Performance monitoring method and apparatus, and communication system

Abstract

Embodiments of the present application provide a performance monitoring method and apparatus, and a communication system. The performance monitoring apparatus is applied to a terminal device. The apparatus comprises a first receiver and a first processor. The first receiver receives first information, wherein the first information is used for configuring at least one channel state information reference signal (CSI-RS) resource set, the CSI-RS resource set is used for performing performance monitoring on an artificial intelligence model or function, the artificial intelligence model or function is used for predicting CSI, and there is at least one CSI-RS in each CSI-RS resource set; the first receiver receives the CSI-RS; and the first processor calculates a performance monitoring metric on the basis of the measurement result of the CSI-RS.

Description

Methods, devices, and communication systems for monitoring performance 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] In CSI prediction, AI / ML functions or models can be configured on the terminal device. The terminal device can measure the reference signal at one or more time instances within an observation window and predict the CSI at one or more time instances within a future prediction window. A time instance can be a slot, etc.

[0004] For Massive Multiple-Input Multiple-Output (MIMO) systems in New Radio (NR) technologies, high-precision Channel Identity (CSI) feedback can effectively improve downlink transmission performance. CSI accuracy is affected by many factors, one important one being channel aging. This means that the instantaneous characteristics of the channel change over time, leading to a mismatch between the CSI obtained from channel estimation and the actual channel conditions. This mismatch is particularly pronounced for fast-fading channels, significantly impacting system performance. To mitigate the impact of channel aging, one approach is to shorten the CSI reporting period; however, this increases feedback overhead and reduces system throughput. Another approach is to use historical time-slot CSI to predict the current time-slot CSI, or to predict the CSI for both current and future time slots. AI / ML-based CSI prediction, in particular, can better capture the temporal correlation of the channel, providing more accurate prediction results.

[0005] For AI / ML-based CSI prediction, the currently used AI / ML functions or models are trained based on historical channel data, matching the statistical characteristics of the historical training channel data. When channel conditions change significantly, the statistical characteristics of the channel also change, and the accuracy of the CSI prediction results output by the current AI / ML function or model may decrease, thus becoming unsuitable for new channel conditions. Lifecycle Management (LCM) can be used to ensure the effectiveness and reliability of AI / ML functions or models throughout their entire lifecycle and to respond promptly to changes in channel conditions.

[0006] Performance monitoring is an important part of LCM. For AI / ML functions or models used for CSI prediction, performance monitoring can provide real-time evaluation and feedback on the model's output to ensure the model's continued accuracy and reliability.

[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] The inventors discovered that in the existing technology, for AI / ML-based CSI prediction, the configuration of the Channel State Information Reference Signal (CSI-RS), the calculation method of the performance monitoring metric, and the reporting mechanism are not determined, which makes the current protocol unable to support performance monitoring. Therefore, how to determine at least one of the following aspects related to performance monitoring: the configuration of the Channel State Information Reference Signal (CSI-RS), the calculation method of the performance monitoring metric, and the reporting mechanism is a problem that needs to be solved.

[0009] To address at least one of the above-mentioned problems, embodiments of this application provide a method, apparatus, and communication system for monitoring performance.

[0010] According to one aspect of the embodiments of this application, a device for monitoring performance is provided, applied to a terminal device, the device comprising a first receiver and a first processor, wherein:

[0011] The first receiver receives first information, which is used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS). The CSI-RS is used to perform performance monitoring on an artificial intelligence model or function. The artificial intelligence model or function is used to predict channel state information. Each of the CSI-RS resource sets contains at least one CSI-RS.

[0012] The first receiver receives the Channel State Information Reference Signal (CSI-RS);

[0013] The first processor calculates performance monitoring metrics based on the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0014] The Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent, the transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information.

[0015] According to another aspect of the embodiments of this application, a device for monitoring performance is provided, applied to a terminal device, the device comprising a first receiver and a first processor, wherein:

[0016] The first receiver receives first information, which is used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS). The CSI-RS is used to perform performance monitoring on an artificial intelligence model or function. The artificial intelligence model or function is used to predict channel state information. Each of the CSI-RS resource sets contains at least one CSI-RS.

[0017] The first receiver receives the Channel State Information Reference Signal (CSI-RS);

[0018] The first processor calculates the performance monitoring metric based on the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0019] For each measurement result:

[0020] The primary performance monitoring metric is calculated based on the entire bandwidth; or

[0021] Calculate the second performance monitoring metric based on a subset of all subbands; or

[0022] The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

[0023] One of the beneficial effects of this application's embodiments is that, for AI / ML-based CSI prediction, it clarifies the configuration of Channel State Information Reference Signal (CSI-RS) related to performance monitoring, the calculation method of performance monitoring metrics, and the reporting mechanism, thereby supporting performance monitoring and ensuring good performance monitoring results with low complexity.

[0024] 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.

[0025] 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.

[0026] 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

[0027] 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.

[0028] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application;

[0029] Figure 2 is a schematic diagram of the temporal relationship between CSI-RS, CSI reporting, and predicted CSI in the observation window;

[0030] Figure 3 is another schematic diagram illustrating the temporal relationship between CSI-RS, CSI reporting, and predicted CSI in the observation window;

[0031] Figure 4 is another schematic diagram of the temporal relationship between CSI-RS, CSI reporting in the observation window, and predicted CSI in the prediction window;

[0032] Figure 5 is a schematic diagram of a method for monitoring performance according to an embodiment of this application;

[0033] Figure 6 is a schematic diagram of the CSI-RS used for performance monitoring in Example 1 of Example 1 of Example 1;

[0034] Figure 7 is a schematic diagram of the CSI-RS used for performance monitoring in Example 2 of Example 1;

[0035] Figure 8 is a schematic diagram of the CSI-RS used for performance monitoring in Example 3 of Example 1;

[0036] Figure 9 is a schematic diagram of the CSI-RS used for performance monitoring in Example 4 of Example 1;

[0037] Figure 10 is a schematic diagram of a method for monitoring performance;

[0038] Figure 11 is a schematic diagram of a device for monitoring performance according to an embodiment of this application;

[0039] Figure 12 is another schematic diagram of the device for monitoring performance according to an embodiment of this application;

[0040] Figure 13 is a schematic diagram of the electronic device according to an embodiment of this application. Detailed Implementation

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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)”.

[0053] 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)”.

[0054] 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.

[0055] 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), dedicated 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.

[0056] In the embodiments of this application, "multiple" refers to at least two, or two or more.

[0057] 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.

[0058] 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.

[0059] The following examples illustrate the scenarios of embodiments of this application, but this application is not limited thereto.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] In various embodiments of this application, AI / ML functions or models may also be referred to as artificial intelligence or machine learning functions or models, artificial intelligence functions or models, AI / ML functionality / model, etc., which have the same meaning and can be used interchangeably in this application.

[0066] First aspect of the embodiments

[0067] Performance monitoring for AI / ML functions or models can be categorized into three types:

[0068] Type 1: The terminal device calculates the performance metric(s) and reports the performance monitoring output. The network device (NW) makes a decision based on the performance monitoring output reported by the terminal device, and the decision is whether to roll back to the traditional CSI prediction scheme (e.g., the scheme that uses codebooks for CSI prediction).

[0069] Type 2: The terminal device directly reports the CSI predicted by the AI / ML function or model and the corresponding real CSI. The network device calculates the performance metric and makes a decision. The decision content is the same as that of Type 1.

[0070] Type 3: The terminal device calculates and reports the performance metrics. The network device makes a decision based on the performance metrics reported by the terminal device. The decision content is the same as that of Type 1.

[0071] Of the three types of performance monitoring mentioned above: Type 1 has the lowest reporting overhead and fewer decision criteria on the network device side; Type 2 has the highest reporting overhead and the most abundant decision criteria on the network device side; Type 3 strikes a good balance between reporting content and monitoring effectiveness.

[0072] The terminal device's reception of the Channel State Information Reference Signal (CSI-RS) and its performance of CSI prediction have the following relationship in the time domain:

[0073] The terminal device receives multiple consecutive CSI-RSs; the time period during which the CSI-RSs are received is referred to as the observation window.

[0074] Based on multiple CSI-RS received within the observation window, the terminal device uses AI / ML models or functions to predict the CSI (i.e., the predicted CSI) at N4 future time-domain locations (e.g., N4∈{1,2,4,8}). Here, the time period corresponding to these N4 predicted CSIs is called the prediction window.

[0075] The prediction results (e.g., the predicted CSI) are fed back to the network devices via CSI reports;

[0076] Network devices determine the data transmission method within the prediction window based on the predicted CSI reported by the CSI.

[0077] In some cases, the output of the AI / ML model or function can be the predicted CSI itself; in other cases, the output of the AI / ML model or function can be the channel matrix H at N4 time points within the prediction window. pred Terminal devices can be based on H pred The corresponding predicted CSI is calculated.

[0078] For example, Figure 2 is a schematic diagram of the temporal relationship between CSI-RS, CSI reporting, and predicted CSIs within the observation window and the prediction window. As shown in Figure 2, the observation window is configured as 5 / 5ms, meaning the number / distance of CSI-RS is 5 / 5ms; the prediction window is configured as 4 / 5ms / 5ms, meaning the number / distance between prediction instances / distance from the last observation instance to the first predicted CSI within the prediction window is 1. st The prediction instance (CSI) is 4 / 5ms / 5ms, where the number of predicted CSIs corresponds to N4 in the 3GPP protocol, i.e., N4 = 4. The terminal device receives 5 CSI-RS for model inference within the observation window, with a 5ms interval between CSI-RS. The AI / ML model or function predicts the channel matrix for the next N4 = 4 time-domain locations based on the CSI-RS within the observation window, and then obtains the predicted CSI for the corresponding time-domain location based on the predicted channel matrix. The predicted CSI is then fed back to the network device in the form of a CSI report. There are 4 predicted CSIs within the prediction window, with a 5ms time interval between each predicted CSI. The time-domain location of the first predicted CSI is 5ms from the last CSI-RS within the observation window.

[0079] For example, Figure 3 is another schematic diagram of the temporal relationship between CSI-RS, CSI reporting, and predicted CSI in the observation window and the prediction window. As shown in Figure 3, the observation window is configured to 5 / 5ms, and the prediction window is configured to 2 / 5ms / 5ms.

[0080] For example, Figure 4 is another schematic diagram of the temporal relationship between CSI-RS, CSI reporting, and predicted CSI in the observation window and the prediction window. As shown in Figure 4, the observation window is configured to 5 / 5ms, and the prediction window is configured to 1 / 5ms / 5ms. Since there is only one predicted CSI, the 5ms temporal distance between predicted CSIs can be ignored in this case.

[0081] For AI / ML-based CSI prediction, performance monitoring can be conducted in various ways. For example, one method might involve the following steps:

[0082] Step 1: The terminal device receives RRC (Radio Resource Control) configuration information, which can be used to configure CSI-RS for performance monitoring;

[0083] Step 2: The terminal device receives the CSI-RS used for AI / ML model inference within the observation window, and based on the received CSI-RS within the observation window (i.e., the CSI-RS used for AI / ML model inference), uses the AI / ML model or function output to predict the future channel H. pred And / or other channel-related information, in addition, the terminal device receives CSI-RS for performance monitoring, and obtains H based on the received CSI-RS for performance monitoring. pred The actual channel H corresponding to the time domain location true and / or other channel-related information (e.g., the terminal device measures the CSI-RS used for performance monitoring to obtain the true channel H). true (and / or other channel-related information);

[0084] Step 3: The terminal device calculates H pred and H true The difference between them can be measured based on Squared Generalized Cosine Similarity (SGCS), and / or based on Normalized Mean-Squared Error (NMSE), and / or based on other methods, where H pred and H true The larger the SGCS between them, or H pred and H true The smaller the NMSE, the better the performance of the detected AI / ML function or model.

[0085] For AI / ML-based CSI prediction, in order to determine at least one of the following: the configuration of the Channel State Information Reference Signal (CSI-RS) related to performance monitoring, the calculation method of the performance monitoring metric, and the reporting mechanism, an embodiment of the first aspect of this application provides a method for monitoring performance, which will be described below from the perspective of the terminal device.

[0086] In an embodiment of the first aspect, the artificial intelligence function or model for CSI prediction can be set on the terminal device side.

[0087] Figure 5 is a schematic diagram of a performance monitoring method according to an embodiment of this application. As shown in Figure 5, the performance monitoring method includes:

[0088] 501. Receive first information, the first information being used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS), the Channel State Information Reference Signal Resource Set being used for performance monitoring of an artificial intelligence model or function, the artificial intelligence model or function being used to predict channel state information, and each of the Channel State Information Reference Signal Resource Sets containing at least one Channel State Information Reference Signal (CSI-RS).

[0089] 502. Receive the Channel State Information Reference Signal (CSI-RS); and

[0090] 503. Calculate the performance monitoring metric based on the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0091] In some embodiments of operation 501, the first information is carried by Radio Resource Control (RRC) signaling.

[0092] In operations 501 and 502, the Channel State Information Reference Signal (CSI-RS) in the Channel State Information Reference Signal Resource Set (CSI-RS resource set) is periodic or semi-persistent. The transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information. The first time window is, for example, a prediction window. For an explanation of the prediction window and the time domain position of the predicted channel state information (predicted CSI), please refer to the illustrations in Figures 2 to 4 above.

[0093] In some examples of Operation 501, each predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS); or, each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

[0094] In some examples of Operation 501, the frequency domain positions of the channel state information reference signals within the channel state information reference signal resource set configured based on the first information are distributed across all or some sub-bands of the terminal device. Here, some sub-bands include sub-bands with indices p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1. For example, this partial sub-band could be all sub-bands with odd indices, or all sub-bands with even indices, or all sub-bands with indices every M sub-bands and an initial offset of j, etc.

[0095] In Operation 501, the Channel State Information Reference Signals (CSI-RS) within each Channel State Information Reference Signal Resource Set used for performance monitoring of artificial intelligence models or functions can also be referred to as Channel State Information Reference Signals (CSI-RS) used for performance monitoring.

[0096] In operation 502, a Channel State Information Reference Signal (CSI-RS) for performance monitoring within the first time window is received, wherein the CSI-RS for performance monitoring is configured by operation 501. For example, in operation 502, the CSI-RS for performance monitoring received within the first time window is a CSI-RS within the CSI-RS resource set configured by the first information in operation 501 for performance monitoring of an artificial intelligence model or function.

[0097] In some embodiments of operation 503, the terminal device calculates performance monitoring metrics based on measurement results of the received Channel State Information Reference Signal (CSI-RS), including, for each measurement result:

[0098] The primary performance monitoring metric is calculated based on the entire bandwidth; or

[0099] Calculate the second performance monitoring metric based on a subset of all subbands; or

[0100] The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

[0101] In some examples, the primary performance monitoring metric based on the entire broadband computing network includes:

[0102] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband; and

[0103] The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

[0104] In some examples, a second performance monitoring metric is calculated based on a subset of all subbands, including:

[0105] For each received Channel State Information Reference Signal (CSI-RS) in the time domain, calculate the performance monitoring metrics for each subband within the subset; and

[0106] The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in this subset.

[0107] In this application, the performance monitoring metric may have the same meaning as the performance metric described above.

[0108] In this application, the performance monitoring metric calculated in operation 503 can be reported by the terminal device to the network device, and the network device makes a performance detection-related decision based on the performance monitoring metric. For example, the terminal device processes the time-domain locations of all received CSI-RS for performance monitoring to obtain the performance monitoring metric for each time-domain location through frequency-domain processing, performs a time-domain average of the performance monitoring metrics for all time-domain locations, and reports the averaged result to the network device.

[0109] The method for monitoring performance of this application will be described below with reference to specific embodiments.

[0110] Example 1

[0111] Example 1 includes the operations 501, 502 and 503 described above.

[0112] In Example 1, the terminal device receives first information carried by RRC. This first information can be used to configure at least one CSI-RS resource set, which can be used for performance monitoring in AI / ML-based CSI prediction. The CSI-RS resource set contains at least one CSI-RS, which is either periodic or semi-persistent, and the period of the CSI-RS is the prediction window period T. predWindow The CSI-RS is an integer multiple of the predicted CSI, and the time domain location of the predicted CSI coincides with that of the predicted CSI.

[0113] Furthermore, for CSI-RS resources within the CSI-RS resource set configured in the first information, their frequency domain locations may be distributed across all subbands, or only within a subset of subbands. This subset of subbands includes subbands with indices p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1. For example, this subset of subbands could be all subbands with odd indices, or all subbands with even indices, or all subbands with indices every M subbands and an initial offset of j, etc.

[0114] The following examples illustrate Example 1.

[0115] Example 1

[0116] Figure 6 is a schematic diagram of the CSI-RS used for performance monitoring in Example 1 of Example 1 of Example 1.

[0117] As shown in Figure 6, in some examples, the observation window is configured as 5 / 5ms, the prediction window is configured as 4 / 5ms / 5ms, and the time interval between every two prediction windows is T. predWindow The first information configuration includes one CSI-RS resource set for performance monitoring. This resource set contains four CSI-RSs, whose time-domain locations coincide with the time-domain locations of four predicted CSIs. The interval between every two CSI-RSs in this resource set is 5ms. The period of each CSI-RS in the resource set is T. predWindow .

[0118] Example 2

[0119] Figure 7 is a schematic diagram of the CSI-RS used for performance monitoring in Example 2 of Example 1.

[0120] As shown in Figure 7, in some examples, the observation window is configured as 5 / 5ms, the prediction window is configured as 4 / 5ms / 5ms, and the time interval between every two prediction windows is T. predWindow The first information configuration includes one CSI-RS resource set for performance monitoring. This resource set contains two CSI-RSs whose time-domain positions coincide with the first and third of the four predicted CSIs (corresponding to the first, second, third, and fourth time-domain positions, respectively). The interval between any two CSI-RSs in this resource set is 10ms. The period of each CSI-RS in this resource set is T. predWindow .

[0121] Example 3

[0122] Figure 8 is a schematic diagram of the CSI-RS used for performance monitoring in Example 3 of Example 1.

[0123] As shown in Figure 8, in some examples, the observation window is configured as 5 / 5ms, the prediction window is configured as 4 / 5ms / 5ms, and the time interval between every two prediction windows is T. predWindow The first information configuration includes one CSI-RS resource set for performance monitoring. This resource set contains two CSI-RSs whose time-domain positions coincide with the first and third positions of the four predicted CSIs (these four predicted CSIs correspond to the first, second, third, and fourth time-domain positions, respectively). The interval between any two CSI-RSs in this resource set is 10ms. The period of each CSI-RS in this resource set is 2*T. predWindow .

[0124] Example 4

[0125] Figure 9 is a schematic diagram of the CSI-RS used for performance monitoring in Example 4 of Example 1.

[0126] As shown in Figure 9, in some examples, the observation window is configured as 5 / 5ms, the prediction window is configured as 1 / 5ms / 5ms, and the time interval between every two prediction windows is T. predWindowThe first information configuration includes one CSI-RS resource set for performance monitoring. This resource set contains one CSI-RS, whose time-domain location coincides with the time-domain location of one predicted CSI within the prediction window. The period of each CSI-RS within this resource set is 2*T. predWindow .

[0127] Example 1 is not limited to the descriptions of Examples 1 to 4 above. For other observation window and prediction window configurations, please refer to Examples 1 to 4 above.

[0128] Example 5

[0129] In Example 5, based on Examples 1 to 4 above, it is possible to restrict the distribution of all CSI-RS resources in the CSI-RS resource set for performance monitoring in the frequency domain to every sub-band, or to a subset of sub-bands. For example, they could be distributed only in sub-bands with even indices, or only in sub-bands with odd indices.

[0130] Example 2

[0131] Example 2 includes the operations 501, 502 and 503 described above.

[0132] In Example 2, for operation 503, calculating the performance monitoring metric based on the measurement results of the Channel State Information Reference Signal (CSI-RS) includes, for each measurement result, the following frequency domain processing can be performed:

[0133] The primary performance monitoring metric is calculated based on the entire bandwidth; or

[0134] Calculate the second performance monitoring metric based on a subset of all subbands; or

[0135] The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

[0136] The following examples illustrate Embodiment 2. In each example of Embodiment 2, SGCS (cosine similarity) is used as the performance metric. This application is not limited to this; the performance metric can also be NMSE (normalized mean square error), that is, SGCS can be replaced with NMSE.

[0137] Example 1

[0138] In some implementations of Example 1, for each received time-domain location of the monitoring CSI-RS, the terminal device calculates a performance metric for each subband and maps the performance metrics of all subbands to a single performance metric. For example, if the terminal device has N subbands across its entire bandwidth, with subband indices i = 0, 1, 2, ..., N-1, a metric SGCS can be calculated for each subband i. i All mappings are represented by a wideband SGCS. wideband (i.e., the primary performance monitoring metric), for example, mapping the metrics to all SGCS. i The average is used to obtain SGCS wideband ,Right now

[0139] The performance metric of a terminal device at a time-domain location where it receives CSI-RS for performance monitoring is SGCS. wideband .

[0140] Example 2

[0141] In some implementations of Example 2, for each time-domain location where the monitoring CSI-RS is received, the terminal device calculates a performance metric for each subband. For example, if there are N subbands across the entire bandwidth of the terminal device, with subband indices i = 0, 1, 2, ..., N-1, a metric SGCS can be calculated for each subband i. i (i.e., the second performance monitoring metric).

[0142] The performance metric of a terminal device at a time-domain location where it receives monitoring CSI-RS is SGCS across all subbands i. i .

[0143] Example 3

[0144] In some implementations, for each time-domain location where a monitoring CSI-RS is received, all subbands are divided into Q subsets (Q is a positive integer). The terminal device calculates a subset performance metric for only one subset. The performance metric of the terminal device at a time-domain location is the SGCS of all subbands in that subset, or a single SGCS mapped from the SGCS of all subbands in that subset.

[0145] For example, there are N subbands with subband indices i = 0, 1, 2, ..., N-1. Divide the N subbands into two sets: one set of subbands with even indices and one set of subbands with odd indices.

[0146] The terminal device calculates the SGCS for subbands with even indices, obtaining SGCS0, SGCS2, SGCS4, ...;

[0147] The performance metric of a terminal device at a time-domain location where it receives monitoring CSI-RS data is SGCS0, SGCS2, SGCS4, ... corresponding to all even-numbered subbands; or it is a single SGCS mapped from SGCS0, SGCS2, SGCS4, ... corresponding to all even-numbered subbands. subset For example, the mapping method is to take the average of SGCS0, SGCS2, SGCS4, ... to obtain SGCS. subset (That is, the second performance monitoring metric), namely:

[0148] in This indicates the rounding up operation.

[0149] For example, there are N subbands with subband indices i = 0, 1, 2, ..., N-1. Divide the N subbands into two sets: one set of subbands with even indices and one set of subbands with odd indices.

[0150] The terminal device only calculates SGCS for subbands with odd indices, resulting in SGCS1, SGCS3, SGCS5, ...;

[0151] The performance metric for a terminal device at a time-domain location where it receives monitoring CSI-RS data is either SGCS1, SGCS3, SGCS5, ... corresponding to all odd-numbered subbands; or a single SGCS mapped from SGCS1, SGCS3, SGCS5, ... corresponding to all odd-numbered subbands. subset For example, the mapping method is to take the average of SGCS1, SGCS3, SGCS5, ... to obtain SGCS. subset (That is, the second performance monitoring metric), namely:

[0152] in This indicates the floor function.

[0153] In Example 3, the above explanation regarding whether the subband index is odd or even is merely illustrative, and this application is not limited to this. For example, the odd or even number mentioned above can be replaced with p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1. Accordingly, SGCS subset The calculation method for (i.e., the second performance monitoring metric) has also been adjusted accordingly.

[0154] Example 4

[0155] In some implementations, for each time-domain location where the monitoring CSI-RS is received, the terminal device calculates a broadband performance metric and a subset performance metric. The broadband performance metric is the same as in Example 1 of Embodiment 2, and the subset performance metric is the same as in Example 2 and / or Example 3 of Embodiment 2.

[0156] The performance metric for a terminal device at a time-domain location where it receives monitoring CSI-RS is a combination of broadband performance metric and subset performance metric.

[0157] It is worth noting that Figure 5 above is only an illustrative description of the embodiments of this application, but this 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 content, and are not limited to the description in Figure 5 above.

[0158] 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.

[0159] According to the embodiments of the first aspect, for AI / ML-based CSI prediction, the configuration of Channel State Information Reference Signal (CSI-RS), the calculation method of performance monitoring metrics, and the reporting mechanism are clarified, thereby supporting performance monitoring and ensuring good performance monitoring results with low complexity.

[0160] Second aspect of the embodiments

[0161] This application provides a method for monitoring performance, described from the perspective of a network device. In a second aspect, the artificial intelligence function or model for CSI prediction can be configured on the terminal device side.

[0162] Figure 10 is a schematic diagram of a method for monitoring performance. As shown in Figure 10, the method includes:

[0163] 1001. Send first information, the first information being used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS), the Channel State Information Reference Signal Resource Set being used for performance monitoring of an artificial intelligence model or function, the artificial intelligence model or function being used to predict channel state information, and each of the Channel State Information Reference Signal Resource Sets containing at least one Channel State Information Reference Signal (CSI-RS).

[0164] 1002. Transmit the Channel State Information Reference Signal (CSI-RS); and

[0165] 1003. Receive the performance monitoring metric obtained by calculating the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0166] In Operation 1001, the Channel State Information Reference Signals (CSI-RS) within each Channel State Information Reference Signal Resource Set used for performance monitoring of artificial intelligence models or functions can also be referred to as Channel State Information Reference Signals (CSI-RS) used for performance monitoring.

[0167] In operation 1002, a Channel State Information Reference Signal (CSI-RS) for performance monitoring is transmitted, wherein the CSI-RS for performance monitoring is configured by operation 1001. For example, in operation 1002, the CSI-RS for performance monitoring transmitted in the first time window is a CSI-RS within the CSI-RS resource set configured by the first information in operation 1001 for performance monitoring of an artificial intelligence model or function.

[0168] In some embodiments, the Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent, the transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information.

[0169] In some embodiments, each of the predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS).

[0170] In some embodiments, each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

[0171] In some embodiments, the frequency domain position of the channel state information reference signal within the channel state information reference signal resource set configured based on the first information is distributed across all or part of the sub-bands of the terminal device.

[0172] In some embodiments, the subband includes a subband with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

[0173] In some embodiments, the performance monitoring metric includes:

[0174] The first performance monitoring metric calculated based on the entire bandwidth; or

[0175] A second performance monitoring metric calculated based on a subset of all subbands; or

[0176] The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

[0177] In some embodiments, the first performance monitoring metric is obtained through the following steps:

[0178] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metrics for each subband; and

[0179] The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

[0180] In some embodiments, the second performance monitoring metric is obtained through the following steps:

[0181] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metrics for each subband within that subset; and

[0182] The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in the subset.

[0183] It is worth noting that Figure 10 above is only an illustrative description of the embodiments of this application, but this 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 content, and are not limited to the description in Figure 10 above.

[0184] 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.

[0185] According to the second aspect of the embodiment, for AI / ML-based CSI prediction, the configuration of Channel State Information Reference Signal (CSI-RS), the calculation method of performance monitoring metrics, and the reporting mechanism are clarified, thereby supporting performance monitoring and ensuring good performance monitoring results with low complexity.

[0186] Third aspect of the embodiments

[0187] This application provides an apparatus for monitoring performance. This apparatus may be, for example, a terminal device, or one or more components or parts configured within a terminal device; details identical to those in the first and second aspects will not be repeated.

[0188] Figure 11 is a schematic diagram of a performance monitoring device according to an embodiment of the present application. As shown in Figure 11, the performance monitoring device 1100 according to an embodiment of the present application includes a first receiver 1101 and a first processor 1102.

[0189] In some embodiments, a first receiver 1101 receives first information, which is used to configure at least one channel state information reference signal resource set (CSI-RS). The CSI-RS resource set is used to perform performance monitoring on an artificial intelligence model or function, which is used to predict channel state information. Each CSI-RS resource set contains at least one channel state information reference signal (CSI-RS).

[0190] The first receiver 1101 receives the Channel State Information Reference Signal (CSI-RS);

[0191] The first processor 1102 calculates performance monitoring metrics based on the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0192] In some embodiments, the Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent, the transmission period of the Channel State Information Reference Signal (CSI-RS) is an integer multiple of the period of the first time window, and the time domain position of the Channel State Information Reference Signal (CSI-RS) coincides with that of the predicted channel state information.

[0193] In some embodiments, each of the predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS).

[0194] In some embodiments, each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

[0195] In some embodiments, the frequency domain position of the channel state information reference signal within the channel state information reference signal resource set configured based on the first information is distributed across all or part of the sub-bands of the terminal device.

[0196] In some embodiments, the subband includes a subband with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

[0197] In some embodiments, calculating the performance monitoring metric based on measurements of the Channel State Information Reference Signal (CSI-RS) includes, for each measurement:

[0198] The primary performance monitoring metric is calculated based on the entire bandwidth; or

[0199] Calculate the second performance monitoring metric based on a subset of all subbands; or

[0200] The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on a subset of all subbands.

[0201] In some embodiments, the first performance monitoring metric is calculated based on the entire bandwidth, including:

[0202] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metrics for each subband; and

[0203] The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

[0204] In some embodiments, a second performance monitoring metric is calculated based on a subset of all subbands, including:

[0205] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metrics for each subband within that subset; and

[0206] The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in the subset.

[0207] 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.

[0208] 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. Figure 11 may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0209] Furthermore, for simplicity, Figure 11 only illustrates the connection relationships or signal flow between the various components or modules, but those skilled in the art should understand that various related technologies such as bus connections can be used. 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.

[0210] Fourth aspect of the embodiment

[0211] This application provides an apparatus for monitoring performance. This apparatus may be, for example, a network device, or one or more components or parts configured in a terminal device; details identical to those in the first and second aspects will not be repeated.

[0212] Figure 12 is a schematic diagram of a performance monitoring device according to an embodiment of the present application. As shown in Figure 12, the performance monitoring device 1200 according to an embodiment of the present application includes a first transmitter 1201 and a second receiver 1202.

[0213] In some embodiments, the first transmitter 1201 transmits first information for configuring at least one channel state information reference signal resource set (CSI-RS), which is used for performance monitoring of an artificial intelligence model or function, which is used to predict channel state information, and each of the channel state information reference signal resource sets contains at least one channel state information reference signal (CSI-RS).

[0214] The first transmitter 1201 transmits the Channel State Information Reference Signal (CSI-RS); and

[0215] The second receiver 1202 receives the performance monitoring metric calculated from the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0216] In some embodiments, the Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent, the transmission period of the Channel State Information Reference Signal (CSI-RS) is an integer multiple of the period of the first time window, and the time domain position of the Channel State Information Reference Signal (CSI-RS) coincides with that of the predicted channel state information.

[0217] In some embodiments, each of the predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS).

[0218] In some embodiments, each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

[0219] In some embodiments, the frequency domain position of the channel state information reference signal within the channel state information reference signal resource set configured based on the first information is distributed across all or part of the sub-bands of the terminal device.

[0220] In some embodiments, the subband includes a subband with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

[0221] In some embodiments, the performance monitoring metric includes:

[0222] The first performance monitoring metric calculated based on the entire bandwidth; or

[0223] A second performance monitoring metric calculated based on a subset of all subbands; or

[0224] The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

[0225] In some embodiments, the first performance monitoring metric is obtained through the following steps:

[0226] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metrics for each subband; and

[0227] The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

[0228] In some embodiments, the second performance monitoring metric is obtained through the following steps:

[0229] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metrics for each subband within that subset; and

[0230] The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in the subset.

[0231] 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.

[0232] 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. Figure 12 may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0233] Furthermore, for simplicity, Figure 12 only illustrates the connection relationships or signal flow between the various components or modules, but those skilled in the art should understand that various related technologies such as bus connections can be used. 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.

[0234] Fifth aspect of the embodiment

[0235] 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.

[0236] In some embodiments, the communication system 100 may include at least: network equipment and terminal equipment.

[0237] In this application embodiment, a scenario including network devices and / or terminal devices is taken as an example.

[0238] 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.

[0239] 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.

[0240] Third-party application devices can be OTT services (over the top server) or other third-party devices.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] This application also provides an electronic device, but the application is not limited thereto, and other devices may also be used. At least one of the network device and terminal device in the communication system of this application may have the structure of this electronic device.

[0246] Figure 13 is a schematic diagram of the structure of an electronic device according to an embodiment of this application. As shown in Figure 13, the electronic device 1300 may include: a processor 1310 (e.g., a central processing unit CPU) and a memory 1320; the memory 1320 is coupled to the processor 1310. The memory 1320 can store various data; in addition, it also stores an information processing program 1330, and executes the program 1330 under the control of the processor 1310.

[0247] For example, processor 1310 may be configured to execute a program to implement the methods described in the embodiments of the first and / or second aspects.

[0248] Furthermore, as shown in Figure 13, the electronic device 1300 may also include a transceiver 1340 and an antenna 1350, 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 1300 does not necessarily include all the components shown in Figure 13; in addition, the electronic device 1300 may also include components not shown in Figure 13, which can be referred to in the prior art.

[0249] This application also provides a computer program or computer program product, 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.

[0250] 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.

[0251] This application also provides a computer program or computer program product, 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 embodiments.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] Regarding the implementation methods including the above embodiments, the following notes are also disclosed:

[0259] 1. A device for monitoring performance, applied to network equipment, the device comprising a first transmitter and a second receiver, wherein,

[0260] The first transmitter sends first information, which is used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS). The CSI-RS is used to perform performance monitoring on an artificial intelligence model or function. The artificial intelligence model or function is used to predict channel state information. Each of the CSI-RS resource sets contains at least one CSI-RS.

[0261] The first transmitter transmits the Channel State Information Reference Signal (CSI-RS); and

[0262] The second receiver receives the performance monitoring metric calculated from the measurement results of the Channel State Information Reference Signal (CSI-RS).

[0263] The performance monitoring metrics include:

[0264] The first performance monitoring metric calculated based on the entire bandwidth; or

[0265] A second performance monitoring metric calculated based on a subset of all subbands; or

[0266] The first performance monitoring metric is calculated based on the entire broadband bandwidth, and the second performance monitoring metric is calculated based on subsets of all subbands.

[0267] 2. The apparatus as described in Appendix 1, wherein,

[0268] The first performance monitoring metric is obtained through the following steps:

[0269] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband; and

[0270] The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

[0271] 3. The apparatus as described in Appendix 1, wherein,

[0272] The second performance monitoring metric is obtained through the following steps:

[0273] For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband in the subset; and

[0274] The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in the subset.

[0275] 4. The apparatus as described in Appendix 1, wherein,

[0276] The Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent. The transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information.

[0277] in,

[0278] Each predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS); or

[0279] Each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where N is a natural number greater than or equal to 2.

[0280] 6. The apparatus as described in Appendix 4, wherein,

[0281] The frequency domain positions of the channel state information reference signals within the channel state information reference signal resource set configured based on the first information are distributed across all or some sub-bands of the terminal device.

[0282] 7. The apparatus as described in Appendix 4, wherein,

[0283] The sub-bands include sub-bands with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

Claims

1. A device for monitoring performance, applied to a terminal device, the device comprising a first receiver and a first processor, wherein, The first receiver receives first information, which is used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS). The CSI-RS is used to perform performance monitoring on an artificial intelligence model or function. The artificial intelligence model or function is used to predict channel state information. Each of the CSI-RS resource sets contains at least one CSI-RS. The first receiver receives the Channel State Information Reference Signal (CSI-RS); The first processor calculates performance monitoring metrics based on the measurement results of the Channel State Information Reference Signal (CSI-RS). The Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent, the transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information.

2. The apparatus of claim 1, wherein, Each predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS).

3. The apparatus of claim 1, wherein, Each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

4. The apparatus of claim 1, wherein, The frequency domain positions of the channel state information reference signals within the channel state information reference signal resource set configured based on the first information are distributed across all or some sub-bands of the terminal device.

5. The apparatus of claim 4, wherein, The sub-bands include sub-bands with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

6. The apparatus of claim 1, wherein, The performance monitoring metrics are calculated based on the measurement results of the Channel State Information Reference Signal (CSI-RS), including, for each measurement result: The primary performance monitoring metric is calculated based on the entire bandwidth. or The second performance monitoring metric is calculated based on a subset of all subbands; or The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

7. The apparatus of claim 6, wherein, The first performance monitoring metric, based on the entire broadband computing, includes: For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband; and The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

8. The apparatus of claim 6, wherein, The second performance monitoring metric is calculated based on a subset of all subbands, including: For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband in the subset; and The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in the subset.

9. A device for monitoring performance, applied to a terminal device, the device comprising a first receiver and a first processor, wherein, The first receiver receives first information, which is used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS). The CSI-RS is used to perform performance monitoring on an artificial intelligence model or function. The artificial intelligence model or function is used to predict channel state information. Each of the CSI-RS resource sets contains at least one CSI-RS. The first receiver receives the Channel State Information Reference Signal (CSI-RS); The first processor calculates the performance monitoring metric based on the measurement results of the Channel State Information Reference Signal (CSI-RS). For each measurement result: The primary performance monitoring metric is calculated based on the entire bandwidth; or Calculate the second performance monitoring metric based on a subset of all subbands; or The first performance monitoring metric is calculated based on the entire broadband, and the second performance monitoring metric is calculated based on subsets of all subbands.

10. The apparatus of claim 9, wherein, The first performance monitoring metric, based on the entire broadband computing, includes: For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband; and The first performance monitoring metric is obtained based on the performance monitoring metrics across all subbands.

11. The apparatus of claim 9, wherein, The second performance monitoring metric is calculated based on a subset of all subbands, including: For each time-domain location where the Channel State Information Reference Signal (CSI-RS) is received, calculate the performance monitoring metric for each subband in the subset; and The second performance monitoring metric is obtained based on the performance monitoring metrics on each subband in the subset.

12. The apparatus of claim 9, wherein, The Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent. The transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information. in, Each predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS); or Each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

13. The apparatus of claim 12, wherein, The frequency domain positions of the channel state information reference signals within the channel state information reference signal resource set configured based on the first information are distributed across all or some sub-bands of the terminal device.

14. The apparatus of claim 13, wherein, The sub-bands include sub-bands with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

15. A device for monitoring performance, applied to network equipment, the device comprising a first transmitter and a second receiver, wherein, The first transmitter sends first information, which is used to configure at least one Channel State Information Reference Signal Resource Set (CSI-RS) for the terminal device. The CSI-RS resource set is used for performance monitoring of an artificial intelligence model or function. The artificial intelligence model or function is used to predict channel state information. Each CSI-RS resource set contains at least one CSI-RS. The first transmitter transmits the Channel State Information Reference Signal (CSI-RS); as well as The second receiver receives the performance monitoring metric calculated from the measurement results of the Channel State Information Reference Signal (CSI-RS). The Channel State Information Reference Signal (CSI-RS) is periodic or semi-persistent, the transmission period of the CSI-RS is an integer multiple of the period of the first time window, and the time domain position of the CSI-RS coincides with that of the predicted channel state information.

16. The apparatus of claim 15, wherein, Each predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS).

17. The apparatus of claim 15, wherein, Each K predicted channel state information within the first time window is configured with a corresponding channel state information reference signal (CSI-RS), where K is a natural number greater than or equal to 2.

18. The apparatus of claim 15, wherein, The frequency domain positions of the channel state information reference signals within the channel state information reference signal resource set configured based on the first information are distributed across all or some sub-bands of the terminal device.

19. The apparatus of claim 18, wherein, The sub-bands include sub-bands with index p*M+j, where M is an integer greater than or equal to 2, p is an integer greater than or equal to 0, and j is an integer greater than or equal to 0 and less than or equal to M-1.

20. The apparatus of claim 15, wherein, The performance monitoring metrics include: The first performance monitoring metric calculated based on the entire bandwidth; or A second performance monitoring metric calculated based on a subset of all subbands; or The first performance monitoring metric is calculated based on the entire broadband bandwidth, and the second performance monitoring metric is calculated based on subsets of all subbands.

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