Performance monitoring method and apparatus, and communication system

Abstract

Embodiments of the present application provide a performance monitoring method and apparatus, and a communication system. The performance monitoring apparatus is applied to a terminal device, wherein the apparatus comprises a first processor, and the first processor is configured to: on the basis of the measurement of a reference signal within a first time window, use an artificial intelligence function or model to obtain predicted channel state information within a second time window, the second time window following the first time window, the first time window comprising one or more time instances, and the second time window comprising one or more time instances; and monitor the performance of the artificial intelligence function or model on the basis of the measurement of a reference signal within the second time window and the predicted channel state information within the second time window.

Description

Method and apparatus for monitoring performance, communication system TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of communication technology. BACKGROUND

[0002] In New Radio Release 18 (NR Rel-18), Artificial Intelligence or Machine Learning (AI / ML) over the air interface is studied. AI / ML can be used for the following use cases: Channel State Information (CSI) feedback enhancement, beam management, positioning enhancement. CSI feedback enhancement can include CSI prediction, CSI compression; beam management can include spatial beam prediction (BM case-1), temporal beam prediction (BM case-2); positioning enhancement can include direct positioning, AI / ML assisted positioning.

[0003] Among them, beam management includes: spatial domain beam prediction; temporal beam prediction. The network device can configure a set (for example, set B) of beams (i.e., reference signals) for measurement for the terminal device, and the measurement of set B is used as the input of the AI / ML function / model. The network device can also configure a set (for example, set A) of beams (i.e., reference signals) for prediction for the terminal device, for example, set A can be used for inference.

[0004] In some sub-use cases, a bilateral model can be used, i.e., the AI / ML function or model is at the terminal device side and at the network device side. In other sub-use cases, a unilateral model can be used, i.e., the AI / ML function or model is at the terminal device side or at the network device side. For beam management, the AI / ML model can be at the terminal device side and / or at the network device side.

[0005] In CSI prediction, the AI / ML function or model can be configured at the terminal device side, the terminal device can measure the reference signal on one or more time instances of the observation window, and predict the CSI on one or more time instances in the future prediction window.

[0006] It should be noted that the above introduction to the technical background is only for the convenience of clearly and completely describing the technical solutions of the present application, and for the understanding of those skilled in the art. The above technical solutions cannot be considered as known to those skilled in the art only because they are described in the background section of the present application.

[0007] SUMMARY

[0008] In a scenario where CSI prediction is performed by using an AI / ML function or model, a terminal device can be configured to perform performance monitoring of the AI / ML function or model to check the performance of the AI / ML function or model.

[0009] The inventors have found that the operations related to reference signals need to be further clarified when the terminal device performs performance monitoring of the AI / ML function or model.

[0010] To address at least one of the above issues, embodiments of the present disclosure provide a method and apparatus for monitoring performance, and a communication system, in which a terminal device can perform performance monitoring of an artificial intelligence function or model using reference signals at one or more time instances within a prediction window, so that the terminal device and a network device can have a unified understanding of the operations related to reference signals in the performance monitoring process, and thus facilitate accurate performance monitoring.

[0011] According to an aspect of embodiments of the present disclosure, there is provided an apparatus for monitoring performance, applied to a terminal device, wherein the apparatus comprises a first processor configured to:

[0012] obtain, using an artificial intelligence function or model, predicted channel state information (CSI) within a second time window according to measurements of reference signals within a first time window, the second time window being after the first time window, the first time window comprising one or more time instances, and the second time window comprising one or more time instances; and

[0013] monitor performance of the artificial intelligence function or model according to measurements of reference signals within the second time window and the predicted channel state information within the second time window.

[0014] According to another aspect of embodiments of the present disclosure, there is provided an apparatus for monitoring performance, applied to a network device, wherein the apparatus comprises:

[0015] a second transmitter configured to transmit, to a terminal device, reference signals within a first time window; and

[0016] a second receiver configured to receive, from the terminal device, predicted channel state information (CSI) within a second time window obtained using an artificial intelligence model or model, the second time window being after the first time window, the first time window comprising one or more time instances, and the second time window comprising one or more time instances,

[0017] The second receiver also receives a performance metric of the artificial intelligence function or model sent by the terminal device.

[0018] One of the beneficial effects of the embodiments of the present application is that the terminal device can use the reference signals of one or more time instances in the prediction window to perform performance monitoring on the artificial intelligence function or model, thereby the terminal device and the network device can have a unified understanding of the operations related to the reference signals in the performance monitoring process, and facilitate accurate performance monitoring.

[0019] Specific embodiments of the application are disclosed herein, and represented in the accompanying drawings, illustrating the principles of the application in a manner that can be employed by those skilled in the art. It is understood that the embodiments of the application are not limited in scope to the specific embodiments disclosed. Embodiments of the application encompass many changes, modifications, and equivalents within the spirit and scope of the appended claims.

[0020] Features described and / or illustrated with respect to one implementation can be used in the same or similar manner in one or more other implementations, in combination with or in place of features in other implementations, and / or in combination with or in place of one or more additional features.

[0021] It should be emphasized that the term comprises / comprising, when used in this specification, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof. BRIEF DESCRIPTION OF DRAWINGS

[0022] Elements and features of the embodiments of the application described in one or more figures or implementations can be combined with elements and features illustrated in one or more other figures or implementations. Additionally, in the drawings, like reference numerals designate corresponding parts throughout the several views, and can be used to designate like components in more than one implementation.

[0023] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0024] FIG. 2 is a schematic diagram of a method of monitoring performance according to an embodiment of the present application;

[0025] FIG. 3 is a schematic diagram of reference signals in a first time window and a second time window;

[0026] FIG. 4 is another schematic diagram of reference signals in a first time window and a second time window;

[0027] FIG. 5 is a schematic diagram of a transmission occasion of performance monitoring results;

[0028] FIG. 6 is another schematic diagram of a transmission occasion of performance monitoring results

[0029] FIG. 7 is one schematic diagram for performance monitoring and life cycle management for component carriers;

[0030] FIG. 8 is another schematic diagram for performance monitoring and life cycle management for component carriers;

[0031] FIG. 9 is yet another schematic diagram for performance monitoring and life cycle management for component carriers;

[0032] FIG. 10 is still another schematic diagram for performance monitoring and life cycle management for component carriers;

[0033] FIG. 11 is one schematic diagram for performance monitoring and life cycle management for multi-transmission reception points;

[0034] FIG. 12 is another schematic diagram for performance monitoring and life cycle management for multi-transmission reception points;

[0035] FIG. 13 is a schematic diagram in which a terminal device is configured to perform performance monitoring of an artificial intelligence function or model for at least one monitoring group;

[0036] FIG. 14 is one schematic diagram of a method for monitoring performance according to an embodiment of the present application;

[0037] FIG. 15 is one schematic diagram of an apparatus for monitoring performance according to an embodiment of the present application;

[0038] FIG. 16 is one schematic diagram of an apparatus for monitoring performance according to an embodiment of the present application;

[0039] FIG. 17 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0040] FIG. 18 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] The foregoing and other features of the present application are hereinafter more fully described and understood when considered in connection with the following drawings. In the drawings, specific embodiments of the present application are disclosed, which illustrate the principles of the present application by showing a partial implementation thereof. It should be understood that the present application is not limited to the embodiments described but includes all modifications, variations, and equivalents that fall within the scope of the appended claims.

[0042] In the embodiments of the present application, the terms "first", "second" and the like are used to distinguish different elements from each other, but do not indicate spatial arrangement or time sequence of the elements, and the elements should not be limited by these terms. The term "and / or" includes any one and all combinations of the associated listed terms. The terms "comprise", "include", "have" and the like mean the presence of the stated features, elements, elements or components, but do not exclude the presence or addition of one or more other features, elements, elements or components.

[0043] In the embodiments of the present application, the singular form "a", "an" and the like includes the plural form, should be understood broadly as "one" or "a kind of", and not limited to the meaning of "one"; in addition, the term "said" should be understood as including both singular and plural forms, unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least partially according to", and the term "based on" should be understood as "at least partially based on", unless the context clearly indicates otherwise.

[0044] In the embodiments of the present application, the term "communication network" or "wireless communication network" can refer to a network that conforms to any communication standard, such as Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), etc.

[0045] In addition, the communication between devices in the communication system can be carried out according to any stage communication protocol, which can include but is not limited to the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G and 5G, New Radio (NR), future 6G, etc., and / or other currently known or future to be developed communication protocols.

[0046] In the embodiments of the present application, the term "network device" refers to, for example, a device that accesses a terminal device to a communication network and provides services for the terminal device in a communication system. The network device can include, but is not limited to, the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), and the like.

[0047] Wherein, the base station can include, but is not limited to: Node B (NodeB or NB), evolved Node B (eNodeB or eNB), and 5G base station (gNB), IAB donor, etc., and can also include remote radio head (RRH), remote radio unit (RRU), relay, or low-power node (such as femto, pico, etc.). And the term "base station" can include some or all functions of them, and each base station can provide communication coverage for a specific geographic area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0048] In the embodiments of the present application, the term "user equipment" (UE) or "terminal equipment" (TE) refers to, for example, a device that accesses a communication network through a network device and receives network services. The terminal equipment can be fixed or mobile, and can also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, a mobile terminal (MT), and the like.

[0049] Wherein, the terminal equipment can include, but is not limited to, the following devices: cellular phone, personal digital assistant (PDA), wireless modem, wireless communication device, handheld device, machine type communication device, laptop computer, cordless phone, smart phone, smart watch, digital camera, and the like.

[0050] For another example, in scenarios such as Internet of Things (IoT), a terminal device can also be a machine or an apparatus that performs monitoring or measurement, for example, can include but is not limited to: a machine type communication (MTC) terminal, a vehicle-mounted communication terminal, a device to device (D2D) terminal, a machine to machine (M2M) terminal, and the like.

[0051] In addition, the term "network side" or "network device side" refers to the side of the network, which can be a certain base station, or can include one or more network devices as described above. The term "user side" or "terminal side" or "terminal device side" refers to the side of the user or terminal, which can be a certain UE, or can include one or more terminal devices as described above. In this article, "device" can refer to a network device or a terminal device without special indication.

[0052] In the following description, the terms "uplink control signal" and "uplink control information (UCI)" or "physical uplink control channel (PUCCH)" can be interchangeable without causing confusion, and the terms "uplink data signal" and "uplink data information" or "physical uplink shared channel (PUSCH)" can be interchangeable;

[0053] The terms "downlink control signal" and "downlink control information (DCI)" or "physical downlink control channel (PDCCH)" can be interchangeable, and the terms "downlink data signal" and "downlink data information" or "physical downlink shared channel (PDSCH)" can be interchangeable.

[0054] In addition, the uplink signal can include an uplink data signal and / or an uplink control signal and / or a PRACH and / or an SRS, etc., and can also be referred to as an uplink transmission (UL transmission) or uplink information or an uplink channel. Transmitting / receiving the uplink transmission on the uplink resource can be understood as transmitting / receiving the uplink transmission using the uplink resource. The downlink signal can include a downlink data signal and / or a downlink control signal and / or a synchronization signal (SS, for example, PSS / SSS) and / or a broadcast channel (PBCH) and / or an SSB (SS / PBCH block, including PSS, SSS and PBCH and DMRS thereof) and / or a CSI-RS, etc., and can also be referred to as a downlink transmission (DL transmission) or downlink information or a downlink channel. Transmitting / receiving the downlink transmission on the downlink resource can be understood as transmitting / receiving the downlink transmission using the downlink resource.

[0055] In the embodiments of the present application, the higher layer signaling can be, for example, radio resource control (RRC) signaling; the RRC signaling can include, for example, an RRC message, such as a broadcast / common RRC message / signaling (for example, a master information block (MIB), system information), a dedicated RRC message / signaling; or an RRC information element (RRC IE); or information fields included in the RRC message or the RRC information element (or information fields included in the information fields). The higher layer signaling can also be, for example, medium access control (MAC) signaling; or a MAC control element (MAC CE). However, the present application is not limited thereto.

[0056] In the embodiments of the present application, multiple means at least two, or two or more.

[0057] In the embodiments of the present application, predefined means defined by a protocol or determined according to a rule defined by a protocol, without additional configuration. Configuration / indication means direct or indirect configuration / indication by a network device through higher layer signaling and / or physical layer signaling. The configuration / indication can be configured / indicated by introducing a higher layer parameter in the higher layer signaling, and the higher layer parameter means information fields and / or information elements / units / elements (IEs) in the higher layer signaling, etc. The physical layer signaling can be, for example, control information (DCI) carried by a physical downlink control channel or control information carried by a sequence, but is not limited thereto.

[0058] For ease of description, the following describes a base station as an example of an access network device. In the following description, “if”, “in the case of” and “when” can be used interchangeably without causing confusion.

[0059] The following describes the scenarios of the embodiments of the present application by way of examples, but the present application is not limited thereto.

[0060] FIG. 1 is a schematic diagram of a communication system of the embodiments of the present application, which schematically illustrates the case taking the terminal device and the network device as examples. As shown in FIG. 1, the communication system 100 can include a network device 101 and terminal devices 102 and 103. For simplicity, FIG. 1 only takes two terminal devices and one network device as examples for illustration, but the embodiments of the present application are not limited thereto.

[0061] In the embodiments of the present application, the network device 101 and the terminal devices 102 and 103 can perform existing services or future implementable services transmission. For example, these services can include but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0062] It is worth noting that FIG. 1 shows that both terminal devices 102 and 103 are within the coverage of the network device 101, but the present application is not limited thereto. Both terminal devices 102 and 103 can be outside the coverage of the network device 101, or one terminal device 102 is within the coverage of the network device 101 while the other terminal device 103 is outside the coverage of the network device 101.

[0063] In the embodiments of the present application, one or more AI / ML functions or models can be configured and run in the network device and / or the terminal device. The AI / ML functions or models can be used for various signal processing functions of wireless communication, such as channel state information (CSI) prediction, CSI compression, beam prediction, positioning management, etc.; the present application is not limited thereto.

[0064] In the embodiments of the present application, the following terms have the same meaning, and they can be replaced with each other: AI / ML, artificial intelligence, artificial intelligence or machine learning.

[0065] In the embodiments of the present application, the AI / ML functionality / model can also be referred to as artificial intelligence or machine learning function or model, artificial intelligence function or model, etc., which have the same meaning and can be replaced with each other in the present application.

[0066] Embodiments of the first aspect

[0067] Embodiments of the present application provide a method for monitoring performance, which is described from the terminal device side.

[0068] FIG. 2 is a schematic diagram of a method for monitoring performance according to an embodiment of the present application. As shown in FIG. 2, the method for monitoring performance includes:

[0069] 201. Obtain, using an artificial intelligence functionality or model, predicted channel state information (CSI) in a second time window (prediction window) based on measurements of reference signals in a first time window, the second time window being after the first time window, the first time window including one or more time instances, and the second time window including one or more time instances; and

[0070] 202. Monitor performance of the artificial intelligence functionality or model based on measurements of reference signals in the second time window and the predicted channel state information in the second time window.

[0071] In some embodiments, the artificial intelligence functionality or model refers to an AI / ML feature / feature group enabled by a configuration, wherein the configuration is supported based on a condition indicated by a terminal device capability.

[0072] For example, the AI / ML functionality or model can be one or more functionalities or models, or can be one or more logical models, or can be one or more sub- functionalities, or can be one or more features, or can be one or more feature groups.

[0073] In operation 201, the artificial intelligence functionality or model is set at the terminal device side.

[0074] In some embodiments of operation 202, the terminal device can compare the channel state information obtained by measuring the reference signals in the second time window with the predicted channel state information in the second time window to monitor the performance of the artificial intelligence functionality or model.

[0075] As shown in FIG. 2, the method for monitoring performance further includes:

[0076] 203. The terminal device receives a corresponding reference signal at each time instance or a partial time instance of the second time window.

[0077] In at least some embodiments, operation 203 can be located before operation 202.

[0078] In this application, the first time window can be referred to as an observation window or a measurement window, for example, and the second time window can be referred to as a prediction window, for example. The length of the first time window and the second time window can be pre-configured or configured by a network device to a terminal device.

[0079] FIG. 3 is a schematic diagram of reference signals in the first time window and the second time window, and FIG. 4 is another schematic diagram of reference signals in the first time window and the second time window. As shown in FIG. 3 and FIG. 4, the reference signals in the first time window and the second time window can be channel state information reference signals (CSI-RSs), for example.

[0080] As shown in FIG. 3 and FIG. 4, the second time window is after the first time window. The first time window can include one or more time instances, which can be represented as T1, T2, T3 and T4, for example. The second time window can include one or more time instances, which can be represented as T5, T6, T7 and T8, for example.

[0081] In this application, a time instance is a time point or a time point represented by other time units, for example. The other time units can be a slot, a frame or a sub-frame, for example.

[0082] As shown in FIG. 3 and FIG. 4, the terminal device can receive (e.g., from a network device) the reference signals on the one or more time instances of the first time window. The reference signals received by the terminal device in the first time window can be referred to as first reference signals, which are used for inference or prediction.

[0083] In some examples of operation 201, the terminal device performs measurement on the first reference signals to obtain measurement results, and uses an artificial intelligence function or model to infer the measurement results to obtain predicted channel state information (CSI) in the second time window. The predicted channel state information in the second time window can correspond to one or more time instances in the second time window, for example, and for each time instance in the second time window, the predicted channel state information on the time instance is obtained.

[0084] In some embodiments, the terminal device is configured to perform performance monitoring on the artificial intelligence function or model used in operation 201.

[0085] In operation 203, the terminal device receives a corresponding reference signal at each time instance or part of time instances of the second time window, wherein the reference signal received by the terminal device in the second time window can be referred to as a second reference signal, and the second reference signal is used for performance monitoring of the artificial intelligence function or model.

[0086] In the example shown in FIG. 3, the terminal device receives a corresponding reference signal (i.e., a second reference signal) at each time instance of the second time window, for example, a reference signal is received at T5, T6, T7 and T8. In the example shown in FIG. 4, the terminal device receives a corresponding reference signal (i.e., a second reference signal) at part of the time instances of the second time window, for example, a reference signal is received at T5 and T6. In addition, the terminal device can receive a corresponding reference signal (i.e., a second reference signal) at 1 time instance of the second time window.

[0087] FIGS. 3 and 4 show the case of 1 time instance corresponding to 1 reference signal, and the present application is not limited thereto. For example, 1 time instance can correspond to 2 or more reference signals at the time instance of receiving the reference signal.

[0088] In addition, in the present application, the reference signal received at 1 time instance can also be referred to as the reference signal of the time instance.

[0089] In operation 202, the terminal device can measure the second reference signal, compare the measurement result (for example, the measured channel state information) with the predicted channel state information in the second time window obtained in operation 201, and obtain a performance monitoring result.

[0090] As shown in FIG. 2, the performance monitoring method further comprises:

[0091] 204. The terminal device sends the performance monitoring result to the network device.

[0092] In operation 204, the performance monitoring result obtained in operation 202 can be sent to the network device. For example, the performance monitoring result obtained in operation 202 can be a performance metric of the artificial intelligence function or model, so in operation 204, the terminal device sends the performance metric to the network device.

[0093] FIG. 5 is a schematic diagram of a sending occasion of the performance monitoring result, and FIG. 6 is another schematic diagram of a sending occasion of the performance monitoring result.

[0094] As shown in FIG. 5, in some examples of operation 204, the terminal device sends the performance indicators respectively after measuring the reference signals of each time instance in the second time window (i.e., after obtaining the measured channel state information), where the sent performance indicators can be the performance indicators obtained based on the latest measurement result. For example, after measuring the reference signal of time instance T5, the terminal device obtains the performance indicator corresponding to the reference signal of time instance T5 based on the measurement result, and sends the performance indicator to the network device; after measuring the reference signal of time instance T6, the terminal device obtains the performance indicator corresponding to the reference signal of time instance T6 based on the measurement result, and sends the performance indicator to the network device; and the operations for time instances T6, T7 and T8 are similar to the operation for time instance T5.

[0095] As shown in FIG. 6, in some other examples of operation 204, the terminal device sends the performance indicators after measuring the reference signals of more than two time instances in the second time window (i.e., after obtaining the measured channel state information). Wherein the terminal device can calculate the performance indicators corresponding to the reference signals of the more than two time instances in the second time window respectively based on the measurement of the reference signals of the more than two time instances, send the average of the more than two performance indicators to the network device, or send the more than two performance indicators to the network device together. For example, the terminal device obtains the performance indicators (e.g., 4 performance indicators) corresponding to the reference signals of time instances T5, T6, T7 and T8 respectively based on the measurement results of the reference signals of time instances T5, T6, T7 and T8, and then sends the average of the 4 performance indicators to the network device or sends the 4 performance indicators to the network device together.

[0096] As shown in FIG. 2, the performance monitoring method further comprises:

[0097] 205. sending the channel state information obtained by measuring the reference signals in the second time window to the network device.

[0098] As described above, the terminal device can measure the reference signals in the second time window to obtain the channel state information (i.e., the measured channel state information), and the measured channel state information includes at least one of precoding matrix indicator (PMI), channel quality indicator (CQI) and rank indicator (RI).

[0099] In operation 205, the terminal device sends the channel state information obtained by measuring the reference signals in the second time window to the network device. For example, the terminal device obtains the measured channel state information based on the reference signal of time instance T5, and sends the measured channel state information to the network device.

[0100] In operation 205, the channel state information measured based on the reference signal in the second time window can be transmitted together with the performance indicator or separately. For example, the terminal device obtains the measured channel state information based on the reference signal at time instance T5, and further obtains the performance indicator corresponding to the reference signal at time instance T5. The terminal device can transmit the measured channel state information and the performance indicator together or separately.

[0101] In this application, the terminal device can perform operation 205 under a predetermined condition. For example, when the performance of the artificial intelligence function or model (for example, the performance of the artificial intelligence function or model can be represented by a performance indicator, and the larger the value of the performance indicator, the better the performance) is lower than a threshold value, operation 205 is performed, that is, the terminal device transmits the channel state information measured based on the reference signal in the second time window to the network device. The threshold value can be predefined or configured by the network device to the terminal device.

[0102] As shown in FIG. 2, the performance monitoring method further includes:

[0103] 206. The terminal device is configured to perform lifecycle management and / or performance monitoring of the artificial intelligence function or model for a component carrier of the terminal device or a transmission reception point (TRP) that communicates with the terminal device.

[0104] In this application, lifecycle management (LCM) refers to at least one of the operations of activation, deactivation, switching, selection, and fallback for the artificial intelligence function or model.

[0105] In this application, the performance monitoring method as shown in FIG. 2 includes operation 206. The present application is not limited thereto, for example, operation 206 can also not be included in the performance monitoring method of FIG. 2, that is, operation 206 can be an embodiment independent of operations 201 to 205.

[0106] In the following, operation 206 is described for different scenarios.

[0107] In some embodiments, for the scenario of carrier aggregation, the artificial intelligence function or model used for predicting CSI (i.e., the artificial intelligence function or model used in operation 201) can be configured or enabled for at least one component carrier (CC) of the terminal device. The at least one component carrier is all component carriers of the terminal device or a subset of all component carriers.

[0108] In some embodiments, the terminal device is further configured to perform lifecycle management and / or performance monitoring of the artificial intelligence function or model for at least one component carrier. For example, for a component carrier for which the artificial intelligence function or model is configured or enabled, the component carrier can be configured with lifecycle management and / or performance monitoring of the artificial intelligence function or model.

[0109] FIG. 7 is a schematic diagram of performance monitoring and lifecycle management for component carriers, FIG. 8 is another schematic diagram of performance monitoring and lifecycle management for component carriers, FIG. 9 is yet another schematic diagram of performance monitoring and lifecycle management for component carriers, and FIG. 10 is still another schematic diagram of performance monitoring and lifecycle management for component carriers.

[0110] In a first type of example in a carrier aggregation scenario, the terminal device performs lifecycle management of an artificial intelligence function or model for a same component carrier based on a monitoring result of performance monitoring of the artificial intelligence function or model for the component carrier.

[0111] For example, as shown in FIG. 7, three component carriers CC#1, CC#2 and CC#3 of the terminal device are all configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #1, artificial intelligence function or model #2 and artificial intelligence function or model #3, respectively), are all configured with lifecycle management (e.g., lifecycle management #1, lifecycle management #2 and lifecycle management #3, respectively), and are all configured with performance monitoring (e.g., performance monitoring #1, performance monitoring #2 and performance monitoring #3, respectively).

[0112] In FIG. 7, performance monitoring can be performed independently on each component carrier, or performance monitoring can be performed jointly on multiple component carriers. Lifecycle management on a component carrier can depend on a monitoring result of performance monitoring on the same component carrier.

[0113] For example, the terminal device performs lifecycle management #1 of an artificial intelligence function or model for component carrier CC#1 based on a monitoring result of performance monitoring #1 of the artificial intelligence function or model for component carrier CC#1. If the monitoring result of performance monitoring #1 is that the performance of artificial intelligence function or model #1 is poor, lifecycle management #1 can perform switching or the like on artificial intelligence function or model #1.

[0114] In the second type of example in the carrier aggregation scenario, in some examples, the terminal device can perform, according to a monitoring result of the performance monitoring of the artificial intelligence function or model on one component carrier, the lifecycle management of the artificial intelligence function or model on two or more component carriers that have an association relationship with the performance monitoring. The association relationship is predefined or configured by the network device or sent by the terminal device to the network device.

[0115] For example, as shown in FIG. 8, the terminal device is configured with the artificial intelligence function or model (for example, artificial intelligence function or model #1, artificial intelligence function or model #2 and artificial intelligence function or model #3, respectively) on three component carriers CC#1, CC#2 and CC#3, and is configured with the lifecycle management (for example, lifecycle management #1, lifecycle management #2 and lifecycle management #3, respectively) on the three component carriers CC#1, CC#2 and CC#3, and is configured with the performance monitoring (for example, performance monitoring #1 and performance monitoring #2, respectively) on two component carriers CC#1 and CC#2.

[0116] In FIG. 8, the performance monitoring #2 has an association relationship with the component carriers CC#2 and CC#3, and thus the terminal device performs the lifecycle management #2 and the lifecycle management #3 on the component carriers CC#2 and CC#3 according to the monitoring result of the performance monitoring #2. In addition, the artificial intelligence function or model monitored by the performance monitoring #2 can be one of the artificial intelligence function or model #2 and the artificial intelligence function or model #3.

[0117] In the second type of example in the carrier aggregation scenario, in other examples, the terminal device can configure the performance monitoring of the artificial intelligence function or model on a first predetermined component carrier, and the part or all of the component carriers of the terminal device configured with the artificial intelligence function or model all have an association relationship with the performance monitoring of the first predetermined component carrier. For example, the terminal device can perform, according to a monitoring result of the performance monitoring of the artificial intelligence function or model on the first predetermined component carrier, the lifecycle management of the artificial intelligence function or model on the part or all of the component carriers of the terminal device configured with the artificial intelligence function or model. The association relationship is predefined or configured by the network device or sent by the terminal device to the network device.

[0118] The first predetermined component carrier can have a first identifier, for example, a lowest (or minimum) identifier number or a highest (or maximum) identifier number (lowest / highest ID) in the component carriers of the terminal device configured with the artificial intelligence function or model.

[0119] For example, as shown in FIG. 9, the terminal device is configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #1, artificial intelligence function or model #2, and artificial intelligence function or model #3, respectively) on each of the three component carriers CC#1, CC#2, and CC#3, is configured with a lifecycle management (e.g., lifecycle management #1, lifecycle management #2, and lifecycle management #3, respectively) on each of the three component carriers CC#1, CC#2, and CC#3, and is configured with performance monitoring #1 on the component carrier CC#1 with the lowest identification number.

[0120] In FIG. 9, the performance monitoring #1 is associated with the component carriers CC#1, CC#2, and CC#3, and thus the terminal device performs the lifecycle management #1, lifecycle management #2, and lifecycle management #3 on the component carriers CC#1, CC#2, and CC#3 according to the monitoring result of the performance monitoring #1. In addition, the artificial intelligence function or model monitored by the performance monitoring #1 can be one of the artificial intelligence function or model #1, #2, and #3.

[0121] In a second type of example in the carrier aggregation scenario, in yet other examples, the terminal device can support multiple frequency bands, and the terminal device can be configured to perform performance monitoring of an artificial intelligence function or model on a second predetermined component carrier in at least one frequency band, and the terminal device can be configured to have an association between the performance monitoring of the second predetermined component carrier in the frequency band and the performance monitoring of the artificial intelligence function or model on part or all of the component carriers in the frequency band that are configured with the artificial intelligence function or model. For example, the terminal device can be configured to perform lifecycle management of the artificial intelligence function or model on part or all of the component carriers in the frequency band that are configured with the artificial intelligence function or model according to the monitoring result of the performance monitoring of the artificial intelligence function or model on the second predetermined component carrier in the frequency band. The association can be predefined or configured by the network device or sent by the terminal device to the network device.

[0122] The second predetermined component carrier can have a second identification, such as a lowest (or minimum) identification number or a highest (or maximum) identification number (lowest / highest ID) among the component carriers in the frequency band of the terminal device that are configured with the artificial intelligence function or model.

[0123] For example, as shown in FIG. 10, the terminal device supports two frequency bands, i.e., a frequency band #1 and a frequency band #2, where the frequency band #1 has two component carriers CC#1 and CC#2, and the frequency band #2 has two component carriers CC#3 and CC#4.

[0124] In the frequency band #1, both component carriers CC#1 and CC#2 are configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #1, artificial intelligence function or model #2, respectively), both component carriers CC#1 and CC#2 are configured with a lifecycle management (e.g., lifecycle management #1, lifecycle management #2, respectively), and the component carrier CC#1 with the lowest identity number in the frequency band #1 is configured with the performance monitoring #1.

[0125] In the frequency band #2, both component carriers CC#3 and CC#4 are configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #3, artificial intelligence function or model #4, respectively), both component carriers CC#3 and CC#4 are configured with a lifecycle management (e.g., lifecycle management #3, lifecycle management #4, respectively), and the component carrier CC#3 with the lowest identity number in the frequency band #2 is configured with the performance monitoring #2.

[0126] In FIG. 10, the performance monitoring #1 is associated with the component carriers CC#1 and CC#2 in the frequency band #1, whereby the terminal device performs the lifecycle management #1 and the lifecycle management #2 on the component carriers CC#1 and CC#2 according to the monitoring result of the performance monitoring #1. In addition, the artificial intelligence function or model monitored by the performance monitoring #1 can be one of the artificial intelligence function or model #1 and #2.

[0127] In FIG. 10, the performance monitoring #2 is associated with the component carriers CC#3 and CC#4 in the frequency band #2, whereby the terminal device performs the lifecycle management #3 and the lifecycle management #4 on the component carriers CC#3 and CC#4 according to the monitoring result of the performance monitoring #2. In addition, the artificial intelligence function or model monitored by the performance monitoring #2 can be one of the artificial intelligence function or model #3 and #4.

[0128] In some embodiments, for a multi-TRP scenario, the artificial intelligence function or model used for predicting the CSI (i.e., the artificial intelligence function or model used in operation 201) can be configured or enabled for at least one transmission reception point (TRP) that communicates with the terminal device. Wherein the at least one transmission reception point is all transmission reception points or a subset of all transmission reception points that communicate with the terminal device.

[0129] In some embodiments, the terminal device can also be configured to perform lifecycle management (LCM) and / or performance monitoring of the artificial intelligence function or model for a transmission reception point (TRP) that communicates with the terminal device. For example, for a transmission reception point that is configured or enabled with the artificial intelligence function or model, the transmission reception point can be configured with lifecycle management and / or performance monitoring of the artificial intelligence function or model.

[0130] FIG. 11 is one illustration of performance monitoring and lifecycle management for multi-TRP, and FIG. 12 is another illustration of performance monitoring and lifecycle management for multi-TRP.

[0131] For multi-TRP scenarios, in some examples, the terminal device performs lifecycle management of an artificial intelligence function or model for a transmission reception point based on monitoring results of performance monitoring of the artificial intelligence function or model for the same transmission reception point.

[0132] For example, as shown in FIG. 11, both transmission reception points TRP#1 and TRP#2 that communicate with the terminal device are configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #1 and artificial intelligence function or model #3, respectively), both transmission reception points TRP#1 and TRP#2 are configured with lifecycle management (e.g., lifecycle management #1 and lifecycle management #2, respectively), and both transmission reception points TRP#1 and TRP#2 are configured with performance monitoring (e.g., performance monitoring #1 and performance monitoring #2, respectively).

[0133] In FIG. 11, performance monitoring can be performed independently on each transmission reception point, or jointly across multiple transmission reception points. Lifecycle management on a transmission reception point can depend on performance monitoring results on the same transmission reception point.

[0134] For example, the terminal device performs lifecycle management #1 of an artificial intelligence function or model for transmission reception point TRP#1 based on monitoring results of performance monitoring #1 of the artificial intelligence function or model for transmission reception point TRP#1. If the monitoring results of performance monitoring #1 indicate that the performance of artificial intelligence function or model #1 is poor, then lifecycle management #1 can perform switching or other operations on artificial intelligence function or model #1. Similarly, performance monitoring #2 and lifecycle management #2 of an artificial intelligence function or model for TRP#2.

[0135] For the multi-TRP scenario, in some examples, the terminal device performs the performance monitoring of the artificial intelligence function or model on one of the transmission reception points according to a monitoring result of the performance monitoring.

[0136] For example, as shown in FIG. 12, the four transmission reception points TRP#1, TRP#2, TRP#3 and TRP#4 in communication with the terminal device are all configured with the artificial intelligence function or model (e.g., artificial intelligence function or model #1, artificial intelligence function or model #2, artificial intelligence function or model #3 and artificial intelligence function or model #4, respectively), the four transmission reception points TRP#1, TRP#2, TRP#3 and TRP#4 are all configured with the life cycle management (e.g., life cycle management #1, life cycle management #2, life cycle management #3 and life cycle management #4, respectively), and the two transmission reception points TRP#1 and TRP#2 are configured with the performance monitoring (e.g., performance monitoring #1 and performance monitoring #2, respectively).

[0137] In FIG. 12, the performance monitoring #2 is associated with the transmission reception points TRP#2, TRP#3 and TRP#4, and thus the terminal device performs the life cycle management #2, life cycle management #3 and life cycle management #4 on the transmission reception points TRP#2, TRP#3 and TRP#4 according to the monitoring result of the performance monitoring #2. In addition, the artificial intelligence function or model monitored by the performance monitoring #2 can be one of the artificial intelligence function or model #2, artificial intelligence function or model #3 and artificial intelligence function or model #4.

[0138] In some embodiments of the present application, as a more general case of the operation 206 described above, the terminal device can be configured to perform the performance monitoring of the artificial intelligence function or model on at least one monitoring group.

[0139] In some examples, the terminal device performs the life cycle management of the artificial intelligence function or model on one object in the monitoring group according to a monitoring result of the performance monitoring of the artificial intelligence function or model on the object.

[0140] In some examples, the terminal device performs the life cycle management of the artificial intelligence function or model on one object in the monitoring group according to a monitoring result of the performance monitoring of the artificial intelligence function or model on the object.

[0141] The monitoring group can be referred to as an artificial intelligence function or model group, or a lifecycle management group. One monitoring group can include one or more objects. In some examples, one object can be one component carrier (CC); or one object can be one component carrier in one frequency band; or one object can be one transmission and reception point (TRP), i.e., a transmission and reception point that communicates with the terminal device. For example, in the example described in operation 206 above, one object is one component carrier (CC), or one object is one component carrier in one frequency band, or one object is one transmission and reception point (TRP).

[0142] FIG. 13 is a schematic diagram in which a terminal device is configured to perform performance monitoring of an artificial intelligence function or model for at least one monitoring group.

[0143] As shown in FIG. 13, one monitoring group #1 of the terminal device has two objects, i.e., object #1 and object #2. Both of the two objects #1 and #2 are configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #1 and artificial intelligence function or model #2, respectively), both of the two objects #1 and #2 are configured with lifecycle management (e.g., lifecycle management #1 and lifecycle management #2, respectively), and the object #1 with the smallest identity of the monitoring group #1 is configured with performance monitoring #1.

[0144] In FIG. 13, the performance monitoring #1 is associated with the objects #1 and #2, and thus the terminal device performs lifecycle management #1 and lifecycle management #2 on the objects #1 and #2 according to the monitoring result of the performance monitoring #1. In addition, the artificial intelligence function or model monitored by the performance monitoring #1 can be one of the artificial intelligence function or model #1 and the artificial intelligence function or model #2.

[0145] As shown in FIG. 13, another monitoring group #2 of the terminal device has two objects, i.e., object #3, object #4, and object #4. The three objects #3, #4, and #5 are all configured with an artificial intelligence function or model (e.g., artificial intelligence function or model #3, artificial intelligence function or model #4, and artificial intelligence function or model #5, respectively), the three objects #3, #4, and #5 are all configured with lifecycle management (e.g., lifecycle management #3, lifecycle management #4, and lifecycle management #5, respectively), and the object #3 with the smallest identity of the monitoring group #2 is configured with performance monitoring #2.

[0146] In FIG. 13, the performance monitoring #2 is associated with the object #3, the object #4 and the object #5, and thus, the terminal device performs the lifecycle management #3, the lifecycle management #4 and the lifecycle management #5 on the object #3, the object #4 and the object #5 according to the monitoring result of the performance monitoring #2. In addition, the artificial intelligence function or model monitored by the performance monitoring #2 can be one of the artificial intelligence function or model #3, the artificial intelligence function or model #4 and the artificial intelligence function or model #5.

[0147] It is worth noting that the above FIG. 2 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications according to the above description, and the present application is not limited to the above FIG. 2.

[0148] The above embodiments only exemplarily illustrate the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0149] As can be seen from the above embodiments, the terminal device can use the reference signal of one or more time instances in the prediction window to perform performance monitoring on the artificial intelligence function or model, so that the terminal device and the network device can have a unified understanding of the operations related to the reference signal in the performance monitoring process, and the performance monitoring can be accurately performed. In addition, the objects in the monitoring group of the terminal device can be flexibly configured to perform lifecycle management and / or performance monitoring on the artificial intelligence function or model.

[0150] Embodiments of the second aspect

[0151] The embodiments of the present application provide a method for monitoring performance, which is described from the network device side. The embodiments of the second aspect can be combined with the embodiments of the first aspect.

[0152] FIG. 14 is a schematic diagram of a method for monitoring performance according to an embodiment of the present application. As shown in FIG. 14, the method comprises:

[0153] 1401. sending a reference signal to a terminal device in a first time window;

[0154] 1402. receiving predicted channel state information (CSI) in a second time window obtained by using an artificial intelligence model or model, the second time window being after the first time window, the first time window comprising one or more time instances, and the second time window comprising one or more time instances; and

[0155] 1403、receive the performance indicator of the artificial intelligence function or model sent by the terminal device.

[0156] As shown in FIG. 14, the method further includes:

[0157] 1404、the network device sends a reference signal to the terminal device at each time instance or part of time instance of the second time window.

[0158] In some embodiments, after the terminal device measures the reference signal at each time instance in the second time window, the network device receives the performance indicator respectively; or after the terminal device measures the reference signal at more than two time instances in the second time window, the network device receives the performance indicator.

[0159] In some embodiments, the terminal device calculates the performance indicator corresponding to each reference signal according to the measurement of the reference signal at more than two time instances in the second time window, the network device receives the average value of more than two performance indicators in the second time window, or the network device receives the more than two performance indicators in the second time window.

[0160] As shown in FIG. 14, the method further includes:

[0161] 1405、the network device receives channel state information obtained by measuring the reference signal in the second time window from the terminal device.

[0162] Among them, the channel state information obtained by measuring the reference signal in the second time window includes at least one of precoding matrix indication (PMI), channel quality indication (CQI) and rank indication (RI).

[0163] In some embodiments, the channel state information obtained by measuring the reference signal in the second time window is received together with the performance indicator or separately.

[0164] In some embodiments, the network device receives the channel state information obtained by measuring the reference signal in the second time window in the case that the performance of the artificial intelligence function or model is lower than the threshold.

[0165] As shown in FIG. 14, the method further includes:

[0166] 1406、configure the terminal device to perform life cycle management and / or performance monitoring of the artificial intelligence function or model for component carriers of the terminal device or transmission reception points in communication with the terminal device.

[0167] In some embodiments, the terminal device is configured to perform the performance monitoring of the artificial intelligence function or model for at least one component carrier, wherein the at least one component carrier is a subset of all component carriers of the terminal device.

[0168] In some embodiments, the terminal device is configured to perform the performance monitoring of the artificial intelligence function or model for at least one component carrier, wherein the at least one component carrier is a subset of all component carriers of the terminal device.

[0169] According to the monitoring result of the performance monitoring of the artificial intelligence function or model for one component carrier, the lifecycle management of the artificial intelligence function or model is performed for the same component carrier; or

[0170] According to the monitoring result of the performance monitoring of the artificial intelligence function or model for one component carrier, the lifecycle management of the artificial intelligence function or model is performed for the same component carrier; or

[0171] In some embodiments, the association relationship is predefined or configured by the network device or sent by the terminal device to the network device.

[0172] In some embodiments, the one component carrier is a first predetermined component carrier, and all component carriers in the at least one component carrier have an association relationship with the performance monitoring of the first predetermined component carrier; or the one component carrier is a second predetermined component carrier in a frequency band, and all component carriers in the at least one component carrier located in the frequency band have an association relationship with the performance monitoring of the second predetermined component carrier.

[0173] In some embodiments, the first predetermined component carrier has a first identifier (e.g., lowest / highest ID); or the second predetermined component carrier has a second identifier (e.g., lowest / highest ID in the frequency band).

[0174] In some embodiments, the terminal device is configured to perform the performance monitoring of the artificial intelligence function or model for at least one component carrier, wherein the at least one component carrier is a subset of all component carriers of the terminal device.

[0175] In some embodiments, the terminal device is configured to perform the performance monitoring of the artificial intelligence function or model on one transmission reception point, and perform the life cycle management of the artificial intelligence function or model on the same transmission reception point according to a monitoring result of the performance monitoring; or perform the performance monitoring of the artificial intelligence function or model on one transmission reception point, and perform the life cycle management of the artificial intelligence function or model on more than two transmission reception points having an association relationship with the performance monitoring according to a monitoring result of the performance monitoring.

[0176] In some embodiments, the terminal device is configured to perform the performance monitoring of the artificial intelligence function or model on at least one monitoring group.

[0177] In some embodiments, the terminal device is configured to perform the performance monitoring of the artificial intelligence function or model on at least one monitoring group.

[0178] It is worth noting that the above FIG. 14 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above description, and the present application is not limited to the above FIG. 14.

[0179] The above embodiments only exemplarily illustrate the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0180] Embodiments of the third aspect

[0181] The embodiments of the present application provide a device for monitoring performance. The device may, for example, be a terminal device, or some component or assembly configured in the terminal device, and the same content as the embodiments of the first and second aspects will not be described herein.

[0182] FIG. 15 is a schematic diagram of a device for monitoring performance according to an embodiment of the present application. As shown in FIG. 15, the device 1500 for monitoring performance according to an embodiment of the present application includes a first processor 1501, which is configured to:

[0183] obtaining predicted channel state information (CSI) for a second time window (prediction window) using an artificial intelligence function or model based on measurements of reference signals in a first time window (observation window), the second time window being subsequent to the first time window, the first time window comprising one or more time instances, the second time window comprising one or more time instances; and

[0184] monitoring performance of the artificial intelligence function or model based on measurements of reference signals in the second time window and the predicted channel state information for the second time window.

[0185] In some embodiments, the performance of the artificial intelligence function or model is monitored by comparing channel state information obtained from measurements of reference signals in the second time window with the predicted channel state information for the second time window.

[0186] In some embodiments, the apparatus 1500 further comprises:

[0187] a first receiver 1502 configured to receive a corresponding reference signal at each time instance or a partial time instance of the second time window.

[0188] In some embodiments, the apparatus 1500 further comprises:

[0189] a first transmitter 1503 configured to transmit a performance metric of the artificial intelligence function or model to a network device.

[0190] In some embodiments, the first transmitter transmits the performance metric after the first processor measures the reference signal at each time instance of the second time window, respectively; or the first transmitter transmits the performance metric after the first processor measures the reference signal at more than two time instances of the second time window.

[0191] In some embodiments, the first processor calculates a performance metric corresponding to each reference signal based on measurements of the reference signal at more than two time instances of the second time window, and the first transmitter transmits an average of the performance metrics or transmits the performance metrics together to the network device.

[0192] In some embodiments, the first transmitter further transmits channel state information obtained from measurements of the reference signal in the second time window to the network device.

[0193] In some embodiments, the channel state information measured on the reference signal within the second time window comprises at least one of a precoding matrix indicator (PMI), a channel quality indicator (CQI), and a rank indication (RI).

[0194] In some embodiments, the channel state information measured on the reference signal within the second time window is transmitted by the first transmitter together with or separately from the performance indicator.

[0195] In some embodiments, the channel state information measured on the reference signal within the second time window is transmitted by the first transmitter in a case where the performance of the artificial intelligence function or model is below a threshold.

[0196] In some embodiments, the apparatus is configured to perform the lifecycle management of the artificial intelligence function or model for component carriers (CCs) of the terminal device.

[0197] In some embodiments, the apparatus is further configured to perform the performance monitoring of the artificial intelligence function or model for at least one component carrier (CC), wherein the at least one component carrier is a subset of all component carriers of the terminal device.

[0198] In some embodiments, the first processor performs the lifecycle management of the artificial intelligence function or model for one component carrier according to a monitoring result of the performance monitoring of the artificial intelligence function or model for the one component carrier; or the first processor performs the lifecycle management of the artificial intelligence function or model for more than two component carriers that have an association relationship with the performance monitoring according to the monitoring result of the performance monitoring of the artificial intelligence function or model for one component carrier.

[0199] In some embodiments, the association relationship is predefined or configured by a network device or transmitted by the terminal device to the network device.

[0200] In some embodiments, the one component carrier is a first predetermined component carrier, and all component carriers in the at least one component carrier have an association relationship with the performance monitoring of the first predetermined component carrier; or

[0201] The one component carrier is a second predetermined component carrier in a frequency band, and all component carriers in the at least one component carrier located in the frequency band have an association relationship with the performance monitoring of the second predetermined component carrier.

[0202] In some embodiments, the first predetermined component carrier has a first identity; or the second predetermined component carrier has a second identity.

[0203] In some embodiments, the apparatus is configured to perform the lifecycle management of the artificial intelligence function or model for transmission reception points (TRPs) that communicate with the terminal device.

[0204] In some embodiments, the apparatus is further configured to perform the performance monitoring of the artificial intelligence function or model for at least one transmission reception point, wherein the at least one transmission reception point is a subset of all transmission reception points of the terminal device.

[0205] In some embodiments, the first processor performs the lifecycle management of the artificial intelligence function or model for one transmission reception point according to a monitoring result of the performance monitoring of the artificial intelligence function or model for the transmission reception point; or

[0206] The first processor performs the lifecycle management of the artificial intelligence function or model for more than two transmission reception points that have a correlation relationship with the performance monitoring according to a monitoring result of the performance monitoring of the artificial intelligence function or model for one transmission reception point.

[0207] In some embodiments, the apparatus is configured to perform the performance monitoring of the artificial intelligence function or model for at least one monitoring group, wherein:

[0208] The first processor performs the lifecycle management of the artificial intelligence function or model for one object in the monitoring group according to a monitoring result of the performance monitoring of the artificial intelligence function or model for the object; or

[0209] The first processor performs the lifecycle management of the artificial intelligence function or model for more than two objects in the monitoring group that have a correlation relationship with the performance monitoring according to a monitoring result of the performance monitoring of the artificial intelligence function or model for one object in the monitoring group.

[0210] In some embodiments, the correlation relationship is predefined or configured by a network device or sent by the terminal device to the network device.

[0211] The above various embodiments are only exemplarily described for the embodiments of the present application, but the present application is not limited thereto, and can be appropriately modified on the basis of the above various embodiments. For example, the above various embodiments can be used alone, or one or more of the above various embodiments can be combined.

[0212] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The device 1500 for monitoring performance can also include other components or modules, and the specific content of these components or modules can be referred to related art.

[0213] In addition, for the sake of simplicity, only the connection relationship or signal path between the various components or modules is exemplarily shown in FIG. 15, but those skilled in the art should understand that various related technologies such as bus connection can be used. The various components or modules described above can be implemented by hardware facilities such as processors, memories, transmitters, receivers, etc., and the present application is not limited thereto.

[0214] Embodiments of the fourth aspect

[0215] The embodiments of the present application provide a device for monitoring performance. The device can be a network device or a component or assembly configured in a network device. The same content as the embodiments of the first to third aspects will not be described again.

[0216] FIG. 16 is a schematic diagram of a device for monitoring performance according to an embodiment of the present application. As shown in FIG. 16, the device 1600 for monitoring performance includes:

[0217] a second transmitter 1601 configured to transmit a reference signal to a terminal device in a first time window; and

[0218] a second receiver 1602 configured to receive channel state information (CSI) predicted by the terminal device using an artificial intelligence model or model in a second time window, the second time window being after the first time window, the first time window including one or more time instances, and the second time window including one or more time instances.

[0219] The second receiver is further configured to receive a performance metric of the artificial intelligence function or model sent by the terminal device.

[0220] In some embodiments, the second transmitter transmits a reference signal to the terminal device at each time instance or part of a time instance of the second time window.

[0221] In some embodiments, the second receiver receives the performance metric respectively after the terminal device measures the reference signal at each time instance of the second time window; or

[0222] The second receiver receives the performance metric after the terminal device measures the reference signal at more than two time instances of the second time window.

[0223] In some embodiments, the terminal device calculates a performance indicator corresponding to each reference signal according to measurements on the reference signals at two or more time instances within the second time window, and the second receiver receives an average of the two or more performance indicators within the second time window, or the second receiver receives the two or more performance indicators within the second time window.

[0224] In some embodiments, the second receiver receives channel state information obtained by the terminal device from measurements on the reference signals within the second time window.

[0225] In some embodiments, the channel state information obtained by the terminal device from measurements on the reference signals within the second time window comprises at least one of a precoding matrix indicator (PMI), a channel quality indicator (CQI), and a rank indicator (RI).

[0226] In some embodiments, the channel state information obtained by the terminal device from measurements on the reference signals within the second time window is received together with or separately from the performance indicators.

[0227] In some embodiments, the second receiver receives the channel state information obtained by the terminal device from measurements on the reference signals within the second time window in a case where the performance of the artificial intelligence function or model is below a threshold.

[0228] In some embodiments, the second transmitter further configures the terminal device to perform lifecycle management of the artificial intelligence function or model for component carriers of the terminal device.

[0229] In some embodiments, the terminal device is configured to perform performance monitoring of the artificial intelligence function or model for at least one component carrier, wherein the at least one component carrier is a subset of all component carriers of the terminal device.

[0230] In some embodiments, the terminal device is configured to:

[0231] perform lifecycle management of the artificial intelligence function or model for the same component carrier according to a monitoring result of the performance monitoring of the artificial intelligence function or model for the component carrier; or

[0232] perform lifecycle management of the artificial intelligence function or model for two or more component carriers having a correlation with the performance monitoring according to the monitoring result of the performance monitoring of the artificial intelligence function or model for the component carrier.

[0233] In some embodiments, the association relationship is predefined or configured by the network device or sent by the terminal device to the network device.

[0234] In some embodiments, the one component carrier is a first predefined component carrier, and all component carriers in the at least one component carrier have an association relationship with performance monitoring of the first predefined component carrier; or

[0235] The one component carrier is a second predefined component carrier in a frequency band, and all component carriers in the at least one component carrier located in the frequency band have an association relationship with performance monitoring of the second predefined component carrier.

[0236] In some embodiments, the first predefined component carrier has a first identifier; or the second predefined component carrier has a second identifier.

[0237] In some embodiments, the second transmitter further configures the terminal device to perform lifecycle management of the artificial intelligence function or model for a transmission reception point (TRP) that communicates with the terminal device.

[0238] In some embodiments, the terminal device is configured to perform performance monitoring of the artificial intelligence function or model for at least one transmission reception point, wherein the at least one transmission reception point is all transmission reception points of the terminal device or a subset of all transmission reception points.

[0239] In some embodiments, the terminal device is configured to:

[0240] According to the monitoring result of the performance monitoring of the artificial intelligence function or model for one transmission reception point, lifecycle management of the artificial intelligence function or model is performed for the same transmission reception point; or

[0241] According to the monitoring result of the performance monitoring of the artificial intelligence function or model for one transmission reception point, lifecycle management of the artificial intelligence function or model is performed for two or more transmission reception points associated with the performance monitoring.

[0242] In some embodiments, the second transmitter further configures the terminal device to perform performance monitoring of the artificial intelligence function or model for at least one monitoring group, wherein:

[0243] The terminal device is configured to perform lifecycle management of the artificial intelligence function or model for one object in the monitoring group according to the monitoring result of the performance monitoring of the artificial intelligence function or model for the object; or

[0244] The terminal device is configured to perform, according to a monitoring result of performance monitoring of one object in the monitoring group by the artificial intelligence function or model, lifecycle management of two or more objects in the monitoring group that have an association relationship with the performance monitoring.

[0245] In some embodiments, the association relationship is predefined or configured by a network device or sent by the terminal device to the network device.

[0246] The above embodiments are only exemplary, and the present application is not limited thereto. The above embodiments can be appropriately modified on the basis of the above embodiments. For example, the above embodiments can be used alone or in combination.

[0247] It should be noted that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The device 1600 for monitoring performance can also include other components or modules, and the specific content of these components or modules can be referred to related technologies.

[0248] In addition, for simplicity, only the connection relationship or signal path between the components or modules is exemplarily shown in FIG. 16, but those skilled in the art should understand that various related technologies such as bus connection can be used. The above components or modules can be implemented by hardware facilities such as processors, memories, transmitters, receivers, etc.; the present application is not limited thereto.

[0249] Embodiments of the fifth aspect

[0250] The embodiments of the present application also provide a communication system, which can be referred to FIG. 1, and the same content as the embodiments of the first to fourth aspects will not be described.

[0251] In some embodiments, the communication system 100 can at least include:

[0252] a network device, which sends a reference signal to a terminal device in a first time window, receives predicted channel state information (CSI) in a second time window obtained by using an artificial intelligence model or model sent by the terminal device, receives a performance metric of the artificial intelligence function or model sent by the terminal device, wherein the second time window is after the first time window, the first time window includes one or more time instances, and the second time window includes one or more time instances; and

[0253] A terminal device obtains predicted channel state information (CSI) in a second time window using an artificial intelligence function or model based on measurements of reference signals in a first time window (observation window), and monitors performance of the artificial intelligence function or model based on measurements of reference signals in the second time window and the predicted channel state information in the second time window.

[0254] In the embodiments of the present application, scenarios involving network devices and / or terminal devices are taken as examples.

[0255] In the above scenarios, the network device can include at least one of a core network device, a third-party application device, an operation administration and maintenance (OAM), and an access network device.

[0256] The core network device can refer to a device in a core network (CN) that provides service support for a terminal device. As some examples, the core network device can be at least one of a mobility and management entity (MME), an access and mobility management function (AMF) entity, a session management function (SMF) entity, a user plane function (UPF) entity, a Location Management Function (LMF) entity, and the like, which are not all listed here. Among them, the AMF entity can be responsible for access management and mobility management of the terminal, the SMF entity can be responsible for session management, such as session establishment of a user, the UPF entity can be a functional entity of the user plane, mainly responsible for connecting external networks. The LMF entity can manage the overall coordination and scheduling of resources required for the location of terminal devices registered or accessing the core network device. It should be noted that in the embodiments of the present application, the entity can also be referred to as a network element or a functional entity, such as the AMF entity can also be referred to as an AMF network element or an AMF functional entity, and the like.

[0257] The third-party application device can be an over the top server (OTT) or other third-party device.

[0258] The OAM can be a network device for performing operation (Operation), administration (Administration), and maintenance (Maintenance) of a network according to actual needs of an operator network.

[0259] The access network device is an access device through which a terminal device accesses a communication system in a wireless manner. The access network device can be a base station (BS), a node, an evolved NodeB (eNodeB), a transmission reception point (TRP), a base station (gNB) in a 5th generation (5G) mobile communication system, a base station in a 6th generation (6G) mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. The access network device can also be a module or unit that completes part of the functions of a base station, for example, at least one of the following modules or units: a centralized unit (CU), a distributed unit (DU), a CU control plane (CU-CP), a CU user plane (CU-CP), an integrated access backhaul (IAB), or other modules or units. The embodiments of the present application do not limit the specific technology and / or specific device form adopted by the access network device. The access network device can be deployed on land, including indoors / outdoors, can be handheld or vehicle-mounted; can also be deployed on water, on an airplane, on a balloon, or on a satellite; the access network device can be deployed at a fixed location or on a mobile carrier, and the embodiments of the present application do not limit this.

[0260] In the scenarios described above, the terminal device can be a device with wireless transceiver function, which can send signals to the access network device and / or receive signals from the access network device. The terminal device can also be referred to as a terminal, mobile station, mobile terminal, etc. The terminal device can be a mobile phone, tablet, or other device with wireless smart transceiver function. The terminal device can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, remote medical care, or various smart scenarios.

[0261] In the scenarios described above, the access network device and the terminal device, or the terminal device and the terminal device, can communicate through licensed spectrum, or through unlicensed spectrum, or through both licensed spectrum and unlicensed spectrum. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0262] The embodiments of the present application also provide a terminal device, but the present application is not limited thereto, and can also be other devices.

[0263] FIG. 17 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in FIG. 17, the terminal device 1700 can include a processor 1717 and a memory 1720. The memory 1720 stores data and programs and is coupled to the processor 1717. It is worth noting that this figure is exemplary; other types of structures can also be used to supplement or replace this structure to achieve telecommunication functions or other functions.

[0264] For example, the processor 1010 can be configured to execute programs to implement the method of monitoring performance as described in the embodiments of the first aspect.

[0265] As shown in FIG. 17, the terminal device 1700 can also include a communication module 1730, an input unit 1740, a display 1750, and a power supply 1760. The functions of the above-mentioned components are similar to those of the prior art, and will not be described here. It is worth noting that the terminal device 1700 does not necessarily include all the components shown in FIG. 17, and the above-mentioned components are not essential. In addition, the terminal device 1700 can also include components not shown in FIG. 17, which can be referred to the prior art.

[0266] The embodiments of the present application also provide a network device, which can be a base station, but the present application is not limited thereto, and can also be other network devices.

[0267] Fig. 18 is a schematic diagram of a network device according to an embodiment of the present application. As shown in Fig. 18, the network device 1800 can include a processor 1810 (e.g., a central processing unit, CPU) and a memory 1820 coupled to the processor 1810. The memory 1820 can store various data. In addition, the memory 1820 can store programs 1830 for information processing, and execute the programs 1830 under control of the processor 1810.

[0268] For example, the processor 1810 can be configured to execute the programs to implement the method for monitoring performance according to an embodiment of the second aspect.

[0269] In addition, as shown in Fig. 18, the network device 1800 can further include a transceiver 1840, an antenna 1850, and the like. The functions of the above components are similar to those of the prior art, and will not be described here. It should be noted that the network device 1800 does not necessarily include all the components shown in Fig. 18. In addition, the network device 1800 can include components not shown in Fig. 18, which can be referred to the prior art.

[0270] An embodiment of the present application further provides a computer program, which, when executed in a terminal device, causes the terminal device to perform the method for monitoring performance according to an embodiment of the first aspect.

[0271] An embodiment of the present application further provides a storage medium storing a computer program, which causes a terminal device to perform the method for monitoring performance according to an embodiment of the first aspect.

[0272] An embodiment of the present application further provides a computer program, which, when executed in a network device, causes the network device to perform the method for monitoring performance according to an embodiment of the second aspect.

[0273] An embodiment of the present application further provides a storage medium storing a computer program, which causes a network device to perform the method for monitoring performance according to an embodiment of the second aspect.

[0274] The apparatuses and methods described above can be implemented by hardware, or by hardware in combination with software. The present application relates to a computer readable program, which, when executed by a logic component, can cause the logic component to implement the apparatuses or constituent components described above, or to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, and the like.

[0275] The method / apparatus described in combination with the embodiments of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of both. For example, one or more of the functional blocks shown in the figure and / or a combination of one or more of the functional blocks can correspond to each software module of a computer program flow, and can also correspond to each hardware module. These software modules can correspond to each step shown in the figure, respectively. These hardware modules can be implemented by, for example, fixing the software modules by using a field programmable gate array (FPGA).

[0276] The software module can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium known in the art. One storage medium can be coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and the storage medium can be located in an ASIC. The software module can be stored in a memory of the mobile terminal, or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a MEGA-SIM card or a large-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0277] For one or more of the functional blocks shown in the figure and / or a combination of one or more of the functional blocks, a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or any appropriate combination thereof can be implemented to perform the functions described in the present application. For one or more of the functional blocks shown in the figure and / or a combination of one or more of the functional blocks, a combination of computing devices can also be implemented, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0278] The present application has been described above with reference to specific embodiments. However, it should be clear to those skilled in the art that these descriptions are exemplary and are not limiting to the scope of protection of the present application. Those skilled in the art can make various modifications and changes to the present application according to the spirit and principles of the present application, and these modifications and changes are also within the scope of the present application.

[0279] In connection with the embodiments including the above embodiments, the following notes are also disclosed:

[0280] 1. A method of monitoring performance, applied to a terminal device, wherein the method comprises:

[0281] obtaining predicted channel state information (CSI) in a second time window (prediction window) using an artificial intelligence function or model based on measurements of reference signals in a first time window (observation window), the second time window being subsequent to the first time window, the first time window comprising one or more time instances, the second time window comprising one or more time instances; and

[0282] monitoring performance of the artificial intelligence function or model based on measurements of reference signals in the second time window and the predicted channel state information in the second time window.

[0283] 2. A method of monitoring performance, applied to a network device, wherein the method comprises:

[0284] transmitting reference signals to a terminal device in a first time window;

[0285] receiving predicted channel state information (CSI) in a second time window (prediction window) obtained using an artificial intelligence model or model transmitted by the terminal device, the second time window being subsequent to the first time window, the first time window comprising one or more time instances, the second time window comprising one or more time instances; and

[0286] receiving a performance metric of the artificial intelligence function or model transmitted by the terminal device.

[0287] 3. A terminal device comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement the method of clause 1.

[0288] 4. A network device comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement the method of clause 2.

[0289] 5. A computer program product comprising at least a computer program which, when executed by a processor, causes a terminal device to perform the method of clause 1.

[0290] 6. A computer program product comprising at least a computer program which, when executed by a processor, causes a network device to perform the method of clause 2.

Claims

1. An apparatus for monitoring performance, applied to a terminal device, wherein, The apparatus comprises a first processor configured to: obtain, according to measurements of reference signals in a first time window, predicted channel state information in a second time window using an artificial intelligence function or model, the second time window being after the first time window, the first time window comprising one or more time instances, and the second time window comprising one or more time instances; and monitor performance of the artificial intelligence function or model according to measurements of reference signals in the second time window and the predicted channel state information in the second time window, wherein the reference signals in the second time window are transmitted at each time instance or a partial time instance of the second time window.

2. The apparatus of claim 1, wherein the channel state information obtained by measuring the reference signals in the second time window is compared with the predicted channel state information in the second time window to monitor the performance of the artificial intelligence function or model.

3. The apparatus of claim 1, wherein, The apparatus further comprises: a first receiver configured to receive corresponding reference signals at each time instance or a partial time instance of the second time window.

4. The apparatus of claim 3, wherein, The apparatus further comprises: a first transmitter configured to transmit performance indicators of the artificial intelligence function or model to a network device.

5. The apparatus of claim 4, wherein the first transmitter transmits the performance indicators respectively after the first processor measures the reference signals at each time instance of the second time window; or the first transmitter transmits the performance indicators after the first processor measures the reference signals at more than two time instances of the second time window.

6. The apparatus of claim 5, wherein the first processor calculates performance indicators corresponding to the reference signals respectively according to measurements of the reference signals at more than two time instances of the second time window, and the first transmitter transmits an average of the performance indicators or transmits the performance indicators together to the network device.

7. The apparatus of claim 4, wherein the first transmitter further transmits channel state information obtained by measuring the reference signals in the second time window to the network device.

8. The apparatus of claim 7, wherein the channel state information obtained by measuring the reference signals in the second time window comprises at least one of a precoding matrix indicator, a channel quality indicator, and a rank indicator.

9. The apparatus of claim 7, wherein the first transmitter transmits the channel state information obtained by measuring the reference signals in the second time window together with the performance indicators or separately.

10. The apparatus of claim 1, wherein the apparatus is configured to perform lifecycle management of the artificial intelligence function or model for component carriers of the terminal device.

11. The apparatus of claim 10, wherein the apparatus is further configured to perform performance monitoring of the artificial intelligence function or model for at least one component carrier, ​ The at least one component carrier is all component carriers of the terminal device or a subset of all component carriers.

12. The apparatus of claim 11, wherein, the first processor performs lifecycle management of the artificial intelligence function or model for the same component carrier according to a monitoring result of performance monitoring of the artificial intelligence function or model for one component carrier; or the first processor performs lifecycle management of the artificial intelligence function or model for more than two component carriers having an association relationship with the performance monitoring according to a monitoring result of performance monitoring of the artificial intelligence function or model for one component carrier.

13. The apparatus of claim 12, wherein, the association relationship is predefined or configured by a network device or sent by the terminal device to the network device.

14. The apparatus of claim 12, wherein, the one component carrier is a first predetermined component carrier, and all component carriers in the at least one component carrier have an association relationship with performance monitoring of the first predetermined component carrier; or the one component carrier is a second predetermined component carrier in a frequency band, and all component carriers in the at least one component carrier located in the frequency band have an association relationship with performance monitoring of the second predetermined component carrier.

15. The apparatus of claim 14, wherein, the first predetermined component carrier has a first identifier; or the second predetermined component carrier has a second identifier.

16. The apparatus of claim 1, wherein, the apparatus is configured to perform lifecycle management of the artificial intelligence function or model for a transmission reception point (TRP) in communication with the terminal device.

17. The apparatus of claim 16, wherein, the apparatus is further configured to perform performance monitoring of the artificial intelligence function or model for at least one transmission reception point, wherein the at least one transmission reception point is all transmission reception points in communication with the terminal device or a subset of all transmission reception points.

18. The apparatus of claim 17, wherein, the first processor performs lifecycle management of the artificial intelligence function or model for the same transmission reception point according to a monitoring result of performance monitoring of the artificial intelligence function or model for one transmission reception point; or the first processor performs lifecycle management of the artificial intelligence function or model for more than two transmission reception points having an association relationship with the performance monitoring according to a monitoring result of performance monitoring of the artificial intelligence function or model for one transmission reception point.

19. The apparatus of claim 1, wherein, the apparatus is configured to perform performance monitoring of the artificial intelligence function or model for at least one monitoring group, wherein, the first processor performs lifecycle management of the artificial intelligence function or model for the same object according to a monitoring result of performance monitoring of the artificial intelligence function or model for one object in the monitoring group; or ​ The first processor performs life cycle management of the artificial intelligence function or model on two or more objects in the monitoring group that have a correlation relationship with the performance monitoring of the artificial intelligence function or model on one object in the monitoring group according to a monitoring result of the performance monitoring.

20. The apparatus of claim 19, wherein, The correlation relationship is predefined or configured by a network device or sent by the terminal device to the network device.

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