CSI prediction method and apparatus, and SRS transmission method and apparatus

Abstract

Embodiments of the present application provide a CSI prediction method and apparatus, and an SRS transmission method and apparatus. The method comprises: a terminal device receives CSI-RS configuration information from a network device; and the terminal device measures a CSI-RS on the basis of the CSI-RS configuration information, wherein the measurement result of the CSI-RS is used by the terminal device and / or the network device to perform spatial-domain CSI prediction and / or frequency-domain CSI prediction on the basis of an AI / ML model / function.

Description

CSI prediction and SRS transmission methods and apparatus Technical Field

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

[0002] In NR Rel-18, artificial intelligence / machine learning (AI / ML) for the air interface was studied. 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 beam prediction (BM case-1) and temporal beam prediction (BM case-2); positioning enhancement can include direct positioning and AI / ML-assisted positioning.

[0003] In some sub-use cases, a two-sided model can be used, meaning the AI / ML model is 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 model is on either the terminal device side or the network device side. For beam management, the AI / ML model can be on both the terminal device side and / or the network device side.

[0004] 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.

[0005] Summary of the Invention

[0006] The inventors have discovered that there is currently no specific solution for how to perform channel state information prediction (CSI prediction) or sounding reference signal (SRS) transmission based on AI / ML.

[0007] To address at least one of the above-mentioned problems, embodiments of this application provide a CSI prediction and SRS transmission method and apparatus.

[0008] According to one aspect of the embodiments of this application, a CSI prediction method is provided, comprising:

[0009] The terminal device receives CSI-RS configuration information from the network device;

[0010] The terminal device measures the CSI-RS based on the CSI-RS configuration information; wherein, the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0011] According to another aspect of the embodiments of this application, a CSI prediction apparatus is provided, comprising:

[0012] The receiver receives CSI-RS configuration information from the network device;

[0013] A processor that measures CSI-RS based on the CSI-RS configuration information; wherein the measurement results of CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0014] According to another aspect of the embodiments of this application, a CSI prediction method is provided, comprising:

[0015] The network device sends CSI-RS configuration information to the terminal device; wherein the terminal device performs CSI-RS measurements based on the CSI-RS configuration information; and

[0016] The network device receives the measurement results from the CSI-RS; and

[0017] The network device performs spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0018] According to another aspect of the embodiments of this application, a CSI prediction apparatus is provided, comprising:

[0019] A transmitter that sends CSI-RS configuration information to a terminal device; wherein the terminal device performs CSI-RS measurements based on the CSI-RS configuration information; and

[0020] A receiver that receives the measurement results from the CSI-RS; and

[0021] The processor performs spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0022] According to another aspect of the embodiments of this application, an SRS transmission method is provided, comprising:

[0023] The terminal device receives configuration information from the network device for SRS transmission;

[0024] The terminal device sends an SRS to the network device according to the configuration information; wherein the measurement result of the SRS is used by the network device as input to an AI / ML model / function.

[0025] According to another aspect of the embodiments of this application, an SRS transmission device is provided, comprising:

[0026] The receiver receives configuration information from the network device for SRS transmission;

[0027] A transmitter that sends an SRS to the network device according to the configuration information; wherein the measurement results of the SRS are used by the network device as input to an AI / ML model / function.

[0028] According to another aspect of the embodiments of this application, an SRS transmission method is provided, comprising:

[0029] The network device sends configuration information for SRS transmission to the terminal device;

[0030] The network device receives the SRS sent by the terminal device according to the configuration information; and

[0031] The network device uses the measurement results of the SRS as input to the AI / ML model / function.

[0032] According to another aspect of the embodiments of this application, an SRS transmission device is provided, comprising:

[0033] The transmitter sends configuration information for SRS transmission to the terminal device;

[0034] The receiver receives the SRS sent by the terminal device according to the configuration information; and

[0035] The processor takes the SRS measurement results as input to the AI / ML model / function.

[0036] According to another aspect of the embodiments of this application, a communication system is provided, including a network device and a terminal device;

[0037] The terminal device receives CSI-RS configuration information from the network device; and performs CSI-RS measurement based on the CSI-RS configuration information; wherein the CSI-RS measurement results are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0038] and / or

[0039] The terminal device receives configuration information from the network device for SRS transmission; sends SRS to the network device according to the configuration information; wherein the measurement results of the SRS are used by the network device as input to an AI / ML model / function.

[0040] One of the beneficial effects of this application's embodiments includes: a terminal device receiving CSI-RS configuration information from a network device; measuring CSI-RS based on the CSI-RS configuration information; wherein the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on an AI / ML model / function. Therefore, spatial domain CSI prediction and / or frequency domain CSI prediction can be performed, improving prediction accuracy and enhancing the performance and efficiency of AI / ML.

[0041] 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.

[0042] 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.

[0043] 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

[0044] 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.

[0045] Figure 1 is a schematic diagram of a communication system according to an embodiment of this application;

[0046] Figure 2 is a schematic diagram of the CSI prediction method according to an embodiment of this application;

[0047] Figure 3 is a schematic diagram of AI / ML for CSI prediction according to an embodiment of this application;

[0048] Figure 4 is a schematic diagram of the CSI prediction method according to an embodiment of this application;

[0049] Figure 5 is an example diagram of CSI-RS configuration according to an embodiment of this application;

[0050] Figure 6 is an example diagram of the frequency domain density and PRG level of an embodiment of this application;

[0051] Figure 7 is a schematic diagram of an SRS transmission method according to an embodiment of this application;

[0052] Figure 8 is a schematic diagram of the CSI prediction method according to an embodiment of this application;

[0053] Figure 9 is a schematic diagram of an SRS transmission method according to an embodiment of this application;

[0054] Figure 10 is a schematic diagram of a CSI prediction device or SRS device according to an embodiment of this application;

[0055] Figure 11 is a schematic diagram of a CSI prediction device or SRS device according to an embodiment of this application;

[0056] Figure 12 is a schematic diagram of a terminal device according to an embodiment of this application;

[0057] Figure 13 is a schematic diagram of a network device according to an embodiment of this application. Detailed Implementation

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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. A terminal device can be fixed or mobile, and may also be referred to as a mobile station (MS), terminal, subscriber station (SS), access terminal (AT), station, etc.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] The following examples illustrate the scenarios of embodiments of this application, but this application is not limited thereto.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] In the embodiments of this application, higher-layer signaling may be, for example, Radio Resource Control (RRC) signaling; for example, referred to as an RRC message, including MIB, system information, dedicated RRC messages; or referred to as an RRC information element. Higher-layer signaling may also be, for example, Medium Access Control (MAC) signaling; or referred to as a MAC control element. However, this application is not limited to these.

[0074] In NR Rel-18, AI / ML for CSI feedback enhancement, including CSI compression and CSI prediction, was also investigated. For CSI prediction, the AI / ML model / function resides on the terminal device side. Through time-domain CSI prediction, the terminal device can measure the reference signal at one or more time instances (observation windows) and predict the CSI for one or more future time instances (prediction windows).

[0075] In the embodiments of this application, one or more AI / ML models can be configured and run in network devices and / or terminal devices. AI / ML models can be used for various signal processing functions in wireless communication, such as CSI prediction, CSI compression, beam prediction, positioning management, etc.; this application is not limited thereto.

[0076] First aspect of the embodiments

[0077] This application provides a CSI prediction method, which is described from the perspective of the terminal device.

[0078] Figure 2 is a schematic diagram of a CSI prediction method according to an embodiment of this application. As shown in Figure 2, the method includes:

[0079] 201. The terminal device receives CSI-RS configuration information from the network device;

[0080] 202, the terminal device performs CSI-RS measurements based on the CSI-RS configuration information; and

[0081] 203. The terminal device, based on an AI / ML model / function, performs spatial domain CSI prediction and / or frequency domain CSI prediction according to the measurement results of the CSI-RS.

[0082] It is worth noting that Figure 2 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 2 above.

[0083] In some embodiments, a functionality refers to an AI / ML feature / feature group enabled by a configuration, wherein the configuration is supported based on conditions indicated by UE capabilities.

[0084] For example, an AL / ML function can be one or more functions, or one or more logical models, or one or more sub-functions, or one or more features, or one or more feature groups.

[0085] For example, the function could be to use AI / ML for spatial beam prediction, or to use AI / ML for temporal beam prediction, or to use AI / ML for CSI prediction, or to use AI / ML for direct positioning, or to use AI / ML for assisted positioning, and so on.

[0086] In some embodiments, the AI / ML function / model can be used for CSI prediction. One or more reference signals are used for measurement, and the measurement results are input into the AI / ML function / model. One or more CSIs are used in the output of the AI / ML function / model for inference.

[0087] In some embodiments, an AI / ML function / model can be used for SRS transmission. One or more SRSs are used for measurement, and the measurement results are input into the AI / ML function / model. One or more SRSs are used in the output of the AI / ML function / model for inference.

[0088] For ease of description, the following will refer to CSI prediction or SRS transmission based on AI / ML functionality / model as model inference or inference operation, training data collection based on AI / ML functionality / model as training data collection (training data collection can also be done using non-AI / ML methods), and performance monitoring based on AI / ML functionality / model as performance monitoring.

[0089] In some embodiments, the network device may transmit configuration information for one or more reference signals, such as CSI-RS configuration information, etc. This application is not limited thereto; further details regarding specific configuration information can be found in related technologies. The configuration information may include configuration information for training data collection, and / or configuration information for model inference, and / or configuration information for performance monitoring.

[0090] Figure 3 is a schematic diagram of AI / ML for CSI prediction according to an embodiment of this application. As shown in Figure 3, one or more reference signals can be received and measured by a terminal device. The measurement results can be used as input to AI / ML, and CSI or beamforming can be used by the terminal device as output to AI / ML, for example, the measurement results can be used as labeled data or ground truth data for AI / ML. For details regarding AI / ML, please refer to related technologies, which will not be elaborated here.

[0091] Figure 4 is another schematic diagram of the CSI prediction method according to an embodiment of this application, illustrated using a terminal device configured with AI / ML as an example. As shown in Figure 4, the method includes:

[0092] 401, The terminal device receives configuration information from the network device; for example, the configuration information includes a set of reference signal resources for measurement;

[0093] 402, Network devices send reference signals (e.g., CSI-RS) to terminal devices;

[0094] 403. The terminal device makes predictions based on AI / ML; that is, the terminal device receives reference signals and performs measurements, and inputs the measurement results into the AI / ML function / model; for example, the measurement results of CSI-RS are used as the input of AI / ML, and CSI or beamforming is used for prediction (or inference).

[0095] It is worth noting that Figure 4 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 4 above.

[0096] The above illustrations demonstrate CSI prediction based on AI / ML, but this application is not limited thereto.

[0097] In some embodiments, the terminal device receives a CSI-RS for CSI prediction; the CSI-RS is used for the spatial domain CSI prediction. The AI / ML model / function used for the spatial domain CSI prediction is configured on the terminal device side.

[0098] For example, CSI prediction can be performed using AI / ML in the spatial domain. AI / ML functions / models can be implemented on the UE side.

[0099] In some embodiments, the terminal device is configured with CSI-RS resources of X1 ports, and the terminal device obtains channel information of Y1 ports based on the CSI-RS of X1 ports; wherein Y1 is greater than or equal to X1.

[0100] In some embodiments, the terminal device is configured to have a codebook report with Y1 ports, and the terminal device sends codebook information with Y1 ports to the network device.

[0101] For example, AI / ML functions / models are located on the UE side. The gNB can be configured with CSI-RS resources with X1 ports for UE CSI measurements. Utilizing the AI / ML functions / models for spatial domain CSI prediction, based on measurements of the CSI-RS with X1 ports, the UE can predict channels with Y1 ports, where Y1 >= X1, for example, X1 = 16 and Y1 = 64.

[0102] For example, in the spatial domain CSI prediction on the UE side, in addition to configuring CSI-RS resources with X1 ports, the gNB can also configure codebook reporting with Y1 ports. After CSI prediction, the UE can report the codebook with Y1 ports.

[0103] In some embodiments, the terminal device is configured / instructed to use first pattern information to represent the distribution of the X1 ports among the Y1 ports and / or second pattern information to represent the TxRU mapping.

[0104] For example, the gNB can configure / indicate a CSI-RS pattern with an X1 port in port Y1, and / or, the gNB can configure / indicate a pattern mapped to a TxRU. For example, Y1 = 64, X1 = 16. Then, CSI-RS can be transmitted through antenna ports {0, 1, 2, 3, ..., 15}. As another example, CSI-RS can be transmitted through antenna ports {0, 2, 4, 6, ..., 30}. A CSI-RS pattern with an X1 port in port Y1 can be explicitly configured / indicated to the UE, or implicitly configured / indicated to the UE.

[0105] Figure 5 is an example diagram of CSI-RS configuration according to an embodiment of this application. As shown in Figure 5, the gNB can support 32 antenna ports for downlink transmission. The gNB can configure 8-port CSI-RS for the UE, but the UE can report a codebook with 32 ports.

[0106] In some embodiments, the terminal device is configured with CSI-RS resources having Y1 ports and is configured / instructed to transmit CSI-RS on X1 ports. The terminal device obtains channel information of the Y1 ports based on the CSI-RS transmitted on the X1 ports; wherein Y1 is greater than or equal to X1.

[0107] In some embodiments, the X1 ports that transmit CSI-RS are a subset of the Y1 ports. Port information that does not transmit CSI-RS is configured / indicated via RRC parameters and / or MAC CE and / or DCI, and / or port information that transmits CSI-RS is configured / indicated via RRC parameters and / or MAC CE and / or DCI.

[0108] For example, the gNB can be configured with CSI-RS resources having Y1 ports, and the CSI-RS is transmitted only on a subset of antenna ports, i.e., on port X1 (Y1>=X1). The gNB can be configured to exclude CSI-RS transmission from certain antenna ports, for example, through new RRC parameters, MAC CE, or DCI. This application is not limited to this; existing RRC parameters, MAC CE, or DCI can also be reused.

[0109] In some embodiments, the terminal device is configured to report a codebook with Y1 ports, and the terminal device sends codebook information with Y1 ports to the network device.

[0110] In some embodiments, the AI / ML model / function for spatial domain CSI prediction is configured on the network device side.

[0111] For example, CSI prediction can be performed using AI / ML in the spatial domain. The AI / ML function / model can be located on the gNB side. The gNB can be configured with CSI-RS resources with X ports for UE CSI measurements. The UE can report the measurement raw channel for X ports, or it can report the high-resolution codebook for X ports.

[0112] In some embodiments, spatial domain CSI prediction is applied together with CSI compression and / or temporal domain CSI prediction.

[0113] For example, spatial domain CSI prediction can be applied in conjunction with CSI compression and / or temporal domain CSI prediction, including but not limited to the following:

[0114] - Spatial domain CSI prediction + CSI compression, where spatial domain CSI prediction is performed on the UE side;

[0115] - Spatial domain CSI prediction + CSI compression, where spatial domain CSI prediction is performed on the gNB side;

[0116] - Spatial domain CSI prediction + temporal domain CSI prediction, both on the UE side;

[0117] - Spatial domain CSI prediction on the NB side and temporal domain CSI prediction on the UE side.

[0118] The spatial domain CSI prediction has been explained above. The frequency domain CSI prediction will be explained below.

[0119] In some embodiments, the terminal device receives a CSI-RS for CSI prediction; the CSI-RS is used for frequency domain CSI prediction. The AI / ML model / function for frequency domain CSI prediction is configured on the terminal device side and / or the network device side.

[0120] In some embodiments, in the frequency domain CSI prediction, the density of CSI-RS in the frequency domain is less than a first value (e.g., 0.5), and / or the granularity of CSI reports in the frequency domain is less than a second value (e.g., 4).

[0121] For example, CSI-RS with lower frequency domain overhead can be applied. The CSI-RS density in the frequency domain (ρ, i.e., the number of REs for CSI-RS in a PRB) can be less than 0.5, for example, ρ = 0.25, which means that one RE out of four PRBs is used for CSI-RS.

[0122] For example, the gNB can configure CSI-RS with a frequency density ρ for the UE. The UE is also configured with the frequency domain granularity (P) of the CSI report, i.e., the precoding resource block group (PRG) level, which means the number of PRBs with the same precoder applied. The value of P can be P ≤ 1 / ρ, as predicted by the frequency domain CSI-RS. For example, ρ = 0.25, P = 2.

[0123] Figure 6 is an example diagram of the frequency domain density and PRG level according to an embodiment of this application. As shown in Figure 6, for example, the CSI-RS frequency density is 0.25, that is, one RE is used for CSI-RS out of every four PRBs. As shown in Figure 6, the PRG level used for CSI reporting is two PRBs.

[0124] In some embodiments, the frequency domain CSI prediction is applied together with CSI compression and / or time domain CSI prediction and / or spatial domain CSI prediction.

[0125] For example, frequency domain CSI prediction can be applied in conjunction with CSI compression and / or time domain CSI prediction and / or spatial domain CSI prediction, including but not limited to the following:

[0126] - Frequency domain CSI prediction + CSI compression, where frequency domain CSI prediction is performed on the UE side;

[0127] - Frequency domain CSI prediction + CSI compression, where frequency domain CSI prediction is performed on the gNB side.

[0128] The above describes some aspects of CSI prediction; the following section describes SRS transmission based on AI / ML. This application provides an SRS transmission method, described from the perspective of the terminal device.

[0129] Figure 7 is a schematic diagram of an SRS transmission method according to an embodiment of this application. As shown in Figure 7, the method includes:

[0130] 701, The terminal device receives configuration information from the network device for SRS transmission;

[0131] 702, the terminal device sends an SRS to the network device according to the configuration information; wherein the measurement result of the SRS is used by the network device as input to an AI / ML model / function.

[0132] It is worth noting that Figure 7 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 7 above.

[0133] Therefore, SRS with reduced overhead can be applied through AI / ML functions / models on the network side.

[0134] In some embodiments, the terminal device has X2 antennas and is configured with SRS resources having Y2 ports, where Y2 is less than or equal to X2.

[0135] For example, the number of ports in the SRS resource can be configured to be less than the number of antennas in the UE. For example, if the UE has 8 Tx antennas, the SRS resource can be configured to have 4 ports.

[0136] In some embodiments, port information for transmitting SRS is configured / indicated by the network device, and / or port information for not transmitting SRS is configured / indicated by the network device.

[0137] For example, the gNB can configure / indicate which antenna ports SRS resources are transmitted through, or vice versa. In one example, UE antennas can be divided into multiple subsets or groups (e.g., antenna groups or port groups). The gNB can configure / indicate which port group is used for SRS transmission.

[0138] In some embodiments, the comb size of the SRS is greater than a third value (e.g., 8).

[0139] For example, SRS can introduce larger comb sizes, i.e., comb sizes greater than 8. For example, SRS can introduce Comb-12 / Comb-16 / Comb-24, etc., and this application is not limited to these.

[0140] 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.

[0141] As described in the above embodiments, the terminal device receives CSI-RS configuration information from the network device; it then measures the CSI-RS based on the CSI-RS configuration information; and the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions. This enables spatial domain CSI prediction and / or frequency domain CSI prediction, improves prediction accuracy, and enhances the performance and efficiency of AI / ML.

[0142] On the other hand, the terminal device receives configuration information from the network device for SRS transmission; it then sends SRS to the network device according to the configuration information; the measurement results of the SRS are used by the network device as input to an AI / ML model / function. Thus, SRS with reduced overhead can be applied through network-side AI / ML functions / models.

[0143] Second aspect of the embodiments

[0144] This application provides a CSI prediction method, described from the perspective of a network device. The embodiments of the second aspect can be combined with the embodiments of the first aspect, and the content identical to that of the embodiments of the first aspect will not be repeated.

[0145] Figure 8 is a schematic diagram of a CSI prediction method according to an embodiment of this application. As shown in Figure 8, the method includes:

[0146] 801, The network device sends CSI-RS configuration information to the terminal device; wherein, the terminal device performs CSI-RS measurement based on the CSI-RS configuration information;

[0147] 802, the network device receives the measurement results from the CSI-RS; and

[0148] 803, the network device performs spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0149] It is worth noting that Figure 8 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 8 above.

[0150] In some embodiments, the network device sends a CSI-RS for CSI prediction; the CSI-RS is used for spatial domain CSI prediction.

[0151] In some embodiments, the AI / ML model / function for spatial domain CSI prediction is configured on the terminal device side.

[0152] In some embodiments, the terminal device is configured with CSI-RS resources of X1 ports, and the terminal device obtains channel information of Y1 ports based on the CSI-RS of X1 ports; wherein Y1 is greater than or equal to X1.

[0153] In some embodiments, the terminal device is configured to have a codebook report with Y1 ports, and the network device also receives codebook information with Y1 ports sent by the terminal device.

[0154] In some embodiments, the terminal device is configured / instructed to use first pattern information to represent the distribution of the X1 ports among the Y1 ports and / or second pattern information to represent the TxRU mapping.

[0155] In some embodiments, the terminal device is configured with CSI-RS resources having Y1 ports and is configured / instructed to transmit CSI-RS on X1 ports. The terminal device obtains channel information of the Y1 ports based on the CSI-RS transmitted on the X1 ports; wherein Y1 is greater than or equal to X1.

[0156] In some embodiments, the X1 ports that transmit CSI-RS are a subset of the Y1 ports. Port information that does not transmit CSI-RS is configured / indicated via RRC parameters or MAC-CE or DCI, and / or port information that transmits CSI-RS is configured / indicated via RRC parameters and / or MAC CE and / or DCI.

[0157] In some embodiments, the terminal device is configured to report a codebook with Y1 ports, and the network device receives codebook information with Y1 ports sent by the terminal device.

[0158] In some embodiments, the AI / ML model / function for spatial domain CSI prediction is configured on the network device side.

[0159] In some embodiments, spatial domain CSI prediction is applied together with CSI compression and / or temporal domain CSI prediction.

[0160] In some embodiments, the network device sends a CSI-RS for CSI prediction; the CSI-RS is used for frequency domain CSI prediction.

[0161] In some embodiments, the AI / ML model / function for the frequency domain CSI prediction is configured on the terminal device side and / or the network device side.

[0162] In some embodiments, in the frequency domain CSI prediction, the density of CSI-RS in the frequency domain is less than a first value (e.g., 0.5), and / or the granularity of CSI reports in the frequency domain is less than a second value (e.g., 4).

[0163] In some embodiments, the frequency domain CSI prediction is applied together with CSI compression and / or time domain CSI prediction and / or spatial domain CSI prediction.

[0164] This application also provides an SRS transmission method.

[0165] Figure 9 is a schematic diagram of an SRS transmission method according to an embodiment of this application. As shown in Figure 9, the method includes:

[0166] 901. The network device sends configuration information for SRS transmission to the terminal device;

[0167] 902, the network device receives the SRS sent by the terminal device according to the configuration information; and

[0168] 903, the network device uses the measurement results of the SRS as input to the AI / ML model / function; for example, the network device measures the SRS and makes SRS predictions.

[0169] It is worth noting that Figure 9 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 9 above.

[0170] In some embodiments, the terminal device has X2 antennas and is configured with SRS resources having Y2 ports, where Y2 is less than or equal to X2.

[0171] In some embodiments, port information for transmitting SRS is configured / indicated by the network device, and / or port information for not transmitting SRS is configured / indicated by the network device.

[0172] In some embodiments, the comb size of the SRS is greater than a third value (e.g., 8).

[0173] In some embodiments, the network device may send configuration information, etc., to the terminal device. The network device may receive feedback information and / or report information sent by the terminal device. For example, the terminal device may report inference results and / or performance monitoring results and / or training data collection results to the network device, but this application is not limited thereto.

[0174] 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.

[0175] As described in the above embodiments, the terminal device receives CSI-RS configuration information from the network device; it then measures the CSI-RS based on the CSI-RS configuration information; and the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions. This enables spatial domain CSI prediction and / or frequency domain CSI prediction, improves prediction accuracy, and enhances the performance and efficiency of AI / ML.

[0176] On the other hand, the terminal device receives configuration information from the network device for SRS transmission; it then sends SRS to the network device according to the configuration information; the measurement results of the SRS are used by the network device as input to an AI / ML model / function. Thus, SRS with reduced overhead can be applied through network-side AI / ML functions / models.

[0177] Third aspect of the embodiments

[0178] This application provides a CSI prediction device. This device 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.

[0179] Figure 10 is a schematic diagram of a CSI prediction device according to an embodiment of the present application. As shown in Figure 10, the CSI prediction device 1000 according to an embodiment of the present application includes a receiver 1001 and a processor 1002, and may also include a transmitter 1003.

[0180] Receiver 1001 receives CSI-RS configuration information from the network device;

[0181] The processor 1002 measures the CSI-RS based on the CSI-RS configuration information; wherein the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0182] In some embodiments, receiver 1001 also receives CSI-RS for CSI prediction; the CSI-RS is used for the spatial domain CSI prediction.

[0183] In some embodiments, the AI / ML model / function for spatial domain CSI prediction is configured on the terminal device side.

[0184] In some embodiments, the terminal device is configured with CSI-RS resources of X1 ports, and the terminal device obtains channel information of Y1 ports based on the CSI-RS of X1 ports; wherein Y1 is greater than or equal to X1.

[0185] In some embodiments, the terminal device is configured to have a codebook report with Y1 ports, and the transmitter 1003 sends the codebook information with Y1 ports to the network device.

[0186] In some embodiments, the terminal device is configured / instructed to use first pattern information to represent the distribution of the X1 ports among the Y1 ports and / or second pattern information to represent the TxRU mapping.

[0187] In some embodiments, the terminal device is configured with CSI-RS resources having Y1 ports and is configured / instructed to transmit CSI-RS on X1 ports. The terminal device obtains channel information of the Y1 ports based on the CSI-RS transmitted on the X1 ports; wherein Y1 is greater than or equal to X1.

[0188] In some embodiments, the X1 ports that transmit CSI-RS are a subset of the Y1 ports. Port information that does not transmit CSI-RS is configured / indicated via RRC parameters or MAC-CE or DCI, and / or port information that transmits CSI-RS is configured / indicated via RRC parameters and / or MAC CE and / or DCI.

[0189] In some embodiments, the terminal device is configured to report a codebook with Y1 ports, and the transmitter 1003 sends codebook information with Y1 ports to the network device.

[0190] In some embodiments, the AI / ML model / function for spatial domain CSI prediction is configured on the network device side.

[0191] In some embodiments, spatial domain CSI prediction is applied together with CSI compression and / or temporal domain CSI prediction.

[0192] In some embodiments, the receiver 1001 also receives a CSI-RS for CSI prediction; the CSI-RS is used for the frequency domain CSI prediction.

[0193] In some embodiments, the AI / ML model / function for the frequency domain CSI prediction is configured on the terminal device side and / or the network device side.

[0194] In some embodiments, in the frequency domain CSI prediction, the density of CSI-RS in the frequency domain is less than a first value (e.g., 0.5), and / or the granularity of CSI reports in the frequency domain is less than a second value (e.g., 4).

[0195] In some embodiments, the frequency domain CSI prediction is applied together with CSI compression and / or time domain CSI prediction and / or spatial domain CSI prediction.

[0196] This application also provides an SRS transmission device, which can be referred to FIG10.

[0197] In some embodiments, receiver 1001 receives configuration information from network device for SRS transmission; transmitter 1003 sends SRS to network device according to the configuration information; wherein the measurement results of SRS are used by network device as input to AI / ML model / function.

[0198] In some embodiments, the terminal device has X2 antennas and is configured with SRS resources having Y2 ports, where Y2 is less than or equal to X2.

[0199] In some embodiments, port information for transmitting SRS is configured / indicated by the network device, and / or port information for not transmitting SRS is configured / indicated by the network device.

[0200] In some embodiments, the comb size of the SRS is greater than a third value (e.g., 8).

[0201] 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.

[0202] 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 CSI prediction device or SRS transmission device may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0203] Furthermore, for simplicity, Figure 10 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.

[0204] As described in the above embodiments, the terminal device receives CSI-RS configuration information from the network device; it then measures the CSI-RS based on the CSI-RS configuration information; and the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions. This enables spatial domain CSI prediction and / or frequency domain CSI prediction, improves prediction accuracy, and enhances the performance and efficiency of AI / ML.

[0205] On the other hand, the terminal device receives configuration information from the network device for SRS transmission; it then sends SRS to the network device according to the configuration information; the measurement results of the SRS are used by the network device as input to an AI / ML model / function. Thus, SRS with reduced overhead can be applied through network-side AI / ML functions / models.

[0206] Fourth aspect of the embodiment

[0207] This application provides a CSI prediction device. This device may be, for example, a network device, or one or more components or parts configured within a network device; details identical to those in the embodiments of the first to third aspects will not be repeated.

[0208] Figure 11 is another schematic diagram of the CSI prediction device according to an embodiment of the present application. As shown in Figure 11, the CSI prediction device 1100 includes a transmitter 1101, and may also include a processor 1102 and a receiver 1103.

[0209] In some embodiments, transmitter 1101 sends CSI-RS configuration information to terminal device; wherein, terminal device performs CSI-RS measurement based on the CSI-RS configuration information; receiver 1103 receives the CSI-RS measurement result; and processor 1102 performs spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML model / function.

[0210] This application also provides an SRS transmission device, which can be referred to FIG11.

[0211] In some embodiments, the transmitter 1101 sends configuration information for SRS transmission to the terminal device; the receiver 1103 receives the SRS sent by the terminal device according to the configuration information; and the processor 1102 uses the measurement results of the SRS as input to the AI / ML model / function.

[0212] 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.

[0213] 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 CSI prediction device or SRS transmission device may also include other components or modules, and for details regarding these components or modules, please refer to related technologies.

[0214] 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.

[0215] As described in the above embodiments, the terminal device receives CSI-RS configuration information from the network device; it then measures the CSI-RS based on the CSI-RS configuration information; and the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions. This enables spatial domain CSI prediction and / or frequency domain CSI prediction, improves prediction accuracy, and enhances the performance and efficiency of AI / ML.

[0216] On the other hand, the terminal device receives configuration information from the network device for SRS transmission; it then sends SRS to the network device according to the configuration information; the measurement results of the SRS are used by the network device as input to an AI / ML model / function. Thus, SRS with reduced overhead can be applied through network-side AI / ML functions / models.

[0217] Fifth aspect of the embodiment

[0218] 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.

[0219] In some embodiments, the communication system 100 may include at least network equipment and terminal equipment;

[0220] The terminal device receives CSI-RS configuration information from the network device; and performs CSI-RS measurement based on the CSI-RS configuration information; wherein the CSI-RS measurement results are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0221] and / or

[0222] The terminal device receives configuration information from the network device for SRS transmission; sends SRS to the network device according to the configuration information; wherein the measurement results of the SRS are used by the network device as input to an AI / ML model / function.

[0223] This application also provides a terminal device, but the application is not limited thereto and may also include other devices.

[0224] Figure 12 is a schematic diagram of a terminal device according to an embodiment of this application. As shown in Figure 12, the terminal device 1200 may include a processor 1210 and a memory 1220; the memory 1220 stores data and programs and is coupled to the processor 1210. It is worth noting that this figure is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunications functions or other functions.

[0225] For example, processor 1210 may be configured to execute a program to implement the CSI prediction method as described in the first aspect embodiment. For example, processor 1210 may be configured to perform the following control: receive CSI-RS configuration information from a network device; measure CSI-RS based on the CSI-RS configuration information; wherein the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on an AI / ML model / function.

[0226] For example, processor 1210 may be configured to execute a program to implement the SRS transmission method as described in the first aspect embodiment. For example, processor 1210 may be configured to perform the following control: receive configuration information for SRS transmission from a network device; send SRS to the network device according to the configuration information; wherein the measurement results of the SRS are used by the network device as input to an AI / ML model / function.

[0227] As shown in Figure 12, the terminal device 1200 may further include: a communication module 1230, an input unit 1240, a display 1250, and a power supply 1260. The functions of these components are similar to those in the prior art and will not be described in detail here. It is worth noting that the terminal device 1200 does not necessarily include all the components shown in Figure 12; these components are not essential. Furthermore, the terminal device 1200 may also include components not shown in Figure 12, which can be referred to in the prior art.

[0228] This application also provides a network device, such as a base station, but this application is not limited to this and may also include other network devices.

[0229] Figure 13 is a schematic diagram of the network device according to an embodiment of this application. As shown in Figure 13, the network 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.

[0230] For example, processor 1310 may be configured to execute a program to implement the CSI prediction method as described in the embodiments of the second aspect. For example, processor 1310 may be configured to perform the following control: sending CSI-RS configuration information to a terminal device; wherein the terminal device measures CSI-RS based on the CSI-RS configuration information; receiving the measurement results of the CSI-RS; and performing spatial domain CSI prediction and / or frequency domain CSI prediction based on an AI / ML model / function.

[0231] For example, processor 1310 may be configured to execute a program to implement the SRS transmission method as described in the embodiments of the second aspect. For example, processor 1310 may be configured to perform the following control: sending configuration information for SRS transmission to a terminal device; receiving SRS transmitted by the terminal device according to the configuration information; and using the measurement results of the SRS as input to an AI / ML model / function.

[0232] In addition, as shown in Figure 13, network 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 network device 1300 does not necessarily have to include all the components shown in Figure 13; in addition, network device 1300 may also include components not shown in Figure 13, which can be referred to in the prior art.

[0233] 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 CSI prediction method or SRS transmission method described in the first aspect of the embodiment.

[0234] This application also provides a storage medium storing a computer program, wherein the computer program causes a terminal device to execute the CSI prediction method or SRS transmission method described in the first aspect of the embodiment.

[0235] 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 CSI prediction method or SRS transmission method described in the second aspect of the embodiment.

[0236] This application also provides a storage medium storing a computer program, wherein the computer program causes a network device to perform the CSI prediction method or SRS transmission method described in the second aspect of the embodiment.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] Regarding the implementation methods including the above embodiments, the following notes are also disclosed:

[0243] 1. A CSI prediction method, comprising:

[0244] The terminal device receives CSI-RS configuration information from the network device;

[0245] The terminal device measures the CSI-RS based on the CSI-RS configuration information; wherein, the measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0246] 2. An SRS transmission method, comprising:

[0247] The terminal device receives configuration information from the network device for SRS transmission;

[0248] The terminal device sends an SRS to the network device according to the configuration information; wherein the measurement result of the SRS is used by the network device as input to an AI / ML model / function.

[0249] 3. A CSI prediction method, comprising:

[0250] The network device sends CSI-RS configuration information to the terminal device; wherein the terminal device performs CSI-RS measurements based on the CSI-RS configuration information; and

[0251] The network device receives the measurement results from the CSI-RS; and

[0252] The network device performs spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0253] 4. An SRS transmission method, comprising:

[0254] The network device sends configuration information for SRS transmission to the terminal device;

[0255] The network device receives the SRS sent by the terminal device according to the configuration information; and

[0256] The network device uses the measurement results of the SRS as input to the AI / ML model / function.

[0257] 5. A CSI prediction device, comprising:

[0258] A transmitter that sends CSI-RS configuration information to a terminal device; wherein the terminal device performs CSI-RS measurements based on the CSI-RS configuration information; and

[0259] A receiver that receives the measurement results from the CSI-RS; and

[0260] The processor performs spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

[0261] 6. An SRS transmission device, comprising:

[0262] The transmitter sends configuration information for SRS transmission to the terminal device;

[0263] The receiver receives the SRS sent by the terminal device according to the configuration information; and

[0264] The processor takes the SRS measurement results as input to the AI / ML model / function.

[0265] 7. A terminal device comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement the CSI prediction method as described in Appendix 1 or the SRS transmission method as described in Appendix 2.

[0266] 8. A network device comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement the CSI prediction method as described in Appendix 3 or the SRS transmission method as described in Appendix 4.

[0267] 9. A computer program product comprising at least a computer program that, when executed by a processor, causes a terminal device to perform the CSI prediction method as described in Appendix 1 or the SRS transmission method as described in Appendix 2.

[0268] 10. A computer program product comprising at least a computer program that, when executed by a processor, causes a network device to perform the CSI prediction method as described in Appendix 3 or the SRS transmission method as described in Appendix 4.

Claims

1. A CSI prediction device, comprising: The receiver receives CSI-RS configuration information from the network device; A processor that measures CSI-RS based on the CSI-RS configuration information; wherein the measurement results of CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions.

2. The apparatus according to claim 1, wherein, The receiver also receives CSI-RS for CSI prediction; the CSI-RS is used for spatial domain CSI prediction.

3. The apparatus according to claim 2, wherein, The AI / ML model / function used for CSI prediction in the spatial domain is configured on the terminal device side.

4. The apparatus according to claim 3, wherein, The terminal device is configured with CSI-RS resources of X1 ports, and the terminal device obtains channel information of Y1 ports based on the CSI-RS of X1 ports; wherein Y1 is greater than or equal to X1.

5. The apparatus according to claim 4, wherein, The terminal device is configured to report a codebook with Y1 ports, and the apparatus further includes: A transmitter that sends codebook information with Y1 ports to the network device.

6. The apparatus according to claim 4, wherein, The terminal device is configured / instructed to use first pattern information to represent the distribution of the X1 ports among the Y1 ports and / or second pattern information to represent the TxRU mapping.

7. The apparatus according to claim 3, wherein, The terminal device is configured with CSI-RS resources having Y1 ports and is configured / instructed to transmit CSI-RS on X1 ports. The terminal device obtains the channel information of the Y1 ports based on the CSI-RS transmitted on the X1 ports; wherein Y1 is greater than or equal to X1.

8. The apparatus according to claim 7, wherein, The X1 ports that transmit CSI-RS are a subset of the Y1 ports. Port information for transmitting CSI-RS and / or not transmitting CSI-RS is configured / indicated via RRC parameters or MAC-CE or DCI.

9. The apparatus according to claim 7, wherein, The terminal device is configured to report a codebook with Y1 ports, and the apparatus further includes: A transmitter that sends codebook information with Y1 ports to the network device.

10. The apparatus according to claim 2, wherein, The AI / ML model / function used for CSI prediction in the spatial domain is configured on the network device side.

11. The apparatus according to claim 2, wherein, The spatial domain CSI prediction is applied together with CSI compression and / or temporal domain CSI prediction.

12. The apparatus according to claim 1, wherein, The receiver also receives CSI-RS for CSI prediction; the CSI-RS is used for frequency domain CSI prediction.

13. The apparatus according to claim 12, wherein, The AI / ML model / function used for frequency domain CSI prediction is configured on the terminal device side and / or the network device side.

14. The apparatus according to claim 12, wherein, In the frequency domain CSI prediction, the density of CSI-RS in the frequency domain is less than a first value, and / or the granularity of CSI reports in the frequency domain is less than a second value.

15. The apparatus according to claim 12, wherein, The frequency domain CSI prediction is applied together with CSI compression and / or time domain CSI prediction and / or spatial domain CSI prediction.

16. An SRS transmission device, comprising: The receiver receives configuration information from the network device for SRS transmission; A transmitter that sends SRS to the network device according to the configuration information; The SRS measurement results are used by the network device as input to the AI / ML model / function.

17. The apparatus according to claim 16, wherein, The terminal device has X2 antennas and is configured with SRS resources with Y2 ports, where Y2 is less than or equal to X2.

18. The apparatus according to claim 16, wherein, Port information for transmitting SRS is configured / indicated by the network device, and / or port information for not transmitting SRS is configured / indicated by the network device.

19. The apparatus according to claim 16, wherein, The comb size of the SRS is greater than the third value.

20. A communication system, comprising network equipment and terminal equipment; The terminal device receives CSI-RS configuration information from the network device; and performs CSI-RS measurements based on the CSI-RS configuration information; wherein... The measurement results of the CSI-RS are used by the terminal device and / or the network device to perform spatial domain CSI prediction and / or frequency domain CSI prediction based on AI / ML models / functions. and / or The terminal device receives configuration information from the network device for SRS transmission; and sends SRS to the network device according to the configuration information. The SRS measurement results are used by the network device as input to the AI / ML model / function.

Images