Information processing method and apparatus, and communication system

Abstract

Provided in the embodiments of the present application are an information processing method and apparatus, and a communication system. The method comprises: a terminal device receiving CSI resource configuration information and / or CSI reporting configuration information from a network device; and on the basis of the CSI resource configuration information and / or the CSI reporting configuration information, the terminal device reporting training data used for an AI / ML model.

Description

Information processing method, apparatus, and communication system TECHNICAL FIELD

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

[0002] In NR (New Radio) Rel-18, the air interface artificial intelligence / machine learning (AI / ML) 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. These use cases are only preliminary selected use cases, in Rel-19, new use cases can be added, or the existing use cases can be enhanced.

[0003] With the introduction of these use cases, to support the reliable operation of AI / ML and ensure the effective gain of AI / ML, the standardization direction is developing new protocols, processes, signaling, etc. These protocol-related methods not only apply to 5G-Advanced stage standards and commercial networks and devices, but also can be further applied to 6G or 6G networks and devices.

[0004] It should be noted that the above introduction to the technical background is only to facilitate the clear and complete description of the technical solutions of the present application, and to facilitate the understanding of those skilled in the art. The above technical solutions cannot be considered as known to those skilled in the art merely because they are described in the background section of the present application.

[0005] SUMMARY

[0006] The inventors have found that a terminal device and / or a network device can use an AI / ML model to predict CSI. For an AI / ML model, training data is needed to train it. The training data is an important factor that affects the inference performance or accuracy of the AI / ML model. However, it is currently uncertain how to collect the training data.

[0007] To address at least one of the above problems, embodiments of the present application provide an information processing method, apparatus, and communication system.

[0008] According to an aspect of an embodiment of the present application, an information processing apparatus configured to be applied to a terminal device is provided, and the apparatus includes a receiving unit configured to receive channel state information (CSI) resource configuration information and / or channel state information (CSI) reporting configuration information from a network device, and a sending unit configured to report training data for an AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0009] According to another aspect of an embodiment of the present application, an information processing method applied to a terminal device is provided, and the method includes that the terminal device receives CSI resource configuration information and / or CSI reporting configuration information from a network device, and the terminal device reports training data for an AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0010] According to another aspect of an embodiment of the present application, an information processing apparatus configured to be applied to a network device is provided, and the apparatus includes a sending unit configured to send CSI resource configuration information and / or CSI reporting configuration information to a terminal device, and a receiving unit configured to receive training data for an AI / ML model reported by the terminal device.

[0011] According to another aspect of an embodiment of the present application, an information processing method applied to a network device is provided, and the method includes that a sending unit sends CSI resource configuration information and / or CSI reporting configuration information to a terminal device, and a receiving unit receives training data for an AI / ML model reported by the terminal device.

[0012] According to another aspect of an embodiment of the present application, a communication system including a network device and a terminal device is provided, the network device sends CSI resource configuration information and / or CSI reporting configuration information to the terminal device, and receives training data for an AI / ML model reported by the terminal device, and the terminal device receives the CSI resource configuration information and / or the CSI reporting configuration information, and reports the training data according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0013] One of beneficial effects of an embodiment of the present application is that the terminal device receives CSI resource configuration information and / or CSI reporting configuration information from a network device, and reports training data for an AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information. Thus, the training data for the AI / ML model can be collected, which helps to improve the accuracy and performance of the AI / ML model.

[0014] The particular implementations of the application described in detail herein are illustrative only and not intended to limit the scope of the application as defined by the appended claims. For the purpose of explanation, specific details are set forth in order to provide a thorough understanding of the implementation. It will be apparent to one skilled in the art that specific details can not be required in instances of the application. Descriptions of well-known functions and constructions can be omitted for clarity and conciseness.

[0015] 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, and / or for the same or similar effect.

[0016] It should be emphasized that the term comprises / comprising, when used in this specification, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. BRIEF DESCRIPTION OF DRAWINGS

[0017] Elements and features of one or more embodiments described in the specification and / or one or more figures can be combined with elements and features of one or more other embodiments in the specification and / or one or more figures, in the same or similar manner. Also, in the description of embodiments, similar reference numerals can be used to denote similar elements in the different embodiments.

[0018] FIG. 1 is a schematic diagram of a communication system according to an embodiment of the application;

[0019] FIG. 2 is a schematic diagram of an information processing method according to an embodiment of the application;

[0020] FIG. 3 is another schematic diagram of an information processing method according to an embodiment of the application;

[0021] FIG. 4 is a schematic diagram of an information processing apparatus according to an embodiment of the application;

[0022] FIG. 5 is another schematic diagram of an information processing apparatus according to an embodiment of the application;

[0023] FIG. 6 is a schematic diagram of a network device according to an embodiment of the application;

[0024] FIG. 7 is a schematic diagram of a terminal device according to an embodiment of the application. DETAILED DESCRIPTION

[0025] The foregoing and other features of the present application will become apparent to those skilled in the art upon consideration of the following description of specific embodiments of the application, taken in conjunction with the accompanying drawings. In the description of embodiments of the application, specific terminology is employed for the sake of clarity. However, the application is not intended to be limited to the specific terminology so selected. The above-mentioned and other aspects of the present application will become apparent and the application will be clearly understood from the following description, taken in conjunction with the accompanying drawings in which:

[0026] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements from the point of view, but do not represent the 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 "include", "contain", "have", etc. refer to the existence of the stated features, elements, elements or components, but do not exclude the existence or addition of one or more other features, elements, elements or components.

[0027] In the embodiments of the present application, the singular form "one", "the" and the like includes the plural form, should be understood broadly as "one kind" or "a kind of", rather than limited to the meaning of "one"; In addition, the term "said" should be understood to include 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.

[0028] In the embodiments of the present application, the term "communication network" or "wireless communication network" can refer to a network that complies with any communication standard, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), etc.

[0029] In addition, the communication between devices in the communication system can be carried out according to any stage of the 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), 6G and future communication, etc., and / or other currently known or future to be developed communication protocols.

[0030] In the embodiments of the present application, the term "network device" refers to, for example, a device that accesses a terminal device to a communication network and provides services for the terminal device in a communication system. The network device can include, but is not limited to, the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), and the like.

[0031] Wherein, the base station can include, but is not limited to: Node B (NodeB or NB), evolved Node B (eNodeB or eNB), 5G base station (gNB), 6G base station, and future base station, etc., and can also include remote radio head (RRH), remote radio unit (RRU), relay, or low-power node (such as femto, pico, etc.). And the term "base station" can include some or all functions of them, and each base station can provide communication coverage for a specific geographic area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0032] 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 user equipment can be fixed or mobile, and can also be referred to as a mobile station (MS), a terminal, a user, a subscriber station (SS), an access terminal (AT), a station, a mobile terminal (MT), and the like.

[0033] Wherein, the terminal device can include, but is not limited to, the following devices: cellular phone, personal digital assistant (PDA), wireless modem, wireless communication device, handheld device, machine type communication device, laptop computer, cordless phone, smart phone, smart watch, digital camera, and the like.

[0034] For another example, in scenarios such as Internet of Things (IoT), user equipment can also be a machine or device that performs monitoring or measurement, for example, can include but not limited to: Machine Type Communication (MTC) terminal, vehicle-mounted communication terminal, Device to Device (D2D) terminal, Machine to Machine (M2M) terminal, terminal supporting sidelink communication, etc.

[0035] 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 above. The term "user side" or "terminal side" or "terminal device side" refers to the side of the user or terminal, which can be a certain UE, or can include one or more terminal devices as above. In this article, "device" can refer to network device or terminal device without special indication.

[0036] The terms "uplink control signal" and "uplink control information (UCI)" or "physical uplink control channel (PUCCH)" can be interchangeable without causing confusion, and the terms "uplink data signal" and "uplink data information" or "physical uplink shared channel (PUSCH)" can be interchangeable;

[0037] The terms "downlink control signal" and "downlink control information (DCI)" or "physical downlink control channel (PDCCH)" can be interchangeable, and the terms "downlink data signal" and "downlink data information" or "physical downlink shared channel (PDSCH)" can be interchangeable.

[0038] In addition, the uplink signal can include an uplink data signal and / or an uplink control signal and / or a PRACH and / or a SRS (sounding reference signal) and the like, and can also be referred to as an uplink transmission (UL transmission) or uplink information or an uplink channel. Transmitting / receiving the uplink transmission on the uplink resource can be understood as transmitting / receiving the uplink transmission using the uplink resource. The downlink signal can include a downlink data signal and / or a downlink control signal and / or a synchronization signal (SS, for example, PSS / SSS) and / or a broadcast channel (PBCH) and / or an SSB (SS / PBCH block, including PSS, SSS and PBCH and its DMRS) and / or a CSI-RS and the like, and can also be referred to as a downlink transmission (DL transmission) or downlink information or a downlink channel. Transmitting / receiving the downlink transmission on the downlink resource can be understood as transmitting / receiving the downlink transmission using the downlink resource.

[0039] In the embodiments of the present application, the higher layer signaling can be, for example, radio resource control (RRC) signaling; the RRC signaling can include, for example, an RRC message, such as a broadcast / common RRC message / signaling (for example, a master information block (MIB), system information (SI), a dedicated RRC message / signaling; or an RRC information element (RRC IE); or information fields included in the RRC message or RRC information element (or information fields included in the information fields). The higher layer signaling can also be, for example, medium access control layer (MAC) signaling; or a MAC control element (MAC CE). However, the present application is not limited thereto.

[0040] In the embodiments of the present application, "at least one" and "one or more than one" can be interchangeable, "a plurality of" and "more than one" can be interchangeable, and "a plurality of" means at least two, or two or more.

[0041] In the embodiments of the present application, pre-defined means defined by a protocol or determined according to a rule defined by a protocol, without additional configuration. Configuration / indication means direct or indirect configuration / indication by a network device through higher layer signaling and / or physical layer signaling. The configuration / indication can be configured / indicated by introducing a higher layer parameter in the higher layer signaling, and the higher layer parameter means information fields and / or information elements / units / elements (IEs) in the higher layer signaling. The physical layer signaling can be, for example, control information (DCI) carried by a physical downlink control channel or sequence, but is not limited thereto.

[0042] For ease of description, a base station is taken as an example of an access network device in the following description. In the following description, "if", "in the case of" and "when" can be used interchangeably without causing confusion.

[0043] The scenarios of the embodiments of the present application are described below by way of examples, but the present application is not limited thereto.

[0044] FIG. 1 is a schematic diagram of a communication system of an embodiment of the present application, which schematically illustrates a case taking a terminal device and a network device as examples. As shown in FIG. 1, the communication system 100 can include a network device 101, a terminal device 102 and a terminal device 103. For simplicity, FIG. 1 only takes two terminal devices and one network device as examples for illustration, but the embodiments of the present application are not limited thereto.

[0045] In the embodiments of the present application, the network device 101, the terminal device 102 and the terminal device 103 can perform existing services or future implementable services transmission. For example, these services can include but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), ultra-reliable and low-latency communication (URLLC) and related communication of reduced capability terminal devices, etc.

[0046] Among them, the terminal devices 102 and 103 can be in an RRC_IDLE state, or an RRC_INACTIVE state or an RRC_CONNECTED state, and the terminal devices 102 and 103 can also communicate with the network device 101. For example, taking the terminal device 102 as an example, the terminal device 102 can send data to the network device 101, or can perform data retransmission. The network device 101 can send a paging message to the terminal device 102, and can also send data to the terminal device 102, and the terminal device 102 receives the data sent by the network device 101. In addition, different terminal devices can also communicate with each other, for example, the terminal device 102 and the terminal device 103 can exchange data.

[0047] It is worth noting that FIG. 1 shows that the terminal device 102 and the terminal device 103 are both within the coverage of the network device 101, but the present application is not limited thereto. The terminal device 102 and the terminal device 103 can both be outside the coverage of the network device 101, or one of the terminal device 102 and the terminal device 103 is within the coverage of the network device 101 and the other is outside the coverage of the network device 101.

[0048] In 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 models can be used for various signal processing functions for wireless communication, such as CSI prediction, CSI compression, beam prediction, positioning management, etc.; the present application is not limited thereto.

[0049] Regarding the CSI prediction sub-use case, there are relevant contents as shown in Table 1 and Table 2:

[0050] Table 1

[0051] Table 2

[0052] For AI / ML-based CSI prediction, in order to obtain good CSI prediction accuracy, the consistency of the training and inference of the AI / ML model needs to be ensured. This means that, in the training data collection phase, the configurations, auxiliary information, or additional conditions on the terminal side and the network side must be consistent with the configurations, auxiliary information, or additional conditions in the inference phase. When the corresponding information in the training phase and the inference phase is inconsistent, the input distribution of the AI / ML model will drift, and the AI / ML model cannot work in the best state.

[0053] On the other hand, compared with a universal AI / ML model, a local AI / ML model can generally obtain better performance and lower complexity. In order to train a local AI / ML model or obtain the local gain of a local AI / ML model, we must label the training data. Training AI / ML models using different categories of training data can help obtain the local gain of the AI / ML model.

[0054] Therefore, for the collection of training data, in addition to the data related to the model input and the model output (label / ground truth) that needs to be collected, we also need to collect the configuration information, auxiliary information, and additional conditions on the network side and the terminal side.

[0055] In embodiments of the present application, AI / ML can also be referred to as AI / ML model, AI / ML method, AI / ML unit, AI / ML functionality, or similar names of AI / ML elements.

[0056] In the embodiments of the present application, the term "true value" can be replaced with similar names such as "label", "ground truth", "actual value", and the like.

[0057] Embodiments of the first aspect

[0058] The embodiments of the present application provide an information processing method, which is described from the terminal device side. FIG. 2 is a schematic diagram of an information processing method according to an embodiment of the present application. As shown in FIG. 2, the method comprises:

[0059] 201: The terminal device receives CSI resource configuration information and / or CSI reporting configuration information from the network device; and

[0060] 202: The terminal device reports training data for an AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0061] It is worth noting that the above FIG. 2 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications according to the above description, and the present application is not limited to the above FIG. 2.

[0062] According to the above embodiments, the terminal device receives CSI resource configuration information and / or CSI reporting configuration information from the network device, and reports training data for an AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information. Thus, the training data for the AI / ML model can be collected, which helps to improve the accuracy and performance of the AI / ML model.

[0063] In some embodiments, the UE can use an artificial intelligence (AI) and / or machine learning (ML) based model (AI / ML model) for CSI prediction. For example, the UE can use the AI / ML model to predict one or more CSI-related information (output information, which can be referred to as CSI for short) at one or more future time instants according to one or more CSI-related information (input information, which can be referred to as CSI for short). Thus, at the one or more future time instants, the network device does not need to send CSI-RS to the terminal device, thereby reducing signaling overhead and helping to save energy on the network side; or the terminal device does not need to measure the CSI-RS or report the CSI at the one or more future time instants, thereby reducing signaling overhead and helping to save energy on the terminal side; or the network device uses the one or more future time instants CSI-related information for downlink PDSCH transmission, which reduces the performance loss caused by the aging of the feedback CSI.

[0064] In some embodiments, the UE can utilize the AI / ML model to predict L second CSI-related information from K first CSI-related information, where K and L are integers greater than or equal to 1. For example, K = 4 and L = 2, the UE predicts the second CSI-related information at time t+2△ and t+4△ from the first CSI-related information at time t-3△, t-2△, t-△ and t, where △ is greater than 0.

[0065] The first CSI-related information and the second CSI-related information can be a channel matrix and / or an eigenvector.

[0066] The channel matrix is, for example, a matrix used to describe the characteristics of a transmission channel. The channel matrix can generally be used to describe the attenuation and / or distortion of a transmitted signal in the channel. The channel matrix can be obtained by transmitting a reference signal and performing channel estimation.

[0067] The eigenvector is, for example, a vector obtained by matrix decomposition of the channel matrix, such as SVD (singular value decomposition) or EVD (eigenvalue decomposition), etc. In some embodiments, multiple layers of precoding vectors can be combined into a precoding matrix. In the embodiments of the present application, the terms “eigenvector” and “precoding vector” can be replaced with each other.

[0068] The present application is not limited thereto, and the first CSI-related information (input information) and the second CSI-related information (output information) can also be other forms of information capable of representing the channel state.

[0069] In some embodiments, the AI / ML model can predict L channel matrices from K channel matrices; or the AI / ML model can predict L eigenvectors from K eigenvectors.

[0070] However, the present application is not limited thereto, for example, the AI / ML model can also predict L eigenvectors from K channel matrices, or predict L channel matrices from K eigenvectors. For another example, the input and / or output of the AI / ML model can also be a combination of channel matrices and eigenvectors. For example, the AI / ML model can predict a channel matrix from a channel matrix and an eigenvector, or predict an eigenvector from a channel matrix and an eigenvector, or predict a channel matrix and an eigenvector from a channel matrix, or predict a channel matrix and an eigenvector from an eigenvector, or predict a channel matrix and an eigenvector from a channel matrix and an eigenvector, etc.

[0071] In some embodiments, the training data of the AI / ML model can include at least one of the following: the input information of the model; the true value corresponding to the output information of the model; or the first information of the terminal device.

[0072] The input information of the model can include a channel matrix and / or an eigenvector. The input information can be generated according to a measurement result of a CSI-RS at a first time (which can also be referred to as a measurement time).

[0073] For example, in 202, the UE can measure the CSI-RS at one or more first times (for example, the aforementioned times t-3△, t-2△, t-△, t) according to the received CSI resource configuration information and / or CSI reporting configuration information, generate a channel matrix and / or an eigenvector according to the measurement result, and use the channel matrix and / or the eigenvector as the input information of the model. For ease of description, the CSI-RS corresponding to the input information of the model is referred to as a first CSI-RS, and the resource corresponding to the first CSI-RS is referred to as a first resource.

[0074] The real value corresponding to the output information of the model can include a channel matrix and / or an eigenvector. In an embodiment of the present application, the output information of the model is output information generated according to the input information of the model in a model inference process; and the real value corresponding to the output information of the model is the output information of the model in an ideal case. One of the purposes of training the AL / ML model is to expect that the output information of the AL / ML model is as close as possible to the real value corresponding to the output information.

[0075] The real value corresponding to the output information can be generated according to a measurement result of a CSI-RS at a second time (which can also be referred to as a prediction time).

[0076] For example, in 202, the UE can measure the CSI-RS at one or more second times (for example, the aforementioned times t+2△, t+4△) according to the received CSI resource configuration information and / or CSI reporting configuration information, generate a channel matrix and / or an eigenvector according to the measurement result, and use the channel matrix and / or the eigenvector as the real value corresponding to the output information of the model. For ease of description, the CSI-RS corresponding to the output information of the model is referred to as a second CSI-RS, and the resource corresponding to the second CSI-RS is referred to as a second resource.

[0077] In some embodiments, the first information of the terminal device can also be referred to as assistance information or additional condition of the terminal device.

[0078] The first information of the terminal device can include at least one of the following: antenna configuration information of the terminal device, movement speed information of the terminal device, or time information expected by the terminal device.

[0079] Since the real values corresponding to the input information and the output information of the model are usually related to the first information, by including the first information in the training data, the first information can be used as a label (also referred to as "mark", "index", and the like) of the training data, thereby ensuring the consistency of the model training and the model inference, i.e., the first information in the model training phase can be kept consistent with the first information in the model inference phase. Thus, the prediction accuracy of the AI / ML model can be improved.

[0080] On the other hand, by including the first information in the training data, the real values corresponding to the input information and / or the output information of the model can be classified according to the first information. Thus, according to the classified training data, the AI / ML model corresponding to each classification can be trained, which helps to improve the accuracy of the AI / ML model, reduce the complexity of the model training, and obtain better performance.

[0081] In the model inference phase, the network device and / or the terminal device can perform LCM (Life-Cycle Management) operations such as model selection / model activation / model deactivation / model switching / rollback according to the classification label.

[0082] For example, when the current first information and / or second information is consistent with the classification label of the first model (or the first information and / or second information corresponding to a certain model), the first model should be selected or activated; or when the current first information and / or second information is inconsistent with the classification label of the second model (or the first information and / or second information corresponding to a certain model), the second model should be deactivated.

[0083] For another example, when the current first information and / or second information is inconsistent with the classification label of the current first model (or the first information and / or second information corresponding to a certain model), and the current first information and / or second information is consistent with the classification label of the second model (or the first information and / or second information corresponding to a certain model), the first model can be switched to the second model.

[0084] For another example, when there is no model consistent with the current first information and / or second information, the AI / ML model / function can be rolled back to a non-AI / ML method (i.e., a traditional method) for corresponding processing.

[0085] In some embodiments, the antenna configuration information of the terminal device can include at least one of: a number of horizontal direction panels and a number of vertical direction panels; a horizontal direction panel spacing and a vertical direction panel spacing; a number of horizontal direction antennas within a panel and a number of vertical direction antennas within a panel; a horizontal direction antenna spacing within a panel and a vertical direction antenna spacing within a panel; a mapping relationship between an antenna and a TxRU; or an antenna polarization direction. The application is not limited thereto, and the antenna configuration information of the terminal device can also include other information.

[0086] In some embodiments, the time information expected by the terminal device can include at least one of: first time information for performing CSI-RS measurement, second time information for performing CSI prediction, or an interval between the first time for performing CSI-RS measurement and the second time for performing CSI prediction.

[0087] The first time information can include a number of times of performing CSI-RS measurement and / or an interval. The number of times of performing CSI-RS measurement can refer to the number of channel matrices and / or eigenvectors included in the input information in one inference process. The interval of performing CSI-RS measurement can refer to the time interval of the CSI-RS corresponding to two adjacent channel matrices and / or eigenvectors in the input information. Taking the channel matrices at four time points t-3△, t-2△, t-△, t as an example, and predicting the channel matrices at two time points t+2△, t+4△, the number of times of performing CSI-RS measurement is K=4, and the interval of performing CSI-RS measurement is△.

[0088] The second time information can include a number of predicted CSIs and / or an interval. The number of predicted CSIs can refer to the number of channel matrices and / or eigenvectors included in the output information in one inference process. The interval of predicted CSIs can refer to the time interval of the CSI-RS corresponding to two adjacent channel matrices and / or eigenvectors in the output information. Taking the channel matrices at four time points t-3△, t-2△, t-△, t as an example, and predicting the channel matrices at two time points t+2△, t+4△, the number of predicted CSIs is L=2, and the interval of performing CSI-RS measurement is 2△.

[0089] The number of times of performing CSI-RS measurement can be the same as or different from the number of predicted CSIs, and the interval of performing CSI-RS measurement can be the same as or different from the interval of predicted CSIs.

[0090] In some embodiments, when there are multiple first moments for performing CSI-RS measurement and / or second moments for performing CSI prediction, the interval between the first moment and the second moment can be expressed as the interval between the last first moment and the first second moment. Taking the channel matrices at moments t-3Δ, t-2Δ, t-Δ, and t as an example, predicting the channel matrices at moments t+2Δ and t+4Δ, the interval between the first moment and the second moment is 2Δ. The present application is not limited to this, and the interval can be expressed as the interval between any first moment and any second moment.

[0091] In some embodiments, when classifying and / or labeling training data, the second information of the network device may also be referenced, or the first information of the terminal device and the second information of the network device may be referenced. In some embodiments, the second information of the network device may also be referred to as auxiliary information or additional conditions of the network device.

[0092] In some embodiments, the second information includes at least one of the following: antenna configuration information of the network device, scenario information of the network device, reference signal period, cell / site identification, carrier frequency, frequency domain granularity, or subcarrier spacing.

[0093] In some embodiments, the antenna configuration information of the network device may include at least one of the following: the number of horizontal panels and the number of vertical panels; the horizontal panel spacing and the vertical panel spacing; the number of horizontal antennas within the panel and the number of vertical antennas within the panel; the horizontal antenna spacing within the panel and the vertical antenna spacing within the panel; the mapping relationship between the antenna and the TxRU; or the antenna polarization direction.

[0094] In some embodiments, the scene information of the network device may include: indoor or outdoor, and / or line-of-sight or non-line-of-sight.

[0095] In some embodiments, the frequency domain granularity may include the number of PRBs (Physical Resource Blocks) in the subband. The present application is not limited thereto, and the frequency domain granularity may also include other information.

[0096] In some embodiments, the classification and / or labeling of training data may be performed on the terminal side or on the network side. For example, the UE includes first information in the reported training data, and the network device may classify and / or label the training data based on the first information of the UE and / or the second information of the network device. For another example, the UE receives second information sent by the network device, and the UE may classify and / or label the training data based on the first information and / or the second information, and report the classified and / or labeled training data.

[0097] The following is an exemplary description of the reference signal and reporting configuration used for training data collection.

[0098] In some embodiments, the CSI resource configuration information may be used to configure one or more CSI-RS resources.

[0099] The period or time interval of the CSI-RS resources can be configured based on information about the AI / ML model. For example, the period or time interval of the CSI-RS resources can be configured to be the same as the period or time interval of the training data used by the AI / ML model during training. This ensures the validity and reliability of the collected training data. Alternatively, the period or time interval of the CSI-RS resources can be configured to be the same as the period or time interval of the input data used by the AI / ML model during inference. This ensures consistency between AI / ML model training and inference.

[0100] For example, the training data used by the AI / ML model during training can be of equal period or equally spaced time domain. In this case, the CSI-RS resources include at least one of the following: periodic CSI-RS resources, semi-persistent CSI-RS resources, or aperiodic CSI-RS burst resources with equal time domain intervals.

[0101] However, the present application is not limited thereto, and the periodicity or time domain interval of the CSI-RS resources may be smaller than the periodicity or time interval of the training data used in the training process or the input data used in the inference process. In other words, part of the CSI-RS resources configured by the CSI resource configuration information is used for collecting training data. For example, the periodicity or time interval of the training data or input data may be an integer multiple of the periodicity or time domain interval of the CSI-RS resources. Thus, during the training data collection phase, the UE may perform measurements on part of the CSI-RS resources.

[0102] In some embodiments, the CSI-RS resources may be configured according to at least one of the following: a scenario of the network device, a moving speed of the terminal device, or a configuration desired by the terminal device.

[0103] The number of moments and / or intervals of CSI-RS resources can be configured based on at least one of the above information. For example, when the terminal device is moving at a low speed, the time domain correlation of CSI is large. In this case, the AI / ML model can use fewer first CSI-related information or information with larger time domain intervals to predict second CSI-related information; and / or the AI / ML model can predict more second CSI-related information or information with larger time domain intervals based on the first CSI-related information. Therefore, when collecting training data, less input information and / or input information with larger time domain intervals can be collected, and / or more output information and / or output information with larger time domain intervals and / or output information with larger time domain intervals from the input information can be collected. That is, the number of measurement moments for CSI-RS resources can be reduced, and / or the measurement moment intervals for CSI-RS resources can be increased, and / or the number of prediction moments for CSI-RS resources can be increased, and / or the prediction moment intervals for CSI-RS resources can be increased, and / or the interval between the measurement moments for CSI-RS resources and the prediction moments for CSI-RS resources can be increased.

[0104] On the contrary, when the terminal device moves faster, the channel correlation is poor. In this case, the AI / ML model needs to use more first CSI-related information or smaller time domain intervals to predict the second CSI-related information; and / or the AI / ML model can predict fewer second CSI-related information or smaller time domain intervals based on the first CSI-related information. Thus, the number of measurement moments of CSI-RS resources can be increased, and / or the measurement moment interval of CSI-RS resources can be reduced, and / or the number of prediction moments of CSI-RS resources can be reduced, and / or the prediction moment interval of CSI-RS resources can be reduced, and / or the interval between the measurement moment of CSI-RS resources and the prediction moment of CSI-RS resources can be reduced.

[0105] Similarly, the number of time instants and / or intervals of CSI-RS resources can also be configured based on the scenario of the network device (e.g., indoor or outdoor, and / or line-of-sight or non-line-of-sight). For example, when the scenario is indoors or line-of-sight, the time domain correlation of CSI is relatively good, while when the scenario is outdoor or non-line-of-sight, the time domain correlation of CSI is relatively poor.

[0106] In addition, the number of moments and / or intervals of the CSI-RS resources may also be configured according to the configuration expected by the terminal device. The configuration expected by the terminal device may include, for example, the moment information expected by the terminal device. For example, the number of moments and / or intervals of the CSI-RS resources may be configured to be the same as the moment information expected by the terminal device. Alternatively, the number of moments of the CSI-RS resources may be configured to be a greater number than the number of moments expected by the terminal device, and / or the interval of the CSI-RS resources may be configured to be a smaller number than the interval expected by the terminal device, and the terminal device may select a portion of the CSI-RS resources from the configured CSI-RS resources for the collection of training data.

[0107] In some embodiments, the CSI-RS resources used for reporting training data can also be used for acquiring and / or reporting channel state information (CSI), i.e., legacy CSI acquisition and / or reporting. In other words, the reference signals used for training data collection can be associated with layer 1 CSI reporting. For example, layer 1 CSI reporting can include PMI / RI / CQI / RSRP, etc. This means that the reference signals used for training data collection can be used for both legacy CSI measurement and reporting.

[0108] In some embodiments, the CSI-RS resources used for reporting training data can also be used for model performance monitoring. That is, the CSI-RS resources can be used simultaneously for AI / ML model performance monitoring and training data reporting. In other words, the reference signal used for training data collection can be used for AI / ML model performance monitoring.

[0109] In some embodiments, the UE may report training data based on the results of the performance monitoring of the AI / ML model. For example, when the performance monitoring result of the model is lower than a first threshold, the training data is reported; and / or, when the performance monitoring result of the model is higher than or equal to the first threshold, the training data is not reported. Thus, the reported training data corresponds to the situation where the performance of the AI / ML model is poor, that is, the situation where training needs to be focused, so that training data can be collected in a targeted manner, which helps to improve the performance of the AI / ML model through training.

[0110] The present application is not limited thereto. When the performance monitoring result of the model is higher than the second threshold, the training data is reported; and / or when the performance monitoring result of the model is lower than or equal to the second threshold, the training data is not reported. Thus, the reported training data corresponds to the situation where the performance of the AI / ML model is better. By training the model based on the above training data, the performance of the AI / ML model is improved.

[0111] In some embodiments, the second threshold may be a value greater than the first threshold.

[0112] In some embodiments, the first threshold and / or the second threshold may be predefined or network configured.

[0113] In some embodiments, the CSI-RS resources may include a first resource corresponding to a first CSI-RS and a second resource corresponding to a second CSI-RS. As described above, the measurement result of the first CSI-RS is used to generate input information of the AI / ML model, and the measurement result of the second CSI-RS is used to generate a true value corresponding to the output information of the AI / ML model.

[0114] The first resource and the second resource may be configured in various ways.

[0115] For example, the first resource and the second resource may be configured separately. For example, the network device may indicate which CSI-RS resources are the first resource and which CSI-RS resources are the second resource.

[0116] The first resource is associated with the second resource. For example, the network device may associate the first resource with the second resource through signaling, such as by performing resource association through a new IE during RRC configuration. Alternatively, the terminal device may associate the first resource with the second resource based on desired time information.

[0117] For another example, the first resource and the second resource may be jointly configured. For example, the network device may not explicitly distinguish between the first resource and the second resource. For example, the network device may configure CSI-RS resources for a period, and the terminal device may select the first resource and the second resource from the CSI-RS resources of the period based on desired time information.

[0118] In some embodiments, the measurement results of the first CSI-RS and the second CSI-RS may correspond to the same receive beam (Rx beam). This eliminates the impact of different receive beams on the measurement results, reduces the variables in the training data, and simplifies the training process. The present application is not limited to this, and the measurement results of the first CSI-RS and the second CSI-RS may also correspond to different receive beams.

[0119] In some embodiments, the CSI reporting configuration information and / or the CSI resource configuration information may include first indication information for instructing the collection and / or reporting of training data. Thus, after receiving the first indication information, the UE may collect and / or report the training data.

[0120] In some embodiments, the first indication information can be displayed by an indication, for example, by the appearance or absence of the first indication information, or a value, to indicate whether to report the training data. For example, when the first indication information appears in the CSI reporting configuration information and / or the CSI resource configuration information, it indicates that the UE reports the training data; when the first indication information is absent in the CSI reporting configuration information and / or the CSI resource configuration information, it indicates that the UE does not report the training data; and vice versa. For another example, the first indication information is indicated by 1 bit: when the 1 bit information takes the value 1, it indicates that the UE reports the training data; when the 1 bit information takes the value 0, it indicates that the UE does not report the training data; and vice versa. The present application is not limited to this, and the first indication information can also be implicitly indicated, for example, in the case of configuring the reporting information related to the training data or the resources related to the training data, the training data is reported by default, otherwise the training data is not reported.

[0121] In some embodiments, the training data can be reported in various ways.

[0122] In some embodiments, the training data can be reported by non-layer 1 signaling, for example, layer 3 signaling, etc. In this case, the first indication information can be carried on the radio resource control (RRC) signaling.

[0123] In some embodiments, the training data can be reported by layer 1 signaling, for example, uplink control information (UCI), etc. In this case, the first indication information can be carried on at least one of the following: radio resource control (RRC) signaling, medium access control layer control element (MAC CE), or downlink control information (DCI).

[0124] In some embodiments, the training data can be reported once, or divided into multiple times for reporting.

[0125] For example, the corresponding real values of the input information and the output information can be jointly reported. That is, in one reporting, both the input information and the output information are included.

[0126] For example, in the joint reporting, the corresponding real values of the input information and the output information can be jointly reported once as a whole. That is, the channel matrix / eigenvector is reported once after the reference signal transmission is completed.

[0127] Taking the prediction of the channel matrices at t+2△ and t+4△ based on the channel matrices at t-3△, t-2△, t-△ and t as an example, the channel matrices obtained by measuring the CSI-RS at t-3△, t-2△, t-△ and t and the channel matrices obtained by measuring the CSI-RS at t+2△ and t+4△ can be reported to the network device at one time.

[0128] The present application is not limited thereto, and the input information and the output information can also be reported in multiple times in joint reporting.

[0129] For example, the corresponding true values of the input information and the output information can be reported independently. That is, the input information is reported in one time, and the output information is not reported; or the output information is reported in one time, and the input information is not reported.

[0130] For example, when the input information is reported independently, the input information can be reported as a whole in one time; when the output information is reported independently, the corresponding true values of the output information can be reported as a whole in one time.

[0131] Taking the prediction of the channel matrices at t+2△ and t+4△ based on the channel matrices at t-3△, t-2△, t-△ and t as an example, the channel matrices obtained by measuring the CSI-RSs at t-3△, t-2△, t-△ and t can be reported in one time; the channel matrices obtained by measuring the CSI-RSs at t+2△ and t+4△ can be reported in another time.

[0132] The present application is not limited thereto, and the input information can also be reported in multiple times. For example, the input information can include the measurement results (i.e., multiple channel matrices and / or eigenvectors) of the CSI-RSs at multiple first time points, in which case, the measurement results can be reported in multiple times.

[0133] Taking the prediction of the channel matrices at t+2△ and t+4△ based on the channel matrices at t-3△, t-2△, t-△ and t as an example, the channel matrices at t-3△, t-2△, t-△ and t can be reported in four times respectively. For example, the UE reports the channel matrix at t-3△ after completing the measurement of the CSI-RS at t-3△, i.e., the channel matrix at t-3△; the UE reports the channel matrix at t-2△ after completing the measurement of the CSI-RS at t-2△, i.e., the channel matrix at t-2△; and so on. That is, the channel matrix / eigenvector can be reported in multiple times after each time point during the transmission of the reference signal.

[0134] The output information can also be reported in multiple times. For example, the output information can include the measurement results (i.e., multiple channel matrices and / or eigenvectors) of the CSI-RSs at multiple second time points, in which case, the measurement results can be reported in multiple times.

[0135] Taking the channel matrix at t+2△ and t+4△ as an example, which is predicted according to the channel matrix at t-3△, t-2△, t-△, and t, the channel matrix at t+2△ and t+4△ can be reported twice. For example, the UE reports the channel matrix at t+2△ after completing the measurement of the CSI-RS at t+2△, that is, the channel matrix at t+2△; the UE reports the channel matrix at t+4△ after completing the measurement of the CSI-RS at t+4△, that is, the channel matrix at t+4△.

[0136] The above exemplary describes the reporting manner of the input information and the output information. In the case where the first information is included in the training data, one or more times of reporting can be performed.

[0137] For example, the input information, the output information, and the first information can be jointly reported; or, the input information, the output information, and the first information can be independently reported; or, any two of the input information, the output information, and the first information can be jointly reported, and the other one can be independently reported.

[0138] In some embodiments, in the case where multiple channel matrices and / or eigenvectors are reported at one time, the complete content of each channel matrix and / or eigenvector can be reported in the one-time reporting. For example, the complete content of each channel matrix and / or eigenvector is concatenated, and the concatenated result is reported.

[0139] The present application is not limited thereto. In the case where multiple channel matrices and / or eigenvectors are reported at one time, the multiple channel matrices and / or eigenvectors can be appropriately compressed in order to reduce the signaling overhead.

[0140] For example, the complete content of at least one channel matrix and / or eigenvector is reported in the one-time reporting, and the channel matrix and / or eigenvector is taken as a reference channel matrix and / or reference eigenvector. For other channel matrices and / or eigenvectors, the difference with the reference channel matrix and / or reference eigenvector can be reported.

[0141] For another example, the reported training data is compressed by using an AI / ML model, and the like.

[0142] The above embodiments are only exemplary descriptions of the embodiments of the present application, but the present application is not limited thereto. Appropriate modifications can be made on the basis of the above embodiments. For example, each of the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0143] According to the above embodiments, the terminal device receives the CSI resource configuration information and / or the CSI reporting configuration information from the network device, and reports the training data for the AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information. In this way, the training data for the AI / ML model can be collected, which helps to improve the accuracy and performance of the AI / ML model.

[0144] Embodiments of the second aspect

[0145] Embodiments of the present application provide an information processing method, which is described from the network device side. Embodiments of the second aspect can be combined with embodiments of the first aspect, or can be implemented alone. The same content as the embodiments of the first aspect will not be described again.

[0146] FIG. 3 is another schematic diagram of the information processing method according to an embodiment of the present application. As shown in FIG. 3, the method comprises:

[0147] 301. The network device sends CSI resource configuration information and / or CSI reporting configuration information to the terminal device; and

[0148] 302. The network device receives the training data for the AI / ML model reported by the terminal device.

[0149] It is worth noting that the above FIG. 3 only schematically illustrates the embodiments of the present application, but the present application is not limited thereto. For example, the execution order between the operations can be appropriately adjusted, and in addition, some operations can be added or some operations can be reduced. Those skilled in the art can make appropriate modifications according to the above content, and the present application is not limited to the above FIG. 3.

[0150] In some embodiments, the training data comprises at least one of: input information of the model; real values corresponding to output information of the model; or first information of the terminal device.

[0151] In some embodiments, the input information comprises a channel matrix and / or a feature vector.

[0152] In some embodiments, the real values corresponding to the output information comprise a channel matrix and / or a feature vector.

[0153] In some embodiments, the first information comprises at least one of: antenna configuration information of the terminal device, movement speed information of the terminal device, or time information expected by the terminal device.

[0154] In some embodiments, the antenna configuration information comprises at least one of: a number of horizontal direction panels and a number of vertical direction panels; a horizontal direction panel interval and a vertical direction panel interval; a number of horizontal direction antennas within a panel and a number of vertical direction antennas within a panel; a horizontal direction antenna interval within a panel and a vertical direction antenna interval within a panel; a mapping relationship between antennas and TxRUs; or an antenna polarization direction.

[0155] In some embodiments, the time instance information expected by the terminal device comprises at least one of: first time instance information for performing CSI-RS measurement, second time instance information for performing CSI prediction, or an interval between the first time instance for performing CSI-RS measurement and the second time instance for performing CSI prediction.

[0156] In some embodiments, the CSI resource configuration information is used to configure one or more CSI-RS resources.

[0157] In some embodiments, the CSI-RS resource comprises at least one of: a resource of periodic CSI-RS, a resource of semi-static CSI-RS, or a resource of non-periodic CSI-RS with time domain equal interval.

[0158] In some embodiments, the CSI-RS resource is used for reporting of the training data and reporting of channel state information.

[0159] In some embodiments, the CSI-RS resource is used for reporting of the training data and performance monitoring of the model.

[0160] In some embodiments, the performance monitoring result of the model is lower than a first threshold, and the training data is reported; and / or the performance monitoring result of the model is higher than a second threshold, and the training data is reported.

[0161] In some embodiments, the CSI-RS resource is configured according to at least one of: a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0162] In some embodiments, a number of time instances and / or intervals of the CSI-RS resource are configured according to at least one of: a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0163] In some embodiments, the CSI-RS resource includes a first resource corresponding to a first CSI-RS and a second resource corresponding to a second CSI-RS, a measurement result of the first CSI-RS is used to generate input information of the model, and a measurement result of the second CSI-RS is used to generate a true value corresponding to output information of the model.

[0164] In some embodiments, the first resource and the second resource are separately configured.

[0165] In some embodiments, the first resource is associated with the second resource.

[0166] In some embodiments, the first resource and the second resource are jointly configured.

[0167] In some embodiments, the measurement result of the first CSI-RS and the measurement result of the second CSI-RS correspond to a same receive beam.

[0168] In some embodiments, the CSI reporting configuration information and / or the CSI resource configuration information includes first indication information used to indicate reporting of the training data.

[0169] In some embodiments, the training data is reported through non-layer 1 signaling, and the first indication information is carried on radio resource control (RRC) signaling.

[0170] In some embodiments, the training data is reported through layer 1 signaling, and the first indication information is carried on at least one of the following: radio resource control (RRC) signaling, medium access control layer control element (MAC CE), or downlink control information (DCI).

[0171] In some embodiments, the training data is reported once or is divided into multiple times of reporting.

[0172] In some embodiments, the network device further sends, to the terminal device, second information of the network device, so that the terminal device classifies and / or labels the training data according to the second information and / or first information of the terminal device.

[0173] In some embodiments, the training data includes first information of the terminal device, and the network device classifies and / or labels the training data according to second information of a network side and / or the first information.

[0174] In some embodiments, the second information of the network side includes at least one of the following: antenna configuration information of the network device, scene information of the network device, a reference signal period, an identifier of a cell / site, a carrier frequency, a frequency domain granularity, or a subcarrier spacing.

[0175] In some embodiments, the antenna configuration information comprises at least one of: a number of horizontal direction panels and a number of vertical direction panels; a horizontal direction panel interval and a vertical direction panel interval; a number of horizontal direction antennas within a panel and a number of vertical direction antennas within a panel; a horizontal direction antenna interval within a panel and a vertical direction antenna interval within a panel; a mapping relationship between antennas and TxRUs; or an antenna polarization direction.

[0176] In some embodiments, the scenario information comprises: indoor or outdoor, and / or line-of-sight or non-line-of-sight.

[0177] In some embodiments, the network device can train the AI / ML model by using the classified training data, and send the related information of the trained AI / ML model to the terminal device.

[0178] In some embodiments, the network device can also indicate the classification information corresponding to the related information of the AI / ML model to the terminal device, for example, indicate the classification label and / or the first information and / or the second information corresponding to the related information of the AI / ML model.

[0179] The above various embodiments are only exemplarily described, 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.

[0180] According to the above embodiments, the network device sends the CSI resource configuration information and / or the CSI reporting configuration information to the terminal device, and receives the training data for the AI / ML model reported by the terminal device. In this way, the training data for the AI / ML model can be collected, which helps to improve the accuracy and performance of the AI / ML model.

[0181] Embodiments of the third aspect

[0182] Embodiments of the present application provide an information processing apparatus. The apparatus may, for example, be a terminal device, or one or more components or assemblies configured in the terminal device, which corresponds to the embodiments of the first aspect, and the same content as the embodiments of the first aspect will not be described again.

[0183] FIG. 4 is a schematic diagram of an information processing apparatus according to an embodiment of the present application. As shown in FIG. 4, the information processing apparatus 400 comprises:

[0184] a receiving unit 401 configured to receive the CSI resource configuration information and / or the CSI reporting configuration information from the network device; and

[0185] The sending unit 402 reports training data for the AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0186] In some embodiments, the training data comprises at least one of: input information of the model; real values corresponding to output information of the model; or first information of the terminal device.

[0187] In some embodiments, the input information comprises a channel matrix and / or a feature vector; and / or, the real values corresponding to the output information comprise a channel matrix and / or a feature vector; and / or, the first information comprises at least one of: antenna configuration information of the terminal device, moving speed information of the terminal device, or time information expected by the terminal device.

[0188] In some embodiments, the antenna configuration information comprises at least one of: a number of horizontal direction panels and a number of vertical direction panels; a horizontal direction panel spacing and a vertical direction panel spacing; a number of horizontal direction antennas within a panel and a number of vertical direction antennas within a panel; a horizontal direction antenna spacing within a panel and a vertical direction antenna spacing within a panel; a mapping relationship between antennas and TxRUs; or an antenna polarization direction.

[0189] and / or

[0190] The time information expected by the terminal device comprises at least one of: first time information for performing CSI-RS measurement, second time information for performing CSI prediction, or an interval between the first time information for performing CSI-RS measurement and the second time information for performing CSI prediction.

[0191] In some embodiments, the CSI resource configuration information is used to configure one or more CSI-RS resources.

[0192] In some embodiments, the CSI-RS resources comprise at least one of: resources of periodic CSI-RS, resources of semi-static CSI-RS, or resources of aperiodic CSI-RS with time domain equal interval.

[0193] In some embodiments, the CSI-RS resources are used for reporting of the training data and reporting of channel state information.

[0194] In some embodiments, the CSI-RS resources are used for reporting of the training data and performance monitoring of the model.

[0195] In some embodiments, the performance monitoring result of the model is lower than a first threshold, and the training data is reported; and / or, the performance monitoring result of the model is higher than a second threshold, and the training data is reported.

[0196] In some embodiments, the CSI-RS resource is configured according to at least one of the following: a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0197] In some embodiments, the number of time instants and / or interval of the CSI-RS resource is configured according to at least one of the following: a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0198] In some embodiments, the CSI-RS resource includes a first resource corresponding to a first CSI-RS and a second resource corresponding to a second CSI-RS, a measurement result of the first CSI-RS is used to generate input information of the model, and a measurement result of the second CSI-RS is used to generate a true value corresponding to output information of the model.

[0199] In some embodiments, the first resource and the second resource are configured separately; and / or, the first resource is associated with the second resource; and / or, the first resource and the second resource are jointly configured.

[0200] In some embodiments, the measurement result of the first CSI-RS and the measurement result of the second CSI-RS correspond to a same receive beam.

[0201] In some embodiments, the CSI reporting configuration information and / or the CSI resource configuration information includes first indication information used to indicate reporting of the training data.

[0202] In some embodiments, the training data is reported through non-layer 1 signaling, and the first indication information is carried on radio resource control (RRC) signaling; and / or, the training data is reported through layer 1 signaling, and the first indication information is carried on at least one of the following: radio resource control (RRC) signaling, medium access control layer control element (MAC CE), or downlink control information (DCI).

[0203] In some embodiments, the training data is reported once or is divided into multiple times of reporting.

[0204] In some embodiments, the receiving unit further receives second information from the network device; and the apparatus 400 further includes:

[0205] a processing unit 403 configured to classify and / or label the training data according to the second information and / or first information of the terminal device, and

[0206] The above embodiments are only exemplary, and the present application is not limited thereto. For example, one or more of the above embodiments can be combined.

[0207] According to the above embodiments, the terminal device receives the CSI resource configuration information and / or the CSI reporting configuration information from the network device, and reports the training data for the AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information. In this way, the training data for the AI / ML model can be collected, which helps to improve the accuracy and performance of the AI / ML model.

[0208] Embodiments of the fourth aspect

[0209] Embodiments of the present application provide an information processing apparatus. The apparatus may, for example, be a network device, or one or more components or components configured in the network device, which corresponds to the embodiments of the second aspect. The same content as the embodiments of the second aspect will not be described again.

[0210] FIG. 5 is another schematic diagram of an information processing apparatus according to an embodiment of the present application. As shown in FIG. 5, the information processing apparatus 500 includes:

[0211] a sending unit 501 configured to send, to a terminal device, CSI resource configuration information and / or CSI reporting configuration information; and

[0212] a receiving unit 502 configured to receive, from the terminal device, training data for an AI / ML model.

[0213] In some embodiments, the training data includes at least one of: input information of the model; real values corresponding to output information of the model; or first information of the terminal device.

[0214] In some embodiments, the input information includes a channel matrix and / or a feature vector.

[0215] In some embodiments, the real values corresponding to the output information include a channel matrix and / or a feature vector.

[0216] In some embodiments, the first information includes at least one of: antenna configuration information of the terminal device, movement speed information of the terminal device, or time information expected by the terminal device.

[0217] In some embodiments, the antenna configuration information comprises at least one of: a number of horizontal direction panels and a number of vertical direction panels; a horizontal direction panel interval and a vertical direction panel interval; a number of horizontal direction antennas within a panel and a number of vertical direction antennas within a panel; a horizontal direction antenna interval within a panel and a vertical direction antenna interval within a panel; a mapping relationship between antennas and TxRUs; or an antenna polarization direction.

[0218] In some embodiments, the time instance information expected by the terminal device comprises at least one of: first time instance information for performing CSI-RS measurement, second time instance information for performing CSI prediction, or an interval between the first time instance for performing CSI-RS measurement and the second time instance for performing CSI prediction.

[0219] In some embodiments, the CSI resource configuration information is used to configure one or more CSI-RS resources.

[0220] In some embodiments, the CSI-RS resource comprises at least one of: a resource of periodic CSI-RS, a resource of semi-static CSI-RS, or a resource of aperiodic CSI-RS with time domain equal interval.

[0221] In some embodiments, the CSI-RS resource is used for reporting of the training data and reporting of channel state information.

[0222] In some embodiments, the CSI-RS resource is used for reporting of the training data and performance monitoring of the model.

[0223] In some embodiments, the performance monitoring result of the model is lower than a first threshold, and the training data is reported; and / or the performance monitoring result of the model is higher than a second threshold, and the training data is reported.

[0224] In some embodiments, the CSI-RS resource is configured according to at least one of: a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0225] In some embodiments, a number of time instances and / or intervals of the CSI-RS resource are configured according to at least one of: a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0226] In some embodiments, the CSI-RS resource includes a first resource corresponding to a first CSI-RS and a second resource corresponding to a second CSI-RS, a measurement result of the first CSI-RS is used to generate input information of the model, and a measurement result of the second CSI-RS is used to generate a true value corresponding to output information of the model.

[0227] In some embodiments, the first resource and the second resource are separately configured.

[0228] In some embodiments, the first resource is associated with the second resource.

[0229] In some embodiments, the first resource and the second resource are jointly configured.

[0230] In some embodiments, the measurement result of the first CSI-RS and the measurement result of the second CSI-RS correspond to a same receive beam.

[0231] In some embodiments, the CSI reporting configuration information and / or the CSI resource configuration information includes first indication information used to indicate reporting of the training data.

[0232] In some embodiments, the training data is reported through non-layer 1 signaling, and the first indication information is carried on radio resource control (RRC) signaling.

[0233] In some embodiments, the training data is reported through layer 1 signaling, and the first indication information is carried on at least one of the following: radio resource control (RRC) signaling, medium access control layer control element (MAC CE), or downlink control information (DCI).

[0234] In some embodiments, the training data is reported once or is divided into multiple times of reporting.

[0235] In some embodiments, the sending unit 501 further sends, to the terminal device, second information of the network device, so that the terminal device classifies and / or labels the training data according to the second information and / or first information of the terminal device.

[0236] In some embodiments, the training data includes the first information of the terminal device; and the apparatus 500 further includes a processing unit 503 configured to classify and / or label the training data according to the second information of the network side and / or the first information.

[0237] In some embodiments, the second information of the network side includes at least one of the following: antenna configuration information of the network device, scene information of the network device, a reference signal period, an identifier of a cell / site, a carrier frequency, a frequency domain granularity, or a subcarrier spacing.

[0238] In some embodiments, the antenna configuration information comprises at least one of: a number of horizontal direction panels and a number of vertical direction panels; a horizontal direction panel interval and a vertical direction panel interval; a number of horizontal direction antennas within a panel and a number of vertical direction antennas within a panel; a horizontal direction antenna interval within a panel and a vertical direction antenna interval within a panel; a mapping relationship between antennas and TxRUs; or an antenna polarization direction.

[0239] In some embodiments, the scenario information comprises: indoor or outdoor, and / or line-of-sight or non-line-of-sight.

[0240] The above various embodiments are only exemplarily described, 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.

[0241] According to the above embodiments, the network device sends CSI resource configuration information and / or CSI reporting configuration information to the terminal device, and receives training data for an AI / ML model reported by the terminal device. Thus, the training data for the AI / ML model can be collected, which helps to improve the accuracy and performance of the AI / ML model.

[0242] Embodiments of the fifth aspect

[0243] The embodiments of the present application further provide a communication system, which can refer to FIG. 1, and the same content as the embodiments of the first aspect to the fourth aspect will not be described herein.

[0244] In some embodiments, the communication system 100 can at least include: a network device and a terminal device. The network device sends CSI resource configuration information and / or CSI reporting configuration information to the terminal device, and receives training data for an AI / ML model reported by the terminal device; and the terminal device receives the CSI resource configuration information and / or the CSI reporting configuration information, and reports the training data according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0245] The embodiments of the present application further provide a network device, which can be a base station, but the present application is not limited thereto, and can also be other network devices.

[0246] Fig. 6 is a schematic diagram of a network device according to an embodiment of the present application. As shown in Fig. 6, the network device 600 can include a processor 610 (e.g., a central processing unit, CPU) and a memory 620, wherein the memory 620 is coupled to the processor 610. The memory 620 can store various data. In addition, the memory 620 can store a program 630 for information processing, and execute the program 630 under the control of the processor 610.

[0247] For example, the processor 610 can be configured to execute the program to implement the operation of the network device in the method according to the embodiments of the second aspect. For example, the processor 610 can be configured to control the network device to send the CSI resource configuration information and / or the CSI reporting configuration information to the terminal device, and receive the training data for the AI / ML model reported by the terminal device.

[0248] In addition, as shown in Fig. 6, the network device 600 can further include a transceiver (receiver and / or transmitter) 640, an antenna 650, and the like. The functions of the above components are similar to those in the related art, which will not be described herein. It should be noted that the network device 600 does not necessarily include all the components shown in Fig. 6. In addition, the network device 600 can include components not shown in Fig. 6, which can be referred to the related art.

[0249] Embodiments of the present application also provide a terminal device, but the present application is not limited thereto, and other devices can also be used.

[0250] Fig. 7 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Fig. 7, the terminal device 700 can include a processor 710 and a memory 720, wherein the memory 720 stores data and programs, and is coupled to the processor 710. It should be noted that the diagram is exemplary, and other types of structures can be used to supplement or replace the structure to implement telecommunication functions or other functions.

[0251] For example, the processor 710 can be configured to execute the program to implement the method according to the embodiments of the first aspect. For example, the processor 710 can be configured to control the terminal device to receive the CSI resource configuration information and / or the CSI reporting configuration information from the network device, and report the training data for the AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0252] As shown in FIG. 7, the terminal device 700 can further include a communication module 730, an input unit 740, a display 750, and a power supply 760. The functions of the above components are similar to those of the related art, and will not be described here. It should be noted that the terminal device 700 does not necessarily include all the components shown in FIG. 7, and the above components are not essential; in addition, the terminal device 700 can also include components not shown in FIG. 7, and can refer to the related art.

[0253] 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 method of the embodiments of the first aspect.

[0254] The embodiments of the present application further provide a storage medium storing a computer program, which causes a terminal device to perform the method of the embodiments of the first aspect.

[0255] 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 method of the embodiments of the second aspect.

[0256] The embodiments of the present application further provide a storage medium storing a computer program, which causes a network device to perform the method of the embodiments of the second aspect.

[0257] 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-described apparatus or components, or to implement the above-described 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.

[0258] The method / apparatus described in conjunction 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 block diagrams shown in the figures and / or a combination of one or more of the functional block diagrams can correspond to each software module of the computer program flow, or to each hardware module. These software modules can correspond to each step shown in the figures, respectively. These hardware modules can be implemented by, for example, a field programmable gate array (FPGA) that solidifies the software modules.

[0259] The software modules can reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium known in the art. One storage medium can be coupled to the processor, 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 (e.g., mobile terminal) employs a MEGA-SIM card or a flash memory device with a large capacity, the software modules can be stored in the MEGA-SIM card or the flash memory device.

[0260] 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 in this application. 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.

[0261] The application has been described above with reference to specific embodiments. Those skilled in the art will understand that the description is illustrative of only the preferred embodiments and is not intended to limit the scope of the application. Various modifications and changes can be made by those skilled in the art which fall in the scope of the present application as defined by the appended claims.

[0262] In connection with the embodiments including the above embodiments, the following supplementary notes are also disclosed:

[0263] 1. An information processing apparatus configured to be installed in a terminal device, wherein the apparatus comprises:

[0264] a receiving unit configured to receive CSI resource configuration information and / or CSI reporting configuration information from a network device; and

[0265] a transmitting unit configured to report training data for an AI / ML model according to the CSI resource configuration information and / or the CSI reporting configuration information.

[0266] 2.The apparatus according to the appended 1, wherein a number of time instants and / or intervals of the CSI-RS resource are configured according to at least one of the following:

[0267] a scenario of the network device, a moving speed of the terminal device, or a configuration expected by the terminal device.

[0268] 3.An information processing apparatus configured in a network device, wherein the apparatus comprises:

[0269] a sending unit configured to send CSI resource configuration information and / or CSI reporting configuration information to a terminal device; and

[0270] a receiving unit configured to receive training data for an AI / ML model reported by the terminal device.

[0271] 4.The apparatus according to the appended 3, wherein the second information of the network side comprises at least one of the following:

[0272] antenna configuration information of the network device, scenario information of the network device, a reference signal period, an identity of a cell / site, a carrier frequency, a frequency domain granularity, or a subcarrier spacing.

[0273] 5.The apparatus according to the appended 4, wherein,

[0274] the antenna configuration information comprises at least one of the following:

[0275] a number of horizontal panels and a number of vertical panels;

[0276] a horizontal panel spacing and a vertical panel spacing;

[0277] a number of horizontal antennas within a panel and a number of vertical antennas within a panel;

[0278] a horizontal antenna spacing within a panel and a vertical antenna spacing within a panel;

[0279] a mapping relationship between antennas and TxRUs; or

[0280] an antenna polarization direction.

[0281] 6.The apparatus according to the appended 4, wherein,

[0282] the scenario information comprises indoor or outdoor, and / or line-of-sight or non-line-of-sight.

Claims

1. An information processing device, configured in a terminal device, wherein: The device comprises: a receiving unit configured to receive channel state information (CSI) resource configuration information and / or CSI reporting configuration information from a network device; and A sending unit, which reports training data for an AI / ML model based on the CSI resource configuration information and / or the CSI reporting configuration information.

2. The device according to claim 1, wherein The training data includes at least one of the following: input information of the model; The true value corresponding to the output information of the model; or The first information of the terminal device.

3. The device according to claim 2, wherein The input information includes a channel matrix and / or an eigenvector; and / or The real value corresponding to the output information includes a channel matrix and / or a eigenvector; and / or The first information includes at least one of the following: antenna configuration information of the terminal device, moving speed information of the terminal device, or expected time information of the terminal device.

4. The device according to claim 3, wherein The antenna configuration information includes at least one of the following: the number of panels in the horizontal direction and the number of panels in the vertical direction; the horizontal panel spacing and the vertical panel spacing; the number of antennas in the horizontal direction and the number of antennas in the vertical direction within the panel; Horizontal and vertical antenna spacing within the panel; mapping relationship between antennas and TxRUs; or antenna polarization direction; and / or The time information expected by the terminal device includes at least one of the following: the first time information for performing CSI-RS measurement, the second time information for performing CSI prediction, or the interval between the first time for performing CSI-RS measurement and the second time for performing CSI prediction.

5. The device according to claim 1, wherein The CSI resource configuration information is used to configure one or more CSI-RS resources.

6. The device according to claim 5, wherein The CSI-RS resources include at least one of the following: periodic CSI-RS resources, semi-static CSI-RS resources, or aperiodic CSI-RS resources with equal intervals in the time domain.

7. The device according to claim 5, wherein The CSI-RS resources are used for reporting the training data and reporting channel state information.

8. The device according to claim 5, wherein The CSI-RS resources are used for reporting the training data and monitoring the performance of the model.

9. The device according to claim 8, wherein The performance monitoring result of the model is lower than a first threshold, and the training data is reported; and / or The performance monitoring result of the model is higher than a second threshold, and the training data is reported.

10. The device according to claim 5, wherein The CSI-RS resource is configured according to at least one of the following: The scenario of the network device, the moving speed of the terminal device, or the expected configuration of the terminal device.

11. The device according to claim 5, wherein The CSI-RS resources include a first resource corresponding to a first CSI-RS and a second resource corresponding to a second CSI-RS. The measurement result of the first CSI-RS is used to generate input information of the model, and the measurement result of the second CSI-RS is used to generate a true value corresponding to the output information of the model.

12. The device according to claim 11, wherein The first resource and the second resource are configured separately; and / or The first resource is associated with the second resource; and / or The first resource and the second resource are jointly configured.

13. The device according to claim 11, wherein The measurement result of the first CSI-RS and the measurement result of the second CSI-RS correspond to the same receive beam.

14. The device according to claim 1, wherein The CSI reporting configuration information and / or the CSI resource configuration information includes first indication information for instructing reporting of the training data.

15. The device according to claim 14, wherein The training data is reported via non-layer 1 signaling, and the first indication information is carried on radio resource control (RRC) signaling; and / or The training data is reported via layer 1 signaling, and the first indication information is carried on at least one of the following: radio resource control (RRC) signaling, media access control layer control element (MAC CE), or downlink control element (DCE). Direct Control Information (DCI).

16. The device according to claim 15, wherein The training data is reported once or divided into multiple reports.

17. The device according to claim 1, wherein The receiving unit further receives second information from the network device; The device further comprises: A processing unit classifies and / or labels the training data according to the second information and / or the first information of the terminal device, and the sending unit reports the classified and / or labeled training data.

18. An information processing device, configured in a network device, wherein: The device comprises: a sending unit, configured to send CSI resource configuration information and / or CSI reporting configuration information to a terminal device; and A receiving unit receives training data for the AI / ML model reported by the terminal device.

19. The device according to claim 18, wherein The training data includes first information of the terminal device; The device further comprises: A processing unit is configured to classify and / or label the training data according to the second information on the network side and / or the first information.

20. A communication system, wherein: The system includes network equipment and terminal equipment, The network device sends CSI resource configuration information and / or CSI reporting configuration information to the terminal device, and receives training data for the AI / ML model reported by the terminal device; The terminal device receives the CSI resource configuration information and / or CSI reporting configuration information, and reports the training data according to the CSI resource configuration information and / or CSI reporting configuration information.

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