Report sending method and apparatus, report receiving method and apparatus, report configuration method and apparatus, and communication system

Abstract

Embodiments of the present application provide a report sending method and apparatus, a report receiving method and apparatus, a report configuration method and apparatus, and a communication system. The report sending apparatus is an apparatus applied to a terminal device side. The apparatus on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction. The report sending apparatus comprises a first communication unit, and the first communication unit performs the following operations: configuring a first resource set used for performing performance monitoring on the artificial intelligence model or function, and a second resource set used for inference, time-domain behavior of the first resource set or the second resource set being at least one of the following: periodic, semi-persistent, and aperiodic; and sending a channel state information report used for performance monitoring and a channel state information report used for inference, time-domain behavior of the channel state information report used for performance monitoring or the channel state information report used for inference being at least one of the following: periodic, semi-persistent, or aperiodic, wherein a terminal device measures the first resource set to acquire ground truth data and / or compute performance metrics, and the terminal device measures the second resource set, the measurement results being used as input to the artificial intelligence model or function.

Description

Methods, apparatus, and communication systems for sending, receiving, and configuring reports. Technical Field

[0001] The embodiments of this application relate to the field of communication technology. Background Technology

[0002] In the new Radio Release 18 (NR Rel-18), artificial intelligence or machine learning (AI / ML) for the air interface was investigated. AI / ML can be used for the following use cases: Channel State Information (CSI) feedback enhancement, beam management, and positioning enhancement. CSI feedback enhancement can include CSI prediction and CSI compression; beam management can include spatial domain beam prediction (i.e., BM case-1) and temporal beam prediction (i.e., BM case-2); positioning enhancement can include direct positioning and AI / ML-assisted positioning.

[0003] Beam management includes: spatial domain beam prediction and temporal beam prediction. The network device can configure a set of beams (i.e., reference signals) for measurement (e.g., set B) for the terminal device, and measurements for set B are used as input to AI / ML functions / models. The network device can also configure a set of beams (i.e., reference signals) for prediction (e.g., set A), for example, set A can be used for inference.

[0004] In some sub-use cases, a two-sided model can be used, meaning the AI / ML function or model resides on both the terminal device side and the network device side. In other sub-use cases, a one-sided model can be used, meaning the AI / ML function or model resides either on the terminal device side or on the network device side. For beam management, the AI / ML model can reside on both the terminal device side and / or on the network device side.

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

[0006] In scenarios where AI / ML functions or models are used for CSI prediction, performance monitoring can be performed on the AI / ML functions or models to check their performance, thereby facilitating the control of the AI / ML functions or models. This control may be, for example, at least one of activation, deactivation, selection, switching, and fallback of the AI / ML function or model.

[0007] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of this application and facilitating understanding by those skilled in the art. It should not be assumed that these technical solutions are known to those skilled in the art simply because they have been described in the background section of this application. Summary of the Invention

[0008] In CSI prediction, AI / ML functions or models can be configured on the terminal device side. Network device-side equipment can configure resource sets for inference and performance monitoring for the terminal device. The AI / ML function or model can utilize the resource set for inference to perform CSI prediction, and the AI / ML function or model can utilize the resource set for performance monitoring to monitor its performance.

[0009] The inventors discovered that when a terminal device is equipped with an artificial intelligence model or function for Channel State Information (CSI) prediction, a problem needs to be solved regarding how to define the time-domain behavior of the resource set for performance monitoring, the resource set for inference, the CSI report for performance monitoring, and the CSI report for inference. Furthermore, in carrier aggregation scenarios, how to correlate the CSI report for inference with the CSI report for performance monitoring for a single component carrier or a bandwidth portion is also a problem that needs to be addressed.

[0010] To address at least one of the above-mentioned problems, embodiments of this application provide a method, apparatus, and communication system for sending, receiving, and configuring reports.

[0011] According to one aspect of the embodiments of this application, a report transmitting apparatus is provided, applied to a terminal device side, wherein the terminal device side apparatus is configured with an artificial intelligence model or function for channel state information prediction, and the report transmitting apparatus includes a first communication unit, the first communication unit performing the following operations:

[0012] A first resource set configured for performance monitoring of the artificial intelligence model or function, and a second resource set configured for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic; and

[0013] The system transmits Channel State Information (CSI) reports for performance monitoring and Channel State Information (CSI) reports for inference. The time-domain behavior of either the CSI report for performance monitoring or the CSI report for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0014] The terminal device measures the first resource set to obtain ground truth data and / or calculates performance metrics, and the terminal device measures the second resource set. The results of the measurements are used as input to the artificial intelligence model or function.

[0015] According to another aspect of the embodiments of this application, a report configuration apparatus is provided, applied to an apparatus on a terminal device side, wherein the apparatus on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction, and the report configuration apparatus includes a second communication unit, the second communication unit performing the following operations:

[0016] In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0017] The inference parameter set or associated ID is configured in the CSI reporting configuration for inference.

[0018] One of the beneficial effects of the embodiments of this application is that, when the device on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction, the temporal behavior of the resource set for performance monitoring, the resource set for inference, the channel state information report for performance monitoring, and the channel state information report for inference is defined; in addition, in the scenario of carrier aggregation, the association method between the channel state information report for inference and the channel state information report for performance monitoring is defined for a component carrier or a bandwidth portion.

[0019] Specific embodiments of this application are disclosed in detail with reference to the following description and accompanying drawings, indicating how the principles of this application can be adopted. It should be understood that the embodiments of this application are not limited in scope. Within the spirit and scope of the appended claims, embodiments of this application include many changes, modifications, and equivalents.

[0020] Features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0021] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, whole, step, or component, but does not exclude the presence or addition of one or more other features, wholes, steps, or components. Attached Figure Description

[0022] The elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. Furthermore, in the drawings, similar reference numerals denote corresponding parts in several drawings and can be used to indicate corresponding parts used in more than one embodiment.

[0023] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application;

[0024] Figure 2 is a schematic diagram of a report sending method according to an embodiment of this application;

[0025] Figure 3 is a schematic diagram of a report configuration method according to an embodiment of this application;

[0026] Figure 4 is a schematic diagram of an example of the first alternative scheme of Embodiment 1;

[0027] Figure 5 is a schematic diagram of another example of the first alternative scheme of Embodiment 1;

[0028] Figure 6 is a schematic diagram of yet another example of the first alternative scheme of Embodiment 1;

[0029] Figure 7 is a schematic diagram of an example of the second alternative scheme of Embodiment 1;

[0030] Figure 8 is a schematic diagram of an example of the third alternative scheme of Embodiment 1;

[0031] Figure 9 is a schematic diagram of another example of the third alternative scheme of Embodiment 1;

[0032] Figure 10 is a schematic diagram of an example of the fourth alternative scheme of Embodiment 1;

[0033] Figure 11 is a schematic diagram of an example of the first alternative scheme of Embodiment 2;

[0034] Figure 12 is a schematic diagram of another example of the first alternative scheme of Embodiment 2;

[0035] Figure 13 is a schematic diagram of yet another example of the first alternative scheme of Embodiment 2;

[0036] Figure 14 is a schematic diagram of an example of the second alternative scheme of Embodiment 2;

[0037] Figure 15 is a schematic diagram of an example of the third alternative scheme of Embodiment 2;

[0038] Figure 16 is a schematic diagram of another example of the third alternative scheme of Embodiment 2;

[0039] Figure 17 is a schematic diagram of an example of the fourth alternative scheme of Embodiment 2;

[0040] Figure 18 is a schematic diagram of a report receiving method;

[0041] Figure 19 is a schematic diagram of the report configuration method;

[0042] Figure 20 is a schematic diagram of a report sending device according to an embodiment of this application;

[0043] Figure 21 is a schematic diagram of a report configuration device according to an embodiment of this application;

[0044] Figure 22 is a schematic diagram of a report receiving device according to an embodiment of this application;

[0045] Figure 23 is a schematic diagram of a report configuration device according to an embodiment of this application;

[0046] Figure 24 is a schematic diagram of the electronic device according to an embodiment of this application. Detailed Implementation

[0047] Referring to the accompanying drawings, the foregoing and other features of this application will become apparent from the following description. Specific embodiments of this application are specifically disclosed in the description and drawings, illustrating partial implementations in which the principles of this application may be employed. It should be understood that this application is not limited to the described embodiments; rather, it includes all modifications, variations, and equivalents falling within the scope of the appended claims.

[0048] In the embodiments of this application, the terms "first," "second," etc., are used to distinguish different elements by name, but do not indicate the spatial arrangement or chronological order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one or more of the terms listed in association and all combinations thereof. The terms "comprising," "including," "having," etc., refer to the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0049] In the embodiments of this application, the singular forms "a," "the," etc., including the plural forms, should be broadly understood as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0050] In the embodiments of this application, the term "communication network" or "wireless communication network" may refer to a network that conforms to any of the following communication standards, such as Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), etc.

[0051] Furthermore, communication between devices in a communication system can be carried out according to communication protocols at any stage, including but 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 communication protocols.

[0052] In the embodiments of this application, the term "network device" refers, for example, to a device in a communication system that connects a terminal device to a communication network and provides services to that terminal device. Network devices may include, but are 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), etc.

[0053] Base stations can include, but are not limited to: NodeBs (or NBs), evolved NodeBs (eNodeBs or eNBs), and 5G base stations (gNBs), IAB hosts, etc. They can also include Remote Radio Heads (RRHs), Remote Radio Units (RRUs), relays, or low-power nodes (e.g., femeto, pico, etc.). The term "base station" can encompass some or all of their functions, and each base station can provide communication coverage to 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.

[0054] In the embodiments of this application, the terms "User Equipment" (UE) or "Terminal Equipment" (TE) refer, for example, to a device that accesses a communication network and receives network services through a network device. Terminal equipment can be fixed or mobile, and may also be referred to as a mobile station (MS), terminal, subscriber station (SS), access terminal (AT), station, mobile terminal (MT), etc.

[0055] The terminal device may 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, smartphone, smartwatch, digital camera, etc.

[0056] For example, in scenarios such as the Internet of Things (IoT), terminal devices can also be machines or devices for monitoring or measurement, such as including but not limited to: machine-type communication (MTC) terminals, vehicle communication terminals, device-to-device (D2D) terminals, machine-to-machine (M2M) terminals, and so on.

[0057] Furthermore, the terms "network side" or "network equipment side" refer to one side of the network, which can be a base station or include one or more network devices as described above. The terms "user side," "terminal side," or "terminal equipment side" refer to the side of the user or terminal, which can be a UE or include one or more terminal devices as described above. Unless otherwise specified, "equipment" can refer to either network equipment or terminal equipment.

[0058] In the following description, without causing confusion, the terms “uplink control signal” and “uplink control information (UCI)” or “physical uplink control channel (PUCCH)” are used interchangeably, as are the terms “uplink data signal” and “uplink data information” or “physical uplink shared channel (PUSCH)”.

[0059] The terms “downlink control signal” and “downlink control information (DCI)” or “physical downlink control channel (PDCCH)” are interchangeable, as are the terms “downlink data signal” and “downlink data information (PDSCH)” or “physical downlink shared channel (PDSCH)”.

[0060] Additionally, uplink signals can include uplink data signals and / or uplink control signals and / or PRACH and / or SRS, etc., and can also be referred to as uplink transmission (UL transmission), uplink information, or uplink channel. Sending / receiving uplink transmission on uplink resources can be understood as using that uplink resource to send / receive the uplink transmission. Downlink signals can include downlink data signals and / or downlink control signals and / or synchronization signals (SS, such as PSS / SSS) and / or broadcast channel (PBCH) and / or SSB (SS / PBCH block, including PSS, SSS, and PBCH and their DMRS) and / or CSI-RS, etc., and can also be referred to as downlink transmission (DL transmission), downlink information, or downlink channel. Sending / receiving downlink transmission on downlink resources can be understood as using that downlink resource to send / receive the downlink transmission.

[0061] In the embodiments of this application, higher-layer signaling may be, for example, Radio Resource Control (RRC) signaling; RRC signaling may include, for example, RRC messages, such as broadcast / public RRC messages / signaling (e.g., Master Information Block (MIB), system information), private RRC messages / signaling; or RRC information elements (RRC IE); or information fields (or information fields included in information fields) included in RRC messages or RRC information elements. Higher-layer signaling may also be, for example, Medium Access Control (MAC) signaling; or referred to as MAC control elements (MAC CE). However, this application is not limited to these.

[0062] In the embodiments of this application, "multiple" refers to at least two, or two or more.

[0063] In this application embodiment, "predefined" refers to what is specified by the protocol or determined according to the rules specified by the protocol, and does not require additional configuration. "Configuration / instruction" refers to what the network device directly or indirectly configures / instructs through higher-layer signaling and / or physical layer signaling. Configuration / instruction can be achieved by introducing higher-layer parameters into the higher-layer signaling. Higher-layer parameters refer to information fields and / or information elements / information units / information cells (IEs) in the higher-layer signaling. Physical layer signaling refers to, for example, control information (DCI) carried by the physical downlink control channel or control information carried by the sequence, but is not limited to these.

[0064] For ease of description, the following description uses a base station as an example of an access network device. In the following description, without causing confusion, "if...", "in the case of...", and "when..." can be used interchangeably.

[0065] The following examples illustrate the scenarios of embodiments of this application, but this application is not limited thereto.

[0066] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application, illustrating the case of a terminal device and a network device as examples. As shown in Figure 1, the communication system 100 may include a network device 101 and terminal devices 102 and 103. For simplicity, Figure 1 only illustrates the case of two terminal devices and one network device, but the embodiments of this application are not limited to this.

[0067] In this embodiment of the application, network device 101 and terminal devices 102 and 103 can transmit existing services or services that can be implemented in the future. For example, these services may include, but are not limited to: enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0068] It is worth noting that Figure 1 shows that both terminal devices 102 and 103 are within the coverage area of ​​network device 101, but this application is not limited to this. Both terminal devices 102 and 103 may be outside the coverage area of ​​network device 101, or one terminal device 102 may be within the coverage area of ​​network device 101 while the other terminal device 103 may be outside the coverage area of ​​network device 101.

[0069] In the embodiments of this application, one or more AI / ML functions or models may be configured and run in the network device and / or 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.; this application is not limited thereto.

[0070] In the various embodiments of this application, the following terms have the same meaning and can be used interchangeably: AI / ML, artificial intelligence, artificial intelligence, or machine learning.

[0071] In various embodiments of this application, AI / ML functionality / model may also be referred to as AI / ML function or model, artificial intelligence or machine learning function or model, artificial intelligence function or model, etc., which have the same meaning and can be used interchangeably in this application.

[0072] First aspect of the embodiments

[0073] This application provides a method for sending a report, described from the perspective of a terminal device.

[0074] Figure 2 is a schematic diagram of a report sending method according to an embodiment of this application. As shown in Figure 2, the report sending method includes:

[0075] 201. A first resource set configured for performance monitoring of the artificial intelligence model or function, and a second resource set configured for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic.

[0076] 202. Send Channel State Information Reports (CSI reports) for performance monitoring and Channel State Information Reports for inference. The time-domain behavior of the Channel State Information Reports for performance monitoring or the Channel State Information Reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0077] In the report transmission method shown in Figure 2, the device on the terminal side is configured with an artificial intelligence model or function for channel state information (CSI) prediction. The device on the terminal side is, for example, a terminal device or a server on the terminal side (e.g., an OTT server).

[0078] In this application, the terminal device can measure a first resource set to obtain ground truth data and / or calculate a performance metric, thereby monitoring the performance of an artificial intelligence model or function used for channel state information prediction (CSI prediction).

[0079] For example, performance monitoring can be assisted by terminal devices or performed on the network device side. For terminal device-assisted monitoring, the terminal device can measure the reference signal used for monitoring, calculate the performance metric, and report the performance metric to the network device. For network device-side monitoring, the terminal device can measure the reference signal used for monitoring, report the measurement results to the network device, and the network device calculates the performance metric.

[0080] In this application, the terminal device can measure a second resource set, and the result of the measurement is used as input to the artificial intelligence model or function.

[0081] In some embodiments of this application, when the terminal device is configured with an artificial intelligence model or function for channel state information prediction, a dedicated CSI report for performance monitoring can be configured on the terminal device. This dedicated CSI report (configuration) for performance monitoring can be associated with a CSI report (configuration) for inference. The identifier (ID) of the CSI report configuration for inference can be configured into the dedicated CSI report configuration for performance monitoring.

[0082] In some cases, a dedicated resource set (or proprietary resources of the reference signal) can be configured for performance monitoring, such as a first resource set.

[0083] In other examples, the set of reference signal resources (or reference signal resources) used for inference can be used for performance monitoring, or a portion of the set of reference signal resources (or a portion of reference signal resources) used for inference can be used for performance monitoring. That is, the first resource set can be a subset of the second resource set, or the first resource set can be the same as the second resource set. In this case, which time instances are used for performance monitoring can be predefined or configured.

[0084] In other embodiments of this application, after the terminal device sends a supported artificial intelligence model or function to the network device, it can receive a Channel State Information Report Configuration (CSI) configuration and / or an Inference Parameter Set configured by the network device. The CSI configuration may be configured with an Associated ID. Furthermore, the terminal device can further select an available CSI configuration and / or Inference Parameter Set and report it to the network device. The Inference Parameter Set may include at least one Inference Parameter. In this application, the Inference Parameter Set may be replaced with an Inference Parameter.

[0085] In this application, the inference parameter set or inference parameters may have at least one of the following information:

[0086] Associated ID;

[0087] The number of time instances in the measurement window;

[0088] The number of time instances in the prediction window;

[0089] The time interval between two consecutive time instances in the measurement window;

[0090] The time interval between two consecutive time instances in the prediction window.

[0091] In some examples of this application, the channel state information report for performance monitoring can be initiated or triggered by a device on the terminal device side. For example, the channel state information report for performance monitoring can be initiated or triggered by the terminal device.

[0092] In other examples of this application, non-periodic channel state information reporting for performance monitoring is not supported.

[0093] The following examples illustrate the limitations made in this application regarding the time-domain behavior of resource sets for performance monitoring, resource sets for inference, channel state information reports for performance monitoring, and channel state information reports for inference.

[0094] In the examples of the first aspect, in some instances, the time-domain behavior of the first resource set and the channel state information report used for performance monitoring can be as follows:

[0095] The first resource set is periodic, and the channel state information report associated with the first resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0096] The first resource set is semi-persistent, and the channel state information report associated with the first resource set for performance monitoring is either semi-persistent or aperiodic; or

[0097] The first resource set is aperiodic, and the channel state information report associated with the first resource set for performance monitoring is aperiodic.

[0098] The above information is shown in Table 1.

[0099] Table 1

[0100] Furthermore, in some examples, in the example of the first aspect, aperiodic first resource sets are not supported. For example, aperiodic first resource sets are not associated with the channel state information reports used for performance monitoring.

[0101] In the examples of the second aspect, in some instances, the time-domain behavior of channel state information reports used for inference and channel state information reports used for performance monitoring can be as follows:

[0102] The channel state information report used for inference is periodic, and the associated channel state information report used for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0103] The channel state information report used for inference is semi-persistent, and the associated channel state information report used for performance monitoring is either semi-persistent or aperiodic; or

[0104] The channel state information report used for inference is aperiodic, and the associated channel state information report used for performance monitoring is also aperiodic.

[0105] The above information is shown in Table 2.

[0106] Table 2

[0107] Furthermore, in some examples of the second aspect, aperiodic channel state information reports for inference are not supported for association with channel state information reports for performance monitoring. For example, aperiodic channel state information reports for inference are not associated with the channel state information reports for performance monitoring.

[0108] In the examples of the third aspect, in some instances, the time-domain behavior of the second resource set and the channel state information reporting for performance monitoring can be as follows:

[0109] The second resource set is periodic, and the channel state information report associated with the second resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0110] The second resource set is semi-persistent, and the channel state information report associated with the second resource set for performance monitoring is either semi-persistent or aperiodic; or

[0111] The second resource set is aperiodic, and the channel state information report associated with the second resource set for performance monitoring is aperiodic.

[0112] The above information is shown in Table 3.

[0113] Table 3

[0114] Furthermore, in the examples of the third aspect, and in other examples, the association of a non-periodic second resource set with the channel state information report used for performance monitoring is not supported. For example, the non-periodic second resource set is not associated with the channel state information report used for performance monitoring.

[0115] In the examples of the fourth aspect, in some instances, the temporal behavior of the second resource set and the first resource set can be the same.

[0116] In the example of the fourth aspect, and in other examples, the temporal behavior of the second resource set and the first resource set can be as follows:

[0117] The second resource set is periodic, and the first resource set associated with the second resource set is periodic, semi-persistent, or aperiodic; or

[0118] The second resource set is semi-persistent, and the first resource set associated with the second resource set is either semi-persistent or aperiodic; or

[0119] The second resource set is aperiodic, and the first resource set associated with the second resource set is also aperiodic.

[0120] The above information is shown in Table 4.

[0121] Table 4

[0122] Furthermore, in some examples of the fourth aspect, the use of a non-periodic second resource set for performance monitoring is not supported; for example, the non-periodic second resource set is not associated with the first resource set.

[0123] This application also provides a method for configuring a report, which is described from the perspective of the terminal device.

[0124] Figure 3 is a schematic diagram of a report configuration method according to an embodiment of this application. As shown in Figure 3, the report configuration method includes:

[0125] 301. In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0126] In the report configuration method shown in Figure 3, the device on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction. The device on the terminal device side is, for example, a terminal device or a server on the terminal device side (e.g., an OTT server).

[0127] In Operation 301, a carrier can refer to a component carrier (CC) in a carrier aggregation technique. A carrier or a bandwidth portion can be written as CC / BWP.

[0128] In operation 301, the channel state information reporting configuration for inference is configured with either an inference parameter set or an associated ID.

[0129] According to the report configuration method shown in Figure 3, in the carrier aggregation scenario, the association method between the channel state information report used for inference and the channel state information report used for performance monitoring is defined for a component carrier or a bandwidth portion.

[0130] The following will further illustrate operation 301 through some embodiments.

[0131] Example 1

[0132] On a carrier or a bandwidth portion, one or more artificial intelligence functions or models for channel state information prediction are activated, wherein at least one of the one or more activated artificial intelligence functions or models may be associated with performance monitoring reporting, or at least one artificial intelligence function or model may not be associated with performance monitoring reporting.

[0133] In some examples, the activated AI function or model may correspond to a configured Channel State Information (CSI) reporting configuration for inference, wherein the temporal behavior of the CSI reporting for inference is at least one of the following: periodic, semi-persistent, or aperiodic. The performance monitoring report could be the CSI report configuration for performance monitoring.

[0134] In Example 1, the Channel State Information Reporting Configuration for Inference can be configured with an inference parameter set or an inference parameter, and the inference parameter set can have at least one inference parameter.

[0135] The inference parameter set has at least one of the following:

[0136] The number of time instances in the measurement window;

[0137] The number of time instances in the prediction window;

[0138] The time interval between two consecutive time instances in the measurement window;

[0139] The time interval between two consecutive time instances in the prediction window.

[0140] When using inference parameter sets, you can introduce or configure an inference parameter set ID.

[0141] Example 1 will be described below using several alternative solutions.

[0142] In the first alternative, multiple channel state information report configurations for inference are configured with the same set of inference parameters (or the same inference parameters), wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. This specific channel state information report configuration for inference is periodic or semi-persistent, and is predefined or configurable. For example, the specific channel state information report configuration for inference may have a highest or lowest identifier (e.g., CSI-ReportConfigId).

[0143] Figure 4 is a schematic diagram of an example of the first alternative scheme of Embodiment 1. As shown in Figure 4, multiple channel state information report configurations for inference are configured with the same set of inference parameters (or the same inference parameters), wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. This specific channel state information report configuration for inference is periodic or semi-persistent and may have a minimum identifier (e.g., CSI-ReportConfigId).

[0144] Figure 5 is a schematic diagram of another example of the first alternative scheme of Embodiment 1. In the example shown in Figure 5, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting for performance monitoring.

[0145] Figure 6 is a schematic diagram of another example of the first alternative scheme of Embodiment 1. In the example shown in Figure 6, the device on the terminal device side is configured with a non-periodic channel state information reporting configuration for inference, and is also configured with a periodic or semi-persistent channel state information reporting configuration for inference that has the same inference parameter set (or the same inference parameters) as the non-periodic channel state information reporting configuration for inference.

[0146] Furthermore, in other examples of the first alternative, if the device on the terminal side is configured with an aperiodic channel state information reporting configuration for inference, the device on the terminal side is not configured with a periodic or semi-persistent channel state information reporting configuration for inference that has the same set of inference parameters (or the same inference parameters) as the aperiodic channel state information reporting configuration for inference.

[0147] In the second alternative, multiple channel state information report configurations for inference are configured with the same set of inference parameters (or the same inference parameters), and at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. This specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and is predefined or configurable. For example, the specific channel state information report configuration for inference may have a highest or lowest identifier (e.g., CSI-ReportConfigId).

[0148] Figure 7 is a schematic diagram of an example of the second alternative scheme of Embodiment 1. In the example shown in Figure 7, multiple channel state information report configurations for inference are configured with the same set of inference parameters (or the same inference parameters), and at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. The specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and may have a minimum identifier (e.g., CSI-ReportConfigId).

[0149] Figure 8 is a schematic diagram of an example of a third alternative scheme of Embodiment 1. In the example shown in Figure 8, the non-periodic channel state information report configuration for inference is not associated with the channel state information report for performance monitoring, and each of one or more periodic or semi-persistent channel state information report configurations for inference is associated with the channel state information report for performance monitoring.

[0150] In another example of the third alternative, the non-periodic channel state information report configuration for inference is not associated with the channel state information report configuration for performance monitoring, and one or more periodic or semi-persistent channel state information report configurations for inference are associated with the channel state information report configuration for performance monitoring.

[0151] Figure 9 is a schematic diagram of another example of the third alternative scheme of Embodiment 1. In the example shown in Figure 9, the non-periodic channel state information report configuration for inference is not associated with the channel state information report for performance monitoring. Instead, a specific periodic or semi-persistent channel state information report configuration for inference is associated with the channel state information report for performance monitoring. This specific periodic or semi-persistent channel state information report configuration for inference is predefined or configurable. For example, this specific periodic or semi-persistent channel state information report configuration for inference may have a highest or lowest identifier (e.g., CSI-ReportConfigId).

[0152] Figure 10 is a schematic diagram of an example of the fourth alternative scheme of Embodiment 1. In the example shown in Figure 10, on a carrier or a bandwidth portion, each channel state information report for inference of each activated artificial intelligence model or function is associated with a channel state information report for performance monitoring.

[0153] Example 2

[0154] On a carrier or a bandwidth portion, one or more artificial intelligence functions or models for channel state information prediction are activated, wherein at least one of the one or more activated artificial intelligence functions or models may be associated with performance monitoring reporting, or at least one artificial intelligence function or model may not be associated with performance monitoring reporting.

[0155] In some examples, the activated artificial intelligence function or model may correspond to a configured Channel State Information Reporting (CSI) configuration for inference, wherein the temporal behavior of the CSI report for inference is at least one of the following: periodic, semi-persistent, or aperiodic. Performance monitoring reports may be CSI reporting configurations for performance monitoring.

[0156] In Example 2, an Associated ID is configured in the Channel State Information Reporting Configuration for Inference.

[0157] Example 2 will be described below using several alternative solutions.

[0158] In the first alternative, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. This specific channel state information report configuration for inference is periodic or semi-persistent, and is predefined or configurable. For example, the specific channel state information report configuration for inference may have a highest or lowest ID (e.g., CSI-ReportConfigId).

[0159] Figure 11 is a schematic diagram of an example of the first alternative scheme of Embodiment 2. As shown in Figure 11, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. This specific channel state information report configuration for inference is periodic or semi-persistent, and may have a minimum identifier (e.g., CSI-ReportConfigId).

[0160] Figure 12 is a schematic diagram of another example of the first alternative scheme of Embodiment 2. In the example shown in Figure 12, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting for performance monitoring.

[0161] Figure 13 is a schematic diagram of another example of the first alternative scheme of Embodiment 2. In the example shown in Figure 13, the device on the terminal device side is configured with a non-periodic channel state information report configuration for inference, and is also configured with a periodic or semi-persistent channel state information report configuration for inference that has the same associated ID as the non-periodic channel state information report for inference.

[0162] Furthermore, in other examples of the first alternative, if the device on the terminal side is configured with an aperiodic channel state information reporting configuration for inference, the device on the terminal side is not configured with a periodic or semi-persistent channel state information reporting configuration for inference that has the same associated ID as the aperiodic channel state information reporting configuration for inference.

[0163] In the second alternative, multiple channel state information report configurations for inference are configured with the same associated ID. The at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. The specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic. The specific channel state information report configuration for inference is predefined or configurable. For example, the specific channel state information report configuration for inference may have a highest or lowest ID (e.g., CSI-ReportConfigId).

[0164] Figure 14 is a schematic diagram of an example of the second alternative scheme of Embodiment 2. In the example shown in Figure 14, multiple channel state information report configurations for inference are configured with the same associated ID. The at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring. The specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and may have a minimum identifier (e.g., CSI-ReportConfigId).

[0165] Figure 15 is a schematic diagram of an example of the third alternative scheme of Embodiment 2. In the example shown in Figure 15, the non-periodic channel state information report configuration for inference is not associated with the channel state information report for performance monitoring, and each of one or more periodic or semi-persistent channel state information report configurations for inference is associated with the channel state information report for performance monitoring.

[0166] In another example of the third alternative, the non-periodic channel state information report configuration for inference is not associated with the channel state information report configuration for performance monitoring, and one or more periodic or semi-persistent channel state information report configurations for inference are associated with the channel state information report configuration for performance monitoring.

[0167] Figure 16 is a schematic diagram of another example of the third alternative scheme of Embodiment 2. In the example shown in Figure 16, the non-periodic channel state information report configuration for inference is not associated with the channel state information report for performance monitoring. Instead, a specific periodic or semi-persistent channel state information report configuration for inference is associated with the channel state information report for performance monitoring. This specific periodic or semi-persistent channel state information report configuration for inference is predefined or configurable. For example, this specific periodic or semi-persistent channel state information report configuration for inference may have a highest or lowest identifier (e.g., CSI-ReportConfigId).

[0168] Figure 17 is a schematic diagram of an example of the fourth alternative scheme of Embodiment 2. In the example shown in Figure 17, on a carrier or a bandwidth portion, each channel state information report for inference of each activated artificial intelligence model or function is associated with a channel state information report for performance monitoring.

[0169] Example 3

[0170] In Embodiment 3, an artificial intelligence model or set of functions is configured within the carrier or bandwidth portion. This set includes one or more artificial intelligence models or functions for channel state information prediction. This set of artificial intelligence models or functions may also be referred to as an artificial intelligence model or function group.

[0171] In some cases, an artificial intelligence model or set of features can be explicitly or implicitly defined or configured through the following options:

[0172] Artificial intelligence models or functions with the same inference parameter set are defined or configured as a set of artificial intelligence models or functions;

[0173] Artificial intelligence models or functions with the same inference parameter are defined or configured as a set of artificial intelligence models or functions;

[0174] Artificial intelligence models or functions with the same associated ID are defined or configured as a set of artificial intelligence models or functions.

[0175] In addition, in other examples, multiple artificial intelligence models or functions defined or configured as a set of artificial intelligence models or functions may have different inference parameter sets, inference parameters, or associated IDs.

[0176] Devices on the terminal side can report information about artificial intelligence models or sets of functions; or devices on the network side can configure artificial intelligence models or sets of functions for devices on the terminal side via Radio Resource Control (RRC) signaling or Media Access Control (MAC-CE) elements.

[0177] In some examples, within an artificial intelligence model or feature set, at least one specific periodic or semi-persistent channel state information (CSI) report configuration for inference is associated with a CSI report configuration for performance monitoring. This specific periodic or semi-persistent CSI report configuration for inference is predefined or configurable. For example, within the artificial intelligence model or feature set, this specific periodic or semi-persistent CSI report configuration for inference has a highest or lowest identifier, such as CSI-ReportConfigId.

[0178] In some examples, the configuration for non-periodic channel state information reports used for inference can be as follows:

[0179] In an artificial intelligence model or feature set, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0180] In an artificial intelligence model or function set, if the terminal device is configured with a non-periodic channel state information reporting configuration for inference, then the artificial intelligence model or function set of the terminal device is also configured with at least one periodic or semi-persistent channel state information reporting configuration for inference; or

[0181] In an artificial intelligence model or set of functions, if the device on the terminal side is configured with a non-periodic channel state information reporting configuration for inference, then the artificial intelligence model or set of functions on the terminal side is not configured with a periodic or semi-persistent channel state information reporting configuration for inference.

[0182] In some cases, when a network device on the terminal side receives a signal from another network device to deactivate an AI model or function in a set of AI models or functions, it can deactivate all AI models or functions in that set of AI models or functions.

[0183] Second aspect of the embodiments

[0184] This application provides a method for receiving reports, described from the perspective of a network device. The network device-side apparatus includes a network device or a server on the network device side. In a second aspect embodiment, artificial intelligence functions or models are configured on the terminal device side.

[0185] Figure 18 is a schematic diagram of a report receiving method. As shown in Figure 18, the method includes:

[0186] 1801. Configure a first resource set for performance monitoring of the artificial intelligence model or function, and a second resource set for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic.

[0187] 1802. Receive channel state information reports (CSI reports) for performance monitoring and channel state information reports for inference, wherein the time-domain behavior of the channel state information reports for performance monitoring or the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0188] In some embodiments, the terminal device measures the first resource set to obtain ground truth data and / or calculates a performance metric.

[0189] The terminal device measures the second resource set, and the result of the measurement is used as input to the artificial intelligence model or function.

[0190] In some embodiments, after the device on the terminal device side sends the supported artificial intelligence model or function to the network device, it receives the Channel State Information Report Configuration (CSI) and / or Inference Parameter Set configured by the network device; and

[0191] The device on the terminal side selects a channel state information reporting configuration and / or inference parameter set, and reports it to the network device.

[0192] In some embodiments, the channel state information report for performance monitoring is initiated or triggered by a device on the terminal device side, or non-periodic channel state information reports for performance monitoring are not supported.

[0193] In some embodiments, the first resource set is periodic, and the channel state information report associated with the first resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0194] The first resource set is semi-persistent, and the channel state information report associated with the first resource set for performance monitoring is either semi-persistent or aperiodic; or

[0195] The first resource set is aperiodic, and the channel state information report associated with the first resource set for performance monitoring is aperiodic; or

[0196] The aperiodic first resource set is not associated with the channel state information report used for performance monitoring.

[0197] In some embodiments, the channel state information report for inference is periodic, and the associated channel state information report for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0198] The channel state information report used for inference is semi-persistent, and the associated channel state information report used for performance monitoring is either semi-persistent or aperiodic; or

[0199] The channel state information report used for inference is aperiodic, and the associated channel state information report used for performance monitoring is also aperiodic; or

[0200] Non-periodic channel state information reports used for inference are not associated with the channel state information reports used for performance monitoring.

[0201] In some embodiments, the second resource set is periodic, and the channel state information report associated with the second resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0202] The second resource set is semi-persistent, and the channel state information report associated with the second resource set for performance monitoring is either semi-persistent or aperiodic; or

[0203] The second resource set is aperiodic, and the channel state information report associated with the second resource set for performance monitoring is aperiodic; or

[0204] The aperiodic second resource set is not associated with the channel state information report used for performance monitoring.

[0205] In some embodiments, the second resource set is periodic, and the first resource set associated with the second resource set is periodic, semi-persistent, or aperiodic; or

[0206] The second resource set is semi-persistent, and the first resource set associated with the second resource set is either semi-persistent or aperiodic; or

[0207] The second resource set is aperiodic, and the first resource set associated with the second resource set is aperiodic; or

[0208] The aperiodic second resource set is not associated with the first resource set.

[0209] A second aspect embodiment also provides a reporting configuration method applied to a network device-side device, wherein the terminal device-side device is configured with an artificial intelligence model or function for channel state information prediction.

[0210] Figure 19 is a schematic diagram of the report configuration method. As shown in Figure 19, the method includes:

[0211] 1901. In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, configure channel state information reports for inference and channel state information reports for performance monitoring, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0212] In some embodiments, an inference parameter set or associated ID is configured in the CSI reporting configuration for inference.

[0213] In some embodiments, the inference parameter set has at least one of the following:

[0214] The number of time instances in the measurement window;

[0215] The number of time instances in the prediction window;

[0216] The time interval between two consecutive time instances in the measurement window;

[0217] The time interval between two consecutive time instances in the prediction window.

[0218] In some embodiments, multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic or semi-persistent, and the specific channel state information reporting configuration for inference is predefined or configurable.

[0219] In some embodiments, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0220] When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference is also configured, which has the same inference parameter set as the non-periodic channel state information report for inference.

[0221] In some embodiments, multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information reporting configuration for inference is predefined or configurable.

[0222] In some embodiments, non-periodic channel state information reporting configurations for inference are not associated with channel state information reporting for performance monitoring, and one or more periodic or semi-persistent channel state information reporting configurations for inference are associated with channel state information reporting for performance monitoring.

[0223] In some embodiments, on a carrier or a bandwidth portion, each channel state information report for inference of each activated AI model or function is associated with a channel state information report for performance monitoring.

[0224] In some embodiments, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic or semi-persistent, and the specific channel state information report configuration for inference is predefined or configurable.

[0225] In some embodiments, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0226] When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference with the same associated ID as the non-periodic channel state information report for inference is also configured.

[0227] In some embodiments, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information report configuration for inference is predefined or configurable.

[0228] In some embodiments, an artificial intelligence model or set of functions is configured in one carrier or one bandwidth portion, the artificial intelligence model or set of functions including one or more artificial intelligence models or functions for channel state information prediction, wherein, in one of the artificial intelligence models or set of functions, at least one specific periodic or semi-persistent channel state information reporting configuration for inference is associated with a channel state information reporting configuration for performance monitoring, the specific periodic or semi-persistent channel state information reporting configuration for inference being predefined or configurable.

[0229] In some embodiments, within one of the artificial intelligence models or feature sets, non-periodic channel state information reporting configurations for inference are not associated with channel state information reporting for performance monitoring; or

[0230] If a non-periodic channel state information reporting configuration for inference is configured in one of the aforementioned artificial intelligence models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is also configured in the artificial intelligence model or feature set; or

[0231] If a non-periodic channel state information reporting configuration for inference is configured in one of the AI ​​models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is not configured in the AI ​​model or feature set.

[0232] In some embodiments, when a signaling is sent to deactivate one of the artificial intelligence models or functions in the set of artificial intelligence models or functions, the signaling deactivates all the artificial intelligence models or functions in the set of artificial intelligence models or functions.

[0233] It is worth noting that the above figures are merely illustrative of embodiments of this application, and the application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above description, and are not limited to the description in the above figures.

[0234] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0235] Third aspect of the embodiments

[0236] This application provides a report transmitting apparatus. The report transmitting apparatus is applied to a device on the terminal device side, such as the terminal device itself, or one or more components or parts configured on the terminal device; details identical to those in the first aspect of the embodiment will not be repeated. The terminal device side apparatus is configured with an artificial intelligence model or function for channel state information prediction.

[0237] Figure 20 is a schematic diagram of a report sending device according to an embodiment of this application. As shown in Figure 20, the report sending device 2000 according to an embodiment of this application includes a first communication unit 2001, which performs the following operations:

[0238] A first resource set configured for performance monitoring of the artificial intelligence model or function, and a second resource set configured for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic; and

[0239] The channel state information report (CSI report) for performance monitoring and the channel state information report for inference are sent. The time-domain behavior of the channel state information report for performance monitoring or the channel state information report for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0240] The terminal device measures the first resource set to obtain ground truth data and / or calculates performance metrics, and the terminal device measures the second resource set. The results of the measurements are used as input to the artificial intelligence model or function.

[0241] In some embodiments, after the device on the terminal device side sends the supported artificial intelligence model or function to the network device, it receives the Channel State Information Report Configuration (CSI) and / or Inference Parameter Set configured by the network device; and

[0242] The device on the terminal side selects a channel state information reporting configuration and / or inference parameter set, and reports it to the network device.

[0243] In some embodiments, the channel state information report for performance monitoring is initiated or triggered by a device on the terminal device side, or non-periodic channel state information reports for performance monitoring are not supported.

[0244] In some embodiments, the first resource set is periodic, and the channel state information report associated with the first resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0245] The first resource set is semi-persistent, and the channel state information report associated with the first resource set for performance monitoring is either semi-persistent or aperiodic; or

[0246] The first resource set is aperiodic, and the channel state information report associated with the first resource set for performance monitoring is aperiodic; or

[0247] The aperiodic first resource set is not associated with the channel state information report used for performance monitoring.

[0248] In some embodiments, the channel state information report for inference is periodic, and the associated channel state information report for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0249] The channel state information report used for inference is semi-persistent, and the associated channel state information report used for performance monitoring is either semi-persistent or aperiodic; or

[0250] The channel state information report used for inference is aperiodic, and the associated channel state information report used for performance monitoring is also aperiodic; or

[0251] Non-periodic channel state information reports used for inference are not associated with the channel state information reports used for performance monitoring.

[0252] In some embodiments, the second resource set is periodic, and the channel state information report associated with the second resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0253] The second resource set is semi-persistent, and the channel state information report associated with the second resource set for performance monitoring is either semi-persistent or aperiodic; or

[0254] The second resource set is aperiodic, and the channel state information report associated with the second resource set for performance monitoring is aperiodic; or

[0255] The aperiodic second resource set is not associated with the channel state information report used for performance monitoring.

[0256] In some embodiments, the second resource set is periodic, and the first resource set associated with the second resource set is periodic, semi-persistent, or aperiodic; or

[0257] The second resource set is semi-persistent, and the first resource set associated with the second resource set is either semi-persistent or aperiodic; or

[0258] The second resource set is aperiodic, and the first resource set associated with the second resource set is aperiodic; or

[0259] The aperiodic second resource set is not associated with the first resource set.

[0260] This application also provides a report configuration device. This report configuration device is applied to a device on the terminal device side, such as the terminal device itself, or one or more components or parts configured on the terminal device; details identical to those in the first aspect embodiment will not be repeated. The terminal device side device is configured with an artificial intelligence model or function for channel state information prediction.

[0261] Figure 21 is a schematic diagram of a report configuration device according to an embodiment of this application. As shown in Figure 21, the report configuration device 2100 according to an embodiment of this application includes a second communication unit 2101, which performs the following operations:

[0262] In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0263] The inference parameter set or associated ID is configured in the CSI reporting configuration for inference.

[0264] In some embodiments, the inference parameter set has at least one of the following:

[0265] The number of time instances in the measurement window;

[0266] The number of time instances in the prediction window;

[0267] The time interval between two consecutive time instances in the measurement window;

[0268] The time interval between two consecutive time instances in the prediction window.

[0269] In some embodiments, multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic or semi-persistent, and the specific channel state information reporting configuration for inference is predefined or configurable.

[0270] In some embodiments, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0271] When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference is also configured, which has the same inference parameter set as the non-periodic channel state information report for inference.

[0272] In some embodiments, multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information reporting configuration for inference is predefined or configurable.

[0273] In some embodiments, non-periodic channel state information reporting configurations for inference are not associated with channel state information reporting for performance monitoring, and one or more periodic or semi-persistent channel state information reporting configurations for inference are associated with channel state information reporting for performance monitoring.

[0274] In some embodiments, on a carrier or a bandwidth portion, each channel state information report for inference of each activated AI model or function is associated with a channel state information report for performance monitoring.

[0275] In some embodiments, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic or semi-persistent, and the specific channel state information report configuration for inference is predefined or configurable.

[0276] In some embodiments, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0277] When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference with the same associated ID as the non-periodic channel state information report for inference is also configured.

[0278] In some embodiments, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information report configuration for inference is predefined or configurable.

[0279] In some embodiments, an artificial intelligence model or set of functions is configured in one carrier or one bandwidth portion, the artificial intelligence model or set of functions including one or more artificial intelligence models or functions for channel state information prediction, wherein, in one of the artificial intelligence models or set of functions, at least one specific periodic or semi-persistent channel state information reporting configuration for inference is associated with a channel state information reporting configuration for performance monitoring, the specific periodic or semi-persistent channel state information reporting configuration for inference being predefined or configurable.

[0280] In some embodiments, within one of the artificial intelligence models or feature sets, non-periodic channel state information reporting configurations for inference are not associated with channel state information reporting for performance monitoring; or

[0281] If a non-periodic channel state information reporting configuration for inference is configured in one of the aforementioned artificial intelligence models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is also configured in the artificial intelligence model or feature set; or

[0282] If a non-periodic channel state information reporting configuration for inference is configured in one of the AI ​​models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is not configured in the AI ​​model or feature set.

[0283] In some embodiments, upon receiving a signal from a network device to deactivate one of the artificial intelligence models or functions in the set of artificial intelligence models or functions, all artificial intelligence models or functions in the set of artificial intelligence models or functions are deactivated.

[0284] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0285] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The above figures may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0286] Furthermore, for simplicity, the above figures only exemplarily illustrate the connection relationships or signal flows between the various components or modules. However, those skilled in the art should understand that various related technologies, such as bus connections, can be employed. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit this implementation.

[0287] Fourth aspect of the embodiment

[0288] This application provides a report receiving device. This report receiving device is applied to a network device side device, such as a network device itself, or one or more components or parts configured in a terminal device; details identical to those in the second aspect of the embodiment will not be repeated. The terminal device side device is configured with an artificial intelligence model or function for channel state information prediction.

[0289] Figure 22 is a schematic diagram of a report receiving device according to an embodiment of this application. As shown in Figure 22, the report receiving device 2200 according to an embodiment of this application includes a third communication unit 2201, which performs the following operations:

[0290] Configure a first resource set for performance monitoring of the artificial intelligence model or function, and a second resource set for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic; and

[0291] Receive channel state information reports (CSI reports) for performance monitoring and channel state information reports for inference. The time-domain behavior of the channel state information reports for performance monitoring or channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0292] In some embodiments, the terminal device measures the first resource set to obtain ground truth data and / or calculates a performance metric.

[0293] The terminal device measures the second resource set, and the result of the measurement is used as input to the artificial intelligence model or function.

[0294] In some embodiments, after the device on the terminal device side sends the supported artificial intelligence model or function to the network device, it receives the Channel State Information Report Configuration (CSI) and / or Inference Parameter Set configured by the network device; and

[0295] The device on the terminal side selects a channel state information reporting configuration and / or inference parameter set, and reports it to the network device.

[0296] In some embodiments, the channel state information report for performance monitoring is initiated or triggered by a device on the terminal device side, or non-periodic channel state information reports for performance monitoring are not supported.

[0297] In some embodiments, the first resource set is periodic, and the channel state information report associated with the first resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0298] The first resource set is semi-persistent, and the channel state information report associated with the first resource set for performance monitoring is either semi-persistent or aperiodic; or

[0299] The first resource set is aperiodic, and the channel state information report associated with the first resource set for performance monitoring is aperiodic; or

[0300] The aperiodic first resource set is not associated with the channel state information report used for performance monitoring.

[0301] In some embodiments, the channel state information report for inference is periodic, and the associated channel state information report for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0302] The channel state information report used for inference is semi-persistent, and the associated channel state information report used for performance monitoring is either semi-persistent or aperiodic; or

[0303] The channel state information report used for inference is aperiodic, and the associated channel state information report used for performance monitoring is also aperiodic; or

[0304] Non-periodic channel state information reports used for inference are not associated with the channel state information reports used for performance monitoring.

[0305] In some embodiments, the second resource set is periodic, and the channel state information report associated with the second resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or

[0306] The second resource set is semi-persistent, and the channel state information report associated with the second resource set for performance monitoring is either semi-persistent or aperiodic; or

[0307] The second resource set is aperiodic, and the channel state information report associated with the second resource set for performance monitoring is aperiodic; or

[0308] The aperiodic second resource set is not associated with the channel state information report used for performance monitoring.

[0309] In some embodiments, the second resource set is periodic, and the first resource set associated with the second resource set is periodic, semi-persistent, or aperiodic; or

[0310] The second resource set is semi-persistent, and the first resource set associated with the second resource set is either semi-persistent or aperiodic; or

[0311] The second resource set is aperiodic, and the first resource set associated with the second resource set is aperiodic; or

[0312] The aperiodic second resource set is not associated with the first resource set.

[0313] This application also provides a report configuration device. This report configuration device is applied to a device on the network device side, such as a network device itself, or one or more components or parts configured on a terminal device. Details identical to those in the second aspect of the embodiments will not be repeated. The device on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction.

[0314] Figure 23 is a schematic diagram of a report configuration device according to an embodiment of this application. As shown in Figure 23, the report configuration device 2300 according to an embodiment of this application includes a fourth communication unit 2301, which performs the following operations:

[0315] In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0316] In some embodiments, an inference parameter set or associated ID is configured in the CSI reporting configuration for inference.

[0317] In some embodiments, the inference parameter set has at least one of the following:

[0318] The number of time instances in the measurement window;

[0319] The number of time instances in the prediction window;

[0320] The time interval between two consecutive time instances in the measurement window;

[0321] The time interval between two consecutive time instances in the prediction window.

[0322] In some embodiments, multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic or semi-persistent, and the specific channel state information reporting configuration for inference is predefined or configurable.

[0323] In some embodiments, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0324] When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference is also configured, which has the same inference parameter set as the non-periodic channel state information report for inference.

[0325] In some embodiments, multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information reporting configuration for inference is predefined or configurable.

[0326] In some embodiments, non-periodic channel state information reporting configurations for inference are not associated with channel state information reporting for performance monitoring, and one or more periodic or semi-persistent channel state information reporting configurations for inference are associated with channel state information reporting for performance monitoring.

[0327] In some embodiments, on a carrier or a bandwidth portion, each channel state information report for inference of each activated AI model or function is associated with a channel state information report for performance monitoring.

[0328] In some embodiments, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic or semi-persistent, and the specific channel state information report configuration for inference is predefined or configurable.

[0329] In some embodiments, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or

[0330] When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference with the same associated ID as the non-periodic channel state information report for inference is also configured.

[0331] In some embodiments, multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information report configuration for inference is predefined or configurable.

[0332] In some embodiments, an artificial intelligence model or set of functions is configured in one carrier or one bandwidth portion, the artificial intelligence model or set of functions including one or more artificial intelligence models or functions for channel state information prediction, wherein, in one of the artificial intelligence models or set of functions, at least one specific periodic or semi-persistent channel state information reporting configuration for inference is associated with a channel state information reporting configuration for performance monitoring, the specific periodic or semi-persistent channel state information reporting configuration for inference being predefined or configurable.

[0333] In some embodiments, within one of the artificial intelligence models or feature sets, non-periodic channel state information reporting configurations for inference are not associated with channel state information reporting for performance monitoring; or

[0334] If a non-periodic channel state information reporting configuration for inference is configured in one of the aforementioned artificial intelligence models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is also configured in the artificial intelligence model or feature set; or

[0335] If a non-periodic channel state information reporting configuration for inference is configured in one of the AI ​​models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is not configured in the AI ​​model or feature set.

[0336] In some embodiments, when a signaling is sent to deactivate one of the artificial intelligence models or functions in the set of artificial intelligence models or functions, the signaling deactivates all the artificial intelligence models or functions in the set of artificial intelligence models or functions.

[0337] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0338] It is worth noting that the above description only covers the components or modules relevant to this application, but this application is not limited thereto. The above figures may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0339] Furthermore, for simplicity, the above figures only exemplarily illustrate the connection relationships or signal flows between the various components or modules. However, those skilled in the art should understand that various related technologies, such as bus connections, can be employed. The aforementioned components or modules can be implemented using hardware facilities such as processors, memory, transmitters, and receivers; this application does not limit this implementation.

[0340] Fifth aspect of the embodiment

[0341] This application also provides a communication system, which can be referred to FIG1. ​​The contents that are the same as those in the embodiments of the first to fourth aspects will not be repeated.

[0342] In some embodiments, the communication system 100 may include at least: a network device; and a terminal device.

[0343] In this application embodiment, a scenario including network devices and / or terminal devices is taken as an example.

[0344] In the above scenario, network devices may include at least one of core network devices, third-party application devices, operation administration and maintenance (OAM) devices, and access network devices.

[0345] Core network equipment refers to equipment in the core network (CN) that provides service support to terminal equipment. As examples, core network equipment can be at least one of the following: Mobility and Management Entity (MME), Access and Mobility Management Function (AMF) entity, Session Management Function (SMF) entity, User Plane Function (UPF) entity, Location Management Function (LMF) entity, etc., and not all will be listed here. The AMF entity is responsible for terminal access management and mobility management; the SMF entity is responsible for session management, such as user session establishment; the UPF entity can be a user plane function entity, mainly responsible for connecting to external networks; and the LMF entity manages the overall coordination and scheduling of resources required for the location of terminal equipment registered with or accessing the core network equipment. It should be noted that in the embodiments of this application, an entity can also be called a network element or functional entity; for example, an AMF entity can also be called an AMF network element or an AMF functional entity, etc.

[0346] Third-party application devices can be OTT services (over the top server) or other third-party devices.

[0347] OAM (Operation, Administration, Maintenance) is a network device that performs network management tasks such as operation, administration, and maintenance according to the actual needs of the operator's network operation.

[0348] Access network equipment is an access device that allows terminal devices to wirelessly access a communication system. Access network equipment can be a base station (BS), a node, an evolved NodeB (eNodeB), a transmission reception point (TRP), a base station in a 5G mobile communication system (gNB), a base station in a 6G mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. Access network equipment can also be a module or unit that performs some of the functions of a base station. For example, it can be at least one of the following modules or units: a central 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. This application does not limit the specific technology and / or specific equipment form used in the access network equipment. Access network equipment can be deployed on land, including indoors / outdoors, and can be handheld or vehicle-mounted; it can also be deployed on water, on airplanes, balloons, or satellites; access network equipment can be deployed in fixed locations or on mobile carriers, and this application embodiment does not limit this.

[0349] In the above scenarios, the terminal device can be a device with wireless transceiver capabilities, capable of sending signals to and / or receiving signals from the access network device. The terminal device can also be called a terminal, mobile station, mobile terminal, etc. It can be a mobile phone, tablet, or other device with wireless intelligent transceiver capabilities. Terminal devices 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, telemedicine, or various smart scenarios.

[0350] In the above scenarios, communication between access network devices and terminal devices, and between terminal devices, can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. This application does not limit the spectrum resources used for wireless communication.

[0351] Figure 24 is a schematic diagram of the structure of an electronic device according to an embodiment of this application. At least one of the network device side device and the terminal device side device may have the structure of the electronic device of Figure 24. As shown in Figure 24, the electronic device 2400 may include: a processor 2410 (e.g., a central processing unit CPU) and a memory 2420; the memory 2420 is coupled to the processor 2410. The memory 2420 can store various data; in addition, it also stores an information processing program 2430, and executes the program 2430 under the control of the processor 2410.

[0352] For example, processor 2410 may be configured to execute a program to implement the methods described in the embodiments of the first and / or second aspects.

[0353] Furthermore, as shown in Figure 24, the electronic device 2400 may also include a transceiver 2440 and an antenna 2450, etc.; the functions of the above components are similar to those in the prior art, and will not be described in detail here. It is worth noting that the electronic device 2400 does not necessarily include all the components shown in Figure 24; in addition, the electronic device 2400 may also include components not shown in Figure 24, which can be referred to in the prior art.

[0354] This application also provides a computer program, wherein when the program is executed in a terminal device, the program causes the terminal device to perform the method described in the first aspect of the embodiment.

[0355] This application also provides a storage medium storing a computer program, wherein the computer program causes a terminal device to perform the method described in the first aspect of the embodiment.

[0356] This application also provides a computer program, wherein when the program is executed in a network device, the program causes the network device to perform the method described in the second aspect of the embodiment.

[0357] This application also provides a storage medium storing a computer program, wherein the computer program causes a network device to perform the method described in the second aspect of the embodiment.

[0358] The apparatus and methods described above in this application can be implemented in hardware or in combination with software. This application relates to a computer-readable program that, when executed by a logic component, enables the logic component to implement the apparatus or components described above, or to implement the various methods or steps described above. This application also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, flash memory, etc.

[0359] The methods / apparatus described in conjunction with the embodiments of this application can be directly embodied in hardware, software modules executed by a processor, or a combination of both. For example, one or more and / or combinations of one or more functional block diagrams shown in the figures can correspond to various software modules in a computer program flow, or to various hardware modules. These software modules can correspond to the various steps shown in the figures, respectively. These hardware modules can be implemented, for example, using a field-programmable gate array (FPGA) to embed these software modules.

[0360] The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to the processor, enabling the processor to read information from and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and storage medium can reside in an ASIC. The software module can be stored in the memory of a 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 high-capacity MEGA-SIM card or a high-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the high-capacity flash memory device.

[0361] One or more and / or one or more combinations of functional blocks described in the accompanying drawings can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described herein. One or more and / or one or more combinations of functional blocks described in the accompanying drawings can also be implemented as a combination of computing devices, 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.

[0362] The present application has been described above with reference to specific embodiments. However, those skilled in the art should understand that these descriptions are exemplary and not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on its spirit and principles, and these modifications and variations are also within the scope of the present application.

[0363] Regarding the implementation methods including the above embodiments, the following notes are also disclosed:

[0364] 1. A method for transmitting a report, applied to a device on a terminal device side, the device on the terminal device side being configured with an artificial intelligence model or function for channel state information prediction, the method comprising:

[0365] A first resource set configured for performance monitoring of the artificial intelligence model or function, and a second resource set configured for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic; and

[0366] The system transmits Channel State Information (CSI) reports for performance monitoring and Channel State Information (CSI) reports for inference. The time-domain behavior of either the CSI report for performance monitoring or the CSI report for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0367] in,

[0368] The terminal device measures the first resource set to obtain ground truth data and / or calculates performance metrics.

[0369] The terminal device measures the second resource set, and the result of the measurement is used as input to the artificial intelligence model or function.

[0370] 2. A method for configuring a report, applied to a device on a terminal device side, the device on the terminal device side being configured with an artificial intelligence model or function for channel state information prediction, the method comprising:

[0371] In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0372] The inference parameter set or associated ID is configured in the CSI reporting configuration for inference.

[0373] 3. A method for receiving a report, applied to a device on the network device side, wherein a device on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction, the method comprising:

[0374] Configure a first resource set for performance monitoring of the artificial intelligence model or function, and a second resource set for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic; and

[0375] The system receives Channel State Information (CSI) reports for performance monitoring and Channel State Information (CSI) reports for inference. The time-domain behavior of either the CSI report for performance monitoring or the CSI report for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0376] in,

[0377] The terminal device measures the first resource set to obtain ground truth data and / or calculates performance metrics.

[0378] The terminal device measures the second resource set, and the result of the measurement is used as input to the artificial intelligence model or function.

[0379] 4. A method for configuring a report, applied to a device on the network device side, wherein a device on the terminal device side is configured with an artificial intelligence model or function for channel state information prediction, the method comprising:

[0380] In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic.

[0381] The inference parameter set or associated ID is configured in the channel state information reporting configuration for inference.

[0382] 5. A terminal device comprising a memory and a processor, the memory storing a computer program and the processor being configured to execute the computer program to implement the method as described in Appendix 1 or 2.

[0383] 6. A network device comprising a memory and a processor, the memory storing a computer program and the processor being configured to execute the computer program to implement the method as described in Appendix 3 or 4.

[0384] 7. A computer program product comprising at least a computer program that, when executed by a processor, causes a terminal device to perform the method as described in Appendix 1 or 2.

[0385] 8. A computer program product comprising at least a computer program that, when executed by a processor, causes a network device to perform the method as described in Appendix 3 or 4.

Claims

1. A report transmitting apparatus, applied to a terminal device side, the terminal device side apparatus being configured with an artificial intelligence model or function for channel state information prediction, the report transmitting apparatus comprising a first communication unit, the first communication unit performing the following operations: A first resource set configured for performance monitoring of the artificial intelligence model or function, and a second resource set configured for inference, wherein the temporal behavior of the first resource set or the second resource set is at least one of the following: periodic, semi-persistent, or aperiodic; and The system transmits Channel State Information Reports (CSIreports) for performance monitoring and Channel State Information Reports for inference. The time-domain behavior of either the CSIreport for performance monitoring or the CSIreport for inference is at least one of the following: Periodic, Semi-persistent, or Aperiodic. in, The terminal device measures the first resource set to obtain ground truth data and / or calculates performance metrics. The terminal device measures the second resource set, and the result of the measurement is used as input to the artificial intelligence model or function.

2. The method as described in claim 1, wherein, After the device on the terminal device side sends the supported artificial intelligence model or function to the network device, it receives the Channel State Information Report configuration (CSIreport configuration) and / or inference parameter set configured by the network device; and The device on the terminal side selects a channel state information reporting configuration and / or inference parameter set, and reports it to the network device.

3. The apparatus of claim 1, wherein, The channel state information report used for performance monitoring is initiated or triggered by the device on the terminal device side, or non-periodic channel state information reports used for performance monitoring are not supported.

4. The apparatus of claim 1, wherein, The first resource set is periodic, and the channel state information report associated with the first resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or The first resource set is semi-persistent, and the channel state information report associated with the first resource set for performance monitoring is either semi-persistent or aperiodic; or The first resource set is aperiodic, and the channel state information report associated with the first resource set for performance monitoring is aperiodic; or The aperiodic first resource set is not associated with the channel state information report used for performance monitoring.

5. The apparatus of claim 1, wherein, The channel state information report used for inference is periodic, and the associated channel state information report used for performance monitoring is periodic, semi-persistent, or aperiodic; or The channel state information report used for inference is semi-persistent, and the associated channel state information report used for performance monitoring is either semi-persistent or aperiodic; or The channel state information report used for inference is aperiodic, and the associated channel state information report used for performance monitoring is also aperiodic; or Non-periodic channel state information reports used for inference are not associated with the channel state information reports used for performance monitoring.

6. The apparatus of claim 1, wherein, The second resource set is periodic, and the channel state information report associated with the second resource set for performance monitoring is periodic, semi-persistent, or aperiodic; or The second resource set is semi-persistent, and the channel state information report associated with the second resource set for performance monitoring is either semi-persistent or aperiodic; or The second resource set is aperiodic, and the channel state information report associated with the second resource set for performance monitoring is aperiodic; or The aperiodic second resource set is not associated with the channel state information report used for performance monitoring.

7. The apparatus of claim 1, wherein, The second resource set is periodic, and the first resource set associated with the second resource set is periodic, semi-persistent, or aperiodic; or The second resource set is semi-persistent, and the first resource set associated with the second resource set is either semi-persistent or aperiodic; or The second resource set is aperiodic, and the first resource set associated with the second resource set is aperiodic; or The aperiodic second resource set is not associated with the first resource set.

8. A reporting configuration apparatus, applied to a terminal device side apparatus, the terminal device side apparatus being configured with an artificial intelligence model or function for channel state information prediction, the reporting configuration apparatus comprising a second communication unit, the second communication unit performing the following operations: In a carrier or a bandwidth portion, for one or more activated artificial intelligence models or functions for channel state information prediction, channel state information reports for inference and channel state information reports for performance monitoring are configured, wherein the time-domain behavior of the channel state information reports for inference is at least one of the following: periodic, semi-persistent, or aperiodic. The inference parameter set or associated ID is configured in the CSI reporting configuration for inference.

9. The apparatus of claim 8, wherein, The inference parameter set has at least one of the following: The number of time instances in the measurement window; The number of time instances in the prediction window; The time interval between two consecutive time instances in the measurement window; The time interval between two consecutive time instances in the prediction window.

10. The apparatus of claim 8, wherein, Multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic or semi-persistent, and the specific channel state information reporting configuration for inference is predefined or configurable.

11. The apparatus of claim 10, wherein, The non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference is also configured, which has the same inference parameter set as the non-periodic channel state information report for inference.

12. The apparatus of claim 8, wherein, Multiple channel state information reporting configurations for inference are configured with the same set of inference parameters, wherein at least one specific channel state information reporting configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information reporting configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information reporting configuration for inference is predefined or configurable.

13. The apparatus of claim 8, wherein, Non-periodic channel state information report configurations for inference are not associated with channel state information reports for performance monitoring, and one or more periodic or semi-persistent channel state information report configurations for inference are associated with channel state information reports for performance monitoring.

14. The apparatus of claim 8, wherein, On a carrier or a bandwidth portion, each channel state information report for inference of each activated AI model or function is associated with a channel state information report for performance monitoring.

15. The apparatus of claim 8, wherein, Multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic or semi-persistent, and the specific channel state information report configuration for inference is predefined or configurable.

16. The apparatus of claim 8, wherein, The non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or When a non-periodic channel state information report configuration for inference is configured, a periodic or semi-persistent channel state information report configuration for inference with the same associated ID as the non-periodic channel state information report for inference is also configured.

17. The apparatus of claim 8, wherein, Multiple channel state information report configurations for inference are configured with the same associated ID, wherein at least one specific channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific channel state information report configuration for inference is periodic, semi-persistent, or aperiodic, and the specific channel state information report configuration for inference is predefined or configurable.

18. The apparatus of claim 8, wherein, Within a single carrier or a bandwidth portion, an artificial intelligence model or set of functions is configured, including one or more artificial intelligence models or functions for channel state information prediction. in, In one of the AI ​​models or feature sets, at least one specific periodic or semi-persistent channel state information report configuration for inference is associated with a channel state information report for performance monitoring, wherein the specific periodic or semi-persistent channel state information report configuration for inference is predefined or configurable.

19. The apparatus of claim 18, wherein, In one of the aforementioned artificial intelligence models or sets of functions, the non-periodic channel state information reporting configuration for inference is not associated with the channel state information reporting configuration for performance monitoring; or If an aperiodic channel state information reporting configuration for inference is configured in one of the aforementioned artificial intelligence models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is also configured in the aforementioned artificial intelligence models or feature sets. or If a non-periodic channel state information reporting configuration for inference is configured in one of the AI ​​models or feature sets, then a periodic or semi-persistent channel state information reporting configuration for inference is not configured in the AI ​​model or feature set.

20. The apparatus of claim 18, wherein, When receiving a signal from a network device to deactivate one of the AI ​​models or functions in the set of AI models or functions, all AI models or functions in the set of AI models or functions are deactivated.

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