Measurement configuration method and communication device

Abstract

A measurement configuration method and communication device are disclosed to ensure the practical application effect of AI services in wireless communication scenarios. In this method, a first node can send a first message to a second node, which requests QoE measurement of the AI ​​service. The QoE measurement of the AI ​​service is performed by a third node, which is the node used to execute the AI ​​service. Therefore, the above technical solution can realize QoE measurement for AI service scenarios, thereby ensuring the practical application effect of AI services in wireless communication scenarios.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically to a measurement configuration method and a communication device. Background Technology

[0002] With the development of artificial intelligence (AI) and machine learning (ML) technologies, more and more applications are leveraging AI / ML models to achieve richer service functions. Further deep integration of AI with wireless networks can realize inherent network intelligence and terminal intelligence, thereby better meeting future new demands and scenarios.

[0003] However, in current wireless communication scenarios, there is a lack of wireless resource management solutions for AI business scenarios, resulting in poor actual application effects of AI business in wireless communication scenarios. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a measurement configuration method and communication device that can ensure the practical application effect of AI services in wireless communication scenarios.

[0005] Firstly, a measurement configuration method is provided. This method can be executed by a first node, or by a component of the first node, such as its processor, chip, or chip system, or by a logic module or software capable of implementing all or part of the first node's functions. The following explanation uses the execution of this method by the first node as an example. The measurement configuration method includes: sending a first message to a second node; the first message requesting Quality of Experience (QoE) measurement of an AI service; the QoE measurement of the AI ​​service being executed by a third node; and the third node being the node used to execute the AI ​​service.

[0006] Based on the above technical solution, in this application, the first node sends a first message to the second node, requesting QoE measurement of the AI ​​service through the first message. Subsequently, the second node can instruct the third node to perform QoE measurement of the AI ​​service. The third node is the node used to execute the AI ​​service. Therefore, the above technical solution can realize QoE measurement for AI service scenarios, so as to adjust / schedule resources used to execute the AI ​​service according to the QoE measurement results of the AI ​​service scenario, thereby ensuring the actual application effect of the AI ​​service in wireless communication scenarios.

[0007] In conjunction with the first aspect mentioned above, in one possible design, the first message includes configuration information and / or measurement metrics for QoE measurement. Thus, the first node can configure the QoE measurement configuration information and / or measurement metrics to the second node while initiating a QoE measurement request, enabling the second node to trigger the third node to actively perform QoE measurement based on this configuration, thereby improving configuration efficiency.

[0008] In conjunction with the first aspect mentioned above, in one possible design, the configuration information for QoE measurement includes at least one of the following: the task identifier of the AI ​​service, the description of the test dataset for the AI ​​service, or the measurement path for the AI ​​service; the measurement metrics include at least one of the following: processing latency, processing accuracy, or processing speed. Based on this possible design, more comprehensive and richer information can be configured for QoE measurement to improve its accuracy.

[0009] In conjunction with the first aspect mentioned above, in one possible design, the third node is a node on the measurement path. For example, the third node could be one or more nodes on the measurement path that execute AI services in a distributed manner. This would trigger each node on the measurement path to perform QoE measurement, improving the accuracy of QoE measurement for AI services.

[0010] In conjunction with the first aspect mentioned above, in one possible design, the description of the test dataset for AI operations includes at least one of the following: the type of the test dataset, the source of the test dataset, the amount of data in the test dataset, or the data precision of the test dataset. The test dataset is the dataset used by the third node to perform QoE measurements. Based on this possible design, the test dataset description can be configured more comprehensively and accurately for the third node, improving the accuracy of QoE measurements performed by the third node.

[0011] In conjunction with the first aspect described above, in one possible design, the first message further includes a measurement identifier and / or the node identifier of the third node; the measurement identifier is used to identify the first message as a message for requesting QoE measurement. Thus, the first node can configure the measurement identifier and / or the node identifier of the third node to the second node while initiating the QoE measurement request, enabling the second node to prepare to learn about the third node based on this configuration and trigger the third node to actively perform QoE measurement, thereby improving configuration efficiency and QoE measurement accuracy.

[0012] In conjunction with the first aspect mentioned above, in one possible design, the first message includes configuration information corresponding to the trigger event; the trigger event is the QoE measurement operation of the candidate node triggered when the measurement index obtained from the QoE measurement on the third node meets the triggering conditions.

[0013] In one possible design, the configuration information corresponding to the triggering event includes at least one of the following: measurement metrics, triggering conditions, candidate nodes, associated operations, or the initiation conditions of associated operations. Thus, the first node can configure the triggering event through the first message, thereby passively triggering QoE measurement via an event-driven mechanism. In this embodiment, QoE measurement and real-time response based on the QoE measurement results can be implemented through events, thereby adjusting the execution plan of AI services in a timely manner. This adapts to changes in node performance within the current communication system, reduces response latency, and improves the actual execution effect of AI services.

[0014] In conjunction with the first aspect mentioned above, in one possible design, the dataset used by the candidate node to perform QoE measurements is the same as the dataset used by the third node to perform QoE measurements. This ensures that the QoE measurement results are not affected by the dataset, facilitating an accurate comparison of the performance of the third node and the candidate node.

[0015] In conjunction with the first aspect mentioned above, in one possible design, there are one or more candidate nodes, each corresponding to a specific association operation. The association operation has one or more activation conditions, including one or more. The association operation can include at least one of the following: diverting a portion of the AI ​​service's data stream to a candidate node; redirecting the entire AI service's data stream to a candidate node; or reducing the data precision of the AI ​​service. This allows for the design of diverting AI services to one or more candidate nodes, thus expanding the applicability of AI services across various scenarios.

[0016] In conjunction with the first aspect mentioned above, in one possible design, the method further includes: receiving a third message from a third node via a second node; the third message includes the QoE measurement results of the AI ​​service. This allows the second node to receive the QoE measurement results of the AI ​​service, enabling adjustments to the resources used to execute the AI ​​service based on these results, thus ensuring the effective application of the AI ​​service.

[0017] In conjunction with the first aspect described above, in one possible design, the first message includes a first identifier and / or first indication information; the first identifier indicates whether the second node is permitted to obtain the QoE measurement results of the AI ​​service; the first indication information indicates the scope of publication of the QoE measurement results of the AI ​​service. This allows the second node to be informed of its ability to obtain the QoE measurement results of the AI ​​service and the scope of publication of the QoE measurement results of the AI ​​service simultaneously with sending the first message, enabling the second node to accurately publish the QoE measurement results of the AI ​​service.

[0018] In conjunction with the first aspect mentioned above, in one possible design, the QoE measurement results of the AI ​​service are sent by the second node to nodes within the publishing range via multicast or broadcast messages.

[0019] In conjunction with the first aspect mentioned above, in one possible design, the first message is a Radio Resource Control (RRC) message, which is transmitted via a radio bearer. This allows the first message to be sent via an RRC message, improving signaling overhead and message delivery reliability.

[0020] In conjunction with the first aspect mentioned above, in one possible design, the first node is the terminal, and the second node is the access network device. Since current QoE measurement schemes are typically used for network optimization and are usually initiated by network-side nodes, it is difficult for terminals to measure the execution status of AI services using current QoE measurement schemes for terminal-oriented AI service scenarios. Therefore, in this embodiment, the terminal can initiate QoE measurement as the first node, allowing the terminal to trigger corresponding QoE measurements based on its own AI service usage needs, thus ensuring the actual application effect of the AI ​​service.

[0021] Secondly, a measurement configuration method is provided. This method can be executed by a second node, or by a component of the second node, such as its processor, chip, or chip system. It can also be implemented by a logic module or software capable of performing all or part of the second node's functions. The following explanation uses the method executed by a second node as an example. The second node can be the node following the first node on the transmission path. The measurement configuration method includes: receiving a first message from the first node; the first message is used to request QoE measurement of an AI service; sending a second message to a third node; the third node is a node used to perform the AI ​​service, and the second message instructs the third node to perform QoE measurement of the AI ​​service.

[0022] In one possible design, the first message includes configuration information and / or measurement metrics for QoE measurement.

[0023] In one possible design, the configuration information for QoE measurement includes at least one of the following: the task identifier of the AI ​​service, the test dataset description of the AI ​​service, or the measurement path of the AI ​​service; the measurement metrics include at least one of the following: processing latency, processing accuracy, or processing speed.

[0024] In one possible design, the third node is a node on the measurement path.

[0025] In one possible design, the test dataset description for the AI ​​business includes at least one of the following: the type of the test dataset, the source of the test dataset, the amount of data in the test dataset, or the data precision of the test dataset. The test dataset is the dataset used by the third node to perform QoE measurements.

[0026] In one possible design, the first message may also include a measurement identifier and / or a node identifier of a third node; the measurement identifier is used to identify the first message as a message for requesting QoE measurement.

[0027] In one possible design, the first message includes configuration information corresponding to the trigger event; the trigger event is the QoE measurement operation of the candidate node triggered when the measurement index obtained from the QoE measurement on the third node meets the triggering conditions.

[0028] In one possible design, the configuration information corresponding to the triggering event includes at least one of the following: measurement metric, triggering condition, candidate node, associated operation, or the start condition of associated operation.

[0029] In one possible design, the dataset used by the candidate node to perform QoE measurements is the same as the dataset used by the third node to perform QoE measurements.

[0030] In one possible design, there are one or more candidate nodes, and each candidate node corresponds to an association operation.

[0031] In one possible design, there are one or more associated operations, and the initiation conditions for associated operations include one or more.

[0032] In one possible design, the association operation includes at least one of the following:

[0033] Distribute a portion of the AI ​​business data stream to candidate nodes;

[0034] Redirect all data streams from AI operations to candidate nodes; or...

[0035] Reduce the data accuracy of AI business.

[0036] In one possible design, the method further includes receiving a third message from a third node; the third message includes the QoE measurement results of the AI ​​business.

[0037] In one possible design, the first message includes a first identifier and / or a first indication; the first identifier is used to indicate whether the second node is allowed to obtain the QoE measurement results of the AI ​​service; the first indication is used to indicate the scope of the release of the QoE measurement results of the AI ​​service.

[0038] In one possible design, the QoE measurement results of the AI ​​business are sent by the second node to nodes within the publishing range via multicast or broadcast messages.

[0039] In one possible design, the first message is a Radio Resource Control (RRC) message, which is transmitted via a radio bearer.

[0040] In one possible design, the first node is the terminal and the second node is the access network device.

[0041] The technical effects of any possible design in the second aspect can be referred to the technical effects of the corresponding or similar designs in the first aspect mentioned above, and will not be repeated here.

[0042] Thirdly, a communication device is provided for implementing various methods. The communication device includes modules, units, or means corresponding to the implementation of the methods, wherein the modules, units, or means can be implemented in hardware, software, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the functions.

[0043] In some possible designs, the communication device may include a processing module and a transceiver module. The processing module can be used to implement the processing functions in any of the above aspects and any possible implementations thereof. The transceiver module may include a receiving module and a transmitting module, respectively used to implement the receiving function and the transmitting function in any of the above aspects and any possible implementations thereof.

[0044] In some possible designs, the transceiver module can consist of transceiver circuits, transceivers, transceivers, or communication interfaces.

[0045] Fourthly, a communication device is provided, comprising: a processor and a memory; the memory being used to store computer instructions that, when executed by the processor, cause the communication device to perform the method described in any of the above aspects and any possible design thereof.

[0046] Fifthly, a communication device is provided, comprising: a processor and a communication interface; the communication interface being used to communicate with a module outside the communication device; the processor being used to execute computer programs or instructions to cause the communication device to perform the methods described in any of the above aspects and any possible designs thereof.

[0047] A sixth aspect provides a communication device comprising: at least one processor; said processor being configured to execute a computer program or instructions stored in a memory to cause the communication device to perform the methods described in any of the foregoing aspects and any possible designs thereof. The memory may be coupled to the processor, or may be independent of the processor.

[0048] In a seventh aspect, a communication device (e.g., a chip or chip system) is provided, the communication device including a processor for implementing the functions involved in any of the above aspects and any possible designs thereof.

[0049] In some possible designs, the communication device includes a memory for storing necessary program instructions and data.

[0050] In some possible designs, when the device is a chip system, it can be composed of chips or contain chips and other discrete components.

[0051] The communication device described in the fifth to seventh aspects may be the first node in the first aspect, or a device included in the first node, such as a chip or chip system; or the communication device may be the second node in the second aspect, or a device included in the second node, such as a chip or chip system.

[0052] Eighthly, a communication system is provided, the communication system including a first node, and may also include modules or units (e.g., chips, chip systems, or circuits) in the first node that perform the methods / operations / steps / actions described in the first aspect, or modules or units that can be used in conjunction with the first node; and / or, the communication system including a second node, and may also include modules or units (e.g., chips, chip systems, or circuits) in the second node that perform the methods / operations / steps / actions described in the second aspect, or modules or units that can be used in conjunction with the second node.

[0053] It is understandable that when the communication device provided by any of the third to eighth aspects is a chip, the sending action / function of the communication device can be understood as outputting information, and the receiving action / function of the communication device can be understood as inputting information.

[0054] A ninth aspect provides a computer-readable storage medium storing a computer program or instructions that, when executed on a communication device, enable the communication device to perform the methods described in any of the foregoing aspects and any possible design thereof.

[0055] In a tenth aspect, a computer program product containing instructions is provided, which, when run on a communication device, enables the communication device to perform the methods described in any of the foregoing aspects and any possible design thereof.

[0056] The technical effects of any of the design methods in aspects three through ten can be found in the technical effects of different design methods in aspects one or two, and will not be repeated here. Attached Figure Description

[0057] Figure 1 A schematic diagram of the structure of a communication system provided in this application;

[0058] Figure 2A schematic diagram of the structure of another communication system provided in this application;

[0059] Figure 3 A schematic diagram illustrating a scenario for performing a reasoning task, as provided in this application;

[0060] Figure 4 A flowchart illustrating a measurement configuration method provided in this application;

[0061] Figure 5 A flowchart illustrating yet another measurement configuration method provided in this application;

[0062] Figure 6 A schematic diagram of an associated operation provided for this application;

[0063] Figure 7 A flowchart illustrating yet another measurement configuration method provided in this application;

[0064] Figures 8-10 A schematic diagram of the communication device provided in this application. Detailed Implementation

[0065] In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between the related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. A and B can be singular or plural.

[0066] In the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0067] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0068] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0069] It is understood that the term "embodiment" used throughout the specification means that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, various embodiments throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It is understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0070] It is understood that in this application, "...when" and "if" both refer to the corresponding processing that will be carried out under certain objective circumstances, and are not limited to a specific time, nor do they require a judgment action to be performed during implementation, nor do they imply any other limitations.

[0071] It is understood that some optional features in the embodiments of this application can be implemented independently in certain scenarios without relying on other features, such as the current solution on which they are based, to solve the corresponding technical problems and achieve the corresponding effects. Alternatively, they can be combined with other features as needed in certain scenarios. Correspondingly, the apparatus given in the embodiments of this application can also implement these features or functions, which will not be elaborated here.

[0072] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, unless otherwise specified or there is a logical conflict, the terminology and / or descriptions between different embodiments are consistent and can be mutually referenced. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0073] To facilitate understanding of the technical solutions of the embodiments of this application, a brief introduction to the relevant technologies of this application is given below.

[0074] With the development of AI / ML technologies, more and more applications are leveraging AI / ML models to achieve richer service functions. Further deep integration of AI with wireless networks can realize intrinsic network intelligence and terminal intelligence, thereby better meeting future new demands and scenarios.

[0075] For example, network-native intelligence can enable communication networks to provide not only traditional communication connectivity services, but also inclusive, real-time, and highly secure computing and AI services. Terminal intelligence can enable the diversification of terminal types, such as terminals for different scenarios like the Internet of Things, connected vehicles, industry, and healthcare, making massive connectivity more flexible.

[0076] In current wireless communication scenarios, adaptive management can be achieved through quality of experience (QoE) measurement. However, current QoE measurements are mainly used for network optimization and are typically initiated by network-side nodes such as the core network (CN) or operation administration and maintenance (OAM) nodes. For example, network-side nodes manage radio resources based on QoE measurement results, ensuring that transmit power, channel allocation, data rate, handover standards, modulation schemes, or error coding schemes are matched as closely as possible to the radio link, thus efficiently utilizing limited time and frequency resources.

[0077] For example, a typical QoE process can be as follows: the CN or OAM sends the QoE configuration to the radio access network (RAN) node, and the RAN node forwards the QoE configuration to the user equipment (UE). Then, the UE performs QoE measurements and generates a QoE measurement report, which is then reported to the RAN node, and subsequently to the relevant nodes via the RAN node.

[0078] However, for AI service scenarios oriented towards terminals, the application layer experience of the terminal related to AI services can also serve as a basis for UE to select a network. The current QoE measurement scheme is not applicable to AI service scenarios, resulting in poor actual application effect of AI services in wireless communication scenarios.

[0079] Based on this, in this application, the first node sends a first message to the second node, requesting QoE measurement of the AI ​​service through the first message. Subsequently, the second node can instruct the third node to perform the QoE measurement of the AI ​​service. The third node is the node used to execute the AI ​​service; therefore, the above technical solution can achieve QoE measurement for AI service scenarios, thereby ensuring the practical application effect of AI services in wireless communication scenarios.

[0080] The technical solutions of this application embodiment can be used in various communication systems, including third-generation partnership project (3GPP) communication systems, such as fourth-generation (4G) systems like Long Term Evolution (LTE), fifth-generation (5G) systems like New Radio (NR), LTE and 5G hybrid networking systems, integrated communication and sensing systems, non-terrestrial networks (NTN), device-to-device (D2D) communication systems, vehicle-to-everything (V2X) communication systems, machine-type communication (MTC) systems, Internet of Things (IoT) systems, satellite communication systems, short-range systems, Global System for Mobile Communications (GSM), Enhanced Data Rate for GSM Evolution (EDGE), Wideband Code Division Multiple Access (WCDMA), and Code Division Multiple Access 2000 systems. The system can be CDMA2000, Time Division-Synchronization Code Division Multiple Access (TD-SCDMA), or other future communication systems. This communication system can also be a non-3GPP communication system; there are no restrictions.

[0081] The communication systems described above are merely illustrative examples, and are not limited to those described herein. The communication systems provided in this application do not impose any limitations on the solutions described herein. This will be explained uniformly here and will not be repeated below.

[0082] Figure 1 This diagram illustrates the structure of a possible, non-limiting communication system. (For example...) Figure 1As shown, the communication system 10 includes a radio access network (RAN) 100 and a core network (CN) 200. RAN 100 includes at least one RAN node (e.g., ...). Figure 1 110a and 110b (collectively referred to as 110) and at least one terminal (such as Figure 1 RAN100, denoted as RAN100, comprises RAN nodes 120a-120j, collectively referred to as RAN120. RAN100 may also include other RAN nodes, such as wireless relay equipment and / or wireless backhaul equipment. Figure 1 (Not shown in the image). Terminal 120 is connected to RAN node 110 wirelessly. RAN node 110 is connected to core network 200 wirelessly or via wired connection. The core network node in core network 200 and RAN node 110 in RAN 100 can be different physical devices, or they can be the same physical device integrating core network logical functions and radio access network logical functions.

[0083] In one possible implementation, a core network node can refer to equipment in the core network 200 that provides service support to terminal 120. The core network node in core network 200 may include at least one of the following: access and mobility management function (AMF) network elements, session management function (SMF) network elements, user plane function (UPF) network elements, policy control function (PCF) network elements, unified data management (UDM) network elements, application function (AF) network elements, network exposure function (NEF) network elements, network slice selection function (NSSF) network elements, or location management function (LMF) network elements, etc. Of course, core network 200 may also include other core network nodes, without limitation.

[0084] The AMF (Agency Flow Management) network element is deployed in the core network 200 to provide mobility management and connectivity management for the network, such as user location updates, user registration with the network, and user handover. The AMF network element can act as an intermediate route between the LMF, SMF, and RAN 100. The SMF network element is mainly responsible for session management in the mobile network, such as session establishment, modification, and release. The UPF (User Plane Function) network element is a user plane function element, mainly responsible for connecting to external networks and processing user packets, such as forwarding and charging. The PCF (Programmable Flow Function) network element is mainly responsible for providing policies to the AMF and SMF, such as Quality of Service (QoS) policies and slice selection policies. The UDM (User DM) network element is used to store user data, such as subscription information and authentication / authorization information. The AF (Agency Flow) network element is responsible for providing services to the 3GPP network. The NEF (Network Flow Function) network element is mainly used to open the capabilities of various network functions and is responsible for converting internal and external information. The LMF network element is a device or component deployed in the core network 200 to provide positioning functions for the terminal 120; for example, the LMF network element can initiate a positioning process to locate a specific terminal.

[0085] In this application, network elements may also be referred to as entities or functional entities. For example, an AMF network element may also be referred to as an AMF entity or an AMF functional entity. In addition, the aforementioned SMF network elements, UPF network elements, PCF network elements, UDM network elements, AF network elements, NEF network elements, and LMF network elements may have other names in future communication systems, and this application does not impose specific limitations on them.

[0086] In one possible implementation, RAN 100 can be a cellular system related to the 3rd Generation Partnership Project (3GPP), such as a 4G, 5G mobile communication system, or a future-oriented evolution system. RAN 100 can also be an open RAN (O-RAN or ORAN), a cloud radio access network (CRAN), an NTN network (such as an NTN supporting pass-through mode and / or regenerative mode, or an NTN supporting eye-viewing mode (earth fixed cell) and / or non-eye-viewing mode (earth moving cell), or a wireless fidelity (WiFi) system. RAN 100 can also be a communication system that integrates two or more of the above systems.

[0087] RAN node 110, sometimes also referred to as access network equipment, RAN entity, or access node, constitutes part of the communication system and assists terminals in achieving wireless access. Multiple RAN nodes 110 in RAN 100 can be of the same type or different types. In some scenarios, the roles of RAN node 110 and terminal 120 are relative, for example... Figure 1 Network element 120i can be a helicopter or a drone, and it can be configured as a mobile base station. For terminals 120j that access RAN 100 through network element 120i, network element 120i is a base station; however, for base station 110a, network element 120i is a terminal. RAN node 110 and terminal 120 are sometimes referred to as communication devices, for example... Figure 1 Network elements 110a and 110b can be understood as communication devices with base station functions, while network elements 120a-120j can be understood as communication devices with terminal functions.

[0088] For RAN node 110, in one possible scenario, RAN node 110 can be a base station, an evolved NodeB (eNodeB, also known as eNB), an access point (AP), a transmission reception point (TRP), a next-generation NodeB (gNB), a next-generation base station in a future mobile communication system, or an access node in a WiFi system, etc. RAN node 110 can also be a macro base station (such as...) Figure 1 110a), micro base stations or indoor stations (such as Figure 1The network equipment can be a relay node or donor node, or a wireless controller in a CRAN scenario. Examples include: satellite base stations, radio network controllers (RNCs), base station controllers (BSCs), base transceiver stations (BTSs), home base stations (e.g., home evolved NodeBs, or home NodeBs, HNBs), relay stations, balloon stations, drone stations, wireless backhaul nodes, or grant nodes (G nodes) in satellite telemetry. It is understood that network equipment can be ground-based or non-ground-based (e.g., satellites, drones, high-altitude communication equipment). Furthermore, the names of network equipment with base station functions may differ in communication systems employing different wireless access technologies; this application does not limit this. Optionally, RAN node 110 can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, in vehicle-to-everything (V2X) technology, the access network equipment can be a roadside unit (RSU). RAN node 110 is also known as the next generation radio access network (NG-RAN) node.

[0089] In another possible scenario, multiple RAN nodes 110 collaborate to assist the terminal in achieving wireless access, with each RAN node 110 implementing a portion of the base station's functions. For example, a RAN node 110 can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radioheads (RRHs).

[0090] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules.

[0091] In one possible scenario, terminal 120 can be a device used to implement wireless communication functions, such as a terminal, a chip or circuit that can be used in the terminal, or an entity associated with the terminal. Specifically, terminal 120 can be user equipment (UE), access terminal, terminal unit, terminal station, mobile station (MS), mobile station, remote station, remote terminal, mobile device, wireless communication equipment, terminal agent or terminal device, subscriber unit, smartphone, wireless data card, tablet computer, wireless modem, laptop computer, machine type communication (MTC) terminal, tag, etc., in a 5G network or a future evolved public land mobile network (PLMN). The access terminal can be a cellular phone, cordless phone, Session Initiation Protocol (SIP) phone, Wireless Local Loop (WLL) station, Personal Digital Assistant (PDA), handset with wireless communication capabilities, computing device or other processing device connected to a wireless modem, in-vehicle device or wearable device, virtual reality (VR) terminal, augmented reality (AR) terminal, wireless terminal in industrial control, wireless terminal in self-driving, wireless terminal in remote medical care, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, or terminal node (T-node) in StarSpark, etc. In one possible implementation, terminal 120 can be mobile or fixed. It is understood that the terminal and the mobile user can be completely independent. All user-related information can be stored in a subscriber identity module (SIM) card, which can be used on the terminal device. The terminal can then interact with network-side devices by sending and / or receiving signals over the air interface.

[0092] The chip or circuit in the terminal includes components inside the terminal, such as at least one of a chip, a central processing unit (CPU), a network processing unit (NPU), and a terminal radio frequency module.

[0093] Entities associated with the terminal include terminal-side servers, computing / processing nodes, computing / processing entities, computing / processing units, and servers such as over-the-top (OTT) servers. OTT refers to various services provided to users by a third party other than the network operator via the operator's network. Examples of OTT services include OTT voice communication services, OTT multimedia services, and OTT data processing services. The terminal interacts with relevant information (e.g., data) through communication with this associated network entity. For example, this associated network entity and the terminal may belong to the same vendor. Since model training, model selection, etc., may not be executed on the terminal but rather on the terminal-side OTT server, the term "terminal" in this embodiment also includes the terminal-side OTT server.

[0094] It should be understood that the terminal in this embodiment may also be referred to as the "UE side" or the "UE part".

[0095] For example, such as Figure 2 As shown, Figure 1 The illustrated system is an exemplary implementation. The communication system may include RAN node 110, terminal 120, and AI network element 130. The AI ​​network element 130 can be deployed as an independent node in the communication system, or it can be an AI module within the aforementioned RAN node 110, terminal 120, and other network elements or devices.

[0096] AI models can be deployed on the AI ​​Network Element 130, providing computing power support for AI services. One or more AI Network Elements 130 can be deployed, and depending on the actual deployment, it can be divided into single-sided model deployment and double-sided model deployment.

[0097] One-sided model deployment refers to the process of completing the entire inference process for an air interface feature, use case, or function by deploying an AI / ML model on the network side (referred to as the network-side AI / ML model) for inference, or by deploying an AI / ML model on the terminal side (referred to as the terminal-side AI / ML model).

[0098] The unilateral model includes a network-side model and a UE-side model. For the network-side model, the terminal can report relevant information to the network side as input data for model inference / training / monitoring / management. For the terminal-side model, the terminal can perform model inference / training / monitoring based on the acquired relevant information and send the output results to the network side.

[0099] Two-sided model deployment refers to deploying AM / ML models on both the terminal and network sides for a given air interface feature / use case / function. In this case, the network-side model and the terminal-side model need to be paired to complete the entire inference process for that air interface feature.

[0100] Taking the deployment of a bilateral model as an example, such as Figure 3 As shown, the communication system includes multiple AI network elements, deployed on the terminal and in the AI ​​node cluster on the network side. Each AI network element in both the terminal and the AI ​​node cluster deploys a sub-model for inference tasks related to AI services. The inference task can be executed by these multiple AI network elements. After generating service data, the terminal outputs the inference result through the sub-model deployed locally on its own AI network element and sends the inference result to the AI ​​node cluster. The AI ​​node cluster sequentially passes through at least one AI network element according to the configured path, obtains the inference result, and then feeds it back to the terminal. In some examples, another sub-model can also be deployed on the terminal's AI network element to obtain the final inference result based on the inference result fed back by the AI ​​node cluster.

[0101] It is understandable that AI network elements can be independent devices, or they can be integrated into the same device to achieve different functions. They can be network components in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., cloud platform). For example, an AI network element can be a server dedicated to computing tasks, or it can be a computing board inside the BBU. Logically, an AI network element can be regarded as an independent network element.

[0102] Understandably, the above Figure 2 This is merely an illustrative diagram and does not constitute a limitation on the applicable scenarios of the technical solutions provided in this application. Those skilled in the art should understand that, in specific implementation processes, Figure 2 The communication system shown may also include more than Figure 2 Showing fewer devices, or, Figure 2 The communication system shown may also include other equipment, which can be determined according to specific needs. Figure 2 The number of devices in the communication system shown is not limited.

[0103] Optional, Figure 2The devices in the process, such as RAN node 110, terminal 120 and AI network element 130, can also be referred to as communication devices. They can be general-purpose devices or special-purpose devices. This application embodiment does not specifically limit them.

[0104] RAN nodes can be devices or components within devices in the aforementioned NG-RAN, such as ng-eNB nodes, gNB nodes, or transmission points (TPs) and transmission and reception points (TRPs) within ng-eNB and gNB nodes, or central units (CUs) integrated into the NG-RAN. RAN nodes can also be network elements with transmission capabilities, such as transmission measurement functions (TMFs). In some embodiments, RAN nodes can also be access nodes in an O-RAN system. A RAN typically consists of a series of modules, such as antennas, RRUs, and BBUs. Traditional RAN architectures define the overall reception and output of a RAN node but do not restrict the transmission and communication between internal modules. O-RAN architectures define the architectural connections and standardized interfaces between various modules within the RAN, allowing the RAN to be decoupled into multiple standard modules, thereby enabling the combination and replacement of modules.

[0105] It should be understood that the AI ​​services involved in the embodiments of this application can be described as tasks (such as AI tasks or ML tasks), functions (such as AI functions or ML functions), features, or algorithms, etc. "Service operations" can also be called "task operations" or "functional operations," such as model training, model delivery, model updating, model inference, and model monitoring. AI services can include functional training, functional updating, functional inference, functional monitoring (or performance monitoring), and functional management. Here, "functionality" can be understood as a function corresponding to artificial intelligence. A model can implement one or more functions, and one or more models can also work together to implement a function.

[0106] It is understood that the system described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0107] The measurement configuration method provided in this application embodiment is described below using the first node as an example. The measurement configuration method provided in this application embodiment is applicable to the communication systems mentioned above, and is also applicable to other communication systems not mentioned. In the following embodiments of this application, the message names, parameter names, or information names between the first node and other nodes are just examples, and may be other names in other embodiments. The method provided in this application does not specifically limit these names.

[0108] It is understood that in the embodiments of this application, each communication device (including the first node or other nodes) may execute some or all of the steps in the embodiments of this application. These steps or operations are merely examples, and the embodiments of this application may also execute other operations or variations thereof. Furthermore, the steps may be executed in different orders as presented in the embodiments of this application, and it is not necessary to execute all the operations in the embodiments of this application.

[0109] It is understood that this application uses the first node and the second node as examples to illustrate the execution of the interaction, but this application does not limit the execution entities of the interaction. For example, the method executed by the first node in this application can also be executed by a module applied to the first node (e.g., a chip, chip system, or processor), or by a logic node, logic module, or software capable of implementing all or part of the functions of the first node. Or, for example, the method executed by the second node in this application can also be executed by a module applied to the second node (e.g., a chip, chip system, or processor), or by a logic node, logic module, or software capable of implementing all or part of the functions of the second node.

[0110] The measurement configuration method provided in the embodiments of this application will be described below. For example... Figure 4 As shown, the measurement configuration method may include:

[0111] Step 401: The first node sends a first message to the second node. Correspondingly, the second node receives the first message from the first node.

[0112] The first message is used to request QoE measurement for AI services.

[0113] For example, the AI ​​service can be an AI service that is currently being executed or a subsequent AI service to be executed. For an AI service that is currently being executed, the current execution status can be obtained by initiating a QoE measurement for that AI service. For a subsequent AI service to be executed, initiating a QoE measurement for that AI service can determine in advance whether the current network can provide the service that meets the demand for the AI ​​service.

[0114] For example, this AI service can be an inference task or a training task for an AI model. The AI ​​model can be a unilaterally deployed model or a bilaterally deployed model. For instance, the AI ​​model can be deployed on the network side, or simultaneously on both the terminal side and the network side. In one example, the AI ​​model can be split into one or more sub-models, each deployed on one or more nodes in the communication system. The inference result data to be processed by the AI ​​service is transmitted sequentially along the corresponding path. The current node receives the inference result data generated by the previous node in the path and generates new inference result data to send to the next node.

[0115] For example, the first message can be a radio resource control (RRC) message, which can be transmitted via a radio bearer. This radio bearer can be a signaling radio bearer (SRB), such as a newly added SRBx, or an existing SRB0, SRB2, SRB3, SRB4, SRB5, etc.

[0116] In some embodiments, the first node can be a terminal, and the second node can be an access network device. Since current QoE measurement schemes are typically used for network optimization and are usually initiated by network-side nodes, it is difficult for terminals to measure the execution status of AI services using current QoE measurement schemes for terminal-oriented AI service scenarios. Therefore, in this embodiment, the terminal can initiate QoE measurement as the first node, allowing the terminal to trigger corresponding QoE measurements based on its own AI service usage requirements, ensuring the actual application effect of the AI ​​service. For example, if the usage requirement of its own AI service is low latency, the first node can trigger QoE measurement for processing latency to determine whether the low latency requirement is met.

[0117] Step 402: The second node sends a second message to the third node. Correspondingly, the third node receives the second message from the second node.

[0118] The third node is used to execute AI services, and the second message is used to instruct the third node to perform QoE measurement of AI services. In other words, the QoE measurement of AI services is performed by the third node.

[0119] For example, the third node is a communication device with AI capabilities, which can be deployed on the terminal side or the network side. For example, the third node can be a terminal, base station, network element, edge computing device, etc. with AI capabilities.

[0120] Based on the above technical solution, in this application, the first node sends a first message to the second node, requesting QoE measurement of the AI ​​service through the first message. Subsequently, the second node can instruct the third node to perform the QoE measurement of the AI ​​service. The third node is the node used to execute the AI ​​service. Therefore, the above technical solution can realize QoE measurement for AI service scenarios, thereby ensuring the practical application effect of AI services in wireless communication scenarios.

[0121] As one possible implementation, combined with Figure 4 ,like Figure 5 As shown, after completing the QoE measurement, the third node can also feed back the QoE measurement results to the decision node for QoE measurement. The decision node for QoE measurement can be the first node, the second node, or other nodes. When the decision node for QoE measurement is the first node, the method further includes step 501. When the decision node for QoE measurement is the second node, the method further includes step 502.

[0122] Step 501: The third node sends a third message to the first node through the second node, and correspondingly, the first node receives the third message from the third node through the second node.

[0123] Step 502: The third node sends a third message to the second node, and correspondingly, the second node receives the third message from the third node.

[0124] The third message includes the QoE measurement results for the AI ​​business.

[0125] In some embodiments, the decision node for QoE measurement can be from the terminal side or the network side. Taking the first node as the terminal and the second node as the access network device as an example, when the decision node is the terminal, the third node can feed back the QoE measurement results to the terminal. When the decision node is the access network device, the third node can feed back the QoE measurement results to the access network device. Furthermore, the decision node can also be a core network element, OAM, or other network-side node. Accordingly, after completing the QoE measurement, the third node can feed back the QoE measurement results to the core network element, OAM, or other network-side nodes.

[0126] As one possible implementation, after obtaining the QoE measurement results, the second node can also publish the QoE measurement results for reference by other nodes that need the AI ​​service.

[0127] In some embodiments, the first message includes a first identifier and / or a first indication information.

[0128] The first identifier is used to indicate whether the second node is allowed to obtain the QoE measurement results of the AI ​​service, and the first indication information is used to indicate the scope of the release of the QoE measurement results of the AI ​​service.

[0129] In some embodiments, the QoE measurement results of the AI ​​service are sent by the second node to nodes within the publishing range via multicast or broadcast messages.

[0130] For example, the second node can send QoE measurement results to terminals within the cell via system messages such as the System Information Block (SIB) and Master Information Block (MIB). The second node can also send QoE measurement results to terminals in a user group via multicast messages. These multicast or broadcast messages can be sent in response to a request from the first node or based on an event. For example, the sending of a multicast or broadcast message is triggered when the QoE measurement results meet preset conditions (such as reaching a preset threshold).

[0131] Furthermore, in this embodiment of the application, QoE measurement can be triggered actively by messages or passively by events.

[0132] The following describes a scheme for triggering proactive measurements via messages:

[0133] As one possible implementation, the first message includes configuration information and / or measurement metrics for QoE measurement. The QoE measurement configuration information is used for the third node to perform QoE measurement operations, and the measurement metrics indicate the metric data to be measured in the QoE measurement. Thus, the first node can initiate a QoE measurement request through this first message, causing the third node to actively perform the QoE measurement.

[0134] In some embodiments, the configuration information for QoE measurement includes at least one of the following: the task identifier of the AI ​​service, the test dataset description of the AI ​​service, or the measurement path of the AI ​​service.

[0135] The test dataset description for an AI service refers to the parameter information used to describe the test dataset. The measurement path for an AI service characterizes the logical order of the nodes involved. In one example, the measurement path for an AI service may include one or more paths, and the terminal can implement the AI ​​service through any one of them. For instance, a first node can request third nodes on multiple test paths to perform QoE measurements. Based on the QoE measurement results obtained from the third nodes on multiple test paths, the optimal path is determined from the multiple test paths. Thus, the first node can update the execution path of the AI ​​service using the optimal path to ensure the actual application effect of the AI ​​service in wireless communication scenarios. As another example, a first node can request a third node on a single test path to perform QoE measurements. Based on the QoE measurement results obtained from the third node on that test path, the first node can determine whether the current path meets the requirements of the AI ​​service.

[0136] For example, the third node can be a node on the measurement path. For instance, the third node can be the first node on the measurement path, the last node on the measurement path, or an intermediate node on the measurement path. Furthermore, there can be one or more third nodes; this application does not limit this.

[0137] In some embodiments, the first message further includes a measurement identifier and / or a node identifier of a third node. The measurement identifier identifies the first message as a message requesting QoE measurement. Thus, the second node can identify, based on the measurement identifier, that the first message is a request message for QoE measurement of AI services, and / or determine the third node that needs to perform the QoE measurement. For example, the first message may be an RRC message, which may include the aforementioned measurement identifier, the node identifier of the third node, and a container. This container carries relevant information about the QoE measurement, such as QoE measurement configuration information and / or measurement metrics.

[0138] For example, the description of the test dataset for AI operations includes at least one of the following: the type of the test dataset, the source of the test dataset, the amount of data in the test dataset, or the data precision of the test dataset. The test dataset is the dataset used by the third node to perform QoE measurements. For example, the type of the test dataset can be text, image, audio, video, etc. The source of the test dataset can characterize the acquisition channel of the test dataset. The data precision of the test dataset refers to the numerical calculation precision of the data in the test dataset, usually referring to the number of bits of floating-point numbers (such as 32-bit, 16-bit, 8-bit). The higher the data precision, the higher the accuracy of the data results, and the lower the corresponding processing speed. Therefore, the appropriate data precision can be configured according to the actual needs of the AI ​​operations to measure the relevant QoE measurement results at that data precision, so as to better reflect the actual business execution.

[0139] In some embodiments, the measurement metrics include at least one of processing latency, processing accuracy, or processing rate.

[0140] For example, processing latency can be average processing latency, maximum processing latency, or the latency of generating the first inference result data. For inference tasks in AI applications, this processing latency can also be called inference latency. Processing accuracy can be the accuracy of the processed data. For example, in a classification task, if the model correctly classifies 95 out of 100 data points, the accuracy is 95%. Processing accuracy can also be expressed as precision, recall, F1 score, etc. Processing rate refers to the amount of data processed per unit time, also known as throughput. The first node can specify the corresponding measurement indicators according to actual needs, enabling QoE measurement of these indicators.

[0141] The following describes a scheme for triggering passive measurement through events:

[0142] In another possible embodiment, the first message includes configuration information corresponding to the trigger event. This trigger event is triggered when the QoE measurement metrics obtained from the QoE measurement performed by the third node meet the triggering conditions, thereby triggering the QoE measurement operation of the candidate node. In this way, the first node can configure the trigger event through the first message, thus passively triggering QoE measurement via an event-driven mechanism.

[0143] In some embodiments, the configuration information corresponding to the triggering event includes at least one of the following: measurement metrics, triggering conditions, candidate nodes, associated operations, or the activation conditions for associated operations. Measurement metrics can be referred to the above description and will not be repeated here. Candidate nodes can be nodes with the same AI capabilities as the third node; that is, candidate nodes can fully or partially replace the third node in performing AI tasks.

[0144] In one example, the dataset used by the candidate node to perform the QoE measurement is the same as that used by the third node. For instance, the dataset used by the candidate node to perform the QoE measurement can be copied from the third node. This ensures that the QoE measurement results are not affected by the dataset, facilitating an accurate comparison of the performance of the third node and the candidate node.

[0145] For example, triggering conditions can be set based on measurement metrics, such as a node's processing latency being greater than or equal to a first latency threshold, a node's processing accuracy being less than a first accuracy threshold, or a node's processing rate being greater than or equal to a first rate threshold. For instance, a third node can periodically perform QoE measurements, and if the triggering conditions are met, it can trigger the QoE measurement operation of the candidate node.

[0146] In some embodiments, there are one or more candidate nodes, and each candidate node corresponds to an association operation. The association operation corresponding to each candidate node can be a different association operation or the same association operation.

[0147] In some embodiments, the associated operations include one or more, and the triggering conditions for the associated operations include one or more. There is a mapping relationship between the associated operations and the QoE measurement results; different QoE measurement results can trigger different triggering conditions, and different triggering conditions can correspond to different associated operations.

[0148] In some embodiments, the association operation includes at least one of the following:

[0149] Distribute a portion of the AI ​​business data stream to candidate nodes;

[0150] Redirect all data streams from AI operations to candidate nodes; or...

[0151] Reduce the data accuracy of AI business.

[0152] For ease of description, diverting a portion of the AI ​​business's data stream to candidate nodes can be called data diversion, redirecting the entire AI business's data stream to candidate nodes can be called data redirection, and reducing the data precision of the AI ​​business can be called data processing.

[0153] For example, taking processing latency as the measurement metric, the first activation condition is that the processing latency measured by the candidate node is less than the second latency threshold, where the second latency threshold can be less than the first latency threshold. When the first activation condition is met, it indicates that the candidate node is in a better state. At this time, the candidate node can process part of the data that was originally processed by the third node. The third node can divert part of the AI ​​business data stream to the candidate node. The associated operation corresponding to the first activation condition is to divert part of the AI ​​business data stream to the candidate node.

[0154] The second start condition is that the processing latency measured by the candidate node is less than the third latency threshold, where the third latency threshold can be less than the second latency threshold. When the second start condition is met, it means that the candidate node can process all the data that was originally processed by the third node. The third node can redirect all data streams of the AI ​​business to the candidate node. At this time, the associated operation corresponding to the second start condition is to redirect all data streams of the AI ​​business to the candidate node.

[0155] The third activation condition is that the processing latency measured by the candidate node is greater than or equal to the fourth latency threshold, where the fourth latency threshold can be greater than the second latency threshold. When the third activation condition is met, it means that the current situation of the candidate node is poor and it is difficult to process the data that was originally processed by the third node. At this time, the associated operation corresponding to the third activation condition is to reduce the data accuracy of the AI ​​business in order to reduce the processing latency.

[0156] For example, such as Figure 6 As shown, the associated operations are mainly divided into data splitting, data redirection, and data processing. Data splitting refers to diverting a portion of the data stream originally processed by node 1 to node 2 for processing, with the final data from both nodes 1 and 2 being fed back to the terminal. Data redirection means that the entire data stream originally processed by node 1 is redirected to node 2, and the terminal sends data to node 2 instead. Data processing involves adjusting the data in the data stream (e.g., data precision), and node 1 processes the adjusted data accordingly.

[0157] For example, the following describes an event-based QoE measurement scenario using a terminal, a trigger node, and a candidate node as examples. The terminal can be the first node mentioned above, and the trigger node can be the third node mentioned above. Figure 7 As shown, the measurement configuration method includes the following steps:

[0158] Step 701: The terminal sends the QoE measurement configuration to the trigger node and candidate nodes. Correspondingly, the trigger node and candidate nodes receive the QoE measurement configuration from the terminal.

[0159] For example, the terminal can send QoE measurement configurations to the triggering node and candidate nodes through the access network device. These QoE measurement configurations may include configuration information corresponding to the triggering event described in the above embodiments.

[0160] Step 702: Trigger the node monitoring (monitor) event.

[0161] For example, the trigger node can periodically monitor whether the current measurement indicator meets the trigger condition. If the trigger condition is met, step 703 is executed.

[0162] Step 703: The triggering node sends a trigger signal to the candidate node. Correspondingly, the candidate node receives the trigger signal from the triggering node.

[0163] Step 704: Perform QoE measurement on candidate nodes.

[0164] For example, if the measurement indicators obtained from the candidate node meet the startup conditions, step 705 is executed.

[0165] Step 705: The candidate node sends an indication message to the triggering node.

[0166] For example, the indication information is used to indicate that the measurement indicators obtained by the candidate node meet the activation conditions, and the triggering node can respond to the indication information and execute step 706.

[0167] Step 706: Trigger the node to execute the associated operation corresponding to the startup condition.

[0168] Based on the above technical solution, in this embodiment of the application, QoE measurement and real-time response based on QoE measurement results can be achieved through events, thereby adjusting the execution plan of AI services in a timely manner, adapting to changes in node performance in the current communication system, reducing response latency, and improving the actual execution effect of AI services.

[0169] The method provided in this application has been described above. In addition, this application also provides a communication device for implementing the functions described in the above method embodiments.

[0170] It is understood that the first node in the above embodiments, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function, such as communication devices. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] This application embodiment can divide the communication device into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. The module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0172] Figure 8 A schematic diagram of a communication device 80 is shown. The communication device 80 includes a processing module 801 and a transceiver module 802. This communication device 80 can be used to implement the functions of the aforementioned first node or second node.

[0173] In some embodiments, the communication device 80 may further include a storage module ( Figure 8 (Not shown in the image) is used to store program instructions and data.

[0174] In some embodiments, the transceiver module 802, also referred to as a transceiver unit, is used to implement sending and / or receiving functions. The transceiver module 802 may consist of a transceiver circuit, a transceiver, a transceiver unit, or a communication interface.

[0175] In some embodiments, the transceiver module 802 may include a receiving module and a sending module, respectively configured to perform the receiving and sending steps in the above method embodiments, and / or other processes to support the technology described herein; the processing module 801 may be configured to perform the processing steps in the above method embodiments, and / or other processes to support the technology described herein.

[0176] When the communication device 80 is used to implement the function of the first node:

[0177] The transceiver module 802 is used to send a first message to the second node; the first message is used to request the quality of experience (QoE) measurement of the artificial intelligence (AI) service; the QoE measurement of the AI ​​service is performed by the third node; the third node is a node used to perform the AI ​​service.

[0178] In one possible design, the first message includes configuration information and / or measurement metrics for QoE measurement.

[0179] In one possible design, the configuration information for QoE measurement includes at least one of the following: the task identifier of the AI ​​service, the test dataset description of the AI ​​service, or the measurement path of the AI ​​service; the measurement metrics include at least one of the following: processing latency, processing accuracy, or processing speed.

[0180] In one possible design, the third node is a node on the measurement path.

[0181] In one possible design, the test dataset description for the AI ​​business includes at least one of the following: the type of the test dataset, the source of the test dataset, the amount of data in the test dataset, or the data precision of the test dataset. The test dataset is the dataset used by the third node to perform QoE measurements.

[0182] In one possible design, the first message may also include a measurement identifier and / or a node identifier of a third node; the measurement identifier is used to identify the first message as a message for requesting QoE measurement.

[0183] In one possible design, the first message includes configuration information corresponding to the trigger event; the trigger event is the QoE measurement operation of the candidate node triggered when the measurement index obtained from the QoE measurement on the third node meets the triggering conditions.

[0184] In one possible design, the configuration information corresponding to the triggering event includes at least one of the following: measurement metric, triggering condition, candidate node, associated operation, or the start condition of associated operation.

[0185] In one possible design, the dataset used by the candidate node to perform QoE measurements is the same as the dataset used by the third node to perform QoE measurements.

[0186] In one possible design, there are one or more candidate nodes, and each candidate node corresponds to an association operation.

[0187] In one possible design, there are one or more associated operations, and the initiation conditions for associated operations include one or more.

[0188] In one possible design, the association operation includes at least one of the following:

[0189] Distribute a portion of the AI ​​business data stream to candidate nodes;

[0190] Redirect all data streams from AI operations to candidate nodes; or...

[0191] Reduce the data accuracy of AI business.

[0192] In one possible design, the transceiver module 802 is used to receive a third message from a third node via a second node; the third message includes the QoE measurement results of the AI ​​service.

[0193] In one possible design, the first message includes a first identifier and / or a first indication; the first identifier is used to indicate whether the second node is allowed to obtain the QoE measurement results of the AI ​​service; the first indication is used to indicate the scope of the release of the QoE measurement results of the AI ​​service.

[0194] In one possible design, the QoE measurement results of the AI ​​business are sent by the second node to nodes within the publishing range via multicast or broadcast messages.

[0195] In one possible design, the first message is a Radio Resource Control (RRC) message, which is transmitted via a radio bearer.

[0196] In one possible design, the first node is the terminal and the second node is the access network device.

[0197] When the communication device 80 is the second node, that is, when the communication device 80 is used to implement the function of the second node:

[0198] The transceiver module 802 is used to receive a first message from the first node; the first message is used to request the quality of experience (QoE) measurement of the artificial intelligence (AI) service; the transceiver module 802 is also used to send a second message to the third node; the third node is a node used to perform AI services, and the second message is used to instruct the third node to perform QoE measurement of the AI ​​service.

[0199] In one possible design, the first message includes configuration information and / or measurement metrics for QoE measurement.

[0200] In one possible design, the configuration information for QoE measurement includes at least one of the following: the task identifier of the AI ​​service, the test dataset description of the AI ​​service, or the measurement path of the AI ​​service; the measurement metrics include at least one of the following: processing latency, processing accuracy, or processing speed.

[0201] In one possible design, the third node is a node on the measurement path.

[0202] In one possible design, the test dataset description for the AI ​​business includes at least one of the following: the type of the test dataset, the source of the test dataset, the amount of data in the test dataset, or the data precision of the test dataset. The test dataset is the dataset used by the third node to perform QoE measurements.

[0203] In one possible design, the first message may also include a measurement identifier and / or a node identifier of a third node; the measurement identifier is used to identify the first message as a message for requesting QoE measurement.

[0204] In one possible design, the first message includes configuration information corresponding to the trigger event; the trigger event is the QoE measurement operation of the candidate node triggered when the measurement index obtained from the QoE measurement on the third node meets the triggering conditions.

[0205] In one possible design, the configuration information corresponding to the triggering event includes at least one of the following: measurement metric, triggering condition, candidate node, associated operation, or the start condition of associated operation.

[0206] In one possible design, the dataset used by the candidate node to perform QoE measurements is the same as the dataset used by the third node to perform QoE measurements.

[0207] In one possible design, there are one or more candidate nodes, and each candidate node corresponds to an association operation.

[0208] In one possible design, there are one or more associated operations, and the initiation conditions for associated operations include one or more.

[0209] In one possible design, the association operation includes at least one of the following:

[0210] Distribute a portion of the AI ​​business data stream to candidate nodes;

[0211] Redirect all data streams from AI operations to candidate nodes; or...

[0212] Reduce the data accuracy of AI business.

[0213] In one possible design, the transceiver module 802 is used to receive a third message from a third node; the third message includes the QoE measurement results of the AI ​​service.

[0214] In one possible design, the first message includes a first identifier and / or a first indication; the first identifier is used to indicate whether the second node is allowed to obtain the QoE measurement results of the AI ​​service; the first indication is used to indicate the scope of the release of the QoE measurement results of the AI ​​service.

[0215] In one possible design, the QoE measurement results of the AI ​​business are sent by the second node to nodes within the publishing range via multicast or broadcast messages.

[0216] In one possible design, the first message is a Radio Resource Control (RRC) message, which is transmitted via a radio bearer.

[0217] In one possible design, the first node is the terminal and the second node is the access network device.

[0218] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0219] In this application, the communication device 80 can be presented in an integrated manner, divided into various functional modules. Here, "module" can refer to an application-specific integrated circuit (ASIC), a circuit, a processor and memory that executes one or more software or firmware programs, integrated logic circuits, and / or other devices that can provide the above functions.

[0220] In some embodiments, when Figure 8When the communication device 80 is a chip or chip system, the function / implementation process of the transceiver module 802 can be implemented through the input / output interface (or communication interface) of the chip or chip system, and the function / implementation process of the processing module 801 can be implemented through the processor (or processing circuit) of the chip or chip system.

[0221] Since the communication device 80 provided in this embodiment can execute the above method, the technical effects it can achieve can be referred to the above method embodiment, and will not be repeated here.

[0222] As one possible product form, the first node described in the embodiments of this application can be implemented using the following: one or more field programmable gate arrays (FPGAs), programmable logic devices (PLDs), controllers, state machines, gate logic, discrete hardware components, any other suitable circuits, or any combination of circuits capable of performing the various functions described throughout this application.

[0223] As another possible product form, the first node described in this application embodiment can be implemented using a general bus architecture. For ease of explanation, see [link to documentation]. Figure 9 , Figure 9 This is a schematic diagram of the structure of a communication device 900 provided in an embodiment of this application. The communication device 900 includes a processor 901 and a transceiver 902. The communication device 900 can be a first node, or a chip or chip system therein. Figure 9 Only the main components of the communication device 900 are shown. In addition to the processor 901 and transceiver 902, the communication device may further include a memory 903 and input / output devices. Figure 9 (Not indicated).

[0224] Optionally, the processor 901 is mainly used to process communication protocols and communication data, control the entire communication device, execute software programs, and process the data of the software programs, thereby implementing the methods provided in the above-described method embodiments. The memory 903 is mainly used to store software programs and data. The transceiver 902 may include radio frequency (RF) circuitry and an antenna. The RF circuitry is mainly used for converting baseband signals to RF signals and processing RF signals. The antenna is mainly used for transmitting and receiving RF signals in the form of electromagnetic waves. Input / output devices, such as touchscreens, displays, and keyboards, are mainly used to receive user input data and output data to the user.

[0225] Optionally, the processor 901, transceiver 902, and memory 903 can be connected via a communication bus.

[0226] When the communication device is powered on, the processor 901 can read the software program in the memory 903, execute the instructions of the software program, and process the data of the software program. When data needs to be transmitted wirelessly, the processor 901 performs baseband processing on the data to be transmitted and outputs the baseband signal to the radio frequency (RF) circuit. The RF circuit processes the baseband signal and transmits the RF signal outward in the form of electromagnetic waves through the antenna. When data is sent to the communication device, the RF circuit receives the RF signal through the antenna, converts the RF signal into a baseband signal, and outputs the baseband signal to the processor 901. The processor 901 converts the baseband signal into data and processes the data.

[0227] In another implementation, the radio frequency circuitry and antenna can be set up independently of the processor performing baseband processing. For example, in a distributed scenario, the radio frequency circuitry and antenna can be arranged remotely, independent of the communication device.

[0228] In some embodiments, those skilled in the art will recognize that the above-described communication device 80 can be implemented in hardware using... Figure 9 The communication device shown is in the form of 900.

[0229] As an example, Figure 8 The function / implementation process of the processing module 801 can be achieved through... Figure 9 The processor 901 in the communication device 900 shown calls computer execution instructions stored in the memory 903 to implement the function. Figure 8 The function / implementation process of the transceiver module 802 in the middle can be obtained through Figure 9 This is achieved through the transceiver 902 in the communication device 900 shown.

[0230] As another possible product form, the first node in this application can adopt... Figure 10 The shown composition structure, or including Figure 10 The components shown. Figure 10 This application provides a schematic diagram of the composition of a communication device 1000, which may be a first node or a chip or system-on-a-chip in the first node.

[0231] like Figure 10 As shown, the communication device 1000 includes at least one processor 1001 and at least one communication interface. Figure 10 (This is merely an example illustration, using a communication interface 1004 and a processor 1001 as examples.) Optionally, the communication device 1000 may also include a communication bus 1002 and a memory 1003.

[0232] Processor 1001 can be a general-purpose central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a PLD, or any combination thereof. Processor 1001 can also be other devices with processing functions, such as circuits, devices, or software modules, without limitation.

[0233] The communication bus 1002 is used to connect different components in the communication device 1000, enabling communication between them. The communication bus 1002 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0234] Communication interface 1004 is used for communicating with other devices or communication networks. For example, communication interface 1004 can be a module, circuit, transceiver, or any device capable of communication. Optionally, communication interface 1004 can also be an input / output interface located within processor 1001, used to implement signal input and signal output for the processor.

[0235] The memory 1003 may be a device with storage function, used to store instructions and / or data. The instructions may be computer programs.

[0236] For example, the memory 1003 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and / or instructions; it may also be a random access memory (RAM) or other type of dynamic storage device capable of storing information and / or instructions; it may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

[0237] It should be noted that the memory 1003 can exist independently of the processor 1001, or it can be integrated with the processor 1001. The memory 1003 can be located inside or outside the communication device 1000, without limitation. The processor 1001 can be used to execute the instructions stored in the memory 1003 to implement the methods provided in the following embodiments of this application.

[0238] Optionally, the processor 1001 and / or memory 1003 may include an artificial intelligence (AI) module, which is used to implement AI-related functions. The AI ​​module can be implemented through software, hardware, or a combination of both. For example, the AI ​​module may include a radio network intelligent controller (RIC) module. For example, the AI ​​module can be a near real-time RIC or a non-real-time RIC.

[0239] As an optional implementation, the communication device 1000 may further include an output device 1005 and an input device 1006. The output device 1005 communicates with the processor 1001 and can display information in various ways. For example, the output device 1005 may be a liquid crystal display (LCD), a light-emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector, etc. The input device 1006 communicates with the processor 1001 and can receive user input in various ways. For example, the input device 1006 may be a mouse, keyboard, touchscreen device, or sensing device, etc.

[0240] In some embodiments, the hardware implementation will be apparent to those skilled in the art as described above. Figure 8 The communication device 80 shown can be adopted Figure 10 The communication device 1000 shown is in the form of this device.

[0241] As an example, Figure 8 The function / implementation process of the processing module 801 can be achieved through... Figure 10 The processor 1001 in the communication device 1000 shown calls computer execution instructions stored in the memory 1003 to implement the function. Figure 8 The function / implementation process of the transceiver module 802 in the middle can be obtained through Figure 10 This is achieved through the communication interface 1004 in the communication device 1000 shown.

[0242] Figure 10The structure shown does not constitute a specific limitation on the first node. For example, in other embodiments of this application, the first node may include more or fewer components than shown, or combine some components, or split some components, or have different component arrangements. The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0243] In some embodiments, this application also provides a communication device, which includes a processor for implementing the methods in any of the above method embodiments.

[0244] As one possible implementation, the communication device also includes a memory. This memory stores necessary computer programs and data. The computer program may include instructions, which a processor can invoke to instruct the communication device to execute the methods described in any of the above method embodiments. Alternatively, the memory may not be present in the communication device.

[0245] As another possible implementation, the communication device also includes an interface circuit, which is a code / data read / write interface circuit, used to receive computer execution instructions (which are stored in memory and may be read directly from memory or may be transmitted through other devices) and transmit them to the processor.

[0246] As another possible implementation, the communication device also includes a communication interface for communicating with modules outside the communication device.

[0247] It is understood that the communication device can be a chip or a chip system. When the communication device is a chip system, it can be composed of chips or may include chips and other discrete devices. This application does not specifically limit this.

[0248] This application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a computer, implements the functions of any of the above-described method embodiments.

[0249] This application also provides a computer program product that, when executed by a computer, implements the functions of any of the above method embodiments.

[0250] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0251] It is understood that the systems, apparatuses, and methods described in this application can also be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0252] The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. The components shown as units may or may not be physical units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0253] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0254] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive (SSD)). In embodiments of this application, the computer may include the aforementioned apparatus.

[0255] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0256] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of the claims and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A measurement configuration method, characterized in that, Applied to the first node, the method includes: A first message is sent to the second node; the first message is used to request the Quality of Experience (QoE) measurement of the AI ​​service; the QoE measurement of the AI ​​service is performed by the third node; the third node is a node used to perform the AI ​​service.

2. The method according to claim 1, characterized in that, The first message includes configuration information and / or measurement metrics for QoE measurement.

3. The method according to claim 2, characterized in that, The configuration information for the QoE measurement includes at least one of the following: the task identifier of the AI ​​service, the test dataset description of the AI ​​service, or the measurement path of the AI ​​service. The measurement metrics include at least one of processing latency, processing accuracy, or processing rate.

4. The method according to claim 3, characterized in that, The third node is a node on the measurement path.

5. The method according to any one of claims 2-4, characterized in that, The first message also includes a measurement identifier and / or the node identifier of the third node; the measurement identifier is used to identify the first message as a message for requesting QoE measurement.

6. The method according to any one of claims 1-5, characterized in that, The first message includes configuration information corresponding to the trigger event; the trigger event is to trigger the QoE measurement operation of the candidate node when the measurement index obtained by the QoE measurement of the third node meets the trigger condition.

7. The method according to claim 6, characterized in that, The configuration information corresponding to the triggering event includes at least one of the following: measurement index, triggering condition, candidate node, associated operation, or the start condition of associated operation.

8. The method according to claim 7, characterized in that, The dataset used by the candidate node to perform QoE measurement is the same as the dataset used by the third node to perform QoE measurement.

9. The method according to claim 7 or 8, characterized in that, There are one or more candidate nodes, and each candidate node corresponds to an association operation.

10. The method according to any one of claims 7-9, characterized in that, The associated operation may be one or more, and the initiation conditions for the associated operation may include one or more.

11. The method according to any one of claims 7-10, characterized in that, The associated operation includes at least one of the following: A portion of the data stream from the AI ​​service is diverted to the candidate node; Redirect all data streams from the AI ​​service to the candidate node; or... The data accuracy of the AI ​​services was reduced.

12. The method according to any one of claims 1-11, characterized in that, The method further includes: The second node receives a third message from the third node; the third message includes the QoE measurement results of the AI ​​service.

13. The method according to claim 12, characterized in that, The first message includes a first identifier and / or a first indication; the first identifier is used to indicate whether the second node is allowed to obtain the QoE measurement results of the AI ​​service; the first indication is used to indicate the scope of publication of the QoE measurement results of the AI ​​service.

14. The method according to claim 13, characterized in that, The QoE measurement results of the AI ​​service are sent by the second node to the nodes within the publishing range via multicast or broadcast messages.

15. A measurement configuration method, characterized in that, Applied to the second node, the method includes: Receive the first message from the first node; the first message is used to request the Quality of Experience (QoE) measurement of the artificial intelligence (AI) service; A second message is sent to a third node; the third node is a node used to execute the AI ​​service, and the second message is used to instruct the third node to perform QoE measurement of the AI ​​service.

16. The method according to claim 15, characterized in that, The method further includes: Receive a third message from the third node; the third message includes the QoE measurement results of the AI ​​service.

17. A communication device, characterized in that, include: A functional unit for performing the method as described in any one of claims 1-14, or a functional unit for performing the method as described in claim 15 or 16; wherein the action performed by the functional unit is implemented by hardware or by hardware executing corresponding software.

18. A communication device, characterized in that, The communication device includes a processor; the processor is configured to run a computer program or instructions to cause the communication device to perform the method as described in any one of claims 1-14, or to perform the method as described in claim 15 or 16.

19. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores computer instructions or programs that, when executed on a computer, cause the method as described in any one of claims 1-14 or the method as described in claim 15 or 16 to be performed.

20. A computer program product, characterized in that, The computer program product includes computer instructions; when some or all of the computer instructions are run on a computer, they cause the method as claimed in any one of claims 1-14 or the method as claimed in claim 15 or 16 to be performed.

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