metric, which may be interpreted as a numeric value or as ranges, e.g., low, medium, high or congested, normal;

[0070] - may be based on PM counters or CTR events recording radio resource utilization, blocking probability and throughput;

[0071] - is applied per groups of cells, which groups may be determined by a clustering method;

[0072] - may utilize joint distribution of cell load and throughput per cell group, where cell load is defined as a function of radio resource utilization and blocking probability;

[0073] - consists of a learning phase, which may be executed by enveloping the most relevant data points of the joint distribution in a bounding polygon, followed by the assessment of the tipping point, i.e. elbow, of the resulting shape in the cell load - throughput space;

[0074] - consists of an operation phase, which may use the tipping point and compares the distance from a cell load - throughput sample from it, thus yielding the congestion level.

[0075] The clustering method that may be used for identifying cell groups which should be handled together, may use the following:

[0076] - Cells are grouped so that similar traffic profiles, usage patterns and user behavior are captured in the same group.

[0077] - Only cell level measurements are used, thereby bypassing the need to include other data sources.

[0078] - Clustering of cells is done incrementally during data ingestion.

[0079] The data from different cell types and traffic profiles will not show separate patterns, where clustering is a proposed way of identifying the groups required to enable the proposed cell congestion detection. A corrective action to solve cell congestion during operation is enabled by monitoring the above counters and detecting congestion by the thresholds determined for the given cell by the above method. Thus, the proposed method enables the optional step of automated corrective actions.

[0080] A number of embodiments will now be described, some of which may be seen as alternatives, while some may be used in combination.

[0081] Figure 2 shows a system architecture for network management. Here it is shown that the network management node 1, here exemplified as a NWDAF is in the core 21 and with NSMF 22 as a part of Network Management 23. It is shown that user plane traffic 2a1 is communicated between a UE 121, or group of UEs, clients or applications 121 , 122, 123, and base stations 111 , 112 in the RAN 24, through radio IF, and user plane traffic 2a2 is communicated between an external cloud network or server 26 and a II PF 25 through the transport layer 27.

[0082] AMF 281 , SMF 282, UPF 25 and PCF 283 are found in the core 21.

[0083] In the figure a group of cells with a similar traffic profile and usage pattern are classified into a cell group 29. This is just a simplified illustration if such cell group and it should be understood that cells might have a similar traffic profile and usage pattern without being positioned close to each other as in this schematic figure.

[0084] Radio events and radio counters may be communicated 2b from the base stations 111 , 112 to network management 23, and communicated 2c from network management 23 to the NWDAF 1. Analytics requests and responses are communicated 2d between the NWDAF 1 and network management 23. Node events and probe reports are communicated 2e from AMF 281, SMF 282 and UPF 25 to the NWDAF 1.

[0085] The invention enables a closed loop action 2f, 2g, 2h, 2i as will be presented below.

[0086] Methods according to embodiments herein are performed by the network management node 1 or network function.

[0087] The method comprises the following actions, which actions may be taken in any suitable order. Optional actions are referred to as dashed boxes in the Figures.

[0088] The following actions are performed in a learning phase 31 as indicated in Figure 3.

[0089] Action 312. Classifying cells in a mobile network, e.g., 4G and 5G networks, having similar traffic profiles and usage patterns as the first cell, into a cell group 29.

[0090] Action 313. Identifying a tipping point indicating a congestion threshold value 71 for the cells in the cell group 29.

[0091] The following actions are performed in an operational phase 32 as indicated in Figure 4: Action 321. Determining a measurement sample 72, represented by a momentary cell load and a momentary throughput value, of the first cell 121.

[0092] Action 323. Determining a cell congestion level for the first cell 121 based on a distance between the measurement sample 72 and the threshold value 71.

[0093] Embodiments herein proposes that the following actions may be part of the learning phase 31.

[0094] Action 311. Collecting cell performance management counters or events for the cells within a cell group.

[0095] Action 312. Classifying cells into cell groups based on data from a data driven algorithm, i.e. a clustering method.

[0096] Action 313. Identifying the tipping point indicating the determined cell congestion threshold value 314, GROUP_A; TIPPING_POINT_A, i.e. a pair of a cell load and a throughput values defining a two-dimensional space for the cell group based on the collected cell performance management counters or events.

[0097] It is also proposed that the operational phase 32 may include the following actions.

[0098] Action 321. Determining a current measurement sample for the first cell 121, where the momentary cell load may be represented by a resource block utilization rate across shared channels and the blocking rate on the shared channels, e.g. shared channel blocking rate and average resource block utilization, where the sum of these two metrics can be observed to be related with the throughput such that the latter was limited by an upper bound, and a momentary throughput value may be represented by an average throughput, a burst throughput, a pipe throughput or a bit rate, etc.

[0099] Action 322. Comparing the determined measurement sample with an indicated congestion threshold value 314 of the said cell group 29.

[0100] Action 323. Determining a cell congestion level for the cell based on a distance between the determined measurement sample and the indicated threshold value.

[0101] Action 324. Deciding that the first cell is exposed to congestion when the threshold value is exceeded.

[0102] It is proposed that collecting 311 during the learning phase 31 may comprises collecting and using cell Radio Frequency, RF, planning data 3111 and per UE data 3112 to increase the accuracy of the model, where UE data are aggregated 3113 per cell, and the per cell aggregated UE data and the per cell RF planning data are blended 3114 with the per cell performance counters or cell performance events, i.e. performance management, PM, counters or cell trace records, CTR, events. It is also proposed that determining 321 during the operational phase 32, may comprises collecting and using cell Radio Frequency, RF, planning data 3211 and per UE data 3212 to increase the accuracy of the model, where UE data are aggregated 3213 per cell, and the per cell aggregated UE data and the per cell RF planning data are blended 3214 with the per cell performance counters or cell performance events, i.e. performance management, PM, counters or cell trace records, CTR, events.

[0103] In this way by using the methods above, a congestion level metric is provided allowing the use of various prediction techniques to warn users about imminent heightened congestion levels. By monitoring the trend of the metrics and the distance from the threshold, it is possible to predict potential cell congestion and act in advance to avoid congestion.

[0104] Embodiments herein such as the embodiments mentioned above will now be further described and exemplified. The text below is applicable to and may be combined with any suitable embodiment described above.

[0105] It is proposed that a new functionality is implemented through a network management node 1 , which in the following will be exemplified by the NWDAF. However, it should be understood that the proposed functionality, or the network management node 1 , can be implemented also in other ways, such as in the network management domain, e.g. in a Management Data Analytics Function, (MDAF).

[0106] NWDAF may receive events and counters from core network NF and CTR events and radio counters from the Operations Support System, OSS. It is also proposed that the radio counters / events may be obtained from radio nodes or any brokering or mediation function. Per flow correlated records, including various KPIs, may be generated, where these KPIs are aggregated per cell and for different time periods. In a pure radio counterbased solution, the KPIs may be generated only per cells.

[0107] NWDAF may implement the cell load metrics and the analytics model for detecting cell congestion as described below.

[0108] NWDAF provides a northbound interface, NBI, where analytics info can be requested, for example cell congestion, for the proposed use case. In case of cell congestion detection or prediction, a Policy Control Function, PCF, and / or network management functions like a Network Slice Management Function, NSMF, analytics consumers, may initiate actions described below to handle cell congestion. Process of cell congestion detection

[0109] The proposed process of cell congestion detection includes two phases: learning 31 and operation 32.

[0110] In the learning phase 31 the cells are classified 312 into cell groups and the cell congestion thresholds are determined for these cell groups by the processes described below. During operation 32, the congestion level is determined 323 and quantified, which may be based on the cell counters and optionally radio planning 3211 and per UE data 3212, and it is decided 323 and indicated if congestion is detected or predicted.

[0111] Learning phase

[0112] In the learning phase 31, cell counters may be collected 311 for a time period long enough for the time aggregation size, e.g. 21 days for PM counters, 2 weekdays and 1 weekend day for CTR. Optionally per cell RF data 3111 and per UE data 3112 may also be used to increase the accuracy of the model. It is proposed that per UE data may be aggregated per cell 3113. The per cell aggregated UE data 3113 and the per cell RF planning data 3111 are blended 3114 with the per cell counter data. Note that if there are events containing per cell aggregated data, best practice is to use these events, in which case the described aggregation step is not needed.

[0113] In the clustering step the cells are classified based on the method described below.

[0114] Operation phase

[0115] In the operation phase 32, the congestion level is determined based on actual cell counter data, which may be obtained for shorter time periods, e.g. 15 min for PM counters, 1 min for CTRs. Optionally, per cell RF data 3211 and per UE event data 3212 may also be used, which may be aggregated per cell 3213 and blended 3214 with the cell level data.

[0116] The actual cell load is determined per cell 321 and compared with the threshold value 322, or tipping point, of the corresponding cell group 314 determined in the learning phase 31. The distance from this point indicates the cell congestion level 323. If the threshold is exceeded, congestion is indicated 324.

[0117] Clustering algorithm

[0118] Cells need to be grouped 312 so that the joint distribution of cell load and throughput metrics form shapes that can be individually recognized. These tend to follow a generic pattern but always depend on the traffic mix associated with the cell group. Figure 5 is an illustration of how cells may be put into groups through clustering.

[0119] Groups or traffic mix types can be found by identifying features that affect the achievable throughput in a cell, i.e. their available bandwidth. This is usually recorded in cell level measurements or cell level data 51 , e.g. a count of available resource blocks or resource block symbols.

[0120] In addition to bandwidth assessed from measurements, RF planning data 52 can be used to describe cells in terms of their respective physical environment, such as inter-site distance, overlap between cells, cell density, operating frequency band, cell type, other RAN technologies on site (e.g., 5G, 4G, EN-DC, NR-DC) and others.

[0121] Clustering of cells 53 may be done via incremental learning, which will provide the groped cells 312.

[0122] It is proposed that cell samples can even be categorized based on the time of day to have cell groups created for temporary situations in which activities that are unusual elsewhere in the network may be the norm, e.g., night life areas, event venues, outdoor resorts.

[0123] Figure 6a shows a graph indicating the throughput as a function of congestion metric without cell classification, for all available cell groups, and Figure 6b shows a graph indicating the throughput as a function of congestion metric with cell classification applied, for a chosen cell group. This clearly shows the need for classification, where it is clear that without classification it is not possible to identify a suitable single threshold value, as will be illustrated in the following.

[0124] Determining congestion threshold for the separate cell groups

[0125] Figure 7 illustrates an example joint cell load and throughput distribution, for a single cell group, in regular conditions, where the X-axis shows cell load.

[0126] It is proposed to define a tipping point, or congestion threshold value 71, beyond which, in terms of cell load, the throughput dips and does not recover.

[0127] As a shape in the cloud of points is searched, bounds 7a are drawn around the pairs of cell load and throughput so that only rare value combinations or outliers are left out of bounds.

[0128] A perimeter around the remaining points can be drawn and it will have an elbow for most regular traffic mixes. It is proposed that the tipping point, or congestion threshold value 71 , is defined as this elbow.

[0129] In case the elbow is not prominent or seems not to exist or the shape formed by the bounds is concave from the positive axis directions for both load and throughput, it can be interpreted as there being no tipping point beyond which throughput dips so markedly as to call it a tipping point. In such cases, the point along the bounding shape at the highest load value translates to a tipping point. It is not a problem if there are no value pairs for which the throughput does not decrease much beyond the tipping point. It only means that the cell group in focus is not saturated enough.

[0130] One approach to achieve the above could be to bin samples along both load and throughput axes so that outliers can be discarded. After discarding outliers, points might undergo a windowed aggregation, e.g. low-pass filter along both axes, to smooth out jagged parts around the shape. The remaining points can be bound by a polygon which will have either of the below attributes:

[0131] - If the shape is concave or flat when observed from the upper right edge of the bounding rectangle around the polygon, in which case the coordinates of the rightmost point, i.e. where the polygon has a maximum along the cell load axis, will be the tipping point.

[0132] - If convex between the coordinates where the polygon is touching its bounding box, the elbow point can be found.

[0133] Once the tipping point, or congestion threshold value 71, has been identified, a pair of a cell load and a throughput value defines a congestion threshold value for the cells in that cell group. Comparing a momentary cell load and a momentary throughput value of the cell with the congestion threshold in its group, will enable a determination of if the cell is in its normal operating range or in the congested range.

[0134] If the cell congestion level of the cell is smaller than the threshold 71 , a metric that measures distance from the zero load case relative to the load threshold can be used.

[0135] Once the threshold is passed, it can be determined that the cell is in a congested state and the degree of this congestion can be defined as another metric of distance from the throughput threshold, a coordinate of the tipping point.

[0136] These distance metrics can be linear, logarithmic or custom-defined. Their purpose is to quantify the congestion level so that network actors can carry out closed loop actions tied to its actual value. This means that scaling of the distances should be customized to best support the business logic built upon the numeric value of the congestion level metric.

[0137] If no values are needed, the tipping point and its relation to the sample under evaluation can define the presence of congestion in a binary manner as well, e.g. normal or congested state. In a more sophisticated approach, a metric for the distance of a measurement under evaluation from the tipping point can be defined, as projected on the bounding polygons upper righthand side.

[0138] Cell load matrix

[0139] On the X-axis of the previous figures 5a, 5b and 6, used metric has been interpreted as the resource demand associated with a given time period, per cell. It measures cell load such that it encapsulates resource block symbol utilization, or just resource block utilization offset by the rate of failed resource block allocations or rate of blocking. Both metrics have to be aggregated for the same period.

[0140] This ensures that situations where resource block utilization is low due to low traffic demand is not confused with situations where resource blocks cannot be efficiently distributed, are accompanied by blocking occasions and result in lower resource block utilization due to excessive load. This can happen when the uplink is so congested that it fails to register acknowledgements or when simply too many users demand traffic in the downlink.

[0141] In the operational phase, the congestion level values for each time step are already evaluated, forming a time series for each monitored cell.

[0142] The charts in Figure 8 provide an example of how the normal and congested states can be numerically represented. A congestion level of 1 in the top chart indicates that the cell has reached its tipping point 71 ; values below this represent the normal range and show little to no correlation with throughput. Values above 1 indicate time steps in the congested state, where throughput has significantly degraded, hence the strong negative correlation with the throughput metric displayed in the lower chart.

[0143] Once these time series are analyzed with any statistical or machine learning methods, it is possible to predict future time steps for which advance warnings can be sent.

[0144] Corrective actions

[0145] In case of congestion, the following examples for closed loop actions may be executed:

[0146] 1. NSMF actions: a) Setting up a new slice for the affected traffic flows b) Modifying slice of the affected traffic c) Move no priority traffic to different slice, e.g. to reduce the load of the affected slice d) Modifying radio resource partitioning (RRP) among slices

[0147] 2. NWDAF actions: a) Allocate the affected traffic to different 5QI b) Move other traffic than the affected one to a lower priority 5QI, which improves the QoS of the affected traffic c) Apply admission control to affected traffic d) Apply admission control to other traffic types e) Apply traffic shaping to the affected traffic or other traffic types

[0148] Proposed embodiments also relate to a network management node 1 , or network function, e.g., a NWDAF or a MDAF, which may be distributed over several nodes.

[0149] To perform the method actions above the network management node 1 , or network function, is adapted to detect congestion in a first cell 121.

[0150] The network management node 1 is further adapted to detect congestion in a first cell 121 , wherein the network management node 1 , or network function, is adapted to perform a learning phase 31, where the network management node 1 , or network function, is adapted to classify 311 cells in a mobile network, e.g., 4G and 5G networks, having similar traffic profiles and usage patterns as the first cell, into a cell group, and where the network management node 1, or network function, is adapted to identify 313 a tipping point indicating a congestion threshold value for the cells in the cell group.

[0151] The network management node 1 is further adapted to perform an operational phase 32, where the network management node 1, or network function, is adapted to determine 321 a measurement sample, represented by a momentary cell load, i.e. resource block utilization rate across shared channels and the blocking rate on the shared channels, e.g. shared channel blocking rate and average resource block utilization, where the sum of these two metrics can be observed to be related with the throughput such that the latter was limited by an upper bound, and a throughput value, i.e. an average throughput, a burst throughput, a pipe throughput or a bit rate, etc, of the first cell, and where the network management node 1, or network function, is adapted to determine 323 a cell congestion level for the first cell based on a distance between the measurement sample and the threshold value.

[0152] It is proposed that the network management node 1 , or network function, may be adapted to, as part of the learning phase 31 collect 311 cell performance management counters or events for the cells within a cell group, to classify 312 cells into cell groups based on data from a data driven algorithm, i.e. a clustering method; and to identify 313 the tipping point indicating the determined cell congestion threshold value 71 i.e. a pair of cell load and throughput values for the cell group based on the collected cell performance management counters or events.

[0153] The operational phase may comprises that the network management node 1 , or network function, is adapted to determine 321 a current measurement sample for the first cell 121 , the measurement sample being based on a momentary cell load and a momentary throughput value for the first cell 121 , to compare 322 the determined measurement sample with an indicated congestion threshold value of the said cell group, to determine 323 a cell congestion level for the cell based on a distance between the determined measurement sample and the indicated threshold value 71, and to decide 324 that the first cell is exposed to congestion when the threshold value is exceeded.

[0154] Embodiments teaches that collecting 312 or determining 321 may comprise that the network management node 1, or network function, is adapted to collect and use cell RF planning data 3111, 3211 and per UE data 3112, 3212 to increase the accuracy of the model, where UE data are aggregated per cell 3113, 3213, and the per cell aggregated UE data and the per cell RF planning data are blended 3114, 3214 with the per cell performance counters or cell performance events, i.e. PM counters or CTR events.

[0155] Embodiments herein may be implemented through a respective processor or one or more processors, such as the processor 910 of a network management node 1 , or network function, depicted in Figure 9 together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the network management node 1. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the network management node 1. The network management node 1 may further comprise a memory 920 comprising one or more memory units. The memory 920 comprises instructions executable by the processor in the network management node 1. The memory 920 is arranged to be used to store e.g., media functions, indications, tags, information, data, configurations, communication data, and applications to perform the methods herein when being executed in the network management node 1.

[0156] In some embodiments, a computer program 930 comprises instructions, which when executed by the at least one processor 910, cause the at least one processor of the network management node 1 to perform the actions above.

[0157] In some embodiments, a carrier 940 comprises the computer program 930, wherein the carrier 940 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer- readable storage medium.

[0158] Those skilled in the art will appreciate that units in the network management node 1 described above may refer to a combination of analog and digital circuits, and / or one or more processors configured with software and / or firmware, e.g. stored in the network management node 1 , that when executed by the one or more processors such as the processors described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuitry ASIC, or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a- Chip (SoC).

[0159] ADDITIONAL EXPLANATION

[0160] Some of the embodiments contemplated herein will now be 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.

[0161] Figure 10 shows an example of a communication system QQ100 in accordance with some embodiments.

[0162] In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network QQ102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108.

[0163] Examples of an ORAN network node include an open radio unit (0-Rll), an open distributed unit (0-Dll), an open central unit (O-CU), including an O-CU control plane (O- CLI-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1, E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.

[0164] 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 QQ100 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 QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0165] The UEs QQ112 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 QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 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 QQ102.

[0166] In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more host computing systems, such as host QQ116. 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 QQ106 includes one more core network nodes (e.g., core network node QQ108) 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 QQ108. 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).

[0167] The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102. The host QQ116 may host 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. As a whole, the communication system QQ100 of Figure 10 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.

[0168] In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 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) / Massive loT services to yet further UEs.

[0169] In some examples, the UEs QQ112 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 QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. 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).

[0170] In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 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 QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 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 QQ114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0171] The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 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 QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0172] Figure 11 shows a UE QQ200 in accordance with some embodiments. The UE QQ200 presents additional details of some embodiments of the UE QQ112 of Figure 1. 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 smart phone, 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 / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehiclemounted 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-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0173] 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).

[0174] The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 11. 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.

[0175] The processing circuitry QQ202 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 QQ210. The processing circuitry QQ202 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 QQ202 may include multiple central processing units (CPUs). In the example, the input / output interface QQ206 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 QQ200. 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.

[0176] In some embodiments, the power source QQ208 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 QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

[0177] The memory QQ210 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 QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems. The memory QQ210 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 hard disk 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 (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 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 QQ210, which may be or comprise a device-readable storage medium.

[0178] The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 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 QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0179] In the illustrated embodiment, communication functions of the communication interface QQ212 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 control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0180] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, 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).

[0181] 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.

[0182] 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 smartwatch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking 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 QQ200 shown in Figure 11. 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-loT 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.

[0183] 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.

[0184] Figure 12 shows a network node QQ300 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 NR NodeBs (gNBs)), O- RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0185] 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, distributed units (e.g., in an O-RAN access node) 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). 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- cel l / 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).

[0186] The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 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 QQ300 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 QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, 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 QQ300.

[0187] The processing circuitry QQ302 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 QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality. In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 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 QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

[0188] The memory QQ304 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 QQ302. The memory QQ304 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 QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated.

[0189] The communication interface QQ306 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 QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 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 QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0190] In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).

[0191] The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

[0192] The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 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 QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 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.

[0193] The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 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 QQ308. As a further example, the power source QQ308 may comprise 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.

[0194] Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 12 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 QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300. In some embodiments providing a core network node, such as core network node 108 of FIG. QQ1, some components, such as the radio front-end circuitry QQ318 and the RF transceiver circuitry QQ312 may be omitted.

[0195] Figure 13 is a block diagram illustrating a virtualization environment QQ400 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 QQ400 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. In some embodiments, the virtualization environment QQ400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0196] Applications QQ402 (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.

[0197] Hardware QQ404 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 QQ406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ408a and QQ408b (one or more of which may be generally referred to as VMs QQ408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ406 may present a virtual operating platform that appears like networking hardware to the VMs QQ408.

[0198] The VMs QQ408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ406. Different embodiments of the instance of a virtual appliance QQ402 may be implemented on one or more of VMs QQ408, 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.

[0199] In the context of NFV, a VM QQ408 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 QQ408, and that part of hardware QQ404 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 QQ408 on top of the hardware QQ404 and corresponds to the application QQ402.

[0200] Hardware QQ404 may be implemented in a standalone network node with generic or specific components. Hardware QQ404 may implement some functions via virtualization. Alternatively, hardware QQ404 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 QQ410, which, among others, oversees lifecycle management of applications QQ402. In some embodiments, hardware QQ404 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 QQ412 which may alternatively be used for communication between hardware nodes and radio units.

[0201] Although the computing devices described herein (e.g., UEs, network nodes) 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 processing circuitry, 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.

[0202] 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.

[0203] When using the word "comprise" or “comprising” it shall be interpreted as non- limiting, i.e. meaning "consist at least of".

[0204] The embodiments herein are not limited to the preferred embodiments described above. Various alternatives, modifications and equivalents may be used.

Claims

CLAIMS1 . A method performed by a network management node (1), or network function, to detect congestion in a first cell (121), wherein the method comprises: a learning phase (31), where cells in a mobile network, having similar traffic profiles and usage patterns as the first cell (121), are classified (312) into a cell group (29), and where a tipping point indicating a congestion threshold value (71) for the cells in the cell group (29) is identified (313), and an operational phase (32), where a measurement sample, represented by a momentary cell load, and a momentary throughput value, of the first cell (121), is determined (321), and where a cell congestion level for the first cell (121) is determined (323) based on a distance between the measurement sample and the threshold value (71).

2. The method according to claim 1 , the learning phase (31) comprising: collecting (311) cell performance management counters or events for the cells within a cell group (29); classifying (312) cells into cell groups based on data from a data driven algorithm; and identifying (313) the tipping point indicating the determined cell congestion threshold value (71) for the cell group (29) based on the collected cell performance management counters or events.

3. The method according to claim 1 or 2, the operational phase (32), comprising: determining (321) a current measurement sample for the first cell (1), the measurement sample being based on a momentary cell load and a momentary throughput value for the first cell (121); comparing (322) the determined measurement sample with an indicated congestion threshold value (314, 71) of the said cell group (29); determining (323) a cell congestion level for the cell based on a distance between the determined measurement sample and the indicated threshold value (71); and deciding (324) that the first cell (121) is exposed to congestion when the threshold value (71) is exceeded.

4. The method according to claim 2 or 3, wherein wherein collecting (311) in the learning phase (31), or determining (321) in the operational phase (32), comprises collecting and using cell Radio Frequency, RF, planning data (3111 , 3211) and perUser Equipment, UE, data (3112, 3212), where UE data are aggregated (3113, 3213) per cell, and the per cell aggregated UE data and the per cell RF planning data are blended (3114, 3214) with the per cell performance counters or cell performance events, i.e. performance management, PM, counters or cell trace records, CTR, events.

5. A network management node (1), or network function, adapted to detect congestion in a first cell (121), wherein the network management node (1), or network function, is adapted to perform: a learning phase (31), where the network management node (1), or network function, is adapted to classify (312) cells in a mobile network, having similar traffic profiles and usage patterns as the first cell, into a cell group (29), and where the network management node (1), or network function, is adapted to identify (313) a tipping point indicating a congestion threshold value (314, 71) for the cells in the cell group (29), and an operational phase (32), where the network management node (1), or network function, is adapted to determine (321) a measurement sample, represented by a momentary cell load, and a throughput value, of the first cell (121), and where the network management node (1), or network function, is adapted to determine (323) a cell congestion level for the first cell (121) based on a distance between the measurement sample and the threshold value (314, 71).

6. The network management node (1), or network function, according to claim 5, where the learning phase (31) comprises that the network management node (1), or network function, is adapted to: collect (311) cell performance management counters or events for the cells within a cell group (29); classify (312) cells into cell groups based on data from a data driven algorithm; and identify (313) the tipping point indicating the determined cell congestion threshold value (71) for the cell group based on the collected cell performance management counters or events.

7. The network management node (1), or network function, according to claim 5 or 6, where the operational phase (32) comprises that the network management node (1), or network function, is adapted to:determine (321) a current measurement sample for the first cell (121), the measurement sample being based on a momentary cell load and a momentary throughput value for the first cell (121); compare (322) the determined measurement sample with an indicated congestion threshold value (71, 314) of the said cell group; determine (323) a cell congestion level for the cell based on a distance between the determined measurement sample and the indicated threshold value (71); and decide (324) that the first cell (121) is exposed to congestion when the threshold value (71) is exceeded.

8. The network management node (1), or network function, according to claim 6 or 7, wherein collecting (312) in the learning phase (31), or determining (321) in the operational phase (32), comprises that the network management node (1), or network function, is adapted to collect and use cell RF planning data (3111, 3211) and per UE data (3112, 3212), where UE data are aggregated per cell (3113, 3213), and the per cell aggregated UE data and the per cell RF planning data are blended (3114, 3214) with the per cell performance counters or cell performance events, i.e. PM counters or CTR events.

9. A computer program (930) comprising instructions, which when executed by a processor (910), causes the processor to perform actions according to any of the claims 1-4.

10. A carrier (940) comprising the computer program (930) of claim 9, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

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