Patent Description:
There is ongoing study relating to enhanced support for aerial wireless devices, for example, aerial vehicles or drones. The objective of such study is to investigate the ability for aerial wireless devices to be served using Long Term Evolution (LTE) network deployments (or any other suitable technology) with base station antennas targeting terrestrial coverage, for example supporting Release <NUM> functionality.

Some objectives may be to, firstly, identify potential enhancements to LTE so that it is better suited to provide connectivity and positioning services to aerial wireless devices in the identified deployment scenarios.

For example, interference mitigation solutions for improving system-level performance, solutions for detecting whether uplink signals from an air-borne wireless device increases interference in multiple neighbour cells, and solutions for identifying if enhancements in terms of cell selection and handover efficiency as well as robustness in handover signalling can be achieved.

Secondly, an objective may be to serve aerial wireless devices more efficiently and to limit the impact of aerial wireless devices on terrestrial wireless devices.

An air-borne wireless device may experience radio propagation characteristics that are likely to be different from those experienced by a wireless device on the ground. As long as an aerial wireless device is flying at low altitude, relative to the base station antenna height, it can be considered to behave like a conventional wireless device on the ground. However, once an aerial wireless device is flying well above the base station antenna height, the uplink signal from the aerial wireless device may become more visible to multiple cells due to line-of-sight propagation conditions. The uplink signal from an aerial wireless device may increase interference in the neighbour cells and the increased interference may result in a negative impact to wireless devices on the ground, for example smartphones, Internet of Things (IoT) devices, etc. Similarly, these line-of-sight conditions to multiple cells may incur higher downlink interference to the aerial wireless device.

Further, as illustrated in <FIG>, as a base station antenna is <NUM> typically tilted downward such that the main lobe <NUM> of the beamformed signals is directed towards the ground, wireless devices <NUM> on the ground or below the base station height are likely served by the main lobe <NUM> of the beam formed signals. However, when an aerial wireless device <NUM> is flying above boresight, it is likely served by one of the side or back lobes <NUM> of the beamformed signals. These side and back lobes <NUM> have reduced antenna gains compared to the antenna gain of the main lobe.

<FIG>, <FIG> and <FIG> show that the coverage area of a base station in the sky may be fragmented into several discontinuous areas, while the coverage area of a base station on the ground may usually be an approximate closed set. Also, for an aerial wireless device a cell which appears as the best cell may be further away from the aerial wireless device compared to the best cell for terrestrial wireless devices, as illustrated in <FIG>. In <FIG>, <FIG> and <FIG>, locations that are served by the same base station are shaded in the same grey tone, assuming that wireless devices connect to the strongest or best cell. <FIG> illustrates the scenario at ground level. <FIG> illustrates the scenario at <NUM> above ground level. <FIG> illustrates the scenario at <NUM> above ground level.

<FIG>, <FIG> and <FIG> illustrate the geometry of the signal to interference ratio for wireless devices located at different heights above ground level. <FIG> illustrated the geometry at ground level. <FIG> illustrates the geometry at <NUM> above ground level. <FIG> illustrates the geometry at <NUM> above ground level. As expected, the higher the wireless device above ground level, the lower the quality of the signal becomes.

Machine learning can be used to find a predictive function for a given dataset; the dataset is typically a mapping between a given input to an output. The predictive function (or mapping function) is generated in a training phase, where the training phase assumes knowledge of both the input and output. The test phase comprises predicting the output for a given input. Applications of machine learning are for example curve fitting, facial recognition and spam filters. <FIG> illustrate an example of one type of machine learning, namely classification, where the task is to train a predictive function that separates the two categories, a circle category and a cross category. In <FIG>, features <NUM> and <NUM> provide low separation of the output class, hence leading to a worse prediction performance in comparison with <FIG>. In <FIG> using feature <NUM> and <NUM> enables a better separation and classifying performance. In general, the performance of the machine learner is proportional to the correlation between the input and the output, and one key problem in machine learning is to find/create good features.

Aerial wireless devices that provide a video-feed to its flight controller over the mobile network for extended flying range may implicate high uplink streaming for the network. Such aerial devices are appearing more and more due to the application opportunities provided by extended range. Based on the traffic characteristics and the control characteristics, the mobile operators are likely to put the aerial wireless devices into separate service category associating different policies on them. Thus, it is important that mobile networks can identify if a wireless device is an aerial wireless device or a regular ground wireless device in order to provide the right service optimization for aerial wireless devices whilst protecting the performance of the ground wireless devices from the potential interfering signals from the aerial wireless devices.

For legitimate aerial wireless devices, standard mechanisms can be enforced so that these aerial wireless devices can be recognized by the networks. For example, it may be required that an operator of an aerial wireless device acquire a Subscriber Identity Module (SIM) card that is designed or registered for aerial wireless device use if the aerial wireless device is intended for use with a cellular connection, i.e., the aerial wireless device may be required to have a subscription indicative of its status as an aerial wireless device rather than a standard wireless device.

Another method may be to introduce aerial wireless device related radio access capacities into the standards such as for example, a), direct flying-status indication mechanisms so that aerial wireless devices can inform the network when they are in the flying mode; b), and measurement reporting enhancements so that the network can identify whether the aerial wireless device is flying and/or causing excessive interferences. However, these aerial wireless device related radio access capacities may not be applicable for legacy wireless devices.

However, "rogue" aerial wireless devices may be considered as any flying wireless device that either is not registered with the network or does not support aerial wireless device related radio access capacities. For example, there are some cases where a legacy wireless device may be attached to an aerial vehicle and may be flown around within the network. The flying terrestrial wireless device attached to the aerial vehicle may generate excessive interference within the network and may not be allowed by regulations in some regions. This phenomenon is being observed in the field and has drawn much attention from mobile operators. It is critical to identify these unlicensed rogue aerial wireless devices from the perspective of the operator and for the purpose of security measures.

One challenging problem here is that the legacy wireless devices may not have new features introduced as mentioned above to help the network to identify the flying status. The network may therefore have to rely on existing measurement report mechanisms to identify if a legacy wireless device is flying or not and in order to identify it as a "rogue" aerial wireless device.

There currently exist certain challenge(s). Methods for detecting aerial wireless devices, may involve the network evaluating every wireless device to determine whether or not it is an aerial wireless device. However, aerial wireless devices will be a minority in most wireless device populations, and thus evaluating every wireless device (collecting measurements and executing the classifier) may result in an unnecessarily high load on the network. For example, to enable accurate detection of each aerial wireless device, the network first needs to collect enough data to train a machine learning model that represents the wireless device population well enough, and then the network may need to collect data from each wireless device in the wireless device population to in order to accurately classify it as an aerial wireless device or a ground wireless device (regular). This process of collecting data from every wireless device may lead to a huge overhead on the network and have a significant impact in loaded networks since such data and processing is needed continuously. describes the classification of UEs as airborne or terrestrial UEs based on RSSI and ΔRSRP measurements. Another document titled "<NPL>et al. describes the need for identification of potential flying terrestrial UEs when the network detects the increased interference: Another document titled "<NPL>. describes the Machine leaning models for categorizing UEs using RSSI and RSRP measurements.

Further, embodiments of the invention are defined by the claims. Moreover, examples, embodiments and descriptions, which are not covered by the claims are presented not as embodiments of the invention, but as background art or examples useful for understanding the invention.

According to some embodiments there is provided a method, in a network node in a communications network, for determining which of a first category associated with a first wireless device behaviour and a second category associated with a second wireless device behaviour each of a plurality of wireless devices fall into. The method comprises determining based on binary classification of a first information which of the wireless devices meet all of at least one primary criterion; wherein the first information comprises one or more measurements of timing advance (TA) measurements, an amount of requested uplink resources; uplink signal strength measurements; handover statistics; line of sight detection in downlink; transmitting a request for second information to the wireless devices that meet all of the at least one primary criterion; wherein the second information comprises one or more of measurements on dedicated uplink pilot transmissions, sounding reference signal, SRS, a Random Access Channel (RACH)) transmissions and their respective measurements in neighboring base stations, periodic downlink measurement reports comprising measurements of reference signal received power from different cells; channel state information reference signal, CSI-RS, related measurement reports; receiving second information from each of the wireless device that meet all of the at least one primary criterion; determining based on binary classification of the second information which of the wireless devices that meet all of the at least one primary criterion also meet all of at least one secondary criterion; and classifying the wireless devices that meet both all of the at least one primary criterion and all of the at least one secondary criterion into the first category, wherein the first category comprises a drone category and the second category comprises a non-drone category.

According to some embodiments there is provided a network node in a communications network, for determining which of a first category associated with a first wireless device behaviour and a second category associated with a second wireless device behaviour each of a plurality of wireless devices fall into. The network node comprising processing circuitry configured to determine based on binary classification of a first information which of the wireless devices meet all of at least one primary criterion; wherein the first information comprises one or more measurements of timing advance (TA) measurements, an amount of requested uplink resources; uplink signal strength measurements; handover statistics; line of sight detection in downlink; transmit a request for a second information to the wireless devices that meet all of the at least one primary criterion; wherein the second information comprises one or more of measurements on dedicated uplink pilot transmissions, sounding reference signal, SRS, a Random Access Channel (RACH)) transmissions and their respective measurements in neighboring base stations, periodic downlink measurement reports comprising measurements of reference signal received power from different cells; channel state information reference signal, CSI-RS, related measurement reports; receive the second information from each of the wireless device that meet all of the at least one primary criterion; determine based on binary classification of the second information which of the wireless devices that meet all of the at least one primary criterion also meet all of at least one secondary criterion; and classify the wireless devices that meet both all of the at least one primary criterion and all of the at least one secondary criterion into the first category, wherein the first category comprises a drone category and the second category comprises a non-drone category.

Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. The invention describes a framework for efficient detection of wireless devices in a particular category, such as aerial wireless devices, by using a layered detection procedure in order to avoid conducting unnecessary measurements and computations. The idea is to have a two-step procedure. The first step comprises performing a crude classification which does not require dedicated measurements to be performed by the wireless devices, and is preferably simple to process computationally. The first step should produce a subset of wireless devices in the wireless device population. The second step comprising instructing the subset of wireless devices to collect dedicated measurements to perform an accurate classification. The embodiments described herein are be illustrated with a drone (or aerial wireless device) detection problem. The methods and apparatus described herein, however, may equally be used for other types of classification, for example classifying wireless devices be their speed.

There are, proposed herein, various embodiments which address one or more of the issues disclosed herein.

Embodiments described herein are described within the context of Long Term Evolution (LTE), i.e. E-UTRAN. However, it will be appreciated that the methods and apparatus described herein are equally applicable to wireless access networks and wireless devices or user equipments (UEs) implementing other access technologies and standards. LTE is used as an example technology within which the invention is suitable, and using LTE in the description therefore is particularly useful for understanding the problem and solutions solving the problem.

Certain embodiments may provide one or more of the following technical advantage(s). The advantages of the embodiments described herein are significantly reduced signaling overhead since only a subset of wireless devices are explicitly instructed to collect measurements dedicated for determining which category the wireless devices fall into, and likely reduced processing complexity since the more accurate second step is applied on the same subset of wireless devices.

The invention comprises a two-stage procedure which involves two sets of criteria, primary criteria and secondary criteria, both solving the same binary classification problem, for example distinguishing between drone and non-drone (regular) wireless devices. The basic flowchart is summarized in the <FIG>. In some examples, the primary criteria and secondary criteria are generated using machine learning.

<FIG> illustrates a method performed by a network node for for determining which of a first category associated with a first wireless device behaviour and a second category associated with a second wireless device behaviour each of the wireless devices fall into. For example, the first wireless device behaviour may comprise behaviour expected from drone wireless devices and the second wireless device behaviour may comprise behaviour expected from non-drone wireless devices. The examples described below relate to drones being the first category, and non-drones being the second category. However, it will be appreciated that other categories may equally be used.

The first stage of the process provides a crude classification of the wireless devices in the population. In this example, this first stage comprises step <NUM> as illustrated in <FIG>.

In step <NUM> the network node, for every wireless device in the wireless device population of interest, performs a first basic classification procedure. For example, the network node determine based on binary classification of first information which of the wireless devices meet all of at least one primary criterion.

For example, the at least one primary criterion may be generated using a simple machine learning model, which may be trained based measurements that are already available to the network. In particular the at least one primary criterion may be generated using machine learning based on training information of a same type as the first information. The first information comprise measurements such as timing advance (TA) measurements, uplink data streams, and uplink signal strength measurements. The at least one primary criterion may comprise a criterion for each type of first information. By utilizing measurements already available to the network, for example network information provided to the network as part of normal operation of each wireless device, the method provides a "cheap" potentially crude filter which has a low probability of misclassifying a drone wireless device as a regular wireless device.

This low probably of misclassifying a drone wireless device as a non-drone wireless device may result in the sacrifice of a higher probability of misclassifying a non-drone wireless device as a drone wireless device.

In a receiver operating characteristic (ROC) curve, this relationship between the probability of misclassifying a drone wireless device as a non-drone wireless devices and misclassifying a non-drone wireless device as a drone, is represented by the ratio between the true positive rate (TPR) and false positive rate (FPR), where a high value of TPR corresponds to low probability of classifying a drone wireless device a no-drone wireless device. Such a classifier will have a low risk of missing drone wireless devices and will reduce the size of the set of wireless devices to consider as being drone wireless devices in step <NUM> of <FIG>, as will be described later. In this step therefore the the at least one primary criterion are such that wireless devices meeting all of the at least one primary criterion have a first false positive rate and a first true positive rate of being in the first category.

In some embodiments, this classification could be based on first information comprising the amount of uplink resources requested by the wireless device, where for example those wireless devices having larger uplink resource requests are classified as drones and the others are not. In another embodiment, this classification could be based on first information comprising the line of sight (LOS) detection in downlink, where for example those wireless devices having at most a rank-<NUM>/<NUM> transmission are classified as drones and the others are not (where the number of ranks depends on how the polarizations are used). In yet another embodiment, this classification could be based on first information comprising handover statistics of the wireless devices, where for example, those wireless devices that are handed over from a particular cell or list of cells are classified as drones and the others are not. In yet another embodiment, one could use any combination of the above described options for the first information. Specifically the first information may be already available to the network in some way. In addition, the first information may comprise an indication, from the NDA (Network Data Analytics) functionality in the core network, indicating explicitly to the network node that the requested service from a wireless device is of a drone type (based on the application data analysis). It is to be noted that in any of these methods, the wireless device may not be expected to perform and report any additional or dedicated measurements than the ones that it performs for its usual operation in order for the network node to receive the first information.

Steps <NUM>, <NUM> and <NUM> comprise the second "stage" of the process in which the network node instructs the subset of wireless devices produced by the first step in step <NUM>, i.e. the wireless devices that meet all of the at least one primary criterion, to collect dedicated measurements to perform an accurate classification using at least one secondary criterion.

In step <NUM>, the network node transmits a request for second information to the wireless devices that meet all of the at least one primary criterion.

The second information comprise explicitly ordered measurements, such as dedicated uplink pilot (for example, sounding reference signal (SRS), or a Random Access Channel (RACH)) transmissions and their respective measurements in the neighboring base stations and/or periodic downlink measurement reporting including measured reference signal received power (RSRP) values from different cells or channel state information reference signal (CSI-RS) related measurement reports (CSI-RS reports for serving cell and CSI-IM related reports for neighbor cells), or a combination of the above. There may be a secondary criterion for each type of second information.

In step <NUM>, the network node receives the second information from each of the wireless devices that meet all of the at least one primary criterion.

In step <NUM>, the network node determines based on binary classification of the second information which of the wireless devices that meet the at least one primary criterion also meet at least one secondary criterion.

In some examples, step <NUM> also takes into consideration the first information in combination with the second information when determining which of the wireless devices that meet the at least one primary criterion also meet at least one secondary criterion.

The at least one secondary criterion may be generated using machine learning based on training information of a same type as the second information. The machine learning model used to generate the secondary criterion may be more advanced than the machine learning model used to generate the primary criterion.

As the model used to generate the at least one secondary criterion may be executed on a smaller set of wireless devices than the population of wireless device that was filtered in the first step using the at least one primary criterion, this model can afford to be more accurate as the load on the network will be smaller as not so many wireless devices are being evaluated. The purpose of the secondary criterion is to provide a more accurate classification of the wireless devices than the primary criterion. In other words the at least one secondary criterion may produce a larger area under the ROC curve when classifying wireless devices, and operate at a point where the probability of classifying a non-drone wireless device as a drone is very small with the sacrifice of a slightly increased probability of classifying a drone wireless device as a non-drone wireless device (small false positive rate (FPR) at the cost of a lower true positive rate (TPR)). In practice, this tradeoff can be based on the characteristics of the ROC curve, where for example a high gain in TPR at the cost of a small increase of FPR may be considered acceptable.

In other words, the at least one secondary criterion are such that wireless devices meeting all of the at least one secondary criterion have a second false positive rate and a second true positive rate for being in the first category, wherein the second false positive rate is lower than the first false positive rate and the second true positive rate is lower than or equal to the first true positive rate.

The true positive rate and false positive rates may be computed based on all the wireless devices in the considered population.

In some examples, in step <NUM>, the network node classifies the wireless devices that meet both all of the at least one primary criterion and all of the at least one secondary criterion into the first category, for example as drone wireless devices.

Only those wireless devices that are classified as drone wireless devices based on both the at least one primary and the at least one secondary criterion are labelled as actual drone wireless devices.

In some examples, the results from the second step (steps <NUM> to <NUM>) can be used to exclude/include particular wireless devices in future detection procedures, for example to avoid repetitive explicit measurements from a wireless device which has already been classified as a non-drone wireless device, or to perform more measurements to accumulate a stronger certainty that a wireless device is a drone wireless device.

In another embodiment of the invention, the at least one primary criterion used in step <NUM> may be further reinforced/tuned based on the outcome of the steps <NUM> and/or <NUM>. As step <NUM> is very conservative in nature, and steps <NUM> and/or <NUM> are very accurate, any learnings from steps <NUM> and/or <NUM> regarding the primary criterion used in step <NUM> may further reduce the number of wireless devices which require evaluation in steps <NUM> to <NUM> by increasing the accuracy of step <NUM>.

This feedback of the outcome of steps <NUM> and/or <NUM> is illustrated in <FIG>. In the step <NUM>, the at least one primary criterion are adapted based on the outcome of the classification in step <NUM>. In other words, the network node may continually update the at least one primary criterion using machine learning based on wireless devices classified into the second category on the basis of not meeting the secondary criterion.

The two-step procedure illustrated in <FIG> may therefore be very efficient in terms of overhead and at the same time be very accurate when determining whether a wireless device falls into a first category or into a second category. As stated previously, this idea is not restricted to drone and non-drone detection but can also be used for other types of detection/classification where the two-step procedure can provide a more efficient classification. For example, for wireless device speed classification.

In this example, in step <NUM>, the first category, i.e. high speed or a second category, i.e. normal speed based on first information comprising the number of handovers/cell reselections in the past 'X' seconds, wherein wireless devices having more than 'N' handovers/re-selections in the past 'X' seconds may meet the primary criterion. For those wireless devices that meet all of the at least one primary criterion in step <NUM>, the decision may be further reinforced based on second information comprising the time series of RSRP values in for example, an RRC_CONNECTED mode, wherein those wireless devices who have a 'larger' rate of change of RSRP may be determined to meet all of the at least one secondary criterion and may then be classified as high speed wireless devices in the first category.

For each wireless device in a population of wireless devices therefore, the method as illustrated in <FIG> may be performed by a network node.

In step <NUM> the network node determined whether the wireless device meets all of the at least one primary criterion. This step is performed based on first information already available to the network node relating to the wireless device.

If in step <NUM> the network node determines that the wireless device does not meet all of the at least one primary criterion, the network classifies the wireless device into the second category in step <NUM>.

If in step <NUM> the network node determines that the wireless device does meet all of the at least one primary criterion, the network requests and receives secondary information from the wireless device in step <NUM>.

In step <NUM> the network node determines whether the wireless device meets all of the at least one secondary criterion based on the second information.

If in step <NUM> the network node determines that the wireless device does not meet all of the at least one secondary criterion, the network node classifies the wireless device into the second category in step <NUM>.

If in step <NUM> the network node determines that the wireless device does meet all of the at least one secondary criterion, the network node classifies the wireless device into the first category in step <NUM>.

In particular, as described in the background section, the network may decide to provide adjusted or altered services to a wireless device in a particular category, for example to avoid excess interference produced by drone wireless devices at high altitudes. In other words, the network node may adjust a communications service provided to each wireless device based on the category that each wireless device is classified into.

It will also be appreciated that there may be more than two categories of wireless device. In this example, the categories may be split down further by for example, repeating the method for wireless devices in a first category to separate the wireless devices in the first category into a third and fourth category.

It will be appreciated that the network node may comprise any suitable network node, or may be a virtual node. In some examples, the network node comprises a base station. In some examples, the network node comprises a Network Data Analytics node in the core network.

Embodiments disclosed herein therefore provide a multi-stage layered approach to efficiently classify wireless devices into different categories without requiring a heavy load on the network. The first stage of the detection can be a simple machine learning model, or some predefined rules based on statistics to exclude wireless devices that are easily classifiable as not being part of a first category, which in examples above is a drone wireless devices category. The second step comprises a ML model focusing on accuracy for effective classification, where a more advanced procedure may be employed on a smaller set of wireless devices and a very accurate determination of whether a wireless device is classified as being part of the first category (drone UE). The example scenario in the embodiments above describe classification of wireless devices as being drone/non-drone, however, the principle may be applied to any such classification problems.

Although the subject matter described herein may be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a wireless network, such as the example wireless network illustrated in <FIG>. For simplicity, the wireless network of <FIG> only depicts network <NUM>, network nodes <NUM> and 760b, and WDs <NUM>, 710b, and 710c. In practice, a wireless network may further include any additional elements suitable to support communication between wireless devices or between a wireless device and another communication device, such as a landline telephone, a service provider, or any other network node or end device. Of the illustrated components, network node <NUM> and wireless device (WD) <NUM> are depicted with additional detail. The wireless device <NUM> may comprise an aerial wireless device, for example a drone as illustrated in <FIG>. The network node <NUM> may be a network node as described in the above embodiments, and may for example be configured to carry out a method as described with respect to <FIG> and <FIG>. The wireless network may provide communication and other types of services to one or more wireless devices to facilitate the wireless devices' access to and/or use of the services provided by, or via, the wireless network.

As used herein, wireless device (WD) refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Unless otherwise noted, the term WD may be used interchangeably herein with user equipment (UE). Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air. In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction. For instance, a WD may be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network. Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE). a vehicle-mounted wireless terminal device, etc.. A WD may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X) and may in this case be referred to as a D2D communication device. As yet another specific example, in an Internet of Things (IoT) scenario, a WD 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 WD and/or a network node. The WD may in this case be a machine-to-machine (M2M) device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the WD may be a UE implementing the 3GPP narrow band internet of things (NB-IoT) standard. Particular examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g. refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.). In other scenarios, a WD may represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. A WD as described above may represent the endpoint of a wireless connection, in which case the device may be referred to as a wireless terminal. Furthermore, a WD as described above may be mobile, in which case it may also be referred to as a mobile device or a mobile terminal.

The UE <NUM> may comprise a wireless device, for example an aerial wireless device as described with respect to the aforementioned embodiments. UE <NUM> may be any UE identified by the <NUM>rd Generation Partnership Project (3GPP), including a NB-loT UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

Network connection interface <NUM> may be configured to provide a communication interface to network 843a. Network 843a may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 843a may comprise a Wi-Fi network.

In <FIG>, processing circuitry <NUM> may be configured to communicate with network 843b using communication subsystem <NUM>. Network 843a and network 843b may be the same network or networks or different network or networks. Communication subsystem <NUM> may be configured to include one or more transceivers used to communicate with network 843b.

Network 843b may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 843b may be a cellular network, a Wi-Fi network, and/or a near-field network.

In particular, virtualization may be applied to the network node as described in above embodiments, for example a network node configured to carry out the method as described with respect to <FIG> and <FIG>.

Access network <NUM> comprises a plurality of base stations 1012a, 1012b, 1012c, such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 1013a, 1013b, 1013c. Each base station 1012a, 1012b, 1012c is connectable to core network <NUM> over a wired or wireless connection <NUM>. A first UE <NUM> located in coverage area 1013c is configured to wirelessly connect to, or be paged by, the corresponding base station 1012c. A second UE <NUM> in coverage area 1013a is wirelessly connectable to the corresponding base station 1012a. Each base station 1012a, 1012b, 1012c may be configured to carry out the method as described with respect to <FIG> and <FIG>. Each UE <NUM>, <NUM> may be an aerial wireless device or a terrestrial wireless device.

It is noted that host computer <NUM>, base station <NUM> and UE <NUM> illustrated in <FIG> may be similar or identical to host computer <NUM>, one of base stations 1012a, 1012b, 1012c and one of UEs <NUM>, <NUM> of <FIG>, respectively.

Wireless connection <NUM> between UE <NUM> and base station <NUM> is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to UE <NUM> using OTT connection <NUM>, in which wireless connection <NUM> forms the last segment. More precisely, the teachings of these embodiments may improve the method of categorizing wireless devices, where such categorization enables the network to reduce interference of the aerial wireless devices with the terrestrial wireless devices. This improved method of categorization provide benefits such as reduced load on the network.

<FIG> depicts a method in accordance with particular embodiments, the method begins at step <NUM> with determining based on binary classification of the first information which of the wireless devices meet all of at least one primary criterion. In step <NUM> the method comprises transmitting a request for second information to the wireless devices that meet all of the at least one primary criterion. In step <NUM> the method comprises receiving second information from each of the wireless device that meet all of the at least one primary criterion. In step <NUM> the method comprises determining based on binary classification of the second information which of the wireless devices that meet all of the at least one primary criterion also meet all of at least one secondary criterion.

<FIG> illustrates a schematic block diagram of an apparatus <NUM> in a wireless network (for example, the wireless network shown in <FIG>). The apparatus may be implemented in a wireless device or network node (e.g., wireless device <NUM> or network node <NUM> shown in <FIG>). Apparatus <NUM> is operable to carry out the example method described with reference to <FIG> and possibly any other processes or methods disclosed herein. It is also to be understood that the method of <FIG> is not necessarily carried out solely by apparatus <NUM>. At least some operations of the method can be performed by one or more other entities.

Virtual Apparatus <NUM> may comprise processing circuitry, which 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 includes 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 some implementations, the processing circuitry may be used to cause Determination unit <NUM>, Transmitting unit <NUM>, and Receiving Unit <NUM> and any other suitable units of apparatus <NUM> to perform corresponding functions according one or more embodiments of the present disclosure.

As illustrated in <FIG>, apparatus <NUM> includes Determination unit <NUM>, Transmitting unit <NUM>, and Receiving Unit <NUM>. Determination unit <NUM> is configured to determine based on binary classification of the first information which of the wireless devices meet all of at least one primary criterion. Transmitting unit <NUM> is configured to transmit a request for second information to the wireless devices that meet all of the at least one primary criterion. Receiving unit <NUM> is configured to receive second information from each of the wireless device that meet all of the at least one primary criterion. The Determination unit <NUM> is further configured to determine based on binary classification of the second information which of the wireless devices that meet all of the at least one primary criterion also meet all of at least one secondary criterion.

Claim 1:
A method, in a network node (<NUM>, 760b) in a communications network, for determining which of a first category associated with a first wireless device behaviour and a second category associated with a second wireless device behaviour each of a plurality of wireless devices (<NUM>, <NUM>) fall into, the method comprising:
determining (<NUM>) based on binary classification of first information which of the wireless devices (<NUM>, <NUM>) meet all of at least one primary criterion; wherein the first information comprises one or more measurements of timing advance, TA, measurements, an amount of requested uplink resources; uplink signal strength measurements; handover statistics; line of sight detection in downlink;
transmitting (<NUM>) a request for second information to the wireless devices (<NUM>, <NUM>) that meet all of the at least one primary criterion; wherein the second information comprises one or more of measurements on dedicated uplink pilot transmissions, sounding reference signal, SRS, a Random Access Channel, RACH, transmissions and their respective measurements in neighboring base stations, periodic downlink measurement reports comprising measurements of reference signal received power from different cells; channel state information reference signal, CSI-RS, related measurement reports;
receiving (<NUM>) the second information from each of the wireless device (<NUM>, <NUM>) that meet all of the at least one primary criterion;
determining (<NUM>) based on binary classification of the second information which of the wireless devices (<NUM>, <NUM>) that meet all of the at least one primary criterion also meet all of at least one secondary criterion; and
classifying (<NUM>) the wireless devices (<NUM>, <NUM>) that meet both all of the at least one primary criterion and all of the at least one secondary criterion into the first category, wherein the first category comprises a drone category and the second category comprises a non-drone category.