Channel sounding method and apparatus

Abstract

Embodiments of the present application provide a channel sounding method and apparatus. The method comprises: a network device receives, from a terminal device, a sounding reference signal (SRS) used for antenna switching; and the network device uses an AI / ML model / function to predict channel information of a receiving antenna of the terminal device on the basis of a measurement result of the SRS.

Description

Channel sounding method and apparatus TECHNICAL FIELD

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

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

[0003] In some sub-use cases, a bilateral model can be used, i.e., the AI / ML model is at the terminal device side and at the network device side. In other sub-use cases, a unilateral model can be used, i.e., the AI / ML model is at the terminal device side or at the network device side. For beam management, the AI / ML model can be at the terminal device side and / or at the network device side.

[0004] On the other hand, antenna switching based on Sounding Reference Signal (SRS) is supported in 5G NR. For a terminal device with xTyR antennas (x transmit antennas, y receive antennas), a set of SRS resources or SRS resources are configured for SRS antenna switching; where x, y are positive integers and x is less than or equal to y, for example, x is a value in {1, 2, 4} and y is a value in {1, 2, 4, 6, 8}.

[0005] It should be noted that the above introduction to the technical background is only for the convenience of clearly and completely describing the technical solutions of the present application, and for the understanding of those skilled in the art. The above technical solutions cannot be considered as known to those skilled in the art just because they are described in the background section of the present application.

[0006] SUMMARY

[0007] The inventors have found that the current SRS antenna switching scheme can occupy a large amount of uplink resources, and it can take a long time to transmit all the configured SRS resources for antenna switching, which can result in inaccurate channel sounding results and degraded system performance.

[0008] For example, for 1T8R antenna switching, 8 SRS resources can be configured for a terminal device, each SRS resource having one port. In order to perform antenna switching, a gap symbol is configured between two SRS resources. Therefore, a total of 15 symbols are occupied, which occupies a large amount of uplink resources and requires a long time for transmission.

[0009] To address at least one of the above problems, embodiments of the present application provide a channel sounding method and apparatus.

[0010] According to an aspect of embodiments of the present application, a channel sounding method is provided, comprising:

[0011] receiving, by a network device, a sounding reference signal (SRS) from a terminal device for antenna switching; and

[0012] predicting, by the network device, channel information of a receiving antenna of the terminal device based on an AI / ML model / function according to a measurement result of the sounding reference signal.

[0013] According to another aspect of embodiments of the present application, a channel sounding apparatus is provided, comprising:

[0014] a receiving unit configured to receive a sounding reference signal (SRS) from a terminal device for antenna switching; and

[0015] a processing unit configured to predict channel information of a receiving antenna of the terminal device based on an AI / ML model / function according to a measurement result of the sounding reference signal.

[0016] According to another aspect of embodiments of the present application, a channel sounding method is provided, comprising:

[0017] transmitting, by a terminal device, a sounding reference signal (SRS) to a network device for antenna switching;

[0018] wherein a measurement result of the sounding reference signal is used by the network device to predict channel information of a receiving antenna of the terminal device based on an AI / ML model / function.

[0019] According to another aspect of embodiments of the present application, a channel sounding apparatus is provided, comprising:

[0020] a transmitting unit configured to transmit a sounding reference signal (SRS) to a network device for antenna switching;

[0021] wherein a measurement result of the sounding reference signal is used by the network device to predict channel information of a receiving antenna of the terminal device based on an AI / ML model / function.

[0022] According to another aspect of embodiments of the present application, a communication system is provided, comprising:

[0023] a terminal device that transmits a sounding reference signal (SRS) for antenna switching; and

[0024] a network device that receives the sounding reference signal (SRS) for antenna switching from the terminal device, and predicts channel information of a receiving antenna of the terminal device according to a measurement result of the sounding reference signal based on an AI / ML model / function.

[0025] One of the beneficial effects of the embodiments of the present application is that the network device predicts the channel information of the receiving antenna of the terminal device according to the measurement result of the sounding reference signal based on the AI / ML model / function. In this way, the channel information of the receiving antenna can be obtained using fewer SRS resources, so that the antenna switching is completed in a shorter time, and the accuracy of the channel sounding result and the system performance can be improved.

[0026] Specific embodiments of the application are disclosed in detail in the following description and claims, indicating the ways in which the principles of the application can be employed. It should be understood that the embodiments of the application are not limited in scope to the specific embodiments described herein. Embodiments of the application include many changes, modifications, and equivalents within the spirit and scope of the appended claims.

[0027] Features described and / or illustrated with respect to one implementation can be used in one or more other implementations in the same or similar manner, in combination with or in place of features in other implementations, or in combination with or in place of one or more features described and / or illustrated with respect to one or more other implementations.

[0028] It should be emphasized that the term "comprises / comprising" when used in this specification is taken to mean the presence of stated features, integers, steps or components but not the exclusion of one or more other features, integers, steps, components or groups thereof. BRIEF DESCRIPTION OF DRAWINGS

[0029] Elements and features of one or more embodiments of the application described in one figure or implementation can be combined with elements and features of one or more other figures or implementations. Also, in the drawings, like reference numerals indicate corresponding parts throughout the several views, and can be used to indicate corresponding components in more than one implementation.

[0030] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0031] FIG. 2 is a schematic diagram of a channel sounding method according to an embodiment of the present application;

[0032] FIG. 3 is a schematic diagram of AI / ML for channel sounding according to an embodiment of the present application;

[0033] FIG. 4 is a schematic diagram of a channel sounding method according to an embodiment of the present application;

[0034] FIG. 5 is an example diagram of SRS configuration using non-AI / ML and using AI / ML according to embodiments of the present disclosure;

[0035] FIG. 6 is a schematic diagram of a channel sounding method according to embodiments of the present disclosure;

[0036] FIG. 7 is a schematic diagram of a channel sounding apparatus according to embodiments of the present disclosure;

[0037] FIG. 8 is a schematic diagram of a channel sounding apparatus according to embodiments of the present disclosure;

[0038] FIG. 9 is a schematic diagram of a network device according to embodiments of the present disclosure;

[0039] FIG. 10 is a schematic diagram of a terminal device according to embodiments of the present disclosure. DETAILED DESCRIPTION

[0040] The foregoing and other features of the present application will become apparent to those skilled in the art from the following description with reference to the accompanying drawings. In the description and drawings, particular embodiments of the present application are disclosed in detail. It should be understood that the present application is not limited to the embodiments described but includes all modifications, variations, and equivalents that fall within the scope of the appended claims.

[0041] In the embodiments of the present application, the terms "first", "second", and the like are used to distinguish different elements from each other, but do not indicate spatial arrangement or time sequence of the elements, and the elements should not be limited by these terms. The term "and / or" includes any one and all combinations of the associated listed terms. The terms "comprise", "include", "have", and the like mean the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0042] In the embodiments of the present application, the singular form "a", "an", and "the" includes the plural form, should be understood broadly as "one" or "a kind of", and not limited to the meaning of "one"; in addition, the term "said" should be understood as including both singular and plural forms, unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least partially according to", and the term "based on" should be understood as "at least partially based on", unless the context clearly indicates otherwise.

[0043] In the embodiments of the present application, the term "communication network" or "wireless communication network" can refer to a network conforming to any communication standard, such as Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and the like.

[0044] In addition, the communication between devices in the communication system can be carried out according to any phase communication protocol, which can include but is not limited to the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G, and 5G, New Radio (NR), future 6G, and the like, and / or other currently known or to be developed in the future communication protocols.

[0045] In the embodiments of the present application, the term "network device" refers to a device that accesses a terminal device to a communication network and provides services for the terminal device in a communication system, for example. The network device can include but is not limited to the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), and the like.

[0046] Among them, the base station can include but is not limited to: Node B (NodeB or NB), evolved Node B (eNodeB or eNB), and 5G base station (gNB), IAB donor, and the like, and can also include remote radio head (RRH), remote radio unit (RRU), relay, or low-power node (such as femto, pico, and the like). In addition, the term "base station" can include some or all functions thereof, and each base station can provide communication coverage for a specific geographic area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0047] In the embodiments of the present application, the term "user equipment" (UE) or "terminal equipment" (TE) refers to, for example, a device that accesses a communication network through a network device and receives network services. The terminal equipment can be fixed or mobile, and can also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and the like.

[0048] The terminal equipment can include, but is not limited to, the following devices: a cellular phone, a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a machine type communication device, a laptop computer, a cordless phone, a smart phone, a smart watch, a digital camera, and the like.

[0049] For another example, in an Internet of Things (IoT) scenario or the like, the terminal equipment can also be a machine or device that performs monitoring or measurement, and can include, but is not limited to, the following devices: a machine type communication (MTC) terminal, a vehicle-mounted communication terminal, a device-to-device (D2D) terminal, a machine-to-machine (M2M) terminal, and the like.

[0050] In addition, the term "network side" or "network device side" refers to the side of the network, which can be a certain base station, or can include one or more network devices as described above. The term "user side" or "terminal side" or "terminal equipment side" refers to the side of the user or terminal, which can be a certain UE, or can include one or more terminal devices as described above. In this document, "device" can refer to a network device or a terminal device unless otherwise specified.

[0051] The following describes the scenarios of the embodiments of the present application by way of examples, but the present application is not limited thereto.

[0052] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the present application, which schematically illustrates a case taking a terminal equipment and a network device as an example. As shown in FIG. 1, the communication system 100 can include a network device 101 and terminal equipments 102 and 103. For simplicity, FIG. 1 only takes two terminal equipments and one network device as an example for illustration, but the embodiments of the present application are not limited thereto.

[0053] In the embodiments of the present application, the network device 101 and the terminal devices 102, 103 can perform existing services or future implementable service transmission. For example, these services can include, but are not limited to, enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0054] It is worth noting that FIG. 1 shows that both terminal devices 102, 103 are within the coverage of the network device 101, but the present application is not limited thereto. Both terminal devices 102, 103 can be outside the coverage of the network device 101, or one terminal device 102 is within the coverage of the network device 101 and the other terminal device 103 is outside the coverage of the network device 101.

[0055] In the embodiments of the present application, the higher layer signaling can be, for example, radio resource control (RRC) signaling; for example, referred to as an RRC message, for example, including MIB, system information, dedicated RRC message; or referred to as an RRC IE. The higher layer signaling can also be, for example, MAC (Medium Access Control) signaling; or referred to as a MAC CE. However, the present application is not limited thereto.

[0056] In the embodiments of the present application, one or more AI / ML models can be configured and run in the network device and / or the terminal device. The AI / ML model can be used for various signal processing functions of wireless communication, such as CSI prediction, CSI compression, beam prediction, positioning management, etc.; the present application is not limited thereto.

[0057] Embodiments of the first aspect

[0058] The embodiments of the present application provide a channel sounding method, which is described from the network device side.

[0059] FIG. 2 is a schematic diagram of a channel sounding method according to an embodiment of the present application. As shown in FIG. 2, the method includes:

[0060] 201, the network device receives a sounding reference signal (SRS) for antenna switching from the terminal device;

[0061] 202, the network device predicts channel information of a receiving antenna of the terminal device according to a measurement result of the sounding reference signal based on an AI / ML model / function.

[0062] It is worth noting that the above Figure 2 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the respective operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. A person skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above Figure 2.

[0063] In some embodiments, functionality refers to an AI / ML feature / feature group enabled by a configuration, wherein the configuration is supported based on a condition indicated by a UE capability.

[0064] For example, the AL / ML functionality can be one or more functions, or can be one or more logical models, or can be one or more sub-functions, or can be one or more features, or can be one or more feature groups.

[0065] For another example, the functionality can be spatial beam prediction using AI / ML, or can be time beam prediction using AI / ML, or can be CSI prediction using AI / ML, or can be direct positioning using AI / ML, or can be positioning assisted using AI / ML, etc.

[0066] In some embodiments, the AI / ML functionality / model can be used for channel sounding. One or more sounding reference signals (SRS) are used for measurement and the measurement result is input to the AI / ML functionality / model, and another one or more sounding reference signals are used for the output of the AI / ML functionality / model for inference.

[0067] For convenience of description, the channel sounding based on AI / ML functionality / model is referred to as model inference or inference operation, the training data collection based on AI / ML functionality / model is referred to as training data collection (the training data collection can also use a non-AI / ML manner), and the performance monitoring based on AI / ML functionality / model is referred to as performance monitoring.

[0068] In some embodiments, the network device can send configuration information of one or more reference signals, such as SRS or CSI-RS configuration information, and the like. The present application is not limited thereto, and the specific configuration information can also be referred to related technologies. The configuration information can include configuration information for training data collection, and / or configuration information for model inference, and / or configuration information for performance monitoring.

[0069] FIG. 3 is a schematic diagram of AI / ML for channel sounding according to an embodiment of the present application. As shown in FIG. 3, one or more reference signals in the second reference signal resource set (set B) (which can be referred to as SRS for measurement or SRS for inference) can be received and measured by the terminal device, and the measurement result can be used as the input of AI / ML. One or more reference signals in the first reference signal resource set (set A) (which can be referred to as SRS for prediction) can be used by the terminal device as the output of AI / ML, for example, the measurement result as the label data or ground truth data of AI / ML. The specific content of AI / ML can be referred to related technologies, which will not be described here.

[0070] FIG. 4 is another schematic diagram of a channel sounding method according to an embodiment of the present application, taking the network device configured with AI / ML as an example. As shown in FIG. 4, the method includes:

[0071] 401, the terminal device receives configuration information from the network device; for example, the configuration information includes a second reference signal resource set (set B) for measurement, such as SRS configuration for a part of receiving antennas of the terminal device;

[0072] 402, the terminal device sends SRS for antenna switching to the network device;

[0073] 403, the network device makes a prediction based on AI / ML; that is, the network device receives SRS and makes measurements, and inputs the measurement results into an AI / ML functionality / model; for example, the measurement results of SRS in set B are input into AI / ML, and the reference signals in set A (SRS configuration for all receive antennas of the terminal device) are used for prediction (or inference).

[0074] It is worth noting that the above FIG. 4 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above description, and the present application is not limited to the above FIG. 4.

[0075] Therefore, the network device can predict the channel information of all receive antennas of the terminal device according to the SRS for a part of receive antennas of the terminal device. The channel information of the receive antennas can be obtained using fewer SRS resources, so that the antenna switching can be completed in a shorter time.

[0076] The above schematically illustrates the AI / ML-based beam management, and the present application is not limited thereto. The present application uses the uplink SRS, and the downlink channel information can be obtained through channel reciprocity, so as to realize antenna switching. For specific content such as antenna switching and channel reciprocity, reference can be made to related technologies, which will not be described herein.

[0077] For the convenience of description, the reference signal whose measurement result is used for AI / ML model input can be referred to as a second reference signal, and the reference signal whose measurement result is used for AI / ML model output can be referred to as a first reference signal. The reference signal can be used for training data collection and / or model inference and / or performance monitoring.

[0078] In the embodiments of the present application, the AI / ML model / functionality is located at the network device side; the sounding reference signal corresponds to a first subset of receive antennas of the terminal device, and the AI / ML model outputs channel information of all receive antennas of the terminal device or channel information of a second subset of receive antennas of the terminal device.

[0079] For example, for SRS antenna switching for a UE with xTyR (x and y are positive integers, x <= y, T represents Tx, and R represents Rx) configuration, AI / ML can be used to predict the channel based on channel sounding on a subset of UE receive antennas, i.e., AI / ML can be used to predict the channel on the spatial domain. The AI / ML model / function can be located at the gNB side. The input of the AI / ML model / function is the measurement of SRS corresponding to a subset of UE receive antennas (i.e., a part of receive antennas), and the output of the AI / ML model / function is the channel information corresponding to another subset of UE receive antennas (i.e., another part of receive antennas), or the channel information corresponding to all UE receive antennas.

[0080] FIG. 5 is an example diagram of SRS configuration using non-AI / ML and using AI / ML according to an embodiment of the present application.

[0081] As shown in the upper side of FIG. 5, for a UE with 1T4R, if a non-AI / ML scheme is used, 4 SRS resources need to be configured, and thus the channel information of 4 receive antennas can be obtained. As shown in the lower side of FIG. 5, for a UE with 1T4R, if an AI / ML scheme is used, for example, only 2 SRS resources need to be configured, and the channel information of 4 receive antennas can be obtained.

[0082] In some embodiments, the number of sounding reference signal resources (SRS resources) used for antenna switching based on an AI / ML model / function is less than or equal to the number of sounding reference signal resources used for antenna switching based on a non-AI / ML model / function, or the number of sounding reference signal resource sets (SRS resource sets) used for antenna switching based on an AI / ML model / function is less than or equal to the number of sounding reference signal resource sets used for antenna switching based on a non-AI / ML model / function.

[0083] For example, for SRS antenna switching with AI / ML prediction for a UE with xTyR configuration, the number of SRS resources can be M, and the number of SRS resource sets can be N, where M and N can be predefined or configurable, or depend on the capability of the UE. Assuming that there is no AI / ML operation, the number of SRS resources configured for antenna switching for xTyR is K, and the number of SRS resource sets is L, then M can be less than or equal to K, and N can be less than or equal to L.

[0084] In some embodiments, the number of ports of sounding reference signal resources (SRS resources) used for antenna switching based on an AI / ML model / function is less than or equal to the number of ports of sounding reference signal resources used for antenna switching based on a non-AI / ML model / function.

[0085] For example, for xTyR configuration with AI / ML prediction for SRS antenna switching, the port number of SRS resource is P1, assuming no AI / ML operation, the port number of SRS resource configured for xTyR antenna switching is P2, then P1 can be less than or equal to P2.

[0086] In some embodiments, the antenna port information for channel sounding is reported by the terminal device, or configured by the network device, or predefined.

[0087] For example, for xTyR configuration with AI / ML prediction for SRS antenna switching, which UE antenna ports are used for channel sounding can be reported by UE or configured by gNB or predefined.

[0088] In some embodiments, the antenna ports of the terminal device are divided into multiple port groups, one port group including a subset of the antenna ports; the port group used for channel sounding is reported by the terminal device, or configured by the network device, or predefined.

[0089] For example, SRS port groups (or SRS port sets) can be defined. UE antenna ports can be divided into multiple SRS port groups (SRS port sets), each SRS port group (SRS port set) including a subset of UE antenna ports. Which SRS port group (SRS port set) can be used for channel sounding can be reported by UE or configured by gNB or predefined.

[0090] For another example, UE antenna ports can be divided into two groups (group #A and group #B), one group (group #B) for the input of AI / ML model / function, and the other group (group #A) for the output of AI / ML model / function. For example, SRS corresponding to group #B will be transmitted, and the measurement value of the SRS will be used as the input of the AI / ML model / function, and the channel information corresponding to group #A will be used as the output of the AI / ML model / function.

[0091] In some embodiments, the network device receives the indication information reported by the terminal device for indicating the correlation between antennas or between antenna groups in the terminal device.

[0092] For example, UE can report the correlation information between UE antennas to the gNB side, or report the correlation information between subsets of UE receiving antennas, such as low correlation, medium correlation or high correlation. For example, the indication information can indicate that antenna #1 and antenna #2 are highly correlated, antenna #3 and antenna #4 are highly correlated, antenna #1 and antenna #4 are lowly correlated, and so on.

[0093] In one example, if high correlation can be maintained between UE antennas, AI / ML based channel prediction can be applied. For example, if antenna #1 and antenna #2 are highly correlated, and antenna #3 and antenna #4 are highly correlated, as shown on the lower side of FIG. 5, SRS resource #1 (for antenna #1) and SRS resource #2 (for antenna #3) can be configured, and through AI / ML, the channel information corresponding to antenna #2 and antenna #4 can be predicted.

[0094] The above illustrates the channel prediction, and the following further illustrates the training data collection.

[0095] In some embodiments, the training data collection is performed at the network device side. That is, the network device performs the training data collection for the AI / ML model / function.

[0096] In some examples, the sounding reference signal resources for antenna switching based on non-AI / ML model / function are used. For example, the traditional SRS configuration for xTyR antenna switching (i.e., without AI / ML based prediction) can be applied, for example, as shown on the upper side of FIG. 5.

[0097] In some examples, the sounding reference signal configuration for training data collection is the same as the sounding reference signal configuration for antenna switching. In other examples, the sounding reference signal configuration for training data collection is different from the sounding reference signal configuration for antenna switching.

[0098] For example, the sounding reference signal configuration for training data collection and the sounding reference signal configuration for antenna switching can be sent together to the terminal device, or the sounding reference signal configuration for training data collection and the sounding reference signal configuration for antenna switching can be sent separately to the terminal device, or only the sounding reference signal configuration for antenna switching is sent to the terminal device.

[0099] In some embodiments, the training data collection is assisted at the terminal device side. The network device receives the reporting information of the training data collection for the AI / ML model / function sent by the terminal device.

[0100] For example, the terminal device obtains the ground truth data based on the channel state information reference signal (CSI-RS) from the network device, and the CSI-RS is associated with the sounding reference signal for antenna switching. The terminal device can collect data and report the data to the network device.

[0101] The above illustrates the training data collection, and the following further illustrates the performance monitoring.

[0102] In some embodiments, performance monitoring is conducted at the network device side. That is, the network device conducts performance monitoring for the AI / ML model / function.

[0103] In some examples, sounding reference signal resources for antenna switching based on non-AI / ML model / function are used. For example, a traditional SRS configuration for antenna switching of xTyR (i.e., without AI / ML-based prediction) can be applied, for example, as shown in the upper side of FIG. 5.

[0104] In some examples, the sounding reference signal configuration for performance monitoring is the same as the sounding reference signal configuration for antenna switching. In other examples, the sounding reference signal configuration for performance monitoring is different from the sounding reference signal configuration for antenna switching.

[0105] For example, the sounding reference signal configuration for performance monitoring and the sounding reference signal configuration for antenna switching can be sent together to the terminal device, or the sounding reference signal configuration for performance monitoring and the sounding reference signal configuration for antenna switching can be sent separately to the terminal device, or only the sounding reference signal configuration for antenna switching is sent to the terminal device.

[0106] In some embodiments, performance monitoring is assisted at the terminal device side. The network device receives the reporting information for performance monitoring of the AI / ML model / function sent by the terminal device.

[0107] For example, the terminal device obtains ground truth data based on channel state information reference signals (CSI-RS) from the network device, and the CSI-RS is associated with the sounding reference signal for antenna switching. The terminal device can collect data and report the data to the network device.

[0108] In the embodiments of the present application, the SRS resource can be periodic, aperiodic, or semi-persistent, and the present application is not limited thereto. The embodiments of the present application can be applied to TDD or FDD, and can also be applied to half duplex and / or full duplex; in addition, the present application can be applied to 6G, 5G advanced, or 5G, and the present application is not limited thereto.

[0109] The above various embodiments are only exemplarily described, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above various embodiments. For example, the above various embodiments can be used alone, or one or more of the above various embodiments can be combined.

[0110] From the above embodiments, the network device predicts the channel information of the receiving antennas of the terminal device according to the measurement result of the sounding reference signal based on the AI / ML model / function. In this way, the channel information of the receiving antennas can be obtained using fewer SRS resources, so that the antenna switching can be completed in a shorter time, and the accuracy of the channel sounding result and the system performance can be improved.

[0111] Embodiments of the second aspect

[0112] Embodiments of the present application provide a channel sounding method, which is described from the terminal device side. Embodiments of the second aspect can be combined with embodiments of the first aspect, and the same content as that of the first aspect will not be described again.

[0113] FIG. 6 is a schematic diagram of a channel sounding method according to an embodiment of the present application. As shown in FIG. 6, the method comprises:

[0114] 601, the terminal device sends a sounding reference signal (SRS) for antenna switching to the network device.

[0115] As shown in FIG. 6, the method can further comprise:

[0116] 602, the measurement result of the sounding reference signal is used by the network device to predict the channel information of the receiving antennas of the terminal device based on the AI / ML model / function.

[0117] It is worth noting that the above FIG. 6 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications based on the above content, and the present application is not limited to the above FIG. 6.

[0118] In some embodiments, the AI / ML model / function is located at the network device side; the sounding reference signal corresponds to a first subset of receiving antennas of the terminal device, and the AI / ML model outputs the channel information of all receiving antennas of the terminal device or the channel information of a second subset of receiving antennas of the terminal device.

[0119] In some embodiments, the number of sounding reference signal resources (SRS resources) for antenna switching based on the AI / ML model / function is less than or equal to the number of sounding reference signal resources for antenna switching based on the non-AI / ML model / function,

[0120] Alternatively, the number of sounding reference signal resource sets (SRS resource sets) for antenna switching based on AI / ML model / function is less than or equal to the number of sounding reference signal resource sets for antenna switching based on non-AI / ML model / function.

[0121] In some embodiments, the number of ports of sounding reference signal resources (SRS resources) for antenna switching based on AI / ML model / function is less than or equal to the number of ports of sounding reference signal resources for antenna switching based on non-AI / ML model / function.

[0122] In some embodiments, the antenna port information for channel sounding is reported by the terminal device, or is configured by the network device, or is predefined.

[0123] In some embodiments, the antenna ports of the terminal device are divided into a plurality of port groups, and one port group includes a subset of the antenna ports.

[0124] In some embodiments, the port group for channel sounding is reported by the terminal device, or is configured by the network device, or is predefined.

[0125] In some embodiments, the terminal device reports indication information for indicating the correlation between antennas or between antenna groups in the terminal device.

[0126] In some embodiments, the training data collection is performed at the network device side, and the network device performs the training data collection for the AI / ML model / function.

[0127] In some embodiments, the sounding reference signal resources for antenna switching based on non-AI / ML model / function are used.

[0128] In some embodiments, the sounding reference signal configuration for training data collection is the same as the sounding reference signal configuration for antenna switching, or the sounding reference signal configuration for training data collection is different from the sounding reference signal configuration for antenna switching.

[0129] In some embodiments, the training data collection is assisted at the terminal device side, and the terminal device sends reporting information for the training data collection of the AI / ML model / function.

[0130] In some embodiments, the terminal device obtains ground truth data based on channel state information reference signals (CSI-RSs) from the network device, and the channel state information reference signals are associated with the sounding reference signals for antenna switching.

[0131] In some embodiments, performance monitoring is conducted at the network device side, and the network device conducts the performance monitoring for the AI / ML model / function.

[0132] In some embodiments, sounding reference signal resources for antenna switching based on a non-AI / ML model / function are used.

[0133] In some embodiments, the sounding reference signal configuration for performance monitoring is the same as the sounding reference signal configuration for antenna switching, or the sounding reference signal configuration for performance monitoring is different from the sounding reference signal configuration for antenna switching.

[0134] In some embodiments, performance monitoring is assisted at the terminal device side, and the terminal device sends reporting information for performance monitoring of the AI / ML model / function.

[0135] In some embodiments, the terminal device obtains ground truth data based on a channel state information reference signal (CSI-RS) from the network device, and the channel state information reference signal is associated with the sounding reference signal for antenna switching.

[0136] In some embodiments, the network device can send configuration information, etc. to the terminal device. The network device can receive feedback information and / or reporting information sent by the terminal device. For example, the terminal device can report inference results and / or performance monitoring results and / or training data collection results to the network device, and the present application is not limited thereto.

[0137] The above various embodiments only exemplarily illustrate the embodiments of the present application, but the present application is not limited thereto, and can be appropriately modified on the basis of the above various embodiments. For example, the above various embodiments can be used alone, or one or more of the above various embodiments can be combined.

[0138] As can be seen from the above embodiments, the network device predicts the channel information of the receiving antenna of the terminal device according to the measurement result of the sounding reference signal based on the AI / ML model / function. In this way, the channel information of the receiving antenna can be obtained using fewer SRS resources, so that antenna switching can be completed in a shorter time, and the accuracy of the channel sounding result and the system performance can be improved.

[0139] Embodiments of the third aspect

[0140] The embodiments of the present application provide a channel sounding device. The device may, for example, be a network device, or some component or assembly configured in the network device, and the same content as the embodiments of the first and second aspects will not be described again.

[0141] FIG. 7 is a schematic diagram of a channel sounding device according to an embodiment of the present application. As shown in FIG. 7, the channel sounding device 700 according to an embodiment of the present application comprises:

[0142] a receiving unit 701 configured to receive a sounding reference signal (SRS) for antenna switching from a terminal device; and

[0143] a processing unit 702 configured to predict channel information of a receiving antenna of the terminal device according to a measurement result of the sounding reference signal based on an AI / ML model / function.

[0144] In some embodiments, as shown in FIG. 7, the channel sounding device 700 can further comprise a sending unit 703 configured to send configuration information / indication information to the terminal device, and the present application is not limited thereto.

[0145] In some embodiments, the AI / ML model / function is located at a network device side; the sounding reference signal corresponds to a first subset of receiving antennas of the terminal device, and the AI / ML model outputs channel information of all receiving antennas of the terminal device or channel information of a second subset of receiving antennas of the terminal device.

[0146] In some embodiments, a number of sounding reference signal resources (SRS resources) for antenna switching based on the AI / ML model / function is less than or equal to a number of sounding reference signal resources for antenna switching based on a non-AI / ML model / function,

[0147] or a number of sounding reference signal resource sets (SRS resource sets) for antenna switching based on the AI / ML model / function is less than or equal to a number of sounding reference signal resource sets for antenna switching based on the non-AI / ML model / function.

[0148] In some embodiments, a number of ports of sounding reference signal resources (SRS resources) for antenna switching based on the AI / ML model / function is less than or equal to a number of ports of sounding reference signal resources for antenna switching based on the non-AI / ML model / function.

[0149] In some embodiments, antenna port information for channel sounding is reported by the terminal device, or is configured by the network device, or is predefined.

[0150] In some embodiments, the antenna ports of the terminal device are divided into a plurality of port groups, and each port group includes a subset of the antenna ports.

[0151] In some embodiments, the port groups for channel sounding are reported by the terminal device, or are configured by the network device, or are predefined.

[0152] In some embodiments, the receiving unit 701 also receives indication information reported by the terminal device, the indication information being used to indicate correlation between antennas or between antenna groups in the terminal device.

[0153] In some embodiments, training data collection is performed at the network device side, and the processing unit 702 performs training data collection for the AI / ML model / function.

[0154] In some embodiments, sounding reference signal resources used for antenna switching based on a non-AI / ML model / function are used.

[0155] In some embodiments, the sounding reference signal configuration used for training data collection is the same as the sounding reference signal configuration used for antenna switching, or the sounding reference signal configuration used for training data collection is different from the sounding reference signal configuration used for antenna switching.

[0156] In some embodiments, training data collection is assisted at the terminal device side, and the receiving unit 701 also receives reporting information sent by the terminal device for training data collection of the AI / ML model / function.

[0157] In some embodiments, the terminal device obtains ground truth data based on channel state information reference signals (CSI-RS) from the network device, and the channel state information reference signals are associated with sounding reference signals used for antenna switching.

[0158] In some embodiments, performance monitoring is performed at the network device side, and the processing unit 702 also performs performance monitoring for the AI / ML model / function.

[0159] In some embodiments, sounding reference signal resources used for antenna switching based on a non-AI / ML model / function are used.

[0160] In some embodiments, the sounding reference signal configuration used for performance monitoring is the same as the sounding reference signal configuration used for antenna switching, or the sounding reference signal configuration used for performance monitoring is different from the sounding reference signal configuration used for antenna switching.

[0161] In some embodiments, performance monitoring is assisted at the terminal device side, and the receiving unit 701 also receives reporting information sent by the terminal device for performance monitoring of the AI / ML model / function.

[0162] In some embodiments, the terminal device obtains ground truth data based on a channel state information reference signal (CSI-RS) from the network device, and the channel state information reference signal is associated with the sounding reference signal for antenna switching.

[0163] The above embodiments are only exemplary, and the present application is not limited thereto. One or more of the above embodiments can be combined.

[0164] It should be noted that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The channel sounding device 700 can also include other components or modules, and the specific content of these components or modules can be referred to related technologies.

[0165] In addition, for simplicity, only the connection relationship or signal path between the components or modules is exemplarily shown in FIG. 7, but it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above components or modules can be implemented by hardware facilities such as processors, memories, transmitters, receivers, etc.; the present application is not limited thereto.

[0166] As can be seen from the above embodiments, the network device predicts the channel information of the receiving antenna of the terminal device based on the AI / ML model / function according to the measurement result of the sounding reference signal. In this way, the channel information of the receiving antenna can be obtained using fewer SRS resources, so that the antenna switching can be completed in a shorter time, and the accuracy of the channel sounding result and the system performance can be improved.

[0167] Embodiments of the fourth aspect

[0168] The embodiments of the present application provide a channel sounding device. The device can be a terminal device, or one or more components or components configured in the terminal device. The same content as the embodiments of the first to third aspects will not be described again.

[0169] FIG. 8 is another schematic diagram of a channel sounding device according to an embodiment of the present application. As shown in FIG. 8, the channel sounding device 800 includes:

[0170] a sending unit 801, configured to send a sounding reference signal (SRS) for antenna switching to a network device;

[0171] The measurement result of the sounding reference signal is used by the network device to predict the channel information of the receiving antenna of the terminal device based on the AI / ML model / function.

[0172] In some embodiments, as shown in FIG. 8, the channel sounding apparatus 800 can further include:

[0173] The receiving unit 802 receives the configuration information / indication information sent by the network device, and the present application is not limited thereto.

[0174] The above embodiments are only exemplary, but the present application is not limited thereto, and appropriate modifications can be made on the basis of the above embodiments. For example, the above embodiments can be used alone or in combination.

[0175] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The channel sounding apparatus 800 can further include other components or modules, and the specific content of these components or modules can be referred to the related art.

[0176] In addition, for the sake of simplicity, only the connection relationship or signal path between the components or modules is exemplarily shown in FIG. 8, but it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above components or modules can be realized by hardware facilities such as processors, memories, transmitters, receivers, etc.; the present application is not limited thereto.

[0177] As can be seen from the above embodiments, the network device predicts the channel information of the receiving antenna of the terminal device based on the AI / ML model / function according to the measurement result of the sounding reference signal. Thus, the channel information of the receiving antenna can be obtained using fewer SRS resources, so that the antenna switching can be completed in a shorter time, and the accuracy of the channel sounding result and the system performance can be improved.

[0178] Embodiments of the fifth aspect

[0179] The present application also provides a communication system, which can be referred to FIG. 1, and the same content as the embodiments of the first to fourth aspects will not be repeated.

[0180] In some embodiments, the communication system 100 can at least include:

[0181] The terminal device sends a sounding reference signal (SRS) for antenna switching; and

[0182] The network device receives the sounding reference signal (SRS) for antenna switching from the terminal device; and predicts the channel information of the receiving antenna of the terminal device based on the AI / ML model / function according to the measurement result of the sounding reference signal.

[0183] The embodiments of the present application also provide a network device, which can be a base station for example, but the present application is not limited thereto, and can also be other network devices.

[0184] FIG. 9 is a schematic diagram of a network device according to an embodiment of the present application. As shown in FIG. 9, the network device 900 can include a processor 910 (such as a central processing unit, CPU) and a memory 920, wherein the memory 920 is coupled to the processor 910. The memory 920 can store various data, and also store a program 930 for information processing, and execute the program 930 under the control of the processor 910.

[0185] For example, the processor 910 can be configured to execute a program to implement the channel sounding method according to the embodiments of the first aspect. For example, the processor 910 can be configured to control receiving a sounding reference signal (SRS) for antenna switching from a terminal device, and predicting channel information of a receiving antenna of the terminal device according to a measurement result of the sounding reference signal based on an AI / ML model / function.

[0186] In addition, as shown in FIG. 9, the network device 900 can also include a transceiver 940 and an antenna 950, etc., wherein the functions of the above components are similar to those of the prior art, and will not be described here. It is worth noting that the network device 900 does not necessarily include all the components shown in FIG. 9; in addition, the network device 900 can also include components not shown in FIG. 9, which can be referred to the prior art.

[0187] The embodiments of the present application also provide a terminal device, but the present application is not limited thereto, and can also be other devices.

[0188] FIG. 10 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in FIG. 10, the terminal device 1000 can include a processor 1010 and a memory 1020, wherein the memory 1020 stores data and programs, and is coupled to the processor 1010. It is worth noting that the figure is exemplary; other types of structures can also be used to supplement or replace the structure to implement telecommunication functions or other functions.

[0189] For example, the processor 1010 can be configured to execute a program to implement the channel sounding method according to the embodiments of the second aspect. For example, the processor 1010 can be configured to control sending a sounding reference signal (SRS) for antenna switching to a network device, wherein a measurement result of the sounding reference signal is used by the network device to predict channel information of a receiving antenna of the terminal device based on an AI / ML model / function.

[0190] As shown in FIG. 10, the terminal device 1000 can further include a communication module 1030, an input unit 1040, a display 1050, and a power supply 1060. The functions of the above components are similar to those of the prior art, and thus will not be described here. It is worth noting that the terminal device 1000 does not necessarily include all the components shown in FIG. 10, and the above components are not essential; in addition, the terminal device 1000 can include components not shown in FIG. 10, and can refer to the prior art.

[0191] The embodiments of the present application further provide a computer program, which, when executed in a network device, causes the network device to perform the channel sounding method according to the embodiments of the first aspect.

[0192] The embodiments of the present application further provide a storage medium storing a computer program, which causes a network device to perform the channel sounding method according to the embodiments of the first aspect.

[0193] The embodiments of the present application further provide a computer program, which, when executed in a terminal device, causes the terminal device to perform the channel sounding method according to the embodiments of the second aspect.

[0194] The embodiments of the present application further provide a storage medium storing a computer program, which causes a terminal device to perform the channel sounding method according to the embodiments of the second aspect.

[0195] The above apparatus and method of the present application can be implemented by hardware, or by a combination of hardware and software. The present application relates to a computer readable program, which, when executed by a logic component, can cause the logic component to implement the above apparatus or constituent components, or to implement the above various methods or steps. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0196] The method / apparatus described in combination with the embodiments of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional blocks shown in the functional block diagram, and / or a combination of one or more functional blocks, can correspond to a software module of a computer program flow, or to a hardware module. These software modules can correspond to the respective steps shown in the figure. These hardware modules can be implemented by, for example, fixing the software modules with a field programmable gate array (FPGA).

[0197] The software modules can reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, registers, a hard disk, a mobile disk, CD-ROM, or any other form of storage medium known in the art. One storage medium can be coupled to the processor, such that the processor can read information from, and write information to, the storage medium; or the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The software modules can be stored in a memory of the mobile terminal, or in a memory card that can be inserted into the mobile terminal. For example, if the device, such as the mobile terminal, uses a MEGA-SIM card or a flash memory device of large capacity, the software modules can be stored in the MEGA-SIM card or the flash memory device of large capacity.

[0198] One or more of the functional blocks described in the accompanying drawings and / or one or more combinations of the functional blocks can be implemented as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any appropriate combination thereof for performing the functions described herein. One or more of the functional blocks described in the accompanying drawings and / or one or more combinations of the functional blocks 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 conjunction with a DSP core, or any other such configuration.

[0199] The present application has been described above with the attachment to the specific embodiments, but it should be clear to those skilled in the art that these descriptions are exemplary and are not a limitation on the scope of protection of the present application. Those skilled in the art can make various modifications and changes to the present application according to the spirit and principles of the present application, and these modifications and changes are also within the scope of the present application.

[0200] With regard to the embodiments including the above embodiments, the following notes are also disclosed:

[0201] 1. A channel sounding method, comprising:

[0202] The network device receives a sounding reference signal (SRS) for antenna switching from a terminal device; and

[0203] The network device predicts channel information of a receiving antenna of the terminal device according to a measurement result of the sounding reference signal based on an AI / ML model / function.

[0204] 2. A channel sounding method, comprising:

[0205] The terminal device sends a sounding reference signal (SRS) for antenna switching to the network device;

[0206] The measurement result of the sounding reference signal is used by the network device to predict channel information of a receiving antenna of the terminal device based on an AI / ML model / function.

[0207] 3. A network device comprising a memory and a processor, the memory storing a computer program, and the processor configured to execute the computer program to implement the channel sounding method of clause 1.

[0208] 4. A terminal device comprising a memory and a processor, the memory storing a computer program, and the processor configured to execute the computer program to implement the channel sounding method of clause 2.

[0209] 5. A computer program product comprising at least a computer program, the computer program being executed by a processor to cause a network device to perform the channel sounding method of clause 1.

[0210] 6. A computer program product comprising at least a computer program, the computer program being executed by a processor to cause a terminal device to perform the channel sounding method of clause 2.

Claims

1. A channel sounding apparatus, comprising: a receiving unit configured to receive sounding reference signals for antenna switching from a terminal device; and a processing unit configured to predict channel information of receiving antennas of the terminal device based on an AI / ML model / function according to measurement results of the sounding reference signals.

2. The apparatus of claim 1, wherein, The AI / ML model / function is located at a network device side; the sounding reference signals correspond to a first subset of receiving antennas of the terminal device, and the AI / ML model outputs channel information of all receiving antennas of the terminal device or channel information of a second subset of receiving antennas of the terminal device.

3. The apparatus of claim 1, wherein, A number of sounding reference signal resources for antenna switching based on the AI / ML model / function is less than or equal to a number of sounding reference signal resources for antenna switching based on a non-AI / ML model / function, Or, a number of sounding reference signal resource sets for antenna switching based on the AI / ML model / function is less than or equal to a number of sounding reference signal resource sets for antenna switching based on the non-AI / ML model / function.

4. The apparatus of claim 1, wherein, A number of ports of sounding reference signal resources for antenna switching based on the AI / ML model / function is less than or equal to a number of ports of sounding reference signal resources for antenna switching based on the non-AI / ML model / function.

5. The apparatus of claim 1, wherein, Antenna port information for channel sounding is reported by the terminal device, or is configured by a network device, or is predefined.

6. The apparatus of claim 1, wherein, Antenna ports of the terminal device are divided into a plurality of port groups, and one of the port groups includes a subset of the antenna ports.

7. The apparatus of claim 6, wherein, Port groups for channel sounding are reported by the terminal device, or are configured by the network device, or are predefined.

8. The apparatus of claim 1, wherein, The receiving unit further receives indication information reported by the terminal device for indicating correlation between antennas or between antenna groups in the terminal device.

9. The apparatus of claim 1, wherein, Training data collection is performed at the network device side, and the processing unit performs the training data collection for the AI / ML model / function.

10. The apparatus of claim 9, wherein, Sounding reference signal resources for antenna switching based on the non-AI / ML model / function are used.

11. The apparatus of claim 9, wherein, Sounding reference signal configurations for training data collection are the same as sounding reference signal configurations for antenna switching, or sounding reference signal configurations for training data collection are different from sounding reference signal configurations for antenna switching.

12. The apparatus of claim 1, wherein, Training data collection is assisted at the terminal device side, and the receiving unit further receives reporting information sent by the terminal device for the training data collection of the AI / ML model / function.

13. The apparatus of claim 12, wherein, The terminal device obtains reference real data based on channel state information reference signals from a network device, and the channel state information reference signals are associated with sounding reference signals for antenna switching.

14. The apparatus of claim 1, wherein, Performance monitoring is performed at the network device side, and the processing unit further performs the performance monitoring for the AI / ML model / function.

15. The apparatus of claim 14, wherein, Sounding reference signal resources for antenna switching based on the non-AI / ML model / function are used.

16. The apparatus of claim 14, wherein, The sounding reference signal configuration for performance monitoring is the same as the sounding reference signal configuration for antenna switching, or the sounding reference signal configuration for performance monitoring is different from the sounding reference signal configuration for antenna switching.

17. The apparatus of claim 1, wherein, The terminal device side assists in performance monitoring, and the receiving unit further receives the reporting information of the terminal device for performance monitoring of the AI / ML model / function.

18. The apparatus of claim 17, wherein the terminal device obtains reference real data based on a channel state information reference signal from a network device, and the channel state information reference signal is associated with a sounding reference signal for antenna switching.

19. A channel sounding apparatus, comprising: a sending unit that sends a sounding reference signal for antenna switching to a network device; wherein the measurement result of the sounding reference signal is used by the network device to predict the channel information of the receiving antenna of the terminal device based on an AI / ML model / function.

20. A communication system, comprising: a terminal device that sends a sounding reference signal for antenna switching; and a network device that receives the sounding reference signal for antenna switching from the terminal device; and predicts the channel information of the receiving antenna of the terminal device based on an AI / ML model / function according to the measurement result of the sounding reference signal.

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