Wireless telecommunications network

Abstract

The invention provides a network node for a wireless telecommunication network and a method of operating the network node, wherein the wireless telecommunication network comprises a first access point configured to transmit in a first coverage area, a second access point configured to transmit in a second coverage area and a first user equipment, UE, connected to the first access point, the method comprising the steps of: calculating a first distance between the first access point and the first UE and a second distance between the second access point and the first UE; detecting a holdover form of the first UE; calculating a relative strength of a first signal between the UE and the first access point relative to a second signal between the UE and the second access point based on the first distance and the second distance and further based on the holdover form of the first UE; determining that the relative strength is below a threshold value; determining a first transmission parameter for the first access point and / or a second transmission parameter for the second access point such that the relative strength of the first signal between the first UE and the first access point relative to the second signal between the first UE and the second access point is above the threshold value when the first and / or second transmission parameter is implemented; and causing the first and / or second access point to reconfigure to implement the first and / or second transmission parameter.

Description

Technical Field

[0001] This invention relates to wireless telecommunications networks. Background Technology

[0002] Wireless telecommunications networks can be evaluated based on one or more key performance indicators (KPIs). Examples of KPIs include average user throughput and cell-edge user throughput (that is, the throughput of users at the edge of the coverage area of ​​the serving access point). While techniques such as increasing transmission power can be used to improve average user throughput, such techniques may not improve (or may even worsen) cell-edge user throughput.

[0003] Cell-edge users may experience poor service due to interference from signals originating from adjacent access points. Current data on interference mitigation for cell-edge users includes interference suppression and interference coordination. Interference suppression (which may also be referred to as interference cancellation) utilizes signal processing techniques such as using stronger channel codes, lower-order modulation schemes, and / or spatial diversity. Interference coordination techniques are generally based on coordinating resource scheduling and management between adjacent access points, such as frequency reuse, power control, and coordinated multipointing. These mitigation techniques are typically used after reports of significant interference from users (e.g., low signal-to-interference-plus-noise ratio, SINR). Summary of the Invention

[0004] According to a first aspect of the present invention, a method for operating a network node in a wireless telecommunications network is provided, wherein the wireless telecommunications network includes a first access point configured to transmit in a first coverage area, a second access point configured to transmit in a second coverage area, and a first user equipment (UE) connected to the first access point, the method comprising the steps of: calculating a first distance between the first access point and the first UE and a second distance between the second access point and the first UE; detecting a hold-up mode of the first UE; calculating a relative strength of a first signal between the UE and the first access point relative to a second signal between the first UE and the second access point based on the first distance, the second distance, and also based on the hold-up mode of the first UE; determining that the relative strength is below a threshold; determining a first transmission parameter for the first access point and / or a second transmission parameter for the second access point such that, when the first transmission parameter and / or the second transmission parameter are implemented, the relative strength of the first signal between the first UE and the first access point relative to the second signal between the first UE and the second access point is above the threshold; and reconfiguring the first access point and / or the second access point to implement the first and / or the second transmission parameters.

[0005] The first distance can be between the first access point and the future location of the first UE, and the second distance can be between the second access point and the future location of the first UE, and the step of reconfiguring the first access point and / or the second access point can be performed before the first UE reaches the future location.

[0006] The method may further include the following steps: obtaining data captured by an optical source, wherein a first distance and a second distance are calculated from the data. The data may be a visual image. The optical source may be an Internet of Things (IoT) sensor.

[0007] According to a second aspect of the invention, a computer program comprising instructions is provided that, when executed by a computer, cause the computer to perform steps according to the first aspect of the invention. The computer program may be stored on a computer-readable media.

[0008] According to a third aspect of the invention, a network node for a wireless telecommunications network is provided, the network node including a processor configured to perform the steps according to the first aspect of the invention.

[0009] According to a fourth aspect of the invention, a system for a wireless telecommunications network is provided, the system comprising one or more nodes configured to perform the steps according to a first aspect of the invention. Attached Figure Description

[0010] To better understand the present invention, embodiments thereof will now be described by way of example only with reference to the accompanying drawings, wherein:

[0011] Figure 1 This is a schematic diagram of a cellular telecommunications network according to an embodiment of the present invention;

[0012] Figure 2 yes Figure 1 A schematic diagram of the first base station of the network;

[0013] Figure 3 yes Figure 1 A schematic diagram of the beam management node of the network;

[0014] Figure 4 It is by Figure 1 A flowchart illustrating the process of implementing an optical camera in a network;

[0015] Figure 5 It is by Figure 1 A flowchart illustrating the process of implementing beam management nodes in a network;

[0016] Figure 6 It is by Figure 1 A flowchart illustrating the process of implementing the second base station in the network; and

[0017] Figure 7 In achieving Figures 4 to 6 After the process Figure 1 A schematic diagram of the network. Detailed Implementation

[0018] Now refer to Figures 1 to 3 A first embodiment of the wireless telecommunications network 100 of the present invention is described. In this embodiment, the wireless telecommunications network is a cellular telecommunications network 100 including a first base station 110, a second base station 120, an optical camera 130, a beam management node 140, and a first user equipment (UE) 150. Both the first base station 110 and the second base station 120 are configured to communicate according to a cellular telecommunications protocol (e.g., a fifth-generation (5G) protocol defined by the 3rd Generation Partnership Project (3GPP). The first base station 110 and the second base station 120 include a backhaul connection to a cellular core network (not shown).

[0019] like Figure 1 As shown, the first base station 110 and the second base station 120 each include a plurality of transceivers configured for beamforming, such that different beams 110-A, 110-B, 120-A, and 120-B can be formed to provide voice and / or data services to users within a geographic area defined by each beam. The first base station 110 and the second base station 120 can create, reconfigure, and remove each beam in their respective beam sets. One operating parameter for each of the plurality of transceivers used by the first base station 110 and the second base station 120 is transmission power. Typically, increasing the transmission power of a transceiver increases the capacity and range of the transmission. The signal strength of these transmissions decreases with distance from the transceiver (e.g., by the inverse square law for propagation in free space or by some other function in a real-world example), such that each beam has a maximum coverage area outside which the signal strength is below a minimum signal strength threshold representing the signal strength required for another wireless device to successfully decode these transmissions. The coverage area of ​​each beam is also defined by other transmission parameters, such as its angle. In this embodiment, the first UE 150 is located within and connected to the first beam 110-A of the first base station 110.

[0020] Figure 1An optical camera 130 is also illustrated. The optical camera 130 is configured to capture images or videos (i.e., image sequences) in the visible spectrum (that is, electromagnetic radiation with wavelengths in the range of approximately 400 to 700 nanometers). The optical camera 130 is in a fixed position and is positioned to capture images or videos of a geographic area including a first base station 110 and a second base station 120. The optical camera 130 includes a processor 133 for processing the captured images, which will be explained in more detail below. The optical camera 130 also includes a Global Navigation Satellite System (GNSS) module, such as a Global Positioning System (GPS) module, to determine the coordinates of its position.

[0021] exist Figure 2 The first base station 110 is shown in more detail. The first base station includes a first communication interface 111 (which can be connected to an antenna), a second communication interface 112 (which can be connected to the core network via a backhaul connection), a processor 113 for processing wireless signals received / transmitted via the first and second communication interfaces, and a memory 115, all connected via a bus 117. The second base station 120 is substantially the same as the first base station 110.

[0022] The cellular telecommunications network 100 also includes a beam management node 140, which resides in the core network and is connected to both the first base station 110 and the second base station 120. For example... Figure 3 As shown in more detail, the beam management node 140 includes a communication interface 141, a processor 143, and a memory 145, all connected via a bus 147. The memory 145 includes a database of visual training data for a computer vision learning agent. In this embodiment, the memory 145 includes a database that stores:

[0023] 1. Multiple training images, wherein a first subset of the training images includes the UE, and a second subset of the training images does not include the UE;

[0024] 2. For each training image, identify whether the UE exists in the training image.

[0025] The first subset of training images can cover different types of UEs (i.e., mobile phones, tablets, etc.), different models of UEs, and different retention methods of UEs (where retention method is a specific way of retaining the UE). The database can be updated periodically using new images.

[0026] In this embodiment, the processor 143 of the beam management node 140 implements the computer vision process through a learning agent 143a and an inference agent 143b. The learning agent 143a is configured to train a machine learning algorithm, in this case, a classification model, based on visual training data in a database. The classification model maps each training image from the database to a corresponding identifier indicating whether a UE exists in the training image. The inference agent 143b can then use the trained classification model.

[0027] The learning agent 143a performs periodic learning operations to update the classification algorithm, thereby adapting to any new training images.

[0028] The inference agent 143b uses a trained classification model to output whether a UE exists in a given input image captured by the optical camera 130.

[0029] Now refer to Figure 1 and Figures 4 to 7 The first embodiment of the method of the present invention is described. Figure 1 This example illustrates network 100 in its initial state. Figures 4 to 6 This is a flowchart illustrating the process of the first embodiment of the method of the present invention, implemented by an optical camera 130, a beam management node 140, and a second base station 120, respectively. Figure 7 An example is shown of a network 100 in its final state after implementing a first embodiment of the method of the present invention.

[0030] In the initial state, such as Figure 1 As shown, UE 150 is located in the overlapping area of ​​the coverage area of ​​the first beam 110-A of the first base station 110 and the coverage area of ​​the first beam 120-A of the second base station 120.

[0031] In the first step S101, the optical camera 130 captures an image of a geographic area (which includes the UE 150). In step S103, the optical camera's processor 133 processes the image to detect each object in the image. If no object is detected, the process ends or loops back to step S101. In this example, the image includes the first detected object—UE 150 (and may include one or more other objects). In step S105, the optical camera's processor 133 determines the location of each detected object. In this embodiment, this is achieved using phase detection focusing technology to determine the distance between the optical camera 130 and each detected object, and further using the optical camera's location (captured by GPS). Suitable phase detection technologies include those described in International Patent Application Publication No. WO2017 / 052923A1, U.S. Patent No. 10044926, and the paper "Depth map generation using a single image sensor with phase masks," Jinbeum Jang et al., Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science and Film.

[0032] In step S107, the optical camera sends a report to the beam management node 140, which includes a captured image containing at least one detected object and the location of each detected object (determined in step S105).

[0033] Go to Figure 5 In step S109, beam management node 140 inputs the captured image to inference agent 143b. Inference agent 143b uses its trained classification model to output an identifier indicating whether each detected object in the captured image includes or excludes the UE. This may involve inputting image portions of the captured image, where each image portion includes a single detected object, to determine whether each image portion contains the UE. If the UE is not present in the captured image, the process ends or loops back to step S101. In this example, inference agent 153b identifies that the captured image includes UE 150, and the process proceeds to step S111.

[0034] In step S111, beam management node 140 estimates the signal-to-interference-plus-noise ratio (SINR) for each UE in the captured image. This is achieved by dividing the signal strength at each UE from the first base station 110 by the signal strength at each UE from the second base station 120. For example, the signal strength at UE 150 from the first base station 110 can be estimated as follows:

[0035] Transmitter power × transmitter gain × path loss × receiver gain (1)

[0036] The values ​​of transmitter power, transmitter gain, and receiver gain may be known to beam management node 140, or may be retrieved from first base station 110 and UE 150 (which may be preferred for determining the current values). Path loss can be determined based on the position of UE 150 relative to first base station 110 (derived from the position of first base station 110 and the estimated position of UE 150 from step S105) and a suitable path loss model (which may be based on the frequency used in communication between first base station 110 and UE 150, the geography of the area between first base station 110 and UE 150 (which may include local clutter), and the altitude of first base station 110 and UE 150). The same technique can be used to calculate the signal strength from second base station 120 at UE 150.

[0037] It should be noted that in an alternative implementation having more than one serving base station and / or more than one neighboring base station, the SINR can be estimated by dividing the sum of the signal strengths at UE 150 from each serving base station by the sum of the signal strengths at UE 150 from each neighboring base station (that is, each base station that does not serve UE 150 and is therefore a source of interference).

[0038] If the captured image contains multiple UEs, step S110 is performed for each UE (based on the location of the UE determined in step S105) to estimate the SINR of each of the multiple UEs.

[0039] In step S113, beam management node 140 determines whether the estimated SINR of each UE meets a SINR threshold. This SINR threshold can be configured to represent the SINR required to meet service requirements. If the estimated SINR of all UEs is higher than the SINR threshold, the process ends (because this would indicate that all UEs still meet their service requirements despite interference from the second base station 120). If the estimated SINR of at least one UE in the captured image is lower than the SINR threshold, the process proceeds to step S115. In this example, the SINR of UE 150 is lower than the SINR threshold (indicating that the estimated SINR of UE 150 makes it possible that UE 150 does not meet its service requirements), and the process proceeds to step S115.

[0040] In step S115, beam management node 140 determines alternative transmission parameters for the first base station 110 and / or the second base station 120. In this example, where the captured image includes only UE 150, this can be achieved by identifying new transmission power for the first base station and / or the second base station such that the estimated SINR of UE 150 (estimated using Equation 1 above) using these alternative transmission parameters is higher than the SINR threshold. For example, alternative transmission parameters could involve adjusting the transmission power of the second base station 120 such that the signal strength from the second base station 120 at UE 150 is reduced. This could involve reducing the transmission power of the second base station 120 so that the coverage area of ​​the second base station no longer covers UE 150.

[0041] In an example where multiple UEs exist in the captured image, alternative transmission parameters for the first base station 110 and / or the second base station 120 are determined such that, when these alternative transmission parameters are used, the estimated SINR of each of the multiple UEs in the captured image is higher than a SINR threshold. If this is not possible, the step involves identifying alternative transmission parameters to maximize the number of UEs with an estimated SINR higher than the SINR threshold.

[0042] In step S117, beam management node 140 sends an instruction message to each base station containing alternative parameters determined in step S115. In this example, beam management node 140 sends an instruction message to the second base station 120 containing alternative parameters for transmission power.

[0043] Go to Figure 6In step S119, each base station receiving the alternative transmission parameters (in this example, the second base station 120) determines whether it serves any UE that will receive poor service or no service after implementing the alternative parameters. This may include UEs in the captured image, but for which the alternative transmission parameters determined in step S115 still result in an estimated SINR below the SINR threshold, and / or other UEs not in the captured image that will also experience poor service or no service after implementing the alternative parameters. If so, the process continues to step S121. In this example, the second base station 120 determines that it does not serve UEs that will receive poor service or no service after implementing the alternative transmission parameters, therefore the process continues to step S123.

[0044] In step S123, each base station implements alternative transmission parameters. In this example, the second base station 120 implements alternative transmission parameters to reduce the transmission power of the antenna transmitting the first beam 120A (or multiple antennas cooperating to transmit the first beam 120A), so that the SINR of the UE 150 is higher than the SINR threshold. Figure 7 The image shows the state of the network after the reconfiguration.

[0045] This first implementation benefits from utilizing real-time visual data to predict and respond to poor service experienced by UE 150 due to low SINR. This eliminates the need for UE 150 to perform measurements of its radio environment and report these measurements to the first base station 110, thereby reducing the requirements for processing, energy, and control signaling resources in network 100. Furthermore, this first implementation can react faster and more accurately than prior art solutions that measure the UE's radio environment and provide measurement reports. Improved accuracy is achieved because users not communicating with serving base stations (i.e., they are in idle mode) cannot perform measurements of the signal strength of user plane resources. Therefore, in scenarios where idle mode users request new services, the present invention can proactively estimate that the SINR of the service will not meet the SINR threshold (while prior art methods must initiate data transmission, measure the SINR, and then react to a SINR below the SINR threshold).

[0046] Returning to step S119 above, an alternative scenario will now be described, in which the second base station 120 determines that it will indeed serve a UE that will receive poor service or no service after implementing alternative transmission parameters. In this scenario, the process proceeds to step S121, where the second base station 120 transfers the UE to a new serving base station (e.g., by coordinating a handover of the UE). This can be based on a measurement report for the UE.

[0047] A second embodiment of the invention will now be described. This second embodiment is substantially the same as the first embodiment, except that the location of the object is detected by a camera at multiple time instances to determine the object's path. The object's path can then be used to predict the object's future location (or multiple future locations). The accuracy of these future location predictions can be improved by associating the path with map data such as road maps, rail maps, and pedestrian maps. The remaining steps of the second embodiment draw upon those of the first embodiment, but are based on one or more of the object's future locations. The beam management node 140 can estimate the SINR of the UE 150 at each of the UE 150's future locations. The estimated SINR at each of the UE 150's future locations can then be compared to a SINR threshold. If the estimated SINR at any of these future locations drops below the SINR threshold, alternative transmission parameters can be implemented before the UE reaches these future locations. Therefore, this second embodiment also benefits from taking preemptive action to prevent the UE 150 from experiencing poor service due to interference signals from neighboring base stations.

[0048] A third embodiment of the invention will now be described. This third embodiment may be based on the same steps as the first or second embodiment, but the difference is that the SINR estimation also takes into account how the UE 150 is held by the user. This will now be explained.

[0049] In this third embodiment, the beam management node 140 includes a database of antenna radiation patterns, each antenna radiation pattern defining the receiver gain as a function of the angle of arrival of the signal at the receiver. Each antenna radiation pattern in the database is associated with a specific holding configuration. In this context, the holding configuration defines how the user holds the UE; for example, when the user holds the UE in front of their body with one or both hands, it may be in a "browsing position," or when the user holds the UE to one side of their head, it may be in a "speaking position." Due to variations in antenna characteristics and different attenuations at each angle of arrival caused by the signal passing through the body, the antenna radiation pattern may differ for each holding configuration. In this example, the database includes antenna radiation patterns for multiple holding configurations, including "speaking position, right hand" (i.e., the UE is held to the right of the user's head), "speaking position, left hand" (i.e., the UE is held to the left of the user's head), "browsing position, right hand" (i.e., the UE is held in front of the user in their right hand), and "browsing position, left hand" (i.e., the UE is held in front of the user in their left hand).

[0050] Each antenna radiation pattern can be determined in a calibration environment to limit the receiver gain of the UE at all possible angles of arrival (relative to a reference, such as due north) when using a specific hold pattern.

[0051] Furthermore, the database of visual training data for the learning agent 143a of the beam management node 140 identifies whether a UE exists in each training image, and if so, identifies the preservation form of the UE in that training image. Therefore, the learning agent 143a of the beam management node 140 is configured to train a classification model to map each training image from the database to a corresponding identifier indicating whether a UE exists in the training image (and, if so, its preservation form).

[0052] In this third embodiment, in step S109, the beam management node 140 can process each image received from the optical camera 130 to determine whether a UE is present in the image, and if so, determine the holding mode of the UE. The beam management node 140 is also configured to determine the absolute azimuth of each UE in the image. This can be determined by training a classification model using training images of the UE at different absolute azimuths for each holding mode, such that the trained classification model can be used to determine whether a UE is present in the image, and if so, determine the holding mode and absolute azimuth of the UE. In another example implementation, the absolute azimuth of the UE can be calculated by determining the direction the user is facing in the image and comparing it to the known absolute azimuth of the optical camera 130.

[0053] Subsequently, in step S111, beam management node 140 estimates the SINR of the UE, wherein receiver gain is calculated based on the UE's hold-up configuration. That is, based on the UE's hold-up configuration, a lookup operation can be performed using a database of antenna radiation patterns to retrieve the antenna radiation pattern associated with that UE hold-up configuration. Then, the angle of arrival (AHEA) of the signal from the first base station 110 at UE 150 can be determined based on the positions of UE 150 and the first base station 110, and the absolute azimuth of UE 150. The retrieved antenna radiation pattern, which defines the receiver gain as a function of the AHEA, can then be used to determine the receiver gain of UE 150 for the signal from the first base station 110. This receiver gain can then be used to estimate the signal strength from the first base station 110 at UE 150.

[0054] The strength of the signal from the second base station 120 at UE 150 can be determined in a similar manner using the same antenna radiation pattern based on the receiver gain, which is a function of the angle of arrival of the signal from the second base station 120 at UE 150.

[0055] The third implementation can then follow the same steps as the first or second implementation to determine whether the estimated SINR is below a threshold, and if so, to take a response or preemptive action. Therefore, an additional benefit of this third implementation is that the SINR can be predicted more accurately based on the image captured by the optical camera 130 by taking into account the hold-up form of the UE detected by the beam management node 140.

[0056] In a variant of the third embodiment described above, the UE's holding mode may be merely a factor in the SINR estimation of a subset of UEs (e.g., using those specific communication frequencies that have significant performance differences for different holding modes).

[0057] Furthermore, receiver gain estimation can be even more accurate by using antenna radiation patterns that are even more specific to the scenario (such as for a specific communication frequency or a specific UE antenna arrangement).

[0058] Those skilled in the art will understand that the above embodiments are applicable to any form of wireless telecommunication network, such as wireless local area network or wireless wide area network.

[0059] In the above embodiment, beam management node 140 is a core networking node. However, this is not mandatory. For example, the beam management node could be a module of a Radio Access Node (RAN) Intelligent Controller (RIC).

[0060] In the above embodiment, an optical camera is used to detect objects within the coverage area of ​​the base station. However, this is not necessary, and any other suitable sensor (e.g., a LiDAR sensor) can be used for detecting objects and estimating their location.

[0061] In the above implementation, neighboring base stations are instructed to reduce their transmission power to improve the UE's SINR. However, this is not the only way to improve the UE's SINR. For example, the transmission power of the serving base station can be increased. Furthermore, the SINR can be improved by changing the beam angle of the serving base station and / or neighboring base stations. In another example, the serving base station and / or neighboring base stations can use alternative frequencies to change the receiver gain and improve the UE's SINR.

[0062] Those skilled in the art will also understand that it is not necessary for the beam management node 140 to estimate the SINR. That is, any other measurement representing the relative signal strength of one or more signals from one or more serving base stations relative to one or more signals from one or more neighboring base stations can be used.

[0063] In the above embodiments, the optical camera includes a GNSS module. However, this is not necessary, as the location of the optical camera can be known to the beam management node through pre-configuration, or can be determined by other means (e.g., using location estimation techniques from cellular telecommunications protocols). Furthermore, the absolute location is not necessary for estimating relative signal strength, as the relative distance between the base station and the UE can be used to estimate the relative signal strength.

[0064] In the above embodiments, the beam management node may need to identify the UE in the image in order to determine the base station serving the UE and the base station not serving the UE. This can be achieved by the base station sending data related to the UE it serves to the beam management node, which may include the UE's visual characteristics (such as brand and model) and the UE's approximate location (e.g., based on a beam identifier). The beam management node can use this information to identify the UE. If the UE cannot be uniquely identified, such that the UE in the image can be served by one of multiple base stations, alternative transmission parameters can be calculated to improve the SINR for each scenario. The UE can also be identified by sending instructions to each UE to transmit a specific sequence of light pulses (specific to its serving base station), making it possible to identify the base station serving the UE from this sequence.

[0065] Those skilled in the art will understand that any combination of features is possible within the scope of the claimed invention.

Claims

1. A method for operating network nodes in a wireless telecommunications network, wherein, The wireless telecommunications network includes a first access point configured to transmit in a first coverage area, a second access point configured to transmit in a second coverage area, and a first user equipment (UE) connected to the first access point. The method includes the following steps: Calculate the first distance between the first access point and the first UE, and the second distance between the second access point and the first UE; Detect the holding mode of the first UE; Based on the first distance and the second distance, and also based on the holding mode of the first UE, the relative strength of the first signal between the first UE and the first access point is calculated relative to the second signal between the first UE and the second access point; The relative intensity is determined to be below a threshold. Determine a first transmission parameter for the first access point and / or a second transmission parameter for the second access point such that, when the first transmission parameter and / or the second transmission parameter are implemented, the relative strength of the first signal between the first UE and the first access point relative to the second signal between the first UE and the second access point is higher than the threshold; and The first access point and / or the second access point are reconfigured to implement the first transmission parameters and / or the second transmission parameters.

2. The method according to claim 1, wherein, The first distance is between the first access point and the future location of the first UE, and the second distance is between the second access point and the future location of the first UE, and the step of reconfiguring the first access point and / or the second access point is performed before the first UE reaches the future location.

3. The method according to any one of the preceding claims, further comprising the following steps: Data captured by an optical source is obtained, wherein the first distance and the second distance are calculated from the data.

4. The method according to claim 3, wherein, The data is a visual image.

5. The method according to claim 3, wherein, The optical source is an Internet of Things (IoT) sensor.

6. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 5.

7. A computer-readable carrier medium comprising a computer program that, when executed by a computer, causes the computer to perform the method according to any one of claims 1 to 5.

8. A network node for a wireless telecommunications network, the network node comprising a processor configured to perform the method according to any one of claims 1 to 5.

9. A system for a wireless telecommunications network, the system comprising one or more nodes configured to perform the method according to any one of claims 1 to 5.

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