Method for determining predictive quality of service (pQoS) in mobile communications

Abstract

The invention describes a method for determining a predictive quality of service, pQoS, in a mobile communication, the method comprising the steps of receiving (301), from an application of a terminal, an indication of at least one data attribute (202a to 202c) of data to be exchanged between the application and an external device (210, 220), determining (302) a communication link (209) between the application and the external device (210, 220), the invention relates to a method for determining (303) a pQoS model (206, 207, 208) depending on indicated data attributes (202a-202c) and a communication link (209), determining (304) pQoS by applying the pQoS model (206, 207, 208), and providing (305) the determined pQoS to an application, the method being characterized in that it further comprises the steps of: receiving from the application of the terminal an indication of at least one Key Performance Index (KPI) applied by the application to measure actual QoS, a pQoS model is further determined depending on the indicated at least one KPI.

Description

Technical Field

[0001] The invention relates to a method for determining a predictive quality of service pQoS in mobile communications. Background Art

[0002] The prior art defines an autonomous vehicle (also known as AV, autonomous vehicle, self-driving car, driverless car, robo-car or robotic car) as a vehicle that can sense its environment and move safely with little or no human input.

[0003] Furthermore, automotive applications and mobile communications are becoming increasingly entangled, in particular due to the growing interest in autonomous driving, which requires larger amounts of data than conventional driving. These amounts of data are partly provided by the vehicle itself (i.e., by its sensors) and partly provided via the air interface. Communication is performed via the air interface (e.g., vehicle-to-vehicle V2V communication or vehicle-to-infrastructure V2I communication or vehicle-to-everything V2X), the latter including communication with roadside units RSU.

[0004] In such systems, mobile communications are essential and, depending on the criticality of the operation, must also meet certain requirements for Quality of Service (QoS).

[0005] In this context, for example, 5G communication systems can support the demanding QoS requirements of many advanced V2X use cases and their applications. However, efficient driving, especially of autonomous vehicles, may be affected by sudden changes in the provided QoS. For this reason, for example, 5G communication systems have recently enabled the prediction of QoS changes and early notification of these predicted changes to vehicles. This approach enables vehicles to avoid or mitigate the impact of sudden QoS changes at the application level.

[0006] Predicted QoS (pQoS) can be used, for example, to plan the execution of a route or, at a lower level, to plan a specific maneuver. If the pQoS is not met for long enough, the application will not perform a given action. Typically, the application adapts to the pQoS time series so that an improved or even optimal target distance plan can be achieved to fully exploit the predicted QoS.

[0007] However, different pQoS models and learning methods may be applied to predict the future QoS of a user (eg, AV). Therefore, the pQoS profile provided to a user or receiver may vary between different MNOs.

[0008] The 5G communication system provides notifications of pQoS changes based on requests from the V2X application server AS. The V2X AS can subscribe to notifications, or request a single notification from the network. The AS provides the network with information about the following items: location, time window for which notification of potential QoS changes applies, and thresholds indicating (one or more) levels (if the (one or more) levels are exceeded, notification that a potential QoS change may occur is triggered).

[0009] So far, QoS prediction functions are obtained from trained models that are trained with some training data or for real data transmission. When real data is transmitted, network providers usually do not know the exact properties behind the data traffic. In addition, they do not know for what kind of traffic the user requests the pQoS profile.

[0010] A pQoS profile usually consists of different predicted characteristics, such as latency and data rate, but it is foreseen that different applications have different application specific key performance indicators (KPIs), which are sometimes even on the application layer and are unknown and inaccessible to the network provider.

[0011] The pQoS model is obtained by different kinds of data services with different attributes. However, different data services have an impact on the obtained pQoS model, i.e., it is biased depending on the data service attributes. By generating the pQoS model according to different kinds of service types, we obtain an average prediction model, which is not necessarily accurate when the service attributes are changing. In addition to data service attribute related features (KPIs), learning is also foreseeable, and this is true even at the application layer (which the network is not aware of). How the pQoS prediction accuracy can be improved is an important question, especially considering the various different data service attributes and different application related KPIs.

[0012] The prior art indicates the disclosure of US2023 / 0108302 A1, which is related to reporting by an application of current and future locations and trajectories of multiple devices and at least one of: the number, size and periodicity of application messages sent via radio resources in order to obtain predicted QoS from the network. Summary of the invention

[0013] Therefore, an object of the present disclosure is to overcome or reduce at least some of the disadvantages of the prior art and to provide a method for determining a predictive quality of service pQoS in mobile communications. In particular, an object of the present disclosure is to improve QoS prediction accuracy.

[0014] A first aspect of the invention relates to a method for determining a predictive quality of service pQoS in mobile communications, the method comprising the steps of: receiving an indication of at least one data attribute of data to be exchanged between the application and an external device from an application, determining a communication link between the application and the external device; determining a pQoS model depending on the indicated data attribute and the communication link, determining the pQoS by applying the pQoS model, and providing the determined pQoS to the application. Thus, according to the method, the type of data / attribute received from the application is an indicator of how the pQoS is determined, and the pQoS depends on the data type / attribute. Thus, when all other pQoS considerations are the same and the data type / attribute changes, the pQoS model and the output pQoS may also change, because different data types may have different transmission requirements and may therefore result in different pQoS reports / predictions. As a result, advantageously, the pQoS can be determined more accurately.

[0015] The application is executed by the terminal requesting the pQoS indication.

[0016] The method further comprises the step of receiving from the application an indication of at least one key performance indicator KPI used by the application to measure the actual QoS, whereby the pQoS model is determined further depending on the indicated at least one KPI.

[0017] As a result, in addition to data traffic attribute dependencies, there may be KPI utilization (and learning) to further improve the determination of pQoS. That is, understanding how applications evaluate performance (e.g., KPIs for QoE, QoS) is beneficial / advantageous for more accurate determination of pQoS. In an embodiment, KPIs are evaluated at the application layer (i.e., the network (lower layers) are not aware of them by default).

[0018] In some cases, it may be that the pQoS determination module (or other unit) predicts pQoS using a different metric than the one used by the application to evaluate actual QoS. In this case, the pQoS determination may not be well suited for the application requesting pQoS. When the pQoS determination module understands the data type and the application's KPIs for QoS, pQoS can be determined more accurately because the application's expectations are more accurately defined. For example, one application may be more concerned with signal-to-noise ratio, while another application may be concerned with latency. Other KPIs may include one or more of the following: data loss, bandwidth, error rate.

[0019] Preferably, according to any aspect defined above, the data attribute indicates a characteristic of a corresponding communication protocol configuration.

[0020] In some cases, data attributes can generally define the content of the business exchanged between the application and the external device. At a consideration level, data attributes relate to the communication protocol. Different network protocols may have different requirements on QoS and on the amount of transmission overhead added to the data payload by headers, encoding, encryption, error correction or other similar data transmission mechanisms. Therefore, it may happen that although the same amount of data will be transmitted in total through different transmission protocols, the amount of data payload received may be significantly different between different transmission protocols. Other features of the communication protocol may relate to one or more of the following items: message size, protocol type, reliability measure (e.g., FEC), communication type / band.

[0021] As a positive consequence, the indication of the corresponding communication protocol configuration is also intended to be relied upon to improve the pQoS determination.

[0022] Preferably, according to any aspect defined above, the data attribute indicates a data encoding type and / or a data encryption type.

[0023] Similar to the example above, the application may directly indicate the data encoding and / or data encryption type. The encoding may be or may include data compression. For example, when a basic encoding and / or encryption type is applied, less data may be exchanged in a time unit compared to a complex encoding and / or encryption type. By knowing this parameter, the pQoS determination may also be improved. As an example, in order to determine the pQoS of a communication encoded by method X and encrypted by method Y, a specific pQoS model M1 may be applied.

[0024] It is advantageous to consider these aspects when determining pQoS, since some coding or encryption types may be data inefficient and / or less resilient to data errors than other coding or encryption types. Furthermore, applying different error correction methods may affect QoS.

[0025] Preferably, according to any aspect defined above, the data attribute indicates a data communication mode.

[0026] This mode may refer to the following modes, such as one or more of the following: bursty (sudden, high-intensity, relatively short transmissions), greedy (requiring a large amount of bandwidth BW over a considerable period of time), smooth (data flows at a uniform rate, not bursty), benign (not greedy for large amounts of BW).

[0027] Other data communication patterns may be one of the following: (one or more) non-scheduled events, scheduled periodic events, semi-permanent events, permanent events / communications. Such patterns may affect decisions on how to determine (pQoS model) and how to provide pQoS indications to applications (pQoS report content / format, etc.). It is advantageous to consider such aspects when determining pQoS, as the data communication pattern may affect the accuracy of determining pQoS.

[0028] Preferably, according to any aspect defined above, the method further comprises storing a pQoS model corresponding to the indicated application and the indicated at least one data attribute.

[0029] In general, data attributes can be one or more of the following:

[0030] - message size;

[0031] - data content, such as audio and / or video streaming;

[0032] - Application (purpose), e.g. remote boot;

[0033] - Correlation with another flow, e.g. UL and DL are correlated;

[0034] - Network protocols, such as UDP / TCP;

[0035] - Mode signal, such as frame rate;

[0036] -Communication scenarios;

[0037] -Communication modes: periodic, event-based, temporary;

[0038] - Criticality: operational safety, personal safety;

[0039] - Destination and source of payload (e.g., from the vehicle's ECU to the backend).

[0040] The pQoS model is advantageously stored locally for future use, or on a remote device to conserve local memory resources.

[0041] Preferably, according to any aspect defined above, each KPI is indicated by a corresponding relationship between one or more KPI values ​​and a QoS level on an application layer.

[0042] In order to enable the application to formulate the KPI more accurately, the application can indicate which values / ranges etc. correspond to a given QoS. Such indication can be made, for example, by providing a suitable correspondence table. By accurately understanding how the application maps the KPI to the QoS, the pQoS determination module can predict the QoS with higher accuracy, because the application preferably also indicates the QoS it requests. Therefore, as an advantage, the indication of such a correspondence also improves the accuracy of the pQoS indication.

[0043] Preferably, according to any aspect defined above, the method further comprises receiving feedback on pQoS, and adapting the pQoS model according to the received feedback.

[0044] In a static environment, the operation of the pQoS model and thus the pQoS determination process may be predefined. However, it may also be adapted to the current state of the system and / or receiver / terminal (application). The adaptation may be based on feedback on the difference between the pQoS and the QoS actually experienced by a given receiver / terminal / application.

[0045] For example, a given range of SNRs is reported as pQoS. However, after the application provides feedback that the experienced throughput does not actually match the pQoS SNR, the pQoS determination module can adapt its mapping so that the given range of SNRs reported as pQoS is thereafter converted to a different pQoS in view of the application's reported data attributes and optionally also one or more KPIs of the application. Thus, as an advantage, the pQoS determination process can be further improved by this adaptive approach.

[0046] Preferably, according to any of the aspects defined above, the pQoS profile is further determined according to one or more requirements of the application.

[0047] Such a requirement may be one or more of the following: profile length (indicative of the prediction time horizon, e.g. in seconds, in other words, the information indicates how far into the future the prediction applies), confidence, duration. Having an accurate pQoS indication allows the positive effect of efficiently configuring the applications of the receiver / terminal, often reducing the number of adaptations of the application operation due to a mismatch between pQoS and actual QoS.

[0048] Preferably, according to any of the aspects defined above, the pQoS profile is further determined according to environmental characteristics corresponding to a device executing the application.

[0049] The environmental (or otherwise referred to as contextual) characteristics may be one or more of the following: time of day, day, weather, traffic, self-information (e.g. location, speed, past and future paths, etc.) Such indication of parameters advantageously further helps in configuring the pQoS model, which may adapt the outputted pQoS accordingly, since environmental parameters often affect the radio communication.

[0050] Preferably, according to any aspect defined above, determining the pQoS model comprises: determining that a previously defined pQoS model is undefined for the indicated at least one data attribute, creating a new pQoS model for the indicated at least one data attribute based on at least one previous pQoS.

[0051] Another advantage of the present invention is that it can adapt to previously unknown data attributes and / or KPIs of an application. When an application reports unknown data attributes, the pQoS determination model can apply the pQoS model determined to be closest to the reported new data parameters, or apply a hybrid approach determined by more than one pQoS model, whose outputs can then be averaged or weighted according to a preferred ratio.

[0052] Preferably, according to any of the aspects defined above, pQoS is determined per application.

[0053] In order for the pQoS to be most advantageously tailored, the pQoS may be determined per application, however in a more general case, the pQoS may be determined for a device / terminal.

[0054] A second aspect of the present invention is a device comprising a processor, wherein the processor is adapted to execute all steps of the method of the first aspect of the present disclosure according to any of the above variations of the first aspect of the present disclosure.

[0055] The device may be a backend device or a device of a network infrastructure device of a mobile network operator MNO. Such MNO device / function may be one or more of the following: mobility management entity MME; service gateway SGW; packet data network gateway PDN GW; access and mobility management function AMF; session management function SMF; policy control function PCF; network exposure function NEF; etc.

[0056] The third aspect of the present invention is a computer program comprising instructions. When the program is executed by a computer, the instructions cause the computer to perform all the steps of the method of the first aspect of the present disclosure according to any of the above variations of the first aspect of the present disclosure.

[0057] A fourth aspect of the present invention is a computer-readable storage medium comprising instructions, which, when executed by a computer, causes the computer to perform all steps of the method of the first aspect of the present disclosure according to any of the above variations of the first aspect of the present disclosure.

[0058] Such computer programs are typically executed by utilizing computing resources in a computing device (e.g., a processor or controller). The application is stored on a non-transitory medium. An example of a non-transitory medium is a non-volatile memory, such as a flash memory, and an example of a volatile memory is a RAM. Computer instructions are typically executed by a processor. These memories are exemplary recording media for storing a computer program including computer executable instructions that perform all steps of a computer-implemented method according to the technical concepts presented herein.

[0059] The different embodiments of the invention described herein may be advantageously combined, unless the context indicates otherwise. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Reference will now be made to the following drawings for a more detailed description of the preferred embodiments of the present invention.

[0061] Figure 1 A general example of an application requesting pQoS is shown;

[0062] Figure 2 An example of a system according to a preferred embodiment of the present invention is shown; and

[0063] Figure 3 Another exemplary block diagram of a method according to the present disclosure is shown. DETAILED DESCRIPTION

[0064] Some portions of the detailed description that follows are presented in terms of data processing procedures, steps, or other symbolic representations of operations on data bits that can be performed on a computer memory. Accordingly, the computer performs such logical steps which require physical manipulations of physical quantities.

[0065] Typically, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. For general purposes, these signals are referred to as bits, packets, messages, values, elements, symbols, characters, terms, numbers, or the like.

[0066] Furthermore, all of these and similar terms are associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Terms such as "process" or "create" or "transfer" or "execute" or "determine" or "detect" or "obtain" or "select" or "compute" or "generate" refer to the actions and processes of a computer system that manipulates and transforms data represented as physical (electronic) quantities within the computer's registers and memories into other data similarly represented as physical quantities within the memories or registers or other such information storage devices.

[0067] As used herein, the term "example" means serving as a non-limiting example, instance or illustration. As used herein, the term "for example" and "eg" introduce a list of one or more non-limiting examples, instances or illustrations.

[0068] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. In addition, when describing embodiments of the present invention, the use of "may" refers to "one or more embodiments of the present invention." Furthermore, in the following description of embodiments of the present invention, terms in the singular may include plural forms unless the presented context clearly indicates otherwise.

[0069] It will be understood that although the terms "first" and "second" are used to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the present invention, the first element can be named as the second element, and similarly, the second element can be named as the first element. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items, and for example, "at least one of ..." and the like modify the entire element list when before the element list.

[0070] Reference will now be made in detail to the embodiments shown in the accompanying drawings. The effects and features of the exemplary embodiments will be described with reference to the accompanying drawings. Wherein, similar reference numerals represent similar elements, and redundant descriptions are omitted. However, the present invention can be embodied in various different forms and should not be construed as being limited to the embodiments shown herein. On the contrary, these embodiments are provided only as examples to fully convey the aspects and features of the present invention to those skilled in the art.

[0071] Figure 1 A high-level example of an application requesting pQoS is shown.

[0072] In step 101, the application makes a request for pQoS 101a. According to the first aspect of the present disclosure, the request is transmitted together with the application's initial QoS requirements and the specification of the data attributes. The request can be provided by a local pQoS determination module, or transmitted to an external pQoS entity (which is the typical case). In step 103, the pQoS determination module generates the pQoS in view of the data attributes. Once ready, the pQoS is reported to the application. Optionally, in step 104, the pQoS determination module can create a long-term request for a pQoS update for the application's request. This allows monitoring of the requested pQoS changes while taking into account other considerations, such as the above-mentioned environmental and / or situational parameters. Once the updated pQoS meets the trigger condition (for example, the pQoS is reduced by a certain threshold), the application is notified of the updated pQoS, which can then adapt 101b its application on this basis.

[0073] In addition, as described above, the application needs to indicate one or more data service attributes used in the sent message / request, and may also indicate one or more target KPIs, which enables the pQoS determination module to feed the correct pQoS model for determining pQoS and learning pQoS in the case of an adaptable pQoS model.

[0074] Figure 2 An example of a system according to a preferred embodiment of the present invention is shown. The figure shows how to learn different pQoS models (one or more) for different data traffic attributes. The data traffic attributes need to be indicated and understood by the application (data source / data sink). Figure 2 An exemplary architecture of the current pQoS is depicted, which considers different service attributes and generates / applies different pQoS models for / to each of them. The locations of the pQoS client and the pQoS server can be implemented in various ways, such as: (1) the pQoS client can be located at the user and on the backend side, and the server is located on the network side, such as hosted by the MNO; (2) the pQoS client can be located on the user side, and the server is located on the backend side.

[0075] There are three main components of the system, which are the user (which is the user's terminal / receiver 201 ), one or more servers / systems of different MNOs 210 , and the backend 220 .

[0076] The user's terminal 201 performs one or more data transmission / receptions 202a to 202c (eg data for a remote control driving application) under the supervision of a data flow management unit 203 which coordinates any data transmission with the communication unit 204 of the user's terminal.

[0077] The pQoS determination module 205 (in particular the pQoS client / server) may also optionally be present in the user's terminal 201. As described above, this module determines pQoS indications for different applications. This module 305 typically receives input from QoS related applications.

[0078] The pQoS determination module 205 may also have access to one or more pQoS models 206 to 208, wherein the pQoS models correspond to different data attributes. As explained above, the pQoS models may further correspond to KPIs for different applications.

[0079] The second component of the system is one or more servers / systems of different MNOs 201. The user terminal 201 exchanges data with one or more servers / systems of different MNOs 201. Each MNO typically operates a radio communication network 212 and a core network 213. In some instances, the MNO may communicate with a backend 220 through a public or private network, which is responsible for managing the user device 201 with the available MNO at a given time instance. The backend 220 may be an autonomous driving control center.

[0080] Each MNO may optionally include a pQoS determination module 217. The pQoS determination module 217 may be implemented on the MNO side, which may be understood as being implemented by one or more of the following: base station, radio access network, core network, Internet global network. The examples indicated above may also be combined in the system.

[0081] Specifically, the core network may also include different devices, which may include a pQoS module / API across MNOs. Such devices / functions of the core network may be one or more of the following: mobility management entity MME; service gateway SGW; packet data network gateway PDN GW; access and mobility management function AMF; session management function SMF; policy control function PCF; network exposure function NEF; etc.

[0082] Corresponding to 206 to 208, the pQoS determination module 217 may also have access to one or more pQoS models 214 to 216, wherein the pQoS models correspond to different data attributes. As explained above, the pQoS models may further correspond to KPIs of different applications.

[0083] The last component of the system is the backend 220. Typically, the backend 220 comprises the counterparts of the components present in the user's terminal 301.

[0084] The backend 220 executes one or more applications (e.g., a remote control driving application), which exchange data 221 to 223 with (one or more) remote applications under the supervision of a data flow management unit 224, which coordinates data transmission with the communication unit 225 application of the backend 220.

[0085] The backend 220 may optionally include a pQoS determination module 226 .

[0086] Corresponding to 206 to 208, the pQoS determination module 226 may also have access to one or more pQoS models 227 to 229, wherein the pQoS models correspond to different data attributes. As explained above, the pQoS models may further correspond to KPIs of different applications.

[0087] Figure 3 Another exemplary block diagram of a method according to the present disclosure is shown.

[0088] The first step of the method is to receive 301 from the application an indication of at least one data attribute relating to data to be exchanged between the application and the external device. Next, the method performs a step of determining 302 a communication link between the application and the external device and determining 303 a pQoS model depending on the indicated data attribute and the communication link. Thereafter, the method performs a step of determining 304 a pQoS by applying the pQoS model and provides 305 the determined pQoS to the application.

[0089] Reference numerals

[0090] 101 Applications

[0091] 101a Request

[0092] 101b Application Adaptation Based on pQoS

[0093] 102 Initial QoS requirements for data attributes

[0094] 103 pQoS determination

[0095] 104 pQoS update confirmed

[0096] 201 User Terminal

[0097] 202a Data 1

[0098] 202b Data 2

[0099] 202c DataX

[0100] 203 Data Flow Management Unit

[0101] 204 Communication Circuit

[0102] 205 pQoS determination unit (pQoS client / server)

[0103] 206 pQoS model of data service attributes (set) 1

[0104] 207 pQoS model of data service attributes (set) 2

[0105] 208 pQoS model of data service attributes (set) X

[0106] 210 Mobile Network Operators MNO

[0107] 211 pQoS determination unit (pQoS client / server)

[0108] 212 MNO's Radio Access Network RAN

[0109] 213 MNO's core network

[0110] 214 pQoS model of data service attributes (set) 1

[0111] 215 pQoS model of data service attributes (set) 2

[0112] 216 pQoS model of data service attributes (set) X

[0113] 217 Public / Private Networks

[0114] 220 Backend

[0115] 221 Data 1

[0116] 222 Data 2

[0117] 223 DataX

[0118] 224 Data Flow Management Unit

[0119] 225 Communication Circuit

[0120] 226 pQoS determination unit (pQoS client / server)

[0121] 227 pQoS model of data service attributes (set) 1

[0122] 228 pQoS model of data service attributes (set) 2

[0123] 229 pQoS model of data service attributes (set) X

[0124] 301 Received an indication of at least one data attribute from the application

[0125] 302 Determine the communication link of the application

[0126] 303 Determine the pQoS model depending on the indicated data attributes and communication link

[0127] 304 Determine pQoS by applying the PQoS model

[0128] 305 Provide the determined pQoS to the application

Claims

1. A method for determining a predictive quality of service pQoS in mobile communications, the method comprising the following steps: receiving (301) from an application of a terminal an indication of at least one data attribute (202a to 202c) of data to be exchanged between the application and an external device (210, 220), determining (302) a communication link (209) between the application and the external device (210, 220), determining (303) a pQoS model (206, 207, 208) depending on the indicated data attributes (202a to 202c) and the communication link (209), determining (304) pQoS by applying the pQoS model (206, 207, 208), and providing (305) the determined pQoS to the application, The method is characterized in that it further comprises the following steps: receiving, from the application of the terminal, an indication of at least one key performance indicator KPI used by the application to measure actual QoS, The pQoS model is further determined depending on the indicated at least one KPI.

2. The method according to claim 1, wherein: The data attributes indicate characteristics of a corresponding communication protocol configuration.

3. The method according to claim 1, wherein: The data attribute indicates a data encoding type and / or a data encryption type.

4. The method according to claim 1, wherein: The data attribute indicates a data communication mode.

5. The method according to any one of claims 1 to 4, further comprising storing the pQoS model corresponding to the indicated application and the indicated at least one data attribute.

6. The method according to any one of claims 1 to 5, wherein: Each KPI is indicated by a correspondence between one or more KPI values ​​and a QoS level on the application layer.

7. The method according to any one of claims 1 to 6, further comprising receiving feedback on the pQoS, and adapting the pQoS model according to the received feedback.

8. The method according to any one of claims 1 to 7, wherein: The pQoS profile is further determined based on one or more requirements of the application.

9. The method according to any one of claims 1 to 7, wherein: The pQoS profile is further determined according to environmental characteristics corresponding to the device executing the application.

10. The method according to any one of claims 1 to 7, wherein: Determining the pQoS model includes: It is determined that a previously defined pQoS model is undefined for the indicated at least one data attribute, and a new pQoS model is created for the indicated at least one data attribute based on at least one previous pQoS.

11. The method according to any one of claims 1 to 10, wherein: The pQoS is determined according to the application.

12. An apparatus comprising a processor adapted to perform all the steps of the method according to any one of claims 1 to 11.

13. The device according to claim 12, wherein: The device is a backend device.

14. The device according to claim 12, wherein: The device is a device of a network infrastructure device of a mobile network operator MNO.

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