Improved analysis generation in wireless communication network

Abstract

A network data analysis function (NWDAF) in a wireless communication network is provided. The NWDAF includes a transceiver and a processor. The transceiver is arranged to receive a request for analysis, the request for analysis comprising a network analysis identifier. The processor is arranged to identify, from the request for analysis, at least one network function from which data is to be collected. The transceiver is further arranged to send a request to a management entity, the request comprising a request for fault information within the wireless communication network. The transceiver is further arranged to receive fault information from the management entity, the fault information comprising: an identity of the particular network function, a property of the fault, and a time period during which the fault affects operation of the particular network function. The processor is further arranged to determine whether the accuracy of the data collected from the network function is affected by the fault information, and to ignore any collected data from the network function affected by the fault. The transceiver is further arranged to send the analyses, and an indication of which analyses are affected by the fault, in response to a request for the analyses.

Description

Technical Field

[0001] The subject matter disclosed herein relates generally to the field of enabling improved analysis generation in wireless communication networks.This document defines a network data analysis function and methods in a network data analysis function. Background Art

[0002] Failures may occur in wireless communication networks and by their very nature these failures may disrupt normal operation of the wireless communication network. During such failures, analysis of network nodes affected by the failure and collected during operation of the wireless communication network is not representative of normal operation of the wireless communication network. Therefore, any operational decisions based on such failure-affected analysis may not be optimized for normal operation of the wireless communication network. Summary of the invention

[0003] This article proposes an arrangement by which the analysis affected by the fault can be identified so that an accurate analysis can be derived. This accurate analysis can be used to make operational decisions for the wireless communication network, thereby improving the normal operation of the wireless communication network.

[0004] Disclosed herein is a process for improving analysis generation in a wireless communication network. The process may be implemented by a network data analysis function and a method in a network data analysis function.

[0005] Therefore, a network data analysis function (NWDAF) in a wireless communication network is provided, the NWDAF comprising a transceiver and a processor. The transceiver is arranged to receive a request for analysis, the request for analysis comprising a network analysis identifier. The processor is arranged to identify at least one network function from which data is to be collected from the request for analysis. The transceiver is also arranged to send a request to a management entity, the request comprising: a request for fault information within the wireless communication network. The transceiver is also arranged to receive fault information from the management entity, the fault information comprising: an identity of a specific network function, a nature of the fault, and a time period during which the fault affects the operation of the specific network function. The processor is also arranged to determine whether the accuracy of the data collected from the network function is affected by the fault information, and ignore any collected data from the network function that is affected by the fault. The transceiver is also arranged to send an analysis and an indication of which analyses are affected by the fault in response to the request for analysis.

[0006] A method is also provided in a network data analysis function (NWDAF), the NWDAF being in a wireless communication network. The method includes: receiving a request for analysis, the request for analysis including a network analysis identifier; and identifying at least one network function from which data is to be collected from the request for analysis. The method also includes: sending a request to a management entity, the request including: an identity of a specific network function, and a request for fault information within the wireless communication network; and receiving fault information from the management entity, the fault information including: an identity of the specific network function, a nature of the fault, and a time period during which the fault affects the operation of the specific network function. The method also includes: determining whether the accuracy of the data collected from the network function is affected by the fault information, and ignoring any collected data from the network function that is affected by the fault; and sending the analysis, and an indication of which analyses are affected by the fault, in response to the request for analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to describe the advantages and features of the present disclosure and the manner in which they can be obtained, the description of the present disclosure is presented by reference to certain devices and methods shown in the accompanying drawings. Each of these drawings depicts only certain aspects of the present disclosure and therefore should not be considered to limit its scope. These drawings may have been simplified for clarity and are not necessarily drawn to scale.

[0008] Methods and apparatus for improving analysis generation in a wireless communication network will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0009] Figure 1 Embodiments of a wireless communication system directed to improved analytics generation in a wireless communication network are described;

[0010] Figure 2 Depicted is a user equipment device that may be used to implement the methods described herein;

[0011] Figure 3 depicts further details of a network node that may be used to implement the methods described herein;

[0012] Figure 4 Methods for deriving analytics and providing analytics to analytics consumer web functions are shown;

[0013] Figure 5 A management data analysis function including internal business logic associated with management data analysis is shown;

[0014] Figure 6 A system is shown in which the NWDAF subscribes from the MDAF / MDAS to receive information about possible faults in the core network;

[0015] Figure 7A method for improving analysis for fault prediction by interacting with MDAS / MDAF is shown; and

[0016] Figure 8 The method in the network data analysis function is shown. DETAILED DESCRIPTION

[0017] It will be understood by those skilled in the art that aspects of the present disclosure may be embodied as systems, devices, methods, or program products. Therefore, the arrangements described herein may be implemented in full hardware form, full software form (including firmware, resident software, microcode, etc.), or in the form of a combination of software and hardware aspects.

[0018] For example, the disclosed methods and apparatus may be implemented as hardware circuits, including custom very large scale integration ("VLSI") circuits or gate arrays, off-the-shelf semiconductors (such as logic chips, transistors, or other discrete components). The disclosed methods and apparatus may also be implemented in programmable hardware devices, such as field programmable gate arrays, programmable array logic, programmable logic devices, etc. As another example, the disclosed methods and apparatus may include one or more physical or logical blocks of executable code, which may be organized, for example, as objects, procedures, or functions.

[0019] In addition, the method and apparatus may take the form of a program product embodied in one or more computer-readable storage devices storing machine-readable code, computer-readable code, and / or program code (hereinafter referred to as code). The storage device may be tangible, non-transient, and / or non-transmitting. The storage device may not embody a signal. In some arrangements, the storage device employs only a signal for accessing the code.

[0020] Any combination of one or more computer readable media may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, device or apparatus, or any suitable combination of the foregoing.

[0021] More specific examples of storage devices (a non-exhaustive list) would include the following: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory ("RAM"), a read-only memory ("ROM"), an erasable programmable read-only memory ("EPROM" or flash memory), a portable compact disk read-only memory ("CD-ROM"), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0022] Throughout this specification, references to examples or similar language of a particular method or device mean that the specific features, structures, or characteristics described in connection with the example are included in at least one implementation of the methods and devices described herein. Therefore, references to features or similar language of examples of a particular method or device may (but not necessarily) all refer to the same example, but rather represent "one or more but not all examples", unless otherwise expressly provided. The terms "include", "comprise", "have", and their variations mean "including but not limited to", unless otherwise expressly provided. Unless otherwise expressly provided, an enumerated list of items does not mean that any or all items are mutually exclusive. The terms "one", "an", and "the" also refer to "one or more", unless otherwise expressly provided.

[0023] As used herein, a list with the conjunction "and / or" includes any single item in the list or a combination of items in the list. For example, a list of A, B, and / or C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C, or a combination of A, B, and C. As used herein, a list using the term "one or more" includes any single item in the list or a combination of items in the list. For example, one or more of A, B, and C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C, or a combination of A, B, and C. As used herein, a list using the term "one of the following" includes one of any single item in the list, and only one of them. For example, "one of A, B, and C" includes only A, only B, or only C, but not a combination of A, B, and C. As used herein, "a member selected from the group consisting of A, B, and C" includes one and only one of A, B, or C, but not a combination of A, B, and C. As used herein, “a member selected from the group consisting of A, B and C and combinations thereof” includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C, or a combination of A, B and C.

[0024] In addition, the features, structures or characteristics described herein may be combined in any suitable manner. In the following description, many specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of the present disclosure. However, those skilled in the relevant art will recognize that the disclosed methods and apparatus may be practiced without one or more of the specific details, or using other methods, components, materials, etc. In other cases, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0025] Aspects of the disclosed methods and apparatus are described below with reference to schematic flow diagrams and / or schematic block diagrams of methods, apparatuses, systems, and program products. It will be understood that each block in the schematic flow diagrams and / or schematic block diagrams and the combination of blocks in the schematic flow diagrams and / or schematic block diagrams can be implemented by code. The code can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that instructions executed by the processor of the computer or other programmable data processing device create components for implementing the functions / actions specified in the schematic flow diagrams and / or schematic block diagrams.

[0026] The code may also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other device to operate in a specific manner, so that the instructions stored in the storage device produce a product including instructions for implementing the functions / actions specified in the schematic flowchart and / or schematic block diagram.

[0027] The code may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are executed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the code executed on the computer or other programmable apparatus provides a process for implementing the functions / actions specified in the schematic flowchart and / or schematic block diagram.

[0028] The flowchart and / or schematic block diagram in the figure illustrate the possible architecture, function and operation of the device, system, method and program product. In this regard, each block in the schematic flowchart and / or schematic block diagram can represent a module, segment or part of the code, which includes one or more executable instructions for implementing the code of (multiple) specified logical functions.

[0029] It should also be noted that in some alternative implementations, the functions indicated in the blocks may not occur in the order indicated in the figure. For example, two consecutively displayed blocks may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on the functions involved. Other steps and methods may be envisioned to be equivalent in function, logic or effect to one or more blocks, or portions thereof, in the illustrated figure.

[0030] The description of an element in each figure may refer to an element in a previous figure. Like numbers in all figures refer to like elements.

[0031] Figure 1 An embodiment of a wireless communication system 100 for improved analysis generation in a wireless communication network is depicted. In one embodiment, the wireless communication system 100 includes a remote unit 102 and a network unit 104. Although a specific number of remote units 102 and network units 104 are Figure 1 1 , but those skilled in the art will recognize that any number of remote units 102 and network units 104 may be included in the wireless communication system 100.

[0032] In one embodiment, the remote unit 102 may include a computing device such as a desktop computer, a laptop computer, a personal digital assistant ("PDA"), a tablet computer, a smart phone, a smart TV (e.g., a TV connected to the Internet), a set-top box, a game console, a security system (including a security camera), a vehicle computer, a network device (e.g., a router, a switch, a modem), an aircraft, a drone, etc. In some embodiments, the remote unit 102 includes a wearable device such as a smart watch, a fitness band, an optical head-mounted display, etc. In addition, the remote unit 102 may be referred to as a subscriber unit, a mobile device, a mobile station, a user, a terminal, a mobile terminal, a fixed terminal, a subscriber station, a UE, a user terminal, a device, or other terms used in the art. The remote unit 102 may communicate directly with one or more network units 104 via UL communication signals. In some embodiments, the remote unit 102 may communicate directly with other remote units 102 via side link communications.

[0033] The network elements 104 may be distributed over a geographic area. In some embodiments, the network elements 104 may also be referred to as access points, access terminals, stations, base stations, Node Bs, eNBs, gNBs, home Node Bs, relay nodes, devices, core networks, air servers, radio access nodes, APs, NRs, network entities, access and mobility management functions (“AMFs”), unified data management functions (“UDMs”), unified data repositories (“UDRs”), UDM / UDRs, policy control functions (“PCFs”), radio access networks (“RANs”), network slice selection functions (“NSSFs”), or by any other terminology used in the art. The network elements 104 are typically part of a radio access network that includes one or more controllers that may be communicatively coupled to one or more corresponding network elements 104. The radio access network may typically be communicatively coupled to one or more core networks that may be coupled to other networks, such as the Internet and public switched telephone networks, among others. These and other elements of the radio access and core networks are not shown, but are generally known to those of ordinary skill in the art.

[0034] In one implementation, the wireless communication system 100 complies with the New Radio (NR) protocol standardized in 3GPP, wherein the network unit 104 transmits on the downlink (DL) using an orthogonal frequency division multiplexing ("OFDM") modulation scheme, and the remote unit 102 transmits on the uplink (UL) using a single carrier frequency division multiple access ("SC-FDMA") scheme or an OFDM scheme. More generally, however, the wireless communication system 100 may implement some other open or proprietary communication protocol, such as WiMAX, IEEE 802.11 variants, GSM, GPRS, UMTS, LTE variants, CDMA2000, ZigBee, Sigfoxx, etc. The present disclosure is not intended to be limited to any particular wireless communication system architecture or protocol implementation.

[0035] The network unit 104 may serve multiple remote units 102 within a service area (eg, a cell or cell sector) via wireless communication links. The network unit 104 sends DL communication signals to serve the remote units 102 in the time, frequency, and / or spatial domains.

[0036] Currently, when a consumer requests an analysis from the NWDAF, the consumer can include a target area and / or a target UE or group of (multiple) UEs in the request. The NWDAF obtains the analysis by collecting data / events from one or more network functions, ensuring that the data comes from the requested target area or involves (multiple) target UEs. The analysis is based on the AMF determining that the UE enters or leaves a specific area of ​​interest (with a granularity of (multiple) tracking areas or (multiple) cell IDs) while considering the static location of the UE.

[0037] Failures may occur in a wireless communication network and by their very nature these failures may disrupt the normal operation of the wireless communication network. Analysis of network nodes affected by the failure and collected during the operation of the wireless communication network does not represent the normal operation of the wireless communication network. Therefore, any operational decisions based on such analysis affected by the failure may not be optimized for the normal operation of the wireless communication network. Therefore, an arrangement is proposed herein by which analysis affected by the failure can be identified so that an accurate analysis can be derived. This accurate analysis can be used to make operational decisions for the wireless communication network, thereby improving the normal operation of the wireless communication network.

[0038] Figure 2 A user equipment device 200 that can be used to implement the methods described herein is depicted. The user equipment device 200 is used to implement one or more solutions described herein. The user equipment device 200 is in accordance with one or more user equipment devices described in the embodiments herein. Specifically, the user equipment device 200 may include the remote unit 102, UE 440, or consumer 705 described herein. The user equipment device 200 includes a processor 205, a memory 210, an input device 215, an output device 220, and a transceiver 225.

[0039] The input device 215 and the output device 220 may be combined into a single device, such as a touch screen. In some implementations, the user equipment apparatus 200 does not include any input device 215 and / or output device 220. The user equipment apparatus 200 may include one or more of the following: a processor 205, a memory 210, and a transceiver 225, and may not include an input device 215 and / or an output device 220.

[0040] As shown, the transceiver 225 includes at least one transmitter 230 and at least one receiver 235. The transceiver 225 can communicate with one or more cells (or wireless coverage areas) supported by one or more base stations. The transceiver 225 can be operable on an unlicensed spectrum. In addition, the transceiver 225 can include multiple UE panels supporting one or more beams. In addition, the transceiver 225 can support at least one network interface 240 and / or application interface 245. (Multiple) application interface 245 can support one or more APIs. (Multiple) network interface 240 can support 3GPP reference points, such as Uu, N1, PC5, etc. As understood by a person of ordinary skill in the art, other network interfaces 240 can be supported.

[0041] The processor 205 may include any known controller capable of executing computer-readable instructions and / or capable of performing logical operations. For example, the processor 205 may be a microcontroller, a microprocessor, a central processing unit (“CPU”), a graphics processing unit (“GPU”), an auxiliary processing unit, a field programmable gate array (“FPGA”), or a similar programmable controller. The processor 205 may execute instructions stored in the memory 210 to perform the methods and routines described herein. The processor 205 is communicatively coupled to the memory 210, the input device 215, the output device 220, and the transceiver 225.

[0042] Processor 205 may control user equipment device 200 to implement the user equipment device behaviors described herein. Processor 205 may include an application processor (also referred to as a "main processor") that manages application domain and operating system ("OS") functions and a baseband processor (also referred to as a "baseband radio processor") that manages radio functions.

[0043] Memory 210 may be a computer-readable storage medium. Memory 210 may include volatile computer storage media. For example, memory 210 may include RAM, including dynamic RAM ("DRAM"), synchronous dynamic RAM ("SDRAM"), and / or static RAM ("SRAM"). Memory 210 may include non-volatile computer storage media. For example, memory 210 may include a hard drive, flash memory, or any other suitable non-volatile computer storage device. Memory 210 may include volatile and non-volatile computer storage media.

[0044] The memory 210 may store data related to implementing the traffic class fields described herein. The memory 210 may also store program code and related data, such as an operating system or other controller algorithms operating on the device 200.

[0045] Input device 215 may include any known computer input device, including a touch panel, buttons, keyboard, stylus, microphone, etc. Input device 215 may be integrated with output device 220, for example, as a touch screen or similar touch-sensitive display. Input device 215 may include a touch screen so that text can be entered using a virtual keyboard displayed on the touch screen and / or by handwriting on the touch screen. Input device 215 may include two or more different devices, such as a keyboard and a touch panel.

[0046] The output device 220 may be designed to output visual, auditory, and / or tactile signals. The output device 220 may include an electronically controllable display or display device capable of outputting visual data to a user. For example, the output device 220 may include, but is not limited to, a liquid crystal display ("LCD"), a light emitting diode ("LED") display, an organic LED ("OLED") display, a projector, or a similar display device capable of outputting images, text, etc. to a user. As another non-limiting example, the output device 220 may include a wearable display that is separate from the rest of the user device apparatus 200 but communicatively coupled, such as a smart watch, smart glasses, a head-up display, etc. In addition, the output device 220 may be a component of a smartphone, a personal digital assistant, a television, a desktop computer, a notebook (laptop) computer, a personal computer, a vehicle dashboard, etc.

[0047] Output device 220 may include one or more speakers for producing sound. For example, output device 220 may produce an audible alarm or notification (e.g., a buzzer or chime). Output device 220 may include one or more tactile devices for producing vibration, motion, or other tactile feedback. All or part of output device 220 may be integrated with input device 215. For example, input device 215 and output device 220 may form a touch screen or similar touch-sensitive display. Output device 220 may be located near input device 215.

[0048] The transceiver 225 communicates with one or more network functions of the mobile communication network via one or more access networks. The transceiver 225 operates under the control of the processor 205 to send messages, data and other signals, and also receive messages, data and other signals. For example, the processor 205 can selectively activate the transceiver 225 (or part thereof) at a specific time to send and receive messages.

[0049] The transceiver 225 includes at least one transmitter 230 and at least one receiver 235. One or more transmitters 230 can be used to provide uplink communication signals to the base unit of the wireless communication network. Similarly, one or more receivers 235 can be used to receive downlink communication signals from the base unit. Although only one transmitter 230 and one receiver 235 are shown, the user equipment device 200 can have any appropriate number of transmitters 230 and receivers 235. In addition, (multiple) transmitters 230 and (multiple) receivers 235 can be any appropriate type of transmitter and receiver. The transceiver 225 may include a first transmitter / receiver pair used to communicate with a mobile communication network through a licensed radio spectrum and a second transmitter / receiver pair used to communicate with a mobile communication network through an unlicensed radio spectrum.

[0050] The first transmitter / receiver pair can be used to communicate with a mobile communication network via a licensed radio spectrum, while the second transmitter / receiver pair used to communicate with a mobile communication network via an unlicensed radio spectrum can be combined into a single transceiver unit, such as a single chip that performs functions for utilizing both the licensed and unlicensed radio spectrums. The first transmitter / receiver pair and the second transmitter / receiver pair can share one or more hardware components. For example, some of the transceivers 225, transmitters 230, and receivers 235 can be implemented as physically separate components that access shared hardware resources and / or software resources, such as, for example, a network interface 240.

[0051] One or more transmitters 230 and / or one or more receivers 235 may be implemented and / or integrated into a single hardware component, such as a multi-transceiver chip, a system on chip, an application specific integrated circuit ("ASIC"), or other types of hardware components. One or more transmitters 230 and / or one or more receivers 235 may be implemented and / or integrated into a multi-chip module. Other components (such as a network interface 240 or other hardware components / circuits) may be integrated into a single chip with any number of transmitters 230 and / or receivers 235. Transmitters 230 and receivers 235 may be logically configured as transceivers 225 using one or more common control signals, or as modular transmitters 230 and receivers 235 implemented in the same hardware chip or multi-chip module.

[0052] Figure 3 300. Further details of a network node 300 that can be used to implement the methods described herein are shown. The network node 300 can be an implementation of an entity in a wireless communication network, for example, in one or more wireless communication networks described herein. The network node 300 can be, for example, a network element 104, or a NWDAF 410, 420, 620, 720 described herein. The network node 300 includes a processor 305, a memory 310, an input device 315, an output device 320, and a transceiver 325.

[0053] The input device 315 and the output device 320 may be combined into a single device, such as a touch screen. In some implementations, the network node 300 does not include any input device 315 and / or output device 320. The network node 300 may include one or more of the following: a processor 305, a memory 310, and a transceiver 325, and may not include an input device 315 and / or an output device 320.

[0054] As shown, the transceiver 325 includes at least one transmitter 330 and at least one receiver 335. Here, the transceiver 325 communicates with one or more remote units 200. In addition, the transceiver 325 can support at least one network interface 340 and / or application interface 345. (Multiple) application interface 345 can support one or more APIs. (Multiple) network interface 340 can support 3GPP reference points such as Uu, N1, N2 and N3. Other network interfaces 340 can be supported, as understood by those of ordinary skill in the art.

[0055] The processor 305 may include any known controller capable of executing computer-readable instructions and / or capable of performing logical operations. For example, the processor 305 may be a microcontroller, a microprocessor, a CPU, a GPU, an auxiliary processing unit, an FPGA, or a similar programmable controller. The processor 305 may execute instructions stored in the memory 310 to perform the methods and routines described herein. The processor 305 is communicatively coupled to the memory 310, the input device 315, the output device 320, and the transceiver 325.

[0056] Memory 310 may be a computer-readable storage medium. Memory 310 may include volatile computer storage media. For example, memory 310 may include RAM, including dynamic RAM ("DRAM"), synchronous dynamic RAM ("SDRAM"), and / or static RAM ("SRAM"). Memory 310 may include non-volatile computer storage media. For example, memory 310 may include a hard drive, flash memory, or any other suitable non-volatile computer storage device. Memory 310 may include both volatile and non-volatile computer storage media.

[0057] The memory 310 may store data related to establishing a multipath unicast link and / or mobile operation. For example, the memory 310 may store parameters, configurations, resource allocations, policies, etc. described herein. The memory 310 may also store program codes and related data, such as an operating system or other controller algorithms operating on the network node 300.

[0058] Input device 315 may include any known computer input device, including a touch panel, buttons, keyboard, stylus, microphone, etc. Input device 315 may be integrated with output device 320, for example as a touch screen or similar touch-sensitive display. Input device 315 may include a touch screen so that text can be entered using a virtual keyboard displayed on the touch screen and / or handwritten on the touch screen. Input device 315 may include two or more different devices, such as a keyboard and a touch pad.

[0059] Output device 320 may be designed to output visual, auditory and / or tactile signals. Output device 320 may include an electronically controllable display or display device capable of outputting visual data to a user. For example, output device 320 may include, but is not limited to, an LCD display, an LED display, an OLED display, a projector, or a similar display device capable of outputting images, text, etc. to a user. As another non-limiting example, output device 320 may include a wearable display that is separate from the rest of network node 300 but communicatively coupled, such as a smart watch, smart glasses, a head-up display, etc. In addition, output device 320 may be a component of a smart phone, a personal digital assistant, a television, a desktop computer, a notebook (laptop) computer, a personal computer, a vehicle dashboard, etc.

[0060] Output device 320 may include one or more speakers for producing sound. For example, output device 320 may produce an audible alarm or notification (e.g., a buzzer or chime). Output device 320 may include one or more tactile devices for producing vibration, motion, or other tactile feedback. All or part of output device 320 may be integrated with input device 315. For example, input device 315 and output device 320 may form a touch screen or similar touch-sensitive display. Output device 320 may be located near input device 315.

[0061] The transceiver 325 includes at least one transmitter 330 and at least one receiver 335. One or more transmitters 330 may be used to communicate with a UE, as described herein. Similarly, one or more receivers 335 may be used to communicate with a network function in a PLMN and / or RAN, as described herein. Although only one transmitter 330 and one receiver 335 are shown, the network node 300 may have any suitable number of transmitters 330 and receivers 335. In addition, the transmitters 330 and the receivers 335 may be any suitable type of transmitter and receiver.

[0062] Currently, when a consumer requests analytics from the NWDAF, the consumer can include a target area and / or a target UE or group of (multiple) UEs in the request. The NWDAF derives analytics by collecting data / events from one or more network functions, ensuring that the data is from the requested target area or involves (multiple) target UEs. The analytics are derived based on the AMF determining that the UE enters or leaves a specific area of ​​interest with the granularity of (multiple) tracking areas or (multiple) cell IDs, taking into account the static location of the UE.

[0063] Figure 4A method of deriving and providing analytics to an analytics consumer network function is shown. This method is defined in the current Release 16 and Release 17 3GPP architecture, where the NWDAF (defined in 3GPP TS 23.288 v17.2.0) provides analytics output to one or more analytics consumer NFs based on data collected from one or more data producer NFs. Figure 4 A system 400 is shown including network functions operating as analysis consumers, including application functions (AF) 402, 5G network functions (5GNF) 404, operations, administration, and maintenance (OAM) 406. The system 400 also includes a network data analysis function (NWDAF) with an analysis reasoning logic function (AnLF) 410, a network data analysis function (NWDAF) with a model training logic function (MTFL) 420, and a data collection coordination function (DCCF) 430. The system 400 also includes network functions operating as data producers, including application functions (AF) 432, 5G network functions (5GNF) 434, operations, administration, and maintenance (OAM) 436, and user equipment (UE) 440. The user equipment 440 may include a user equipment device 200, a remote unit 102, a UE 440, or a consumer 705, as described herein. The NWDAF 410, 420 may include a network element 104, a network node 300, or a NWDAF 620, 720, as described herein.

[0064] Analytics are also generated at the management plane.Management Data Analytics (MDA) is an enabler for automation and intelligence in service management and orchestration. The MDA MnS (also known as MDAS) defined in 3GPP TS28.102v2.0.0 enables any authorized consumer to request and receive analytics. Figure 5 A Management Data Analysis Function (MDAF) 550 is shown that includes internal business logic associated with management data analysis. MDAF 550 interfaces with MDA Management Services (MnS) consumers 548. MDAF 550 also interfaces with multiple producers, such as other MDA MnS producers 552, MNS producers 554, NWDAF 556, location management functions (LMF) 558, and non-3GPP management systems 560.

[0065] The management function (MDAF) can play the role of MDA MnS producer, MDA MnS consumer, other MnS consumers, NWDAF consumer and LMF service consumer, and can also interact with other non-3GPP management systems.

[0066] The internal business logic associated with the MDA utilizes current and historical data related to:

[0067] • Performance Measures (PM) according to TS 28.552 and Key Performance Indicators (KPI) according to TS 28.554.

[0068] Tracking data according to TS 32.422 and TS 32.423, including MDT / RLF / RCEF.

[0069] QoE and service experience data according to TS28.405 and TS28.406.

[0070] ·Analysis data supplied by NWDAF in accordance with TS23.288 includes 5GC data from AF and external web / app-based information (e.g., a web crawler providing online news).

[0071] · Alarm information and notifications in accordance with TS28.532.

[0072] CM information and notifications.

[0073] UE location information provided by LMF as per TS23.273.

[0074] MDA reports from other MDA MnS producers.

[0075] Management data from non-3GPP systems.

[0076] The analysis output from the MDA internal business logic is made available to authorized consumers (including but not limited to other management functions, network functions / entities, NWDAF, SON functions, optimization tools and human operators) by the management function (MDAF) playing the role of MDA MnS producer.

[0077] The analysis provided by the MDAS includes analysis for fault management prediction / statistics, or generally the MDA can assist in fault management. The MDA can monitor the status of various network functions and resources, and predict the network's operating trends and potential failures to intervene in advance. These predictions can be used by the management system to autonomously maintain the health of the network, for example, rapid recovery actions for network functions related to predicted potential failures.

[0078] As part of the analysis output, the following information elements may be provided by the MDAF. Table 1 indicates the analysis output for the fault prediction analysis as defined in 3GPP TS 28.104 v17.0.1.

[0079]

[0080]

[0081]

[0082] Table 1: Analysis output for failure prediction analysis

[0083] This article gives definitions of the interactions between NWDAF and MDAS / MDAF, as well as examples of how these interactions can be used. For example, analytics from MDAS can assist in the generation of analytics from NWDAF.

[0084] Figure 6 A system 600 is shown in which the NWDAF 620 subscribes from the MDAF / MDAS 640 to receive information about possible failures in the core network 610. The system 600 includes a 5G core 610, an ADRF 632, an OAM 636, a NWDAF 620, and a MDAF / MDAS 640. The 5G core 610 includes an access and mobility management function (AMF) 612, a session management function (SMF) 614, a user plane function (UPF) 616, and a network repository function (NRF) 618. The NWDAF 620 may include a network element 104, a network node 300, or a NWDAF 410, 420, 720, as described herein.

[0085] NWDAF 620 uses analysis output from MDAF / MDAS 640 to verify and / or determine analysis accuracy. NWDAF 620 may require input from MDAF / MDAS 640 to verify analysis accuracy, for example, when generating analysis based on analysis requested by a consumer using an ML model and data from a corresponding data producer. In the event that NWDAF 620 is notified by MDAS / MDAF 640 about analysis inaccuracies, NWDAF 620 may require input from MDAF / MDAS 640 when notifying analysis consumer(s) about analysis inaccuracies.

[0086] The request for fault prediction and / or statistics issued by NWDAF 620 to MDAF / MDAS 640 may depend on the analysis requested by the analysis consumer. The consumer requests an analysis identified by a specific analysis ID, and the analysis request may have one or more parameters as analysis filters. Table 2 shows potential parameters that may be requested by NWDAF 620 to MDAF / MDAS 640 when requesting fault prediction analysis. These parameters may be used as filters to filter the available information.

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] Table 2: Potential Fault Prediction Request Parameters

[0094] Figure 7 A method 700 is shown for improving analysis for fault prediction by interacting with MDAS / MDAF. Figure 7 An analysis consumer 705, NWDAF 720, MDAS / MDAF 740, and data producer 745 are shown. The analysis consumer 705 may include a remote unit 102, a user equipment device 200, or a UE 440, as described herein. The NWDAF 720 may include a network unit 104, a network node 300, or a NWDAF 410, 420, 620, as described herein.

[0095] Method 700 starts at 771, where a consumer 705 requests an analysis including an analysis ID in accordance with 3GPP TS 23.288. For example, a consumer may request an analysis for an analysis ID for NF load. The request may include at least one parameter as described in Table 2.

[0096] At 772 , NWDAF 720 determines the data producer(s) 745 from which to collect data to derive the analysis for the requested analysis ID.

[0097] At 773 , the NWDAF 720 collects data from the identified data producer(s).

[0098] At 774 , NWDAF 720 determines the parameters required to request fault prediction from MDAS / MDAF 740 according to Table 2.

[0099] At 775, NWDAF 720 subscribes to MDAS / MDAF 740 for fault prediction.

[0100] At 776, MDAS / MDAF 740 determines the data producers and collects the data.

[0101] At 777 MDAS / MDAF 740 may determine a fault based on the generated analysis.

[0102] At 778, MDAS / MDAF 740 reports the failure prediction to NWDAF 720. The analysis output may include one or more parameters listed in Table 1, such as an indication of analysis inaccuracy, suspending analysis subscriptions.

[0103] At 779, NWDAF 720 determines whether the failure affects the accuracy of the analysis based on the information provided in step 778. NWDAF 720 can determine whether the failure affects the inference model used to generate the analysis, or whether the analysis predictions that have been provided to consumers are inaccurate due to the predictions or statistics provided by MDAS / MDAF 740.

[0104] One method for the NWDAF 720 to estimate how the analysis accuracy is affected is: if the fault prediction includes an NF identity (NF id or NF instance ID) indicating that the network function will "misbehave" (operate abnormally) for a specific time period, the NWDAF 720 can ignore the data during the time period when the NF "misbehaves" and collect data from the NF when the NF operates in its normal conditions. Based on the ignored data, the NWDAF 720 can estimate how the analysis accuracy is affected (i.e., the NWDAF 720 will have a smaller number of samples to generate the analysis) and indicate to the consumer whether the analysis accuracy is affected. If the NWDAF 720 determines that the analysis is not affected by the fault prediction (e.g., the NF fails only for a short period of time and the NWDAF 720 has enough samples to generate the analysis with the requested accuracy), the NWDAF 720 does not need to report an indication of inaccurate analysis to the consumer.

[0105] NWDAF 720 may also use statistics provided by MDAS / MDAF 740. Such statistics may include MDAS / MDAF 740 notifying NWDAF 720 of an impending network failure. If NWDAF 720 determines that the failure indication will affect the analysis accuracy, NWDAF 720 notifies the consumer to stop using the analysis provided by NWDAF 720 until the network failure is resolved.

[0106] At 780, if the NWDAF 720 determines that the fault analysis information affects the accuracy of the analysis, the NWDAF 720 reports an indication to the consumer to stop making decisions. The NWDAF 720 may include a pause indication to the consumer 705.

[0107] At 781 , after the NWDAF 720 has accumulated sufficient samples from the data producer(s) 745 whose failure was predicted, the NWDAF 720 determines that the analysis accuracy is restored and the accumulated samples are not contaminated by the failure.

[0108] At 782, NWDAF 720 instructs consumer 705 to resume the analytics subscription.

[0109] The above example specifies the collection of network analytics. However, the arrangement described herein may also be applied to the training of machine learning (ML) models that take into account fault predictions from the MDAS / MDAF 740. When the MTLF trains the ML model, an estimate of the accuracy of the ML model is determined by receiving input from the MDAS / MDAF. When the NWDAF MTLF is triggered to train the ML model, the NWDAF determines the data producer to obtain data to train the model based on the analysis ID. The NWDAF MTLF may also subscribe to the MDAS / MDAF to receive notifications of fault predictions / statistics in the area served by the data producer, or if an identified data producer NF that provides data for model training fails (identified by including the NF ID in the fault prediction request to the MDAS / MDAF).

[0110] If MDAS / MDAF provides a fault prediction, the NWDAF MTLF will ignore the data collected for training the ML model based on the information provided in the fault prediction and retrain the ML model using data from data producers whose faults were not predicted. For example, if the fault prediction from MDAS / MDAF indicates that a fault is predicted for a NF within a specific time period, the MTLF will ignore any data collected from that NF during the time period when the fault was predicted.

[0111] If MDAS / MDAF notifies NWDAF MTLF of a fault condition based on statistics, MDAS / MDAF may decide to retrain the ML model using data from data producers affected by the fault after the fault is resolved. NWDAF may be made aware of when the fault is resolved based on the event times provided in the analysis output of MDAS / MDF.

[0112] This process and Figure 7 The difference is that NWDAF is specifically NWDAF MTLF, while consumer is specifically NWDAF AnLF. Figure 7 The analysis requested in step 771 is specifically for the ML model for the analysis ID. AnLF (here the consumer) can include in the request that the ML model be provided at the requested time. In step 780, if the failure affects the ML model training and the MTLF cannot provide the ML model at the requested time, the MTLF indicates to the AnLF that the ML model will not be available at the requested time.

[0113] Therefore, a network data analysis function (NWDAF) is provided in a wireless communication network, the NWDAF comprising a transceiver and a processor. The transceiver is arranged to receive a request for analysis, the request for analysis comprising a network analysis identifier. The processor is arranged to identify at least one network function from which data is to be collected from the request for analysis. The transceiver is also arranged to send a request to a management entity, the request comprising: a request for fault information within the wireless communication network. The transceiver is also arranged to receive fault information from the management entity, the fault information comprising: the identity of a specific network function, the nature of the fault, and the time period during which the fault affects the operation of the specific network function. The processor is also arranged to determine whether the accuracy of the data collected from the network function is affected by the fault information, and ignore any collected data from the network function that is affected by the fault. The transceiver is also arranged to send an analysis and an indication of which analyses are affected by the fault in response to the request for analysis.

[0114] Failures may occur in a wireless communication network and by their very nature these failures may disrupt the normal operation of the wireless communication network. During such a failure, the analysis of the network nodes affected by the failure and collected during the operation of the wireless communication network does not represent the normal operation of the wireless communication network. Therefore, any operational decision based on such analysis affected by the failure may not be optimized for the normal operation of the wireless communication network. Therefore, an arrangement is proposed herein by which the analysis of the impact of the failure is identified so that an accurate analysis can be derived. This accurate analysis can be used to make operational decisions for the wireless communication network, thereby improving the normal operation of the wireless communication network.

[0115] The fault may be a predicted fault, whereby the data analysis indicates that the fault will occur in a particular network function at a particular time, or whereby the data analysis indicates that the fault has occurred in a particular network function at a particular time. The data analysis may be a statistical analysis. The fault may be a detected fault, whereby the fault may be identified by a management function.

[0116] The wireless communication network may be a 5G network. The information request sent to the management entity may include an identity of a specific network function.

[0117] The request for analysis may include a request for a trained machine learning model associated with the network analysis identifier.

[0118] The processor may be arranged to ignore any collected data from the network function affected by the failure by determining that the trained machine learning model requires retraining.

[0119] The request for analysis may include a request for network analysis corresponding to at least one of the network analysis identifiers, a network function identity of a network function for which data is collected, and / or a target region.

[0120] The request for an analysis may include conditions. For example, the conditions may include any combination of target UE, region of interest, or S-NSSAI list. An analysis may be identified by an analysis ID.

[0121] The request for analytics information may be a subscription request.

[0122] The request for fault information may be derived from the request for analysis. For example, where the request for analysis includes conditions for providing the analysis, these conditions are used to create the request for fault information. The request for fault information may include a list of network functions.

[0123] The indication of which analyses were affected by the failure may include an indication for the analyses whose accuracy was affected during the period when the failure occurred.

[0124] The indication of which analyses are affected by the failure may include an indication of a time period during which the accuracy of the analyses was affected when the failure occurred. The identity of a particular network function may be defined as a target area, where the target area is served by the particular network function. Where the failure affects multiple network functions, multiple network functions may be defined as a target area, where the target area is served by the multiple network functions.

[0125] The indication of which analyses were affected by the failure may include an indication of target areas for a plurality of network functions for which the accuracy of the analyses was affected when the failure occurred.

[0126] The failure may affect more than just a specific network function, in which case the failure information may include a target area, whereby the failure affects the operation of a set of network functions serving the target area. The specific network function may be a member of a set of network functions serving the target area. The failure information may include a list of the set of network functions affected by the failure.

[0127] Figure 8A method 800 is shown in a network data analysis function (NWDAF), the NWDAF being located in a wireless communication network. The method 800 includes receiving 810 a request for analysis, the request for analysis including a network analysis identifier; and identifying 820 from the request for analysis at least one network function from which data is to be collected. The method 800 also includes sending 830 a request to a management entity, the request including: an identity of a particular network function, and a request for fault information within the wireless communication network; and receiving 840 fault information from the management entity, the fault information including: an identity of the particular network function, a nature of the fault, and a time period during which the fault affected the operation of the particular network function. The method 800 also includes determining 850 whether the accuracy of data collected from the network function is affected by the fault information, and ignoring any collected data from the network function that is affected by the fault; and sending 860 the analysis, and an indication of which analyses are affected by the fault, in response to the request for analysis.

[0128] Failures may occur in a wireless communication network and by their very nature these failures may disrupt the normal operation of the wireless communication network. During such a failure, the analysis of the network nodes affected by the failure and collected during the operation of the wireless communication network does not represent the normal operation of the wireless communication network. Therefore, any operational decision based on such analysis affected by the failure may not be optimized for the normal operation of the wireless communication network. Therefore, an arrangement is proposed herein by which the analysis of the impact of the failure is identified so that an accurate analysis can be derived. This accurate analysis can be used to make operational decisions for the wireless communication network, thereby improving the normal operation of the wireless communication network.

[0129] The fault may be a predicted fault, whereby the data analysis indicates that the fault will occur in a particular network function at a particular time, or whereby the data analysis indicates that the fault has occurred in a particular network function at a particular time. The data analysis may be a statistical analysis. The fault may be a detected fault, whereby the fault may be identified by a management function.

[0130] The wireless communication network may be a 5G network. The information request sent to the management entity may include an identity of a specific network function.

[0131] The request for analysis may include a request for a trained machine learning model, the trained machine learning model being trained using training data associated with the network analysis identifier.

[0132] Ignoring any collected data from network functions affected by the failure may include determining that a trained machine learning model needs to be retrained.

[0133] The request for analysis may include: a request for a network analysis corresponding to at least one of the network analysis identifiers; a network function identity of the network function for which data is collected, and / or a target area. The request for analysis may include conditions for providing the analysis. For example, the conditions may include any combination of a target UE, an area of ​​interest, or an S-NSSAI list. The analysis may be identified by an analysis ID. The request for analysis information may be a subscription request.

[0134] The request for fault information may be derived from the request for analysis. For example, where the request for analysis includes conditions for providing the analysis, these conditions are used to create the request for fault information. The request for fault information may include a list of network functions.

[0135] The indication of which analyses were affected by the failure may include an indication for the analyses whose accuracy was affected during the period when the failure occurred.

[0136] The indication of which analyses are affected by the failure may include an indication of a time period during which the accuracy of the analyses was affected when the failure occurred. The identity of a particular network function may be defined as a target area, where the target area is served by the particular network function. Where the failure affects multiple network functions, multiple network functions may be defined as a target area, where the target area is served by the multiple network functions.

[0137] The indication of which analyses were affected by the failure may include an indication of target areas for a plurality of network functions for which the accuracy of the analyses was affected when the failure occurred.

[0138] The failure may affect more than just a specific network function, in which case the failure information may include a target area, whereby the failure affects the operation of a set of network functions serving the target area. The specific network function may be a member of a set of network functions serving the target area. The failure information may include a list of the set of network functions affected by the failure.

[0139] As part of the Release 18 work for network analysis, one goal is to improve the analysis accuracy of NWDAF. In addition, a separate goal of the network analysis study is to determine how NWDAF and MDAS / MDAF functions interact to improve the coordination of analysis between the core network and the management plane.

[0140] This paper proposes a solution that suggests NWDAF to obtain fault predictions from MDAS / MDAF analysis. Such information can help NWDAF determine whether the accuracy of the analysis will be affected. NWDAF ignores data from data producer NF based on the fault prediction information. In other words, data from data producers affected by the fault are ignored.

[0141] Prior art solutions typically work by comparing predictions to statistical information. The solution proposed in this paper is further optimized to allow NWDAF to identify scenarios where the collected data may be invalid due to NF failures.

[0142] according to Figure 7 In the arrangement, NWDAF determines the accuracy of the analysis by subscribing to the fault predictions from MDAS / MDAF. However, it should be noted that the present arrangement can also be applied by NWDAF MTLF, which can determine the accuracy of ML model training by subscribing to the fault predictions from MDAS / MDAF.

[0143] Therefore, there is provided a NWDAF that receives a first request for analysis identified by an analysis ID, wherein the first request includes a first condition for providing the analysis. The analysis may include conditions, including a target UE, an area of ​​interest, a list of S-NSSAIs, etc. The NWDAF identifies a first list of network functions to collect data to derive an analysis for the first request; and sends a second request (which may be a subscription request) to a first entity (which may be an MDAS / MDAF) to receive statistics and / or predictions for failures, taking into account the first condition requested in the first request and the first list of network functions that provide data to derive the analysis for the first request. The NWDAF determines whether the accuracy of the analysis is affected in response to receiving a notification from the first entity about the predicted failure, wherein the response includes a time period for which the failure is predicted. The NWDAF ignores any data collected between the time period for which the failure is predicted in response to determining that the accuracy of the analysis is affected, and sends a response to the first request indicating that the accuracy of the analysis is affected during the time period for which the failure is predicted.

[0144] The second request may include an area of ​​interest, a list of network functions, a network slice instance, or a combination thereof.

[0145] The response to the second request may include one or more parameters in Table 1. These parameters may include an indication of the NR cell or NF where a fault-related problem has occurred or may occur, the event time at which the fault occurred or is predicted to occur, "Operational Violation", "Physical Violation" and "Time Domain Violation", and the severity of the fault.

[0146] It should be noted that the above methods and apparatus illustrate rather than limit the present invention, and that those skilled in the art will be able to design many alternative arrangements without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps other than those listed in the claims, "a" or "an" does not exclude a plurality, and a single processor or other unit may perform the functions of several units described in the claims. Any reference signs in the claims should not be construed as limiting their scope.

[0147] Furthermore, although examples have been given in the context of specific communication standards, these examples are not intended to limit the communication standards to which the disclosed methods and apparatus may be applied. For example, although specific examples have been given in the context of 3GPP, the principles disclosed herein may also be applied to another wireless communication system, and indeed to any communication system that uses routing rules.

[0148] The method may also be embodied in a set of instructions stored on a computer readable medium which, when loaded into a computer processor, digital signal processor (DSP) or the like, causes the processor to perform the method described above.

[0149] The method and apparatus may be practiced in other specific forms. The method and apparatus should be considered in all respects to be illustrative only and not restrictive. Therefore, the scope of the present invention is indicated by the appended claims rather than the foregoing description. All changes in the equivalent meaning and scope of the claims should be included within their scope.

[0150] The following abbreviations are related to the domain of this document. ML, machine learning. MnS, management service. MDA, management data analysis. MDAS, management data analysis service. MDAF, management data analysis function. NF, network function. NWDAF, network data analysis function. OAM, operations and maintenance. UE, user equipment. MTLF, model training logic function. AnLF, analytical reasoning logic function.

Claims

1. A network data analysis function (NWDAF) in a wireless communication network, the NWDAF comprising a transceiver and a processor, wherein: The transceiver is arranged to receive a request for analysis, the request for analysis comprising a network analysis identifier; The processor is arranged to identify, from the request for analysis, at least one network function from which data is to be collected; The transceiver is further arranged to send a request to a management entity, the request comprising: a request for fault information within the wireless communication network; The transceiver is further arranged to receive fault information from the management entity, the fault information comprising: an identity of a particular network function, a nature of the fault, and a time period during which the fault affects operation of the particular network function; The processor is further arranged to determine whether the accuracy of data collected from the network function is affected by the fault information, and to ignore any collected data from the network function that is affected by the fault; The transceiver is further arranged to, in response to the request for analysis, transmit the analysis and an indication of which analyses are affected by the fault.

2. The NWDAF of claim 1, wherein the request for analysis comprises: A request for a trained machine learning model associated with the network analysis identifier.

3. The NWDAF of claim 2, wherein the processor ignoring any collected data from the network function affected by the fault comprises: The processor determines that the trained machine learning model requires retraining.

4. The NWDAF of claim 1, wherein the request for analysis comprises: a request for a network analysis corresponding to at least one of the network analysis identifiers; The network function identity, and / or target region of the network function for which the data is collected.

5. A NWDAF according to any preceding claim, wherein the request for analysis comprises a condition.

6. A NWDAF according to any preceding claim, wherein the request for fault information is derived from the request for analysis.

7. A NWDAF according to any preceding claim, wherein the indication of which analyses are affected by the failure comprises: An indication of the analysis whose accuracy was affected during a time period in which the fault occurred.

8. A NWDAF according to any preceding claim, wherein the indication of which analyses are affected by the failure comprises: An indication of a time period during which the accuracy of the analysis was affected when the fault occurred.

9. The NWDAF according to any preceding claim, wherein the identity of the specific network function is defined as a target area, wherein the target area is served by the specific network function.

10. A NWDAF according to any preceding claim, wherein the indication of which analyses are affected by the failure comprises: An indication of target areas for a plurality of network functions for which the accuracy of the analysis is affected when the failure occurs.

11. A method in a network data analysis function (NWDAF), wherein the NWDAF is located in a wireless communication network, the method comprising: receiving a request for analysis, the request for analysis comprising a network analysis identifier; identifying, from the request for analysis, at least one network function from which to collect data; sending a request to a management entity, the request comprising: an identity of a specific network function, and a request for fault information within the wireless communication network; receiving fault information from the management entity, the fault information comprising: the identity of the specific network function, a nature of the fault, and a time period during which the fault affects operation of the specific network function; determining whether accuracy of data collected from the network function is affected by the fault information, and ignoring any collected data from the network function that is affected by the fault; In response to the request for analysis, the analysis is sent, along with an indication of which analyses are affected by the failure.

12. The method of claim 11, wherein the request for analysis comprises: A request for a trained machine learning model, the trained machine learning model being trained using training data associated with the network analysis identifier.

13. The method of claim 12, wherein ignoring any collected data from the network function affected by the fault comprises: It is determined that the trained machine learning model needs to be retrained.

14. The method of claim 11, wherein the request for analysis comprises: a request for a network analysis corresponding to at least one of the network analysis identifiers; The network function identity, and / or target region of the network function for which the data is collected.

15. The method of any one of claims 11 to 14, wherein the request for analysis comprises: Provide conditions for analysis. 16 . The method according to claim 11 , wherein the request for fault information is derived from the request for an analysis.

17. The method of any one of claims 11 to 16, wherein the indication of which analyses are affected by the fault comprises: An indication of the analysis whose accuracy was affected during a time period in which the fault occurred.

18. The method of any one of claims 11 to 17, wherein the indication of which analyses are affected by the fault comprises: An indication of a time period during which the accuracy of the analysis was affected when the fault occurred.

19. The method according to any one of claims 11 to 18, wherein the identity of the specific network function is defined as a target area, wherein the target area is served by the specific network function.

20. The method of any one of claims 11 to 19, wherein the indication of which analyses are affected by the fault comprises: An indication of target areas for a plurality of network functions for which the accuracy of the analysis is affected when the failure occurs.

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