WO2023072435A1

WO2023072435A1 - False cell detection in a wireless communication network

Info

Publication number: WO2023072435A1
Application number: PCT/EP2022/055318
Authority: WO
Inventors: Elif USTUNDAG SOYKAN; Leyli KARAÇAY; Ayse Bilge INCE; Emrah TOMUR
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2021-10-27
Filing date: 2022-03-02
Publication date: 2023-05-04

Abstract

Equipment (50) is configured for false cell detection. The equipment (50) determines how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices(12) are entering or leaving proximity of one or more closed group cells (26), e.g., in a non-public network (20). The equipment (50) detects the presence or absence of a false cell (46)based on that determination.

Description

FALSE CELL DETECTION IN A WIRELESS COMMUNICATION NETWORK

TECHNICAL FIELD

The present application relates generally to a wireless communication network, and relates more particularly to false cell detection in such a network.

BACKGROUND

False cells represent a security threat to a wireless communication network, as they can be used to maliciously eavesdrop on and/or track wireless communication devices. An attacker in this regard can deploy a false base station that deceptively advertises a false cell as being able to provide wireless communication service to wireless communication devices subscribed to a wireless communication network. This false cell thereby masquerades as a genuine cell of the wireless communication network, e.g., in order to lure wireless communication devices into attempting to connect to the false cell. Depending on the capability of the false base station and/or the vulnerabilities of the wireless communication network, security threats from a false cell include, for example: (i) attacks on subscriber privacy, including attempts to identify used subscriptions or track the location of wireless communication devices; (ii) denial of service attacks on wireless communication devices; (iii) denial of service attacks on the wireless communication network; and (iv) rogue service delivery whereby an attacker attempts to deliver unauthorized or unsolicited services (e.g., text messages and calls) to wireless communication devices.

In one approach to false cell detection, the wireless communication network detects a cell as false if it uses an invalid cell identity. However, this approach proves vulnerable to a resourceful attacker that operates its false cell with a valid cell identity, e.g., learned by scanning the network.

Other approaches to false cell detection detect a cell as false if its presence conflicts with the known topology of the network. However, these approaches require large amounts of data about the network topology to be collected from wireless communication devices in measurement reports. Moreover, the approaches require that either up-to-date network topology information exists or topology information be collected before any false base station starts emitting signals. This may prevent third parties from being able to provide false cell detection as a paid service.

Still other approaches to false cell detection, such as those described in US 9838879 B2, detect a cell as false if its presence causes an anomaly in the known radio signal environment of the network. These approaches rely on collecting signal measurements from wireless communication devices, in order to compare the measurements against those expected in the absence of a false cell. The approaches therefore require heavy data collection during online operations. The approaches may also require a dedicated spectrum analyzer, e.g., on a car driven by dedicated technical personnel, to manually scan various locations while the network is offline, to capture baseline measurements in the assumed absence of a false cell.

Known approaches to false cell detection therefore suffer from a number of drawbacks. Many of the approaches are inefficient in terms of radio resources consumed, time, effort, and/or workforce. Moreover, many of the approaches rely on wireless devices to transmit messages, such as measurement reports or location area updates, that create meaningful network traffic, consume significant device energy, and/or are expensive to process at the network. Another drawback of known approaches is that they target wireless communication device operation in a public network.

SUMMARY

One object of the invention to improve the efficiency of false cell detection in a wireless communication network. Another object of the invention is to reduce the amount of data that must be transmitted and/or processed for the purpose of false cell detection. Still another object of the invention is to enable false cell detection in a non-public network. These and/or other objects may be achieved using methods and apparatus described herein for false cell detection.

For example, some embodiments herein perform false cell detection based on proximity indications that wireless communication devices send to a public network for reporting device proximity to one or more closed group cells. One or more embodiments in this regard exploit an anomaly in the number of proximity indications that a radio network node in a public network receives over time as being an indicator of the presence of a false cell. Indeed, such embodiments capitalize on the tendency of a false cell to lure wireless communication devices to report proximity indications to the false cell rather than to the radio network node, meaning that the radio network node would receive fewer proximity indications when a false cell is present than when a false cell is absent. These and other embodiments may thereby detect the presence of a false cell when the radio network node does not receive as many proximity indications as would have been expected in the absence of a false cell. Exploiting proximity indications in this way may prove advantageous for false cell detection even in a non-public network context, e.g., in a public network integrated non-public network (PNI-NPN). Alternatively or additionally, some embodiments advantageously operate based on minimal information, in the form of proximity reports, that does not consume many radio resources on the air interface, does not demand much power to transmit/receive, and/or does not demand significant resources to process. In fact, embodiments that rely simply on the number of proximity reports received operate very efficiently, as the content of the proximity reports need not be examined for purposes of false cell detection. This contrasts with other solutions that require significant information in the form of measurement reports and/or network topology data.

More particularly, embodiments herein include a method performed by equipment usable for false cell detection. The method comprises determining how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering or leaving proximity of one or more closed group cells, e.g., in a non-public network. The method further comprises detecting the presence or absence of a false cell based on determining the number of proximity indications.

In some embodiments, the one or more closed group cells are deployed in a non- public network.

In some embodiments, detecting the presence or absence of a false cell comprises detecting the presence or absence of a false cell based on whether the radio network node receives as many proximity indications as would have been expected in the absence of a false cell.

In some embodiments, determining the number of proximity indications comprises determining how many proximity indications the radio network node receives within an interval of time, and detecting the presence or absence of a false cell comprises detecting the presence or absence of a false cell based on whether the radio network node receives as many proximity indications as would have been expected within that interval of time in the absence of a false cell. In one or more of these embodiments, the interval of time is an interval between a first time of day on a first date and a second time of day on a second date, wherein the first date is the same or different than the second date.

In some embodiments, detecting the presence or absence of a false cell comprises detecting the presence of a false cell if, according to determining the number of proximity indications, the radio network node does not receive as many proximity indications as would have been expected in the absence of a false cell. In some embodiments, determining the number of proximity indications comprises determining how many proximity indications the radio network node receives over time, as part of determining a pattern of how many proximity indications the radio network node receives over time, and detecting the presence or absence of a false cell comprises detecting the presence or absence of a false cell based on an extent to which the determined pattern matches a pattern that would be expected in the presence or absence of a false cell.

In some embodiments, the method further comprises obtaining a trained machine learning model that predicts the presence or absence of a false cell as a function of how many proximity indications the radio network node receives, and detecting the presence or absence of a false cell comprises inputting, into the trained machine learning model, how many proximity indications the radio network node receives according to said determining. In one or more of these embodiments, the method further comprises training a machine learning model in order to obtain the trained machine learning model, wherein the training is based on observations of how many proximity indications the radio network node receives in the absence of a false cell. In one or more of these embodiments, the training is performed according to a k-means clustering machine learning algorithm.

In some embodiments, determining the number of proximity indications comprises determining how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering proximity of one or more closed group cells.

In some embodiments, the proximity indications are included in Radio Resource Control, RRC, measurement report messages that the wireless communication devices send to the radio network node as part of a procedure for handing over from the radio network node to a target radio network node.

Other embodiments herein include a method performed by equipment usable for model training. The method comprises determining, in the absence of a false cell, how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering or leaving proximity of one or more closed group cells, e.g., in a non-public network. Based on determining the number of proximity indications, the method also comprises training a machine learning model to predict the presence or absence of a false cell as a function of how many proximity indications the radio network node receives.

In some embodiments, the training is performed according to a k-means clustering machine learning algorithm. In some embodiments, determining the number of proximity indications comprises determining, in the absence of a false cell, how many proximity indications the radio network node receives within an interval of time. In one or more of these embodiments, the interval of time is an interval between a first time of day and a second time of day on a certain date.

In some embodiments, determining the number of proximity indications comprises determining how many proximity indications the radio network node receives over time in the absence of a false cell, as part of determining a pattern of how many proximity indications the radio network node receives over time in the absence of a false cell.

In some embodiments, determining the number of proximity indications comprises determining, in the absence of a false cell, how many proximity indications the radio network receives from wireless communication devices reporting that the wireless communication devices are entering proximity of one or more closed group cells.

Other embodiments herein include equipment usable for false cell detection. The equipment is configured to make a determination as to how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering or leaving proximity of one or more closed group cells, e.g., in a non-public network, and detect the presence or absence of a false cell based on said determination.

Other embodiments herein include equipment usable for model training. The equipment is configured to make a determination, in the absence of a false cell, how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering or leaving proximity of one or more closed group cells, e.g., in a non-public network. Based on determining the number of proximity indications, the equipment is also configured to train a machine learning model to predicts the presence or absence of a false cell as a function of how many proximity indications the radio network node receives. Other embodiments herein include a computer program comprising instructions which, when executed by at least one processor of equipment usable for false cell detection, causes the equipment to perform the steps described above for detecting a false cell. Other embodiments herein include a computer program comprising instructions which, when executed by at least one processor of equipment usable for model training, causes the equipment to perform the steps described above for model training. In one or more of these embodiments, a carrier containing the computer program above is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

Other embodiments herein include equipment, usable for false cell detection, comprising processing circuitry configured to make a determination as to how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering or leaving proximity of one or more closed group cells, e.g., in a non-public network. The processing circuitry is also configured to detect the presence or absence of a false cell based on said determination.

In some embodiments, the processing circuitry is configured to perform the steps described above for false cell detection.

Other embodiments herein include equipment, usable for model training, comprising processing circuitry configured to make a determination, in the absence of a false cell, how many proximity indications a radio network node in a public network receives from wireless communication devices reporting that the wireless communication devices are entering or leaving proximity of one or more closed group cells, e.g., in a non-public network. Based on determining the number of proximity indications, the processing circuitry is also configured to train a machine learning model to predicts the presence or absence of a false cell as a function of how many proximity indications the radio network node receives.

In some embodiments, the processing circuitry is configured to perform the steps described above for model training.

Of course, the present invention is not limited to the above features and advantages. Indeed, those skilled in the art will recognize additional features and advantages upon reading the following detailed description, and upon viewing the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a block diagram of equipment usable for false cell detection according to some embodiments.

Figure 2 is a graph of proximity indications received over time according to some embodiments.

Figure 3A is a block diagram of machine learning model training for false cell detection according to some embodiments. Figure 3B is a block diagram of false cell detection using a trained machine learning model according to some embodiments.

Figure 4 is a graph of a number of proximity indications received over time for anomaly detection according to some embodiments.

Figure 5 is a call flow diagram of proximity indication reporting according to some embodiments.

Figure 6 is a logic flow diagram of a method performed by equipment usable for false cell detection according to some embodiments.

Figure 7 is a logic flow diagram of a method performed by equipment usable for model training according to some embodiments.

Figure 8 is a block diagram of equipment usable for false cell detection according to some embodiments.

Figure 9 is a block diagram of equipment usable for model training according to some embodiments.

Figure 10 is a block diagram of a communication system in accordance with some embodiments.

Figure 11 is a block diagram of user equipment in accordance with some embodiments.

Figure 12 is a block diagram of a network node in accordance with some embodiments. Figure 13 is a block diagram of a host in accordance with some embodiments.

Figure 14 is a block diagram of a virtualization environment in accordance with some embodiments.

Figure 15 is a block diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.

DETAILED DESCRIPTION

Figure 1 shows a public network 10 that provides wireless communication service to wireless communication devices 12, e.g., based on subscriptions that the wireless communication devices 12 hold to receive such service. The public network 10 may for example be a Public Land Mobile Network (PLMN). To provide wireless coverage to the wireless communication devices 12, the public network 10 includes radio network nodes that each provide one or more cells. Figure 1 for example shows radio network node 14 provides a cell 16, e.g., in the form of a macro cell. Wireless communication devices 12 within the radio coverage of the cell 16 may use the cell 16 to connect to the radio network node 14 and receive wireless communication service.

According to some embodiments shown, another network 20 provides wireless communication service over an area that overlaps with, or is proximate to, the coverage of the public network 10. In some embodiments, the network 20 is non-public, in the sense that it provides wireless communication service only for a dedicated and clearly defined set of users or wireless communication devices, e.g., that may be part of a single organization. The set of users or devices may be those included in a whitelist. Regardless, as shown, the network 20 includes one or more radio network nodes 24 that provides one or more closed group cells 26. A closed group cell 26 may for example be a Closed Subscriber Group (CSG) or a Closed Access Group (CAG), e.g., as specified by the 3^rd Generation Partnership Project (3GPP). Regardless, unlike a cell 16 of the public network 10, a closed group cell 26 in the network 20 is closed except to a certain group of users or wireless communication devices. A closed group cell 26 to which a wireless communication device 12 is allowed access may be referred to as a member cell from that device’s perspective, i.e., the device 12 is a member of the closed group allowed to access the closed group cell 26. In some embodiments, a closed group cell 26 provides coverage over a smaller area than a cell 16 of the public network 10. In one embodiment, for example, a cell 16 of the public network 10 is a macro cell whereas a closed group cell 26 is a picocell, femtocell, or other small cell provided by a short-range, low-power access point. However, a closed group cell 26 may nonetheless operate on a mobile operator’s licensed spectrum.

In some embodiments where the network 20 is a non-public network (NPN), the network 20 may be a public network integrated NPN (PNI-NPN), which is an NPN deployed with the support of a public network, e.g., a PNI-NPN may operate its own core network but have the support of a public network’s access network. For example, in some embodiments, the network 20 is an NPN that employs the support of public network 10, e.g., the support of an access network of the public network 10.

Regardless, a wireless communication device 12 that is connected to a radio network node 14 of the public network 10 may at some point detect that the device 12 is within proximity of one or more closed group cells 26, e.g., provided that the device 12 is a member of the closed group(s) allowed to access such closed group cell(s) 26. The wireless device 12 may for example detect that the wireless device 12 is within proximity to a closed group cell 26 if the device 12 receives a signal from a cell advertising that it is a closed group cell, if the device 12 detects it is at a location known to be covered by a closed group cell, or in any other way.

In any event, in this scenario where a wireless communication device 12 detects proximity to one or more closed group cells 26, the wireless communication device 12 may transmit one or more proximity indications 30 to the radio network node 14 of the public network 10, e.g., as specified by 3GPP TS 25.331 v16.1.0. The proximity indications 30 may for example be transmitted in Radio Resource Control (RRC) measurement report messages that the wireless communication devices 12 send to the radio network node 14 as part of a procedure for handing over from a radio network node 14 of the public network 10 to a radio network node 24 serving a closed group cell 26. A proximity indication 30 herein may report that a wireless communication device 12 is entering or leaving proximity of one or more closed group cells 26. For example, in some embodiments, a wireless communication device 12 transmits a proximity indication 30 upon first detecting that the device 12 is now within proximity of one or more closed group cells 26, after not being within proximity of any closed group cell 26. In this case, the proximity indication 30 effectively reports that the wireless communication device 12 is entering the proximity of one or more closed group cells 26. In one or more embodiments, the proximity indication 30 includes a field that specifies the proximity indication 30 is reporting such an “entering” event. The proximity indication 30 may also include field(s) indicating a radio access technology (RAT) and/or a frequency usable for accessing (one of) the closed group cell(s) 26 for which the proximity indication 30 is sent.

In some embodiments, a wireless communication device 12 may also transmit a proximity indication 30 upon first detecting that the wireless communication device 12 has left the proximity of all closed group cells that the device 12 is allowed to access as a member. In this case, the proximity indication 30 effectively reports that the wireless communication device 12 has been in the proximity of one or more closed group cells 26 but is no longer in the proximity of any closed group cell 26 that the device 12 is allowed to access. In one or more embodiments, the proximity indication 30 includes a field that specifies the proximity indication 30 is reporting such a “leaving” event.

Generally, then, the proximity indications 30 that the radio network node 14 receives from a wireless communication device 12, if any, indicate to the radio network node 14 whether that wireless communication device 12 is entering or leaving proximity of one or more closed group cells 26. With such proximity reporting being performed on a device by device basis, the radio network node 14 may receive a number of proximity indications 30 reporting wireless communication devices 12 as entering or leaving proximity of one or more closed group cells 26.

Some embodiments herein exploit these proximity indications 30 for performing false cell detection. Indeed, such embodiments capitalize on the tendency of a false cell to lure wireless communication devices 12 to connect to and report proximity indications 30 to the false cell rather than to the radio network node 14. This means that the radio network node 14 will generally receive fewer proximity indications 30 when a false cell is present than when a false cell is absent. Figure 1 for example shows that, when a false cell 46 operated by a false base station 44 is present, the false cell 46 lures some wireless communication devices 12 into connecting to it rather than the radio network node 14. The false cell 46 thereby effectively diverts some of the proximity indications 30 that would have been sent to the radio network node 14, so that proximity indications 30D are sent to the false cell 46 instead. Embodiments herein generally detect the diversion of proximity indications 30 away from the radio network node 14 and attribute such diversion to the presence of a false cell 46.

Exploiting proximity indications 30 in this way may prove advantageous for false cell detection even in a non-public network context, e.g., in a public network integrated non-public network (PNI-NPN). Moreover, the proximity indications 30 advantageously do not consume many radio resources on the air interface, do not demand much power to transmit/receive, and/or do not demand significant resources to process. In fact, embodiments that rely simply on the number of proximity indications 30 received operate very efficiently, as the content of the proximity indications need not be examined for purposes of false cell detection in such embodiments. This contrasts with other solutions that require significant information in the form of measurement report content and/or network topology data.

Figure 1 shows additional details of false cell detection according to one or more embodiments. Some embodiments in this regard provide equipment 50 usable for false cell detection. Equipment 50 performs false cell detection based on proximity indication(s) 30 received by a radio network node 14 in the public network 10. Equipment 50 may be, comprise, or be co-located with, the radio network node 14 itself. Or, in other embodiments, equipment 50 is, comprises, or is co-located with, another radio network node of the public network 10 or a core network node in a core network of the public network 10.

More particularly, equipment 50 includes a false cell detector 52 that performs false cell detection, for making a decision 54 about whether a false cell 46 is present or absent. The equipment 50 determines how many proximity indications 30 the radio network node 14 receives from wireless communication devices 12. Such determination may for instance yield information 56 at the equipment 50 indicating how many proximity indications 30 the radio network node 14 receives from wireless communication devices 12. In some embodiments, this information 56 may simply include, as suggested by the example of Figure 1 , a count of how many proximity indications 30 the radio network node 14 receives.

In one embodiment, the determination of how many proximity indications 30 the radio network node 14 receives is agnostic as to whether the proximity indications 30 report the entering or leaving of proximity to one or more closed group cells, e.g., so as to count how many proximity indications 30 the radio network node 14 receives in total, without regard to whether those proximity indications 30 were triggered by the entering or leaving of proximity. In another embodiment, by contrast, the determination of how many proximity indications 30 the radio network node 14 receives is specific to proximity indications 30 that report the entering of proximity to one or more closed group cells 26, e.g., so as to strictly count how many proximity indications 30 the radio network node 14 receives reporting the entering of proximity to one or more closed group cells 26, to the exclusion of proximity indications 30 that report the leaving of proximity to one or more closed group cells 26.

In some embodiments, the false cell detector 52 exploits an anomaly in the number of proximity indications 30 that the radio network node 14 receives as being an indicator of the presence of a false cell 46. In these and other embodiments, then, the false cell detector 52 may detect the presence or absence of a false cell 46 based on whether the radio network node 14 receives as many proximity indications 30 as would have been expected in the absence of a false cell 46. For example, the false cell detector 52 may detect the presence of a false cell 46 when the radio network node 14 does not receive as many proximity indications 30 as would have been expected in the absence of a false cell 46. In one embodiment where this expected number of proximity indications 30 is reflected by a threshold, then, the false cell detector 52 detects the presence of a false cell 46 when the number of proximity indications 30 received by the radio network node 14 is below the threshold.

The false cell detector 52 in some embodiments performs false cell detection in this way on a time interval by time interval basis. In this case, the false cell detector 52 may perform false cell detection based on information 56 that indicates how many proximity indications 30 the radio network node 16 receives within an interval of time. This interval of time may, for example, span a 24 hour interval, a 1 hour interval, or any other interval between a first time of day (e.g., 10:00 AM) and a second time of day (e.g., 11 :00 AM), e.g., on the same or different dates. Regardless, the false cell detector 52 may then detect the presence of absence of a false cell 46 based on whether the radio network node 16 receives as many proximity indications 30 as would have been expected within that interval of time in the absence of a false cell 46. In these embodiments, then, the false cell detector 52 may iteratively or periodically perform false cell detection in this way for each of multiple time intervals.

Figure 2 shows a simplistic example. In this example, equipment 50 determines how many proximity indications 30 the radio network node 16 receives during each of multiple time intervals T1-T14. The false cell detector 52 compares the number of proximity indications 30 received by the radio network node 16 in each time interval to a threshold, and detects the presence of a false cell 26 if the number of proximity indications 30 received by the radio network node 16 in any given time interval is less than the threshold. In this example, the threshold that the false cell detector 52 uses for the comparison in time intervals T1-T9 is shown as threshold TH1. The number of proximity indications 30 received in each of time intervals T1-T5 and T7-T9 exceeds the threshold TH1 , such that the false cell detector 52 detects that a false cell is absent in those time intervals. However, the number of proximity indications 30 received in time interval T6 does not exceed the threshold TH1, such that the false cell detector 52 detects that a false cell is present in that time interval.

Note, though, that the false cell detector 52 may use different thresholds for different time intervals. As shown in the example of Figure 2, for instance, the threshold that the false cell detector 52 uses for the comparison in time intervals T10-T14 is shown as threshold TH2. The number of proximity indications 30 received in each of time intervals T10-T14 exceeds this threshold TH2, such that the false cell detector 52 detects that a false cell is absent in those time intervals. This remains the case even though the number of proximity indications 30 received in each of time intervals T10-T14 would not have exceeded threshold TH1.

Uses of different thresholds in different time intervals may accommodate for factors other than the presence of a false cell impacting how many proximity indications 30 the radio network node 14 receives over time. For example, even in the absence of a false cell 26, the radio network node 14 may naturally receive different numbers of proximity indications 30 at different times of the day, e.g., due to time-dependent fluctuation of the number of wireless communication devices 12 that are active.

More generally, the false cell detector 52 may determine how many proximity indications 30 the radio network node 14 receives over time, as part of determining a pattern of how many proximity indications 30 the radio network node 14 receives over time. The false cell detector 52 may then detect the presence or absence of a false cell 26 based on an extent to which the determined pattern matches a pattern that would be expected in the presence or absence of a false cell 26. The pattern expected may be determined, for example, on historical observations and/or statistical measures.

These and other embodiments may operate based on a machine learning (ML) model trained to predict the presence or absence of a false cell 26 as a function of how many proximity indications 30 the radio network node 16 receives. Training of the ML model may for example be based on observations of how many proximity indications 30 the radio network node 16 receives in the (presumed) absence of a false cell 26 and/or observations of how many proximity indications 30 the radio network node 16 receives in the (known) presence of a false cell 26. Note the equipment that trains the ML model, referred to as equipment usable for model training, may be the same or different than the equipment 50 that uses the trained ML model to detect the presence or absence of a false cell 46. Regardless, with the ML model trained, the false cell detector 52 may input, into the trained ML model, how many proximity indications 30 the radio network node 16 receives, with the output of the trained ML model being a decision as to whether a false cell 26 is present or absent.

Figure 3A-3B illustrate additional details of some embodiments based on machine learning. Figure 3A shows the ML training phase. In the training phase, the equipment 50 usable for false cell detection collects K proximity indications 30 as reported by wireless communication devices 12 in a clean environment, i.e., an environment where no false cell is present. These K proximity indications 30 constitute training data 70 that is used to train the ML model to generate the normal behavior of the public network 10, i.e., without the presence of a false cell 26. The output of the training phase is the trained ML model 72 which will be used for false cell detection.

Figure 3B shows the detection phase that uses the trained ML model 72. In the detection phase, the equipment 50 usable for false cell detection collects T proximity indications 30 as reported by wireless communication devices 12 in a running environment, i.e., an environment where a false cell might be present. The proximity indications 30 constitute test data 74 that is fed to the trained ML model 72 periodically to check if there is a false cell present. The output 76 from the trained ML model 72 is a detection decision. The detection decision is a decision as to whether the number of proximity indications within a time interval (e.g., 1 hour) amounts to an anomaly.

Note that any type of ML model may be used. As an example, if labeled data is available, whereby proximity indication counts are labeled as normal or abnormal, the ML model may be designed as a classification model.

Consider now a specific example wherein ML training is performed according to a k- means clustering machine learning algorithm. In this example, a closed group cell 26 is a CSG configured for PNI-NPN deployment, and wireless communication devices 12 in the form of user equipments (UEs) send a proximity indication report during a handover process for handing over from the radio network node 16 to a CSG.

The equipment 50 usable for false cell detection keeps track of the proximity indications 30 received by the radio network node 16, together with a timestamp of receipt. The resulting data set represents the statistics of the proximity indications 30 over time. In this example, the equipment 50 does not use the content of the proximity indications 30, but only relies on the existence of the proximity indications 30. This means the equipment 50 does not need the actual proximity indications 30 or their content to perform the false cell detection. Rather, the equipment 50 only needs the number of proximity indications 30 and their respective receipt timestamps.

Table 1 shows the format of the dataset used for training the ML model according to this example. The example includes 30 days of data that includes proximity indication IDs and timestamps of reception. This dataset, e.g., as represented in an excel file, represents the statistics of proximity indication reception by the radio network node 16.

Table 1 : Dataset Format

The entries in the dataset in this example approximate a Poisson distribution with A=50, related to the report count, e.g., to simulate the behavior of the UEs. Here, the Poisson distribution is a discrete probability distribution for the counts of events that occur randomly in a given interval of time (or space). In this regard, the probability of observing x events in a given interval is given by

where X is the number of events in a given interval, and A is the mean number of events per interval. X follows a Poisson distribution with parameter A. Table 2 shows the statistics of the dataset on day 1 for 14 hours. For each hour, the number of reported proximity indications is given in the last column. Here, no anomaly is shown in the dataset.

able 2: Snapshot of training dataset on Day 1 Tables 3A and 3B show anomalies within the training dataset according to some embodiments, with the anomalies occurring at Hour 11 on Day 11 and at Hour 22 on Day 22.

Table 3A: Training Dataset with Anomaly at Hour 11 on day 11

Table 3B: Training Dataset with Anomaly at Hour 22 on day 22

On day 11 in this example training dataset, only 7 reports are gathered from UEs between the 11-12 AM. Similarly, on day 22, only 11 reports are gathered from UEs between 10-11 PM. The ML model is trained with this training dataset to identify these report counts as an anomaly.

With the ML model trained in this way, Figure 4 shows an example of anomaly detection performed using a k-means clustering ML algorithm in Python using sklearn library. The example defines k=2, which denotes two clusters representing normal and anomaly behavior. Euclidian distance is used as distance type to calculate distance between cluster centers’. The ML model is trained with the corresponding dataset including two anomalies. Figure 4 shows that the algorithm identified the defined anomalies during clustering. Figure 4 in this regard shows the report counts on y-axis, timestamp in days on the x-axis during 30 days of data. The report counts are changing around 50, yet the report count of the anomalies changes drastically.

These and other embodiments may thereby enable false cell detection in the vicinity of small cell deployments, e.g., in the area where closed group cells are deployed. Some embodiments perform false cell detection in this way, based on the density of proximity indication messages that are sent by wireless communication devices. When a wireless communication device is connected to a radio network node 14 of the public network 10 but approaches and/or leaves a small cell 26 that belongs to a closed subscriber group, that device 12 sends a proximity indication message to the radio network node 14 of the public network 10 before the device 12 hands over from the radio network node 14 in order to connect to the small cell 26. The proximity indication message may for instance be sent via an RRC measurement report. If a false cell is present, however, that false cell tries to attract the wireless communication device 12 by sending more powerful signals, so that the wireless communication device 12 connects to the false cell rather than the radio network node 14 of the public network 10. The false cell therefore prevents the wireless communication device 12 from sending proximity indication messages to the radio network node 14 of the public network 10. Some embodiments accordingly exploit proximity indication reports collected by the public network 10 to detect a false cell as an anomaly in the density of the collected reports, e.g., as compared to the system normal learned in a specified interval of time. One or more embodiments employ ML based detection to detect the anomaly, where the ML based detection may run on in the public network’s radio access network (RAN) or in the public network’s core network.

Some embodiments prove advantageous in that they do not require any modification to wireless communication devices, nor to underlying 3GPP protocols, that are already configured to transmit proximity indication reports. Alternatively or additionally, some embodiments are advantageous because they do not need any information about the network or about the end-users besides already collected RRC measurement reports. For example, some embodiments do not require any network information to be known beforehand such as network topology.

Alternatively or additionally, some embodiments are advantageous in that they do not require an additional device or an additional application that runs on wireless communication devices to collect proximity indication messages. Indeed, wireless communication devices belonging to the public network 10 may already send this information in the RRC measurement reports. Some embodiments simply utilize a ML function running in the public network 10, either in the RAN or the core network.

Some embodiments herein are especially applicable in an Internet of Things (loT) scenario. For example, in one applicable scenario, there is a factory which is deployed with small cells to cover different parts of the factory, including coverage-gaps places. One or more Home eNodeBs (HeNBs) serve respective ones of these small cells. All of these small cells form a Closed Subscriber Group (CSG). The UEs (including loT devices and robots) which are a member of the CSG can connect to and camp on these small cells. In order to connect or handover from a source eNodeB in a public network to a target Home eNB (HeNB) providing a small cell covering a part of the factory, a UE needs to send a proximity indication report to the source eNodeB. With respect to the number of devices in the environment, that source eNodeB is expected to receive a specific number of proximity indications on average during a specific time, e.g., as captured by historical data. Some embodiments exploit these proximity indications to detect a false base station (FBS) deployed around the factory with stronger signal strength than the legitimate base stations, trying to attract UEs to connect it and causing interference which can severely hamper network quality. When such a FBS exists, some loT devices try to connect to this FBS; so, they will not send proximity indication reports to the source eNB. As a result, the number of proximity reports received during a specific time will be decreased in the source eNB. Embodiments herein exploit this as an indicator of an anomaly situation attributable to the presence of a FSB.

Wireless communication devices 12 in some embodiments transmit proximity indications according to the procedure shown in Figure 5, e.g., consistent with 3GPP TS 36.300 v16.6.0. In a public network integrated non-public network (PNI-NPN), when a UE connects to CSG cell, it is handed over from a public network eNodeb (called Source eNodeB) to a CSG cell (provided by a Home eNodeB, HeNB). Before making a handover decision to a HeNB, the Source eNB needs to acquire UE measurement information related to the target CSG cell. Nevertheless, UEs may not be able to continuously make measurements and read the system information of a lot of CSG cells in cases of large scale HeNB deployments. In order to allow the UE to make those measurements efficiently, a proximity report can be configured within the RRC Reconfiguration message. A proximity report allows the UE to send a “proximity indication” to the Source eNB in the uplink whenever the UE is entering or leaving the proximity of one or more cells with CSG IDs that the UE has in its CSG Whitelist.

In particular, as shown in Figure 5, the source eNB configures a UE with proximity indication control (Step 1). The UE sends an "entering" proximity indication when it determines it may be near a CSG member cell (based on autonomous search procedures) (Step 2). The proximity indication includes the Radio Access Technology (RAT) and frequency of the CSG member cell. If a measurement configuration is not present for the concerned frequency/RAT, the source eNB configures the UE with relevant measurement configuration including measurement gaps as needed, so that the UE can perform measurements on the reported RAT and frequency (Step 3). The network may also use the proximity indication to minimize the requesting of handover preparation information of CSG/hybrid cells by avoiding requesting such information when the UE is not in the geographical area where its CSG member cells are located.

In Step 4, the UE sends a measurement report including the PCI (e.g., due to triggered event A3). The source eNB then configures the UE to perform System Information (SI) acquisition and reporting of a particular Physical Cell Identity (PCI) (Step 5). The UE correspondingly performs SI acquisition using autonomous gaps, i.e., the UE may suspend reception and transmission with the source eNB within limits to acquire the relevant system information from the target HeNB (Step 6)

In Step 7, the UE sends a measurement report including (E-)Cell Global Identity (CGI), Tracking Area Identifier (TAI), CSG ID and "member/non-member" indication. If the target cell is a shared CSG/hybrid cell, the measurement report also includes the subset of the broadcast PLMN identities that pass PLMN ID check and for which the CSG whitelist of the UE includes an entry comprising the cell's CSG ID and the respective PLMN identity.

In Step 8, the source eNB includes the target E-CGI and the CSG ID in the Handover Required message sent to the Mobility Management Entity (MME). If the target is a hybrid cell the Cell Access Mode of the target is included. The MME performs UE access control to the CSG cell based on the CSG ID and the selected target PLMN received in the Handover Required message and the stored CSG subscription data for the UE (Step 9). If the access control procedure fails, the MME ends the handover procedure by replying with the Handover Preparation Failure message. If the Cell Access Mode is present, the MME determines the CSG Membership Status of the UE handing over to the hybrid cell and includes it in the Handover Request message. In Steps 10-11 , the MME sends the Handover Request message to the target HeNB including the target CSG ID received in the Handover Required message. If the target is a hybrid cell the CSG Membership Status will be included in the Handover Request message.

In Step 12, the target HeNB verifies that the CSG ID received in the Handover Request message matches the CSG ID broadcast in the target cell and if such validation is successful it allocates appropriate resources. UE prioritisation may also be applied if the CSG Membership Status indicates that the UE is a member. In Steps 13-14, the target HeNB sends the Handover Request Acknowledge message to the MME via the HeNB gateway (GW) if present.

In Step 15, the MME sends the Handover Command message to the source eNB. The source eNB transmits the Handover Command (RRC Connection Reconfiguration message including mobility control information) to the UE (Step 16). After sending an "entering" proximity indication (step 2), if the UE determines that it is no longer near a CSG member cell, the UE sends a "leaving" proximity indication to the source eNB. Upon reception of this indication, the source eNB may reconfigure the UE to stop measurements on the reported RAT and frequency.

In view of the modifications and variations herein, Figure 6 depicts a method performed by equipment 50 usable for false cell detection in accordance with particular embodiments. The method includes determining how many proximity indications 30 a radio network node 14 in a public network 10 receives from wireless communication devices 12 reporting that the wireless communication devices 12 are entering or leaving proximity of one or more closed group cells 26, e.g., in a non-public network 20 (Block 600). The method also comprises detecting the presence or absence of a false cell 46 based on that determination (Block 610).

In some embodiments, the method further comprises obtaining a trained machine learning model that predicts the presence or absence of a false cell 46 as a function of how many proximity indications the radio network node 14 receives (Block 605). In this case, the equipment 50 may use the trained machine learning model to detect the presence or absence of a false cell 46 in Block 610.

Figure 7 depicts a method performed by equipment usable for model training in accordance with other particular embodiments. The method comprises determining, in the absence of a false cell 46, how many proximity indications 30 a radio network node 14 in a public network 10 receives from wireless communication devices 12 reporting that the wireless communication devices 12 are entering or leaving proximity of one or more closed group cells 26, e.g., in a non-public network 20 (Block 700). The method also comprises, based on that determination, training a machine learning model to predict the presence or absence of a false cell 46 as a function of how many proximity indications 30 the radio network node 14 receives (Block 710). Embodiments herein also include a radio network node configured to perform any of the steps of any of the embodiments described above for the radio network node.

Embodiments herein also include corresponding equipment for performing the methods and processing herein. For example, embodiments herein include equipment 50 usable for false cell detection. The equipment 50 comprises processing circuitry and power supply circuitry. The processing circuitry is configured to perform any of the steps of any of the embodiments described above for false cell detection. The power supply circuitry is configured to supply power to the equipment 50.

Embodiments further include equipment 50 usable for false cell detection. The equipment 50 comprises processing circuitry. The processing circuitry is configured to perform any of the steps of any of the embodiments described above for false cell detection. In some embodiments, the equipment 50 further comprises communication circuitry.

Embodiments further include equipment 50 usable for false cell detection. The equipment 50 comprises processing circuitry and memory. The memory contains instructions executable by the processing circuitry whereby the equipment 50 is configured to perform any of the steps of any of the embodiments described above for false cell detection.

Embodiments herein further include equipment usable for model training. The equipment comprises processing circuitry and power supply circuitry. The processing circuitry is configured to perform any of the steps of any of the embodiments described above for model training. The power supply circuitry is configured to supply power to the equipment.

Embodiments further include equipment usable for model training. The equipment comprises processing circuitry. The processing circuitry is configured to perform any of the steps of any of the embodiments described above for model training. In some embodiments, the equipment further comprises communication circuitry.

Embodiments further include equipment usable for model training. The equipment comprises processing circuitry and memory. The memory contains instructions executable by the processing circuitry whereby the equipment is configured to perform any of the steps of any of the embodiments described above for model training.

More particularly, the apparatuses described above may perform the methods herein and any other processing by implementing any functional means, modules, units, or circuitry. In one embodiment, for example, the apparatuses comprise respective circuits or circuitry configured to perform the steps shown in the method figures. The circuits or circuitry in this regard may comprise circuits dedicated to performing certain functional processing and/or one or more microprocessors in conjunction with memory. For instance, the circuitry may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as read-only memory (ROM), random-access memory, cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory may include program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein, in several embodiments. In embodiments that employ memory, the memory stores program code that, when executed by the one or more processors, carries out the techniques described herein.

Figure 8 for example illustrates equipment 50 usable for false cell detection as implemented in accordance with one or more embodiments. As shown, the equipment 50 includes processing circuitry 810 and communication circuitry 820. The communication circuitry 820 is configured to transmit and/or receive information to and/or from one or more other nodes, e.g., via any communication technology. The processing circuitry 810 is configured to perform processing described above, e.g., in Figure 6, such as by executing instructions stored in memory 830. The processing circuitry 810 in this regard may implement certain functional means, units, or modules.

Figure 9 illustrates equipment 900 usable for model training as implemented in accordance with one or more embodiments. As shown, the equipment 900 includes processing circuitry 910 and communication circuitry 920. The communication circuitry 920 is configured to transmit and/or receive information to and/or from one or more other nodes, e.g., via any communication technology. The processing circuitry 910 is configured to perform processing described above, e.g., in Figure 7, such as by executing instructions stored in memory 930. The processing circuitry 910 in this regard may implement certain functional means, units, or modules.

Those skilled in the art will also appreciate that embodiments herein further include corresponding computer programs.

A computer program comprises instructions which, when executed on at least one processor of an apparatus, cause the apparatus to carry out any of the respective processing described above. A computer program in this regard may comprise one or more code modules corresponding to the means or units described above.

Embodiments further include a carrier containing such a computer program. This carrier may comprise one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

In this regard, embodiments herein also include a computer program product stored on a non-transitory computer readable (storage or recording) medium and comprising instructions that, when executed by a processor of an apparatus, cause the apparatus to perform as described above.

Embodiments further include a computer program product comprising program code portions for performing the steps of any of the embodiments herein when the computer program product is executed by a computing device. This computer program product may be stored on a computer readable recording medium.

Figure 10 shows an example of a communication system 1000 in accordance with some embodiments.

In the example, the communication system 1000 includes a telecommunication network 1002 that includes an access network 1004, such as a radio access network (RAN), and a core network 1006, which includes one or more core network nodes 1008. The access network 1004 includes one or more access network nodes, such as network nodes 1010a and 1010b (one or more of which may be generally referred to as network nodes 1010), or any other similar 3^rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 1010 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1012a, 1012b, 1012c, and 1012d (one or more of which may be generally referred to as UEs 1012) to the core network 1006 over one or more wireless connections.

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 1000 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 1000 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

The UEs 1012 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 1010 and other communication devices. Similarly, the network nodes 1010 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 1012 and/or with other network nodes or equipment in the telecommunication network 1002 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 1002.

In the depicted example, the core network 1006 connects the network nodes 1010 to one or more hosts, such as host 1016. 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 1006 includes one more core network nodes (e.g., core network node 1008) 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 1008. 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).

The host 1016 may be under the ownership or control of a service provider other than an operator or provider of the access network 1004 and/or the telecommunication network 1002, and may be operated by the service provider or on behalf of the service provider. The host 1016 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 1000 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.

In some examples, the telecommunication network 1002 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1002 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1002. For example, the telecommunications network 1002 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.

In some examples, the UEs 1012 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 1004 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1004. 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).

In the example, the hub 1014 communicates with the access network 1004 to facilitate indirect communication between one or more UEs (e.g., UE 1012c and/or 1012d) and network nodes (e.g., network node 1010b). In some examples, the hub 1014 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1014 may be a broadband router enabling access to the core network 1006 for the UEs. As another example, the hub 1014 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 1010, or by executable code, script, process, or other instructions in the hub 1014. As another example, the hub 1014 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 1014 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1014 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1014 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 1014 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

The hub 1014 may have a constant/persistent or intermittent connection to the network node 1010b. The hub 1014 may also allow for a different communication scheme and/or schedule between the hub 1014 and UEs (e.g., UE 1012c and/or 1012d), and between the hub 1014 and the core network 1006. In other examples, the hub 1014 is connected to the core network 1006 and/or one or more UEs via a wired connection. Moreover, the hub 1014 may be configured to connect to an M2M service provider over the access network 1004 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1010 while still connected via the hub 1014 via a wired or wireless connection. In some embodiments, the hub 1014 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 1010b. In other embodiments, the hub 1014 may be a non-dedicated hub - that is, a device which is capable of operating to route com munications between the UEs and network node 1010b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

Figure 11 shows a UE 1100 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a 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 device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

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).

The UE 1100 includes processing circuitry 1102 that is operatively coupled via a bus 1104 to an input/output interface 1106, a power source 1108, a memory 1110, a communication interface 1112, 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.

The processing circuitry 1102 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 1110. The processing circuitry 1102 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 1102 may include multiple central processing units (CPUs).

In the example, the input/output interface 1106 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 1100. 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. In some embodiments, the power source 1108 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 1108 may further include power circuitry for delivering power from the power source 1108 itself, and/or an external power source, to the various parts of the UE 1100 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1108. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1108 to make the power suitable for the respective components of the UE 1100 to which power is supplied.

The memory 1110 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 1110 includes one or more application programs 1114, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1116. The memory 1110 may store, for use by the UE 1100, any of a variety of various operating systems or combinations of operating systems.

The memory 1110 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 1110 may allow the UE 1100 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 1110, which may be or comprise a device-readable storage medium.

The processing circuitry 1102 may be configured to communicate with an access network or other network using the communication interface 1112. The communication interface 1112 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1122. The communication interface 1112 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 1118 and/or a receiver 1120 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1118 and receiver 1120 may be coupled to one or more antennas (e.g., antenna 1122) and may share circuit components, software or firmware, or alternatively be implemented separately.

In the illustrated embodiment, communication functions of the communication interface 1112 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.

Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1112, 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).

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.

A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or 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 1100 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.

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.

Figure 12 shows a network node 1200 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)).

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

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).

The network node 1200 includes a processing circuitry 1202, a memory 1204, a communication interface 1206, and a power source 1208. The network node 1200 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 1200 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 1200 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1204 for different RATs) and some components may be reused (e.g., a same antenna 1210 may be shared by different RATs). The network node 1200 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1200, 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 1200.

The processing circuitry 1202 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 1200 components, such as the memory 1204, to provide network node 1200 functionality.

In some embodiments, the processing circuitry 1202 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1202 includes one or more of radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214. In some embodiments, the radio frequency (RF) transceiver circuitry 1212 and the baseband processing circuitry 1214 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 1212 and baseband processing circuitry 1214 may be on the same chip or set of chips, boards, or units.

The memory 1204 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 1202. The memory 1204 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 1202 and utilized by the network node 1200. The memory 1204 may be used to store any calculations made by the processing circuitry 1202 and/or any data received via the communication interface 1206. In some embodiments, the processing circuitry 1202 and memory 1204 is integrated.

The communication interface 1206 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 1206 comprises port(s)/terminal(s) 1216 to send and receive data, for example to and from a network over a wired connection. The communication interface 1206 also includes radio front-end circuitry 1218 that may be coupled to, or in certain embodiments a part of, the antenna 1210. Radio front-end circuitry 1218 comprises filters 1220 and amplifiers 1222. The radio front-end circuitry 1218 may be connected to an antenna 1210 and processing circuitry 1202. The radio front-end circuitry may be configured to condition signals communicated between antenna 1210 and processing circuitry 1202. The radio front-end circuitry 1218 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 1218 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1220 and/or amplifiers 1222. The radio signal may then be transmitted via the antenna 1210. Similarly, when receiving data, the antenna 1210 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1218. The digital data may be passed to the processing circuitry 1202. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

In certain alternative embodiments, the network node 1200 does not include separate radio front-end circuitry 1218, instead, the processing circuitry 1202 includes radio front-end circuitry and is connected to the antenna 1210. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1212 is part of the communication interface 1206. In still other embodiments, the communication interface 1206 includes one or more ports or terminals 1216, the radio front-end circuitry 1218, and the RF transceiver circuitry 1212, as part of a radio unit (not shown), and the communication interface 1206 communicates with the baseband processing circuitry 1214, which is part of a digital unit (not shown).

The antenna 1210 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 1210 may be coupled to the radio front-end circuitry 1218 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 1210 is separate from the network node 1200 and connectable to the network node 1200 through an interface or port.

The antenna 1210, communication interface 1206, and/or the processing circuitry 1202 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 1210, the communication interface 1206, and/or the processing circuitry 1202 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.

The power source 1208 provides power to the various components of network node 1200 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1208 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1200 with power for performing the functionality described herein. For example, the network node 1200 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 1208. As a further example, the power source 1208 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.

Embodiments of the network node 1200 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 1200 may include user interface equipment to allow input of information into the network node 1200 and to allow output of information from the network node 1200. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1200. Figure 13 is a block diagram of a host 1300, which may be an embodiment of the host 1016 of Figure 10, in accordance with various aspects described herein. As used herein, the host 1300 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1300 may provide one or more services to one or more UEs.

The host 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input/output interface 1306, a network interface 1308, a power source 1310, and a memory 1312. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 11 and 12, such that the descriptions thereof are generally applicable to the corresponding components of host 1300.

The memory 1312 may include one or more computer programs including one or more host application programs 1314 and data 1316, which may include user data, e.g., data generated by a UE for the host 1300 or data generated by the host 1300 for a UE. Embodiments of the host 1300 may utilize only a subset or all of the components shown. The host application programs 1314 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAG, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 1314 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 1300 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 1314 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

Figure 14 is a block diagram illustrating a virtualization environment 1400 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 1400 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.

Applications 1402 (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.

Hardware 1404 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 1406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1408a and 1408b (one or more of which may be generally referred to as VMs 1408), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 1406 may present a virtual operating platform that appears like networking hardware to the VMs 1408.

The VMs 1408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1406. Different embodiments of the instance of a virtual appliance 1402 may be implemented on one or more of VMs 1408, 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.

In the context of NFV, a VM 1408 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 1408, and that part of hardware 1404 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 1408 on top of the hardware 1404 and corresponds to the application 1402.

Hardware 1404 may be implemented in a standalone network node with generic or specific components. Hardware 1404 may implement some functions via virtualization. Alternatively, hardware 1404 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 1410, which, among others, oversees lifecycle management of applications 1402. In some embodiments, hardware 1404 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 1412 which may alternatively be used for communication between hardware nodes and radio units.

Figure 15 shows a communication diagram of a host 1502 communicating via a network node 1504 with a UE 1506 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 1012a of Figure 10 and/or UE 1100 of Figure 11), network node (such as network node 1010a of Figure 10 and/or network node 1200 of Figure 12), and host (such as host 1016 of Figure 10 and/or host 1300 of Figure 13) discussed in the preceding paragraphs will now be described with reference to Figure 15.

Like host 1300, embodiments of host 1502 include hardware, such as a communication interface, processing circuitry, and memory. The host 1502 also includes software, which is stored in or accessible by the host 1502 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1506 connecting via an over-the-top (OTT) connection 1550 extending between the UE 1506 and host 1502. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1550.

The network node 1504 includes hardware enabling it to communicate with the host 1502 and UE 1506. The connection 1560 may be direct or pass through a core network (like core network 1006 of Figure 10) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

The UE 1506 includes hardware and software, which is stored in or accessible by UE 1506 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 1506 with the support of the host 1502. In the host 1502, an executing host application may communicate with the executing client application via the OTT connection 1550 terminating at the UE 1506 and host 1502. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1550 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 1550. The OTT connection 1550 may extend via a connection 1560 between the host 1502 and the network node 1504 and via a wireless connection 1570 between the network node 1504 and the UE 1506 to provide the connection between the host 1502 and the UE 1506. The connection 1560 and wireless connection 1570, over which the OTT connection 1550 may be provided, have been drawn abstractly to illustrate the communication between the host 1502 and the UE 1506 via the network node 1504, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

As an example of transmitting data via the OTT connection 1550, in step 1508, the host 1502 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1506. In other embodiments, the user data is associated with a UE 1506 that shares data with the host 1502 without explicit human interaction. In step 1510, the host 1502 initiates a transmission carrying the user data towards the UE 1506. The host 1502 may initiate the transmission responsive to a request transmitted by the UE 1506. The request may be caused by human interaction with the UE 1506 or by operation of the client application executing on the UE 1506. The transmission may pass via the network node 1504, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1512, the network node 1504 transmits to the UE 1506 the user data that was carried in the transmission that the host 1502 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1514, the UE 1506 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1506 associated with the host application executed by the host 1502.

In some examples, the UE 1506 executes a client application which provides user data to the host 1502. The user data may be provided in reaction or response to the data received from the host 1502. Accordingly, in step 1516, the UE 1506 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input/output interface of the UE 1506. Regardless of the specific manner in which the user data was provided, the UE 1506 initiates, in step 1518, transmission of the user data towards the host 1502 via the network node 1504. In step 1520, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1504 receives user data from the UE 1506 and initiates transmission of the received user data towards the host 1502. In step 1522, the host 1502 receives the user data carried in the transmission initiated by the UE 1506.

One or more of the various embodiments improve the performance of OTT services provided to the UE 1506 using the OTT connection 1550, in which the wireless connection 1570 forms the last segment. In an example scenario, factory status information may be collected and analyzed by the host 1502. As another example, the host 1502 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1502 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1502 may store surveillance video uploaded by a UE. As another example, the host 1502 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 1502 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.

In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1550 between the host 1502 and UE 1506, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 1502 and/or UE 1506. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1550 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1550 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 1504. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 1502. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1550 while monitoring propagation times, errors, etc.

Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by 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.

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.

Notably, modifications and other embodiments of the disclosed invention(s) will come to mind to one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the invention(s) is/are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of this disclosure. Although specific terms may be employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

CLAIMS What is claimed is:

1. A method performed by equipment (50) usable for false cell detection, the method comprising: determining (600) how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering or leaving proximity of one or more closed group cells (26); and detecting (610) the presence or absence of a false cell (46) based on said determining (600).

2. The method of claim 1 , wherein the one or more closed group cells (26) are deployed in a non-public network (20).

3. The method of any of claims 1-2, wherein said detecting (610) comprises detecting the presence or absence of a false cell (46) based on whether the radio network node (14) receives as many proximity indications (30) as would have been expected in the absence of a false cell (46).

4. The method of any of claims 1-3, wherein said determining (600) comprises determining how many proximity indications (30) the radio network node (14) receives within an interval of time, and wherein said detecting (610) comprises detecting the presence or absence of a false cell (46) based on whether the radio network node (14) receives as many proximity indications (30) as would have been expected within that interval of time in the absence of a false cell (46).

5. The method of claim 4, wherein the interval of time is an interval between a first time of day on a first date and a second time of day on a second date, wherein the first date is the same or different than the second date.

6. The method of any of claims 1-5, wherein said detecting (610) comprises detecting the presence of a false cell (46) if, according to said determining (600), the radio network node (14) does not receive as many proximity indications (30) as would have been expected in the absence of a false cell (46).

7. The method of any of claims 1-6, wherein said determining (600) comprises determining how many proximity indications (30) the radio network node (14) receives over time, as part of determining a pattern of how many proximity indications (30) the radio network node (14) receives over time, and wherein said detecting (610) comprises detecting the presence or absence of a false cell (46) based on an extent to which the determined pattern matches a pattern that would be expected in the presence or absence of a false cell (46).

8. The method of any of claims 1-7, further comprising obtaining a trained machine learning model that predicts the presence or absence of a false cell (46) as a function of how many proximity indications (30) the radio network node (14) receives, and wherein said detecting (610) comprises inputting, into the trained machine learning model, how many proximity indications (30) the radio network node (14) receives according to said determining (600).

9. The method of claim 8, further comprising training a machine learning model in order to obtain the trained machine learning model, wherein said training is based on observations of how many proximity indications (30) the radio network node (14) receives in the absence of a false cell (46).

10. The method of claim 9, wherein said training is performed according to a k-means clustering machine learning algorithm.

11. The method of any of claims 1-10, wherein said determining (600) comprises determining how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering proximity of one or more closed group cells (26).

12. The method of any of claims 1-11 , wherein the proximity indications (30) are included in Radio Resource Control, RRC, measurement report messages that the wireless communication devices (12) send to the radio network node (14) as part of a procedure for handing over from the radio network node (14) to a target radio network node.

13. A method performed by equipment (900) usable for model training, the method comprising: determining (700), in the absence of a false cell (46), how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering or leaving proximity of one or more closed group cells (26); and based on said determining (700), training (710) a machine learning model to predict the presence or absence of a false cell (46) as a function of how many proximity indications (30) the radio network node (14) receives.

14. The method of claim 13, wherein the one or more closed group cells (26) are deployed in a non-public network (20).

15. The method of any of claims 13-14, wherein said training (710) is performed according to a k-means clustering machine learning algorithm.

16. The method of any of claims 13-15, wherein said determining (700) comprises determining, in the absence of a false cell (46), how many proximity indications (30) the radio network node (14) receives within an interval of time.

17. The method of claim 16, wherein the interval of time is an interval between a first time of day on a first date and a second time of day on a second date, wherein the first date is the same or different than the second date.

18. The method of any of claims 13-17, wherein said determining (700) comprises determining how many proximity indications (30) the radio network node (14) receives over time in the absence of a false cell (46), as part of determining a pattern of how many proximity indications (30) the radio network node (14) receives over time in the absence of a false cell (46).

19. The method of any of claims 13-18, wherein said determining (700) comprises determining, in the absence of a false cell (46), how many proximity indications (30) the radio network receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering proximity of one or more closed group cells (26).

20. The method of any of claims 13-19, wherein the proximity indications (30) are included in Radio Resource Control, RRC, measurement report messages that the wireless communication devices (12) send to the radio network node (14) as part of a procedure for handing over from the radio network node (14) to a target radio network node.

21. Equipment (50) usable for false cell detection, the equipment (50) configured to: make a determination as to how many proximity indications (30) a radio network node

(14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering or leaving proximity of one or more closed group cells (26); and detect the presence or absence of a false cell (46) based on said determination.

22. The equipment (50) of claim 21 , configured to perform the method of any of claims 2-12.

23. Equipment (900) usable for model training, the equipment (900) configured to: make a determination, in the absence of a false cell (46), how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering or leaving proximity of one or more closed group cells (26); and based on said determination, train a machine learning model to predicts the presence or absence of a false cell (46) as a function of how many proximity indications (30) the radio network node (14) receives.

24. The equipment (900) of claim 23, configured to perform the method of any of claims 13- 20.

25. A computer program comprising instructions which, when executed by at least one processor of equipment (50) usable for false cell detection, causes the equipment (50) to perform the method of any of claims 1-12.

26. A computer program comprising instructions which, when executed by at least one processor of equipment (900) usable for model training, causes the equipment (900) to perform the method of any of claims 13-20.

27. A carrier containing the computer program of any of claims 25-26, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

28. Equipment (50) usable for false cell detection, the equipment (50) comprising processing circuitry (810) configured to: make a determination as to how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering or leaving proximity of one or more closed group cells (26); and detect the presence or absence of a false cell (46) based on said determination.

29. The equipment (50) of claim 28, the processing circuitry (810) configured to perform the method of any of claims 2-12.

30. Equipment (900) usable for model training, the equipment (900) comprising processing circuitry (910) configured to: make a determination, in the absence of a false cell (46), how many proximity indications (30) a radio network node (14) in a public network (10) receives from wireless communication devices (12) reporting that the wireless communication devices (12) are entering or leaving proximity of one or more closed group cells (26); and based on said determination, train a machine learning model to predict the presence or absence of a false cell (46) as a function of how many proximity indications (30) the radio network node (14) receives.

31. The equipment (900) of claim 30, the processing circuitry (810) configured to perform the method of any of claims 13-20.