A method and apparatus used in a node for wireless communication data collection

Abstract

This application discloses a method and apparatus for data collection in a node used in wireless communication. A first node receives a first information block indicating a first RS resource set for data collection; executes a process for the data collection; performs measurements included in the first RS resource set as part of the process for the data collection, the data collection being associated with an inference configuration; the amount of inference resources occupied by the process for the data collection depends on at least one of the data types included in the data collection or the inference configuration associated with the data collection. This application solves the resource consumption problem of UE performing data collection, facilitates AI deployment, and improves overall system performance.

Description

Technical Field

[0001] This application relates to signal transmission methods and apparatus in wireless communication systems, and more particularly to data collection methods and apparatus. Background Technology

[0002] In traditional wireless communication, the UE (User Equipment) reports various auxiliary information obtained through measurements of downlink signals and / or channels. This information includes channel information, beam management (BM) related auxiliary information, location-related auxiliary information, HARQ (Hybrid Automatic Repeat Quest) - ACK (ACK knowledge) information, beam / radio link failure auxiliary information, and so on. The UE reports this information to the network equipment, which then selects appropriate transmission parameters for the UE based on this information. These parameters include the cell to be used, MCS (Modulation and Coding Scheme), TPMI (Transmitted Precoding Matrix Indicator), and TCI (Transmission Configuration Indication). Furthermore, UE reporting can be used to optimize network parameters, such as improving cell coverage and switching base stations on / off based on the UE's location.

[0003] The application scenarios of future wireless communication systems are becoming increasingly diversified. To meet the different performance requirements of various scenarios, 3GPP (3rd Generation Partner Project) initiated standard research on the intelligence of RAN (Radio Access Networks) starting with Rel-16 (Release-16), mainly focusing on intelligent use cases, enhanced data collection, and the potential impact on RAN nodes and interfaces. Rel-18 formally established the project for AI / ML-based 5G air interface enhancement, initiating international standardization work on the integration of 5G air interface and AI / ML, mainly focusing on research use cases, lifecycle management (LCM), simulation verification, and data collection.

[0004] Currently, the development of AI / ML has entered the stage of large-scale models. Large-scale communication models can realize autonomous networks and intelligent services, support network operation optimization, and improve network efficiency. The deep integration of communication and AI is an important direction for the future evolution of communication. AI will empower the development and upgrading of 5G, 5.5G to 6G, bringing new management models such as automated management of frequency bands and traffic, real-time analysis of user data and network load, and prediction of network status. Summary of the Invention

[0005] The applicant's research revealed that existing measurement, computation, and resource consumption methods may be insufficient to meet the demands of AI / ML when AI / ML functionalities are introduced. For example, current standards provide explicit definitions of resource consumption and corresponding priority designs for processing physical layer channel information. AI / ML models are training-based, and training data collection is a crucial step for the effective application of AI / ML in wireless communication networks. The impact of measuring and transmitting large amounts of training data on the communication system, and how to reasonably define the processing resources consumed during training data collection, are issues that need to be considered.

[0006] To address the aforementioned issues, this application discloses a solution. It should be noted that while this application is initially intended for AI / ML scenarios, it can also be applied to other non-AI / ML scenarios. Furthermore, adopting a unified design scheme for different scenarios (such as other non-AI / ML scenarios, including but not limited to Vehicle to Everything (V2X), capacity enhancement systems, short-range communication systems, NTN (Non-Terrestrial Network), IoT (Internet of Things), and URLLC (Ultra-Reliable Low-Latency Communication) networks) helps reduce hardware complexity and cost. Where there is no conflict, embodiments and features in any node of this application can be applied to any other node. Where there is no conflict, embodiments and features in any embodiment of this application can be arbitrarily combined with each other.

[0007] In particular, the interpretation of terms, nouns, functions, and variables in this application (unless otherwise specified) can be found in the definitions of the TS38 and TS37 series of 3GPP (3rd Generation Partnership Project) Technical Specifications (TS). Where necessary, reference can be made to TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, and TS38.423 in the 3GPP technical specifications to aid in understanding this application.

[0008] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0009] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS37 series.

[0010] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-17.

[0011] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-18.

[0012] This application discloses a method for a first node in wireless communication data collection, comprising:

[0013] A first receiver receives a first information block, the first information block indicating a first RS resource set, the first RS resource set being for data collection; and executes a process for the data collection.

[0014] The process of performing the data collection includes measurements in the first RS resource set, and the data collection is associated with an inference configuration; the amount of processing resources used by the process of performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0015] As an example, the problem this application aims to solve includes: how to improve model performance.

[0016] As an example, the problem this application aims to solve includes: how to determine the processing resources occupied by the UE in collecting training data.

[0017] As an example, the features of the above method include: In this application, the first node collects datasets for AI / ML through data collection, and real and high-quality data helps to improve model performance, thereby solving the above problems.

[0018] As an example, the features of the above method include: in this application, the processing resource consumption during data collection depends on the data type to be collected, thereby solving the above-mentioned problem.

[0019] As an example, the features of the above method include: in this application, data collection is associated with inference configuration, and the processing resource consumption of data collection depends on inference configuration, thereby solving the above problems.

[0020] As an example, the features of the above method include: the data collected is used for one or more of model training, model monitoring, and model delivery.

[0021] As an example, the features of the above method include: the processing resources include at least one of computing resources, storage resources, and inference resources.

[0022] As an example, the advantages of the above method include: this application supports the deep integration of AI and communication, improves the adaptability and intelligence level of the communication system, and thus enhances the performance, efficiency and user experience of the communication system.

[0023] As an example, the advantages of the above method include: solving the resource consumption problem of UE performing data collection and improving the feasibility of UE performing data collection.

[0024] As an example, the advantages of the above method include: quantifying processing resources by defining the amount of resources for row data collection, facilitating consensus between the base station and the UE, and thus enabling reasonable configuration of the data to be collected.

[0025] As an example, the benefits of the above method include: facilitating AI deployment and improving overall system performance.

[0026] According to one aspect of this application, the method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection; candidates for the included data type include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0027] As an example, the features of the above method include: the CSI includes one or more of CRI, SSBRI, L1-RSRP, L1-SINR, CRI, RI, PMI, CQI, and LI.

[0028] As an example, the characteristics of the above method include: the candidates for the data type include AI inference instructions.

[0029] As an example, the characteristics of the above method include: the UE will consume different amounts of processing resources when calculating data of different data types.

[0030] As an example, the advantages of the above method include: good compatibility.

[0031] As an example, the advantages of the above method include: greater flexibility.

[0032] As an example, the advantages of the above method include: the UE can collect data of various data types, making the training and inference of AI / ML models more compatible, and further improving the performance of AI / ML solutions for communication systems.

[0033] According to one aspect of this application, the above method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the number of data types included in the data collection.

[0034] As an example, the characteristics of the above method include: a larger number of data types require more processing resources to process different data types simultaneously.

[0035] As an example, the characteristics of the above method include: a larger number of data types require the UE to process in more parallel to ensure that the calculation is completed within a limited time.

[0036] As an example, the characteristics of the above method include: the number of data types is configured by higher-level signaling.

[0037] As an example, the advantages of the above method include ease of implementation and deployment.

[0038] As an example, the advantages of the above method include: allowing the base station to reasonably configure data collection based on channel conditions and UE capabilities, thereby optimizing system resource utilization.

[0039] As an example, the advantages of the above method include: good compatibility, and the fact that the same dataset includes multiple types of data, which helps to improve the generalization ability of the model during training.

[0040] According to one aspect of this application, the above method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0041] As an example, the features of the above method include: the dataset collected by the data collection is used for model training, the model is configured for inference, and the model is associated with the inference configuration.

[0042] As an example, the features of the above method include: the data collected by the data collection includes data generated by performing inference according to the inference configuration.

[0043] As an example, the features of the above method include: the processing resources used by the process executing the data collection include inference resources, and the amount of inference resources used by the process executing the data collection depends on the inference configuration associated with the data collection.

[0044] As an example, the advantages of the above method include: data collection, including inference results, can be used for model monitoring or model transfer by collecting at least the inference results.

[0045] As an example, the advantages of the above method include: good compatibility.

[0046] As an example, the benefits of the above method include: optimizing model lifecycle management and improving model management efficiency.

[0047] According to one aspect of this application, the above method is characterized in that the amount of processing resources occupied by the process executing the data collection depends on the number of RS resources occupied by the first RS resource set.

[0048] As an example, the characteristics of the above method include: an excessive amount of RS resources occupied by a set of RS resources may lead to more parallel computing, thereby increasing the consumption of processing resources.

[0049] As an example, the characteristics of the above method include: the UE may need to calculate different data based on the measurement results of multiple RS resources, which leads to increased computational complexity and more processing resources required.

[0050] As an example, the characteristics of the above method include: the UE needs to perform independent channel estimation and measurement for each RS resource in the RS resource set, and the higher data volume and computational load require more processing resources.

[0051] As an example, the advantages of the above method include: the base station can reasonably configure the number of RS resources based on channel quality and training requirements, thereby reducing the consumption of processing resources and avoiding redundant calculations.

[0052] As an example, the advantages of the above method include: ease of implementation.

[0053] As an example, the advantages of the above method include: good compatibility.

[0054] According to one aspect of this application, the above method is characterized in that the amount of processing resources occupied by the process executing the data collection depends on the configuration cycle of the first RS resource set.

[0055] As an example, the features of the above method include: the RS resources included in the first RS resource set have the same configuration period.

[0056] As an example, the features of the above method include: the configuration period includes time-domain behavior, which includes periodic and semi-persistent behavior.

[0057] As an example, the features of the above method include: periodic or semi-persistent RS resource measurement and storage usage can be optimized by predicting processing resource usage in advance based on the configuration period and by adopting implementation-related methods.

[0058] As an example, the advantages of the above method include: considering the optimization of periodic resource occupancy, rationally allocating resource occupancy, and avoiding resource waste.

[0059] As an example, the advantages of the above method include: good compatibility.

[0060] As an example, the advantages of the above method include: when the channel conditions are good, the amount of processing resources used to execute the process for data collection can be reduced by reducing the configuration cycle.

[0061] According to one aspect of this application, the above method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the capabilities of the first node.

[0062] As an example, the features of the above method include: a UE with stronger computing power can process the same type of data with less processing resources.

[0063] As an example, the features of the above method include: UEs with lower computing power can improve computing speed by increasing the amount of resource processing resources required.

[0064] As an example, the features of the above method include: introducing UE capabilities can hide the specific hardware structure between different UEs, thus avoiding UE privacy leakage.

[0065] As an example, the advantages of the above method include: the base station can adjust the content of data collection according to the UE's capabilities, and UEs with higher capabilities can be assigned to perform more complex calculations, thereby improving data accuracy and obtaining more accurate channel information.

[0066] As an example, the advantages of the above method include: excessive data collection tasks may affect the normal communication of the UE, and introducing UE capabilities can achieve a balance between data collection and communication services.

[0067] As an example, the advantages of the above method include: higher compatibility, which facilitates reasonable deployment and implementation on different terminals.

[0068] According to one aspect of this application, the above method is characterized by comprising:

[0069] The first transmitter sends a second information block, which includes the results of the data collection.

[0070] As an example, the features of the above method include: this application is applicable to data collection for non-UE side model training.

[0071] As an example, the features of the above method include: the first information block is carried by an SRB other than Rel-18 and earlier SRBs.

[0072] As an example, the benefits of the above method include: optimized data reporting for AI / ML training.

[0073] As an example, the advantages of the above method include: better meeting the specific needs of AI / ML and optimizing the performance improvement brought by AI / ML solutions.

[0074] As an example, the advantages of the above method include: good forward compatibility.

[0075] According to one aspect of this application, the above method is characterized in that the first node is a user equipment.

[0076] According to one aspect of this application, the above method is characterized in that the first node is a terminal.

[0077] This application discloses a method for a second node in wireless communication data collection, comprising:

[0078] Send a first information block, the first information block indicating a first RS resource set, the first RS resource set being used for data collection;

[0079] Wherein, the recipient of the first information block executes a process for the data collection; the execution of the process for the data collection includes measurements of the first node in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0080] According to one aspect of this application, the method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection; candidates for the included data type include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0081] According to one aspect of this application, the above method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the number of data types included in the data collection.

[0082] According to one aspect of this application, the above method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0083] According to one aspect of this application, the above method is characterized in that the amount of processing resources occupied by the process executing the data collection depends on the number of RS resources occupied by the first RS resource set.

[0084] According to one aspect of this application, the above method is characterized in that the amount of processing resources occupied by the process executing the data collection depends on the configuration cycle of the first RS resource set.

[0085] According to one aspect of this application, the above method is characterized in that the amount of processing resources used to execute the process for the data collection depends on the capabilities of the first node.

[0086] According to one aspect of this application, the above method is characterized by comprising:

[0087] Receive a second information block, which includes the results of the data collection.

[0088] According to one aspect of this application, the method described above is characterized in that the second node is a base station.

[0089] This application discloses a first node for wireless communication data collection, comprising:

[0090] A first receiver receives a first information block, the first information block indicating a first RS resource set, the first RS resource set being for data collection; and executes a process for the data collection.

[0091] The process of performing the data collection includes measurements in the first RS resource set, and the data collection is associated with an inference configuration; the amount of processing resources used by the process of performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0092] This application discloses a second node for wireless communication data collection, comprising:

[0093] The second transmitter sends a first information block, the first information block indicating a first RS resource set, the first RS resource set being used for data collection;

[0094] Wherein, the recipient of the first information block executes a process for the data collection; the execution of the process for the data collection includes measurements of the first node in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0095] As an example, compared with conventional solutions, this application has the following advantages, but is not limited to:

[0096] This application supports the deep integration of AI and communication to improve the adaptability and intelligence of communication systems, thereby enhancing the performance, efficiency, and user experience of communication systems.

[0097] Better compatibility facilitates reasonable deployment and implementation across different user interfaces (UEs).

[0098] UE can collect data of various types, making AI / ML model training and inference more compatible and further improving the performance of AI / ML solutions;

[0099] This solves the resource consumption problem of UE performing data collection and improves the feasibility of UE performing data collection. Attached Figure Description

[0100] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0101] Figure 1A flowchart of the first node transmission according to an embodiment of this application is shown;

[0102] Figure 2 A schematic diagram of a network architecture according to an embodiment of this application is shown;

[0103] Figure 3 A schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application is shown;

[0104] Figure 4 A schematic diagram of a first communication device and a second communication device according to an embodiment of this application is shown;

[0105] Figure 5 A flowchart illustrating the transmission between a first node and a second node according to an embodiment of this application is shown;

[0106] Figure 6 A schematic diagram of a data type according to an embodiment of this application is shown;

[0107] Figure 7 A schematic diagram illustrating the number of data types according to one embodiment of this application is shown;

[0108] Figure 8 A schematic diagram of an associated inference configuration according to one embodiment of this application is shown;

[0109] Figure 9 A schematic diagram of the RS resources occupied by a first RS resource set according to an embodiment of this application is shown;

[0110] Figure 10 A schematic diagram illustrating the configuration cycle of RS resources in a first RS resource set according to an embodiment of this application is shown;

[0111] Figure 11 A schematic diagram illustrating the relationship between UE capabilities and processing resource usage according to an embodiment of this application is shown;

[0112] Figure 12 A schematic diagram illustrating the deployment of RAN domain AI / ML functionality according to an embodiment of this application is shown;

[0113] Figure 13 A schematic diagram illustrating the deployment of AI / ML functions in a UE according to an embodiment of this application is shown;

[0114] Figure 14 A schematic diagram of an artificial intelligence or machine learning-based processing system according to an embodiment of this application is shown;

[0115] Figure 15A schematic diagram illustrating artificial intelligence or machine learning according to an embodiment of this application is shown;

[0116] Figure 16 A structural block diagram of a processing apparatus for a first node according to an embodiment of this application is shown;

[0117] Figure 17 A structural block diagram of a processing apparatus for a second node according to an embodiment of this application is shown. Detailed Implementation

[0118] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Considering performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, including but not limited to the accompanying drawings. Figure 1 Examples and appendices Figure 5 - Appendix Figure 17 The embodiments in the appendix Figure 5 Examples and appendices Figure 6 - Appendix Figure 17 Examples, etc.

[0119] Example 1

[0120] Example 1 illustrates a flowchart of a first node transmission according to an embodiment of this application, as shown in the attached diagram. Figure 1 As shown. In the appendix Figure 1 In this diagram, each box represents a step. Specifically, the order of the steps within the boxes does not indicate a specific temporal sequence between them.

[0121] In step 101, the first node receives a first information block indicating a first RS resource set for data collection; in step 102, it executes a process for the data collection.

[0122] In Example 1, the process of performing the data collection includes measurements in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process of performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0123] As one example, the first node is a user equipment (UE).

[0124] As one example, the first node is a terminal.

[0125] As an example, the first node in this application includes a core network device that provides services for terminal AI.

[0126] As an example, the first node in this application includes an application layer device that provides services for terminal AI.

[0127] As an example, the first node in this application includes an Agent (intelligent agent) that provides services for the terminal AI.

[0128] As an example, the first node in this application includes a Handset (phone).

[0129] As an example, the first node is the first node in this application.

[0130] As an example, RS stands for Reference Signal.

[0131] As one embodiment, the first node receives the first information block.

[0132] As one embodiment, the first information block is carried by higher layer signaling.

[0133] As an example, the first information block is transmitted via RRC (Radio Resource Control) signaling.

[0134] As an example, the first information block includes one or more RRC IEs (Information Elements).

[0135] As an example, the first information block includes one or more fields in an RRC IE.

[0136] As one embodiment, the first information block includes information from one or more fields of each of the plurality of RRC IEs.

[0137] As an example, the first information block is an RRC IE.

[0138] As one example, the first information block includes ServingCellConfig IE.

[0139] As one example, the first information block includes one or more domains in the ServingCellConfig IE.

[0140] As one example, the first information block includes CSI-MeasConfig IE.

[0141] As one example, the first information block includes one or more domains in the CSI-MeasConfig IE.

[0142] As one embodiment, the first information block includes NZP-CSI-RS-Resource IE.

[0143] As one example, the first information block includes one or more fields in the NZP-CSI-RS-Resource IE.

[0144] As one embodiment, the first information block includes NZP-CSI-RS-ResourceSetIE.

[0145] As one embodiment, the first information block includes one or more fields in NZP-CSI-RS-ResourceSetIE.

[0146] As one example, the first information block includes a CSI-IM-Resource IE.

[0147] As one example, the first information block includes one or more fields in the CSI-IM-Resource IE.

[0148] As one embodiment, the first information block includes a CSI-IM-ResourceSet IE.

[0149] As one example, the first information block includes one or more fields in the CSI-IM-ResourceSet IE.

[0150] As one embodiment, the first information block includes CSI-SSB-ResourceSetIE.

[0151] As one example, the first information block includes one or more fields in CSI-SSB-ResourceSetIE.

[0152] As one example, the first information block includes CSI-ResourceConfig IE.

[0153] As one example, the first information block includes one or more domains in the CSI-ResourceConfig IE.

[0154] As one example, the first information block includes MeasConfig IE.

[0155] As one example, the first information block includes one or more domains in the MeasConfig IE.

[0156] As one example, the first information block includes MeasObjectToAddModListIE.

[0157] As one example, the first information block includes one or more fields in MeasObjectToAddModListIE.

[0158] As an example, the first information block includes one or more fields in the MeasObject IE.

[0159] As one example, the first information block includes MeasObjectNR IE.

[0160] As an example, the first information block includes MeasObjec6G IE.

[0161] As an example, the first information block includes MeasObjec6G IE.

[0162] As one example, the first information block includes CSI-RS-ResourceConfigMobility IE.

[0163] As one example, the first information block includes one or more domains in the CSI-RS-ResourceConfigMobility IE.

[0164] As an example, the name of the RRC signaling used to transmit the first information block includes CSI.

[0165] As an example, the name of the RRC signaling used to transmit the first information block includes CSI-RS.

[0166] As an example, the name of the RRC signaling used to transmit the first information block includes Config.

[0167] As an example, the name of the RRC signaling used to transmit the first information block includes Meas.

[0168] As an example, the name of the RRC signaling used to transmit the first information block includes Measurement.

[0169] As an example, the name of the RRC signaling used to transmit the first information block includes Object.

[0170] As an example, the name of the RRC signaling used to transmit the first information block includes Data.

[0171] As an example, the name of the RRC signaling used to transmit the first information block includes DataSet.

[0172] As an example, the name of the RRC signaling used to transmit the first information block includes Collection.

[0173] As an example, the name of the RRC signaling used to transmit the first information block includes Collect.

[0174] As an example, the name of the RRC signaling used to transmit the first information block includes Training.

[0175] As an example, the RS resources in the first RS resource set belong to the same cell.

[0176] As an example, in the first RS resource set, there are two RS resources belonging to different cells.

[0177] As an example, the RS resources in the first RS resource set belong to the same BWP (BandWidthPart).

[0178] As an example, in the first RS resource set, there are two RS resources belonging to different BWPs.

[0179] As an example, each RS resource in the first RS resource set includes at least one port(s).

[0180] As an example, each RS resource in the first RS resource set includes at least one antenna port(s).

[0181] As an example, each RS resource in the first RS resource set includes at least one RS port.

[0182] As an example, each RS resource in the first RS resource set includes a CSI-RS (Channel State Information Reference Signal) port.

[0183] As an example, the first RS resource set includes at least one RS.

[0184] As an example, the first RS resource set includes at least one RS resource.

[0185] As an example, the first RS resource set includes downlink RS resources.

[0186] As one embodiment, the first RS resource set includes RS resources for time-frequency resource tracking.

[0187] As an example, the first RS resource set includes PTRS (Phase-Tracking Reference Signal) resources.

[0188] As one embodiment, the first RS resource set includes RS resources for positioning.

[0189] As an example, the first RS resource set includes PRS (Positioning Reference Signal) resources.

[0190] As one embodiment, the first RS resource set includes RS resources for channel estimation.

[0191] As one embodiment, the first RS resource set includes RS resources for demodulation.

[0192] As an example, the first RS resource set includes DMRS (DeModulation Reference Signal) resources.

[0193] As an example, the first RS resource set includes RS resources for sensing.

[0194] As an example, the first RS resource set includes RS resources for ISAC (Integrated Sensing and Communication).

[0195] As an example, the first RS resource set includes ISAC-RS resources.

[0196] As an example, the first RS resource set includes IRS (ISAC Reference Signal) resources.

[0197] As one embodiment, the first RS resource set includes RS resources for mobility management.

[0198] As one embodiment, the first RS resource set includes RS resources for cell-level mobility management.

[0199] As one embodiment, the first RS resource set includes RS resources for beam-level mobility management.

[0200] As one embodiment, the first RS resource set includes RS resources for synchronization.

[0201] As one embodiment, the first RS resource set includes synchronization signals in at least 5G systems and systems after 5G systems.

[0202] As an example, the first RS resource set includes at least a synchronization signal in a 6G system.

[0203] As one embodiment, the first RS resource set includes at least a synchronization signal (SS).

[0204] As one embodiment, the first RS resource set includes at least a Primary Synchronization Signal (PSS).

[0205] As one embodiment, the first RS resource set includes at least a Secondary Synchronization Signal (SSS).

[0206] As an example, the first RS resource set includes at least PBCH (Physical Broadcast Channel).

[0207] Typically, the PBCH, PSS, and SSS are received in consecutive symbols and form an SS / PBCH block.

[0208] As an example, any RS resource in the first RS resource set is a CSI-RS resource or an SSB resource.

[0209] As an example, any RS resource in the first RS resource set is identified by an NZP-CSI-RS-ResourceId or SSB-Index.

[0210] As an example, the first RS resource set is identified by a CSI-ResourceConfigId.

[0211] As an example, the first RS resource set is a CSI-RS resource set.

[0212] As an example, the first RS resource set is an NZP (Non-Zero-Power) CSI-RS resource set.

[0213] As an example, the first RS resource set is a CSI-SSB resource set.

[0214] As an example, each RS resource in the first RS resource set is a CSI-RS resource.

[0215] As an example, each RS resource in the first RS resource set is an NZP CSI-RS resource.

[0216] As an example, each RS resource in the first RS resource set is identified by an NZP-CSI-RS-ResourceId.

[0217] As an example, the first RS resource set includes SSB.

[0218] As an example, the first RS resource set includes SSB resources.

[0219] As an example, the first RS resource set includes at least one SSB.

[0220] As an example, the first RS resource set includes an SSB.

[0221] As one embodiment, the first RS resource set includes multiple SSBs.

[0222] As an example, the first RS resource set includes at least one SSB in an SSB burst set.

[0223] As an example, each RS resource in the first RS resource set is identified by an SSB-Index.

[0224] As an example, each RS resource in the first RS resource set is an SSB resource.

[0225] As an example, SSB in this application refers to Synchronization Signal Block.

[0226] As an example, the SSB mentioned in this application refers to the SS / PBCH block.

[0227] As one embodiment, the first information block indicates the first RS resource set.

[0228] As an example, the first information block configures the first RS resource set.

[0229] As one embodiment, the first information block indicates the identifier of the first RS resource set.

[0230] As an example, the identifier of the first RS resource set is NZP-CSI-RS-ResourceSetId.

[0231] As an example, the identifier of the first RS resource set is CSI-SSB-ResourceSetId.

[0232] As an example, the identifier of the first RS resource set is CSI-ResourceConfigId.

[0233] As an example, the identifier of the first RS resource set is SSB-Index.

[0234] As an example, the identifier of the first RS resource set is NZP-CSI-RS-ResourceSetId or CSI-SSB-ResourceSetId.

[0235] As one embodiment, the first information block indicates the identifier of each RS resource in the first RS resource set.

[0236] As an example, the identifier of each RS resource in the first RS resource set is NZP-CSI-RS-ResourceId.

[0237] As an example, the identifier of each RS resource in the first RS resource set is SSB-Index.

[0238] As an example, the identifier of any RS resource in the first RS resource set is NZP-CSI-RS-ResourceId or SSB-Index.

[0239] As an example, the first RS resource set is used for the data collection.

[0240] As an example, the meaning of "the first RS resource set for data collection" includes: the RS resources in the first RS resource set are used for data collection.

[0241] As an example, the first RS resource set for data collection means that: the first node measures and stores the measurement results of the first RS resource set to form a first dataset, and the first dataset is for data collection.

[0242] As an example, the meaning of the first RS resource set for data collection includes: configuring the signaling for data collection while simultaneously instructing the first RS resource set.

[0243] As an example, the meaning of "the first RS resource set for data collection" includes: the RS resources in the first RS resource set are for data collection.

[0244] As an example, the meaning of "the first RS resource set for data collection" includes: at least a portion of the RS resources in the first RS resource set are for data collection.

[0245] As an example, the meaning of "the first RS resource set for data collection" includes: all RS resources in the first RS resource set are for data collection.

[0246] As an example, the meaning of the first RS resource set for data collection includes: the measurement results of the first RS resource set are not used for CSI reporting.

[0247] As an example, the first RS resource set for data collection means that at least a portion of the measurement results of the first RS resource set are used for CSI reporting and data collection.

[0248] As an example, the first information block instructs the first RS resource set for the data collection.

[0249] As an example, the first information block configures the data collection for the first RS resource set.

[0250] As an example, the first information block instructs the first node to perform the data collection, which is associated with the first RS resource set.

[0251] As an example, the first information block configures the first node to perform the data collection, which is associated with the first RS resource set.

[0252] As one embodiment, the first information block indicates that the first RS resource set is used for channel measurement, and the data collection includes the results of the channel measurement.

[0253] As one embodiment, the first information block indicates that the first RS resource set is used for interference measurement, and the data collection includes the results of the interference measurement.

[0254] As an example, the first node executes a process for the data collection.

[0255] As an example, the first node measures the data required for the data collection.

[0256] As an example, the first node stores the data required for the data collection.

[0257] As a sub-example of this embodiment, the buffer for storing the data required for data collection is an AS (Access Stratum) layer buffer.

[0258] As an example, the first node calculates the data required for the data collection.

[0259] As an example, the first node sends the data required for the data collection.

[0260] As a sub-example of this embodiment, the transmission includes transmission to a higher layer of the first node.

[0261] As a sub-implementation of this embodiment, the sending includes sending to the second node in this application.

[0262] As one embodiment, the process of the first node performing the data collection includes the first node's measurement in the first RS resource set.

[0263] As one embodiment, the measurement in the first RS resource set includes: measuring RS transmitted on the RS resources included in the first RS resource set.

[0264] As one embodiment, the measurement in the first RS resource set includes: measuring the RS transmitted on the RS resources included in the first RS resource set.

[0265] As one embodiment, the measurement in the first RS resource set includes: measuring the RS transmitted on a portion of the RS resources included in the first RS resource set.

[0266] As one embodiment, the process performed by the first node for the data collection includes: measurement results obtained from measurements stored in the first RS resource set.

[0267] As one embodiment, the process performed by the first node for the data collection includes: measurement results obtained from measurements partially stored in the first RS resource set.

[0268] As one embodiment, the process performed by the first node for the data collection includes: measurement information obtained from measurements stored in the first RS resource set.

[0269] As one embodiment, the process performed by the first node for the data collection includes: measurement information obtained from measurements partially stored in the first RS resource set.

[0270] As one embodiment, the first node performing the process for the data collection includes: generating a first dataset, which is associated with the first RS resource set.

[0271] As one example, the measurement includes: intra-frequency measurement.

[0272] As one example, the measurement includes: inter-frequency measurement.

[0273] As one example, the measurement includes: inter-RAT (RadioAccess Technology) measurement.

[0274] As an example, the measurement includes: Intra-RAT measurement.

[0275] As one example, the measurement includes: channel measurement.

[0276] As one example, the measurement includes: received power measurement.

[0277] As an example, the measurement includes RSRP (Reference Signal Received Power) measurement.

[0278] As an example, the measurement includes: path loss (PL) measurement.

[0279] As one example, the measurement includes: channel matrix measurement.

[0280] As an example, the measurement includes: raw channel matrix measurement.

[0281] As one example, the measurement includes: eigenvector and eigenvalue measurements.

[0282] As an example, the measurement includes: Block Error Rate (BLER) measurement.

[0283] As an example, the measurement includes: Bit Error Rate (BER) measurement.

[0284] As one example, the measurement includes: delay spread measurement.

[0285] As one example, the measurement includes: Doppler shift measurement.

[0286] As an example, the measurement includes a Doppler spread measurement.

[0287] As one example, the measurement includes: average delay measurement.

[0288] As one example, the measurement includes: average gain measurement.

[0289] As one example, the measurement includes: interference measurement.

[0290] As one example, the measurement includes: interference channel matrix measurement.

[0291] As one example, the measurement includes: a measurement of the interference covariance matrix.

[0292] As one example, the measurement includes: interference feature vector measurement.

[0293] As one example, the measurement includes: interference characteristic value measurement.

[0294] As one example, the measurement includes: interference beam measurement.

[0295] As one example, the measurement includes: interference power measurement.

[0296] As one example, the measurement includes: interference variance measurement.

[0297] As one example, the measurement includes: interference power spectral density measurement.

[0298] As an example, the data collected includes a first dataset, which is used for training.

[0299] As a sub-example of this embodiment, the first dataset includes measurement results obtained from measurements in the first RS resource set.

[0300] As a sub-example of this embodiment, the first dataset includes measurement information obtained from measurements in the first RS resource set.

[0301] As a sub-example of this embodiment, the first dataset includes partial measurement results obtained from measurements in the first RS resource set.

[0302] As a sub-example of this embodiment, the first dataset includes partial measurement information obtained from measurements in the first RS resource set.

[0303] As a sub-example of this embodiment, the first dataset is associated with the inference configuration.

[0304] As a sub-example of this embodiment, the first dataset is associated with an associated ID.

[0305] As a sub-implementation of this embodiment, the first dataset is associated with a functionality.

[0306] As a sub-example of this embodiment, the first dataset is associated with an AI model.

[0307] As a sub-implementation of this embodiment, the first dataset is associated with a reporting configuration.

[0308] As an example, the associated ID in this application is a non-negative integer.

[0309] As an example, the associated ID in this application is a positive integer.

[0310] As an example, the associated ID in this application is a string.

[0311] As an example, the associated ID in this application is an associated ID.

[0312] As an example, the associated ID in this application is an Associated ID.

[0313] As an example, the associated ID in this application is an Associated-Id.

[0314] As an example, the associated ID in this application identifies a functionality.

[0315] As an example, the associated ID described in this application is used to indicate the generalization ability of the model.

[0316] As an example, the associated ID described in this application is used to ensure the consistency of network-side (NW-side) additional conditions during model training and model inference.

[0317] As an example, multiple beams, multiple beam sets, or multiple beam lists that are associated with the same association ID described in this application have similar properties.

[0318] As an example, the data collection is associated with an inference configuration.

[0319] As an example, the dataset collected by the data collection is used to train the AI ​​model associated with the inference configuration.

[0320] As an example, the dataset collected by the data collection is used for model training, the model is configured for inference, and the model is associated with the inference configuration.

[0321] As an example, the dataset collected by the data collection is configured to a function.

[0322] As an example, the dataset collected by the data collection includes the results of inference.

[0323] As an example, the first information block includes an inference configuration that instructs the first node to perform a process for the data collection.

[0324] As an example, the data collected includes data associated with the inference configuration.

[0325] As an example, the data collected includes data generated by performing inference according to the inference configuration.

[0326] As an example, the first information block indicates the inference configuration.

[0327] As an example, the amount of processing resources used to execute the process for the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0328] As an example, the amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection.

[0329] As an example, the data collection includes physical layer data.

[0330] As an example, the data collection includes higher-level data.

[0331] As an example, the data collection includes data types such as AI inference data.

[0332] As an example, the data collection includes data related to channel estimation.

[0333] As an example, the data collection includes data of various types, including power-related data.

[0334] As an example, the amount of processing resources used to execute the process for the data collection depends on the data types included in the data collection and the number of data types included in the data collection.

[0335] As an example, the amount of processing resources used to execute the process for the data collection depends on whether the data collection includes only physical layer data.

[0336] As an example, the amount of processing resources used to execute the process for the data collection depends on whether the data collection includes only higher-level data.

[0337] As an example, the amount of processing resources used to execute the process for the data collection depends on whether the data collection includes AI inference data.

[0338] As an example, the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0339] As an example, the amount of processing resources used to execute the process for the data collection depends on the functionality associated with the inference configuration associated with the data collection.

[0340] As an example, the amount of processing resources used to execute the process for the data collection depends on the architecture of the AI ​​model associated with the inference configuration associated with the data collection.

[0341] As an example, the amount of processing resources used to execute the process for the data collection depends on the type of AI model associated with the inference configuration associated with the data collection.

[0342] As a sub-example of this embodiment, the type of the AI ​​model includes at least one of an untrained AI model, a basic AI model, and a supervised fine-tuning AI model.

[0343] As a sub-example of this embodiment, the type of the AI ​​model includes at least one of CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), and Transformer.

[0344] As an example, the amount of processing resources used to execute the process for the data collection depends on the data types included in the data collection and the inference configuration associated with the data collection.

[0345] As an example, the amount of processing resources used to execute the process for the data collection depends on a first factor and a second factor, which depend on the data types included in the data collection and the inference configuration associated with the data collection, respectively; both the first factor and the second factor are non-negative integers.

[0346] As a sub-example of this embodiment, the amount of processing resources used to execute the process for the data collection is equal to the first factor plus the second factor.

[0347] As a sub-example of this embodiment, the amount of processing resources used to execute the process for the data collection is equal to the larger of the first factor and the second factor.

[0348] As a sub-example of this embodiment, the amount of processing resources used by the process executing the data collection is equal to the weighted sum of the first factor and the second factor.

[0349] As an example, the processing resources used by the process for collecting the data include CPU.

[0350] As an example, the CPU in this application is: CSI Processing Unit.

[0351] As an example, the CPU mentioned in this application is: Central Processing Unit.

[0352] As an example, the processing resources occupied by the process for collecting the data include a DPU (Data Processing Unit).

[0353] As an example, the processing resources used by the process for collecting the data include an NPU (Neural Network Processing Unit).

[0354] Typically, the processing resources used by the process for collecting the data include only computing resources.

[0355] As an example, the amount of processing resources used by the process performing the data collection includes only the amount of computing resources.

[0356] As one example, the processing resources include the resources required for computation.

[0357] As one example, the processing resources include computing resources.

[0358] As one embodiment, the processing resources are used for at least one of processing, computing, or measuring.

[0359] As one example, the processing resources are used for at least addition and multiplication operations.

[0360] As an example, one of the processing resources is a computing unit.

[0361] As one embodiment, one of the processing resources is a channel information generation unit.

[0362] As one embodiment, one of the processing resources is a beam information generation unit.

[0363] As one embodiment, one of the processing resources is an Arithmetic and Logic Unit (ALU).

[0364] As an example, one of the processing resources is a Special Function Unit (SFU).

[0365] Typically, the processing resources used by the process for collecting the data include only computing and storage resources.

[0366] As an example, the amount of processing resources used to perform the process for the data collection includes the amount of computing resources and the amount of storage resources.

[0367] As an example, the amount of processing resources used by the process performing the data collection includes the number of computing resources and storage resource groups.

[0368] As an example, the amount of processing resources used by the process performing the data collection includes the amount of computing resources and the amount of storage resources.

[0369] Example 2

[0370] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in the attached diagram. Figure 2 As shown.

[0371] Appendix Figure 2 Network architecture 200 is described. Network architecture 200 refers to the network architectures of LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architectures of LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems are referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture may be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture may be referred to as 6GS (6G System) / EPS or some other suitable terminology.

[0372] The network architecture 200 may include one or more UEs 201, a RAN (Radio Access Network) 202, a core network 210, an HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and an Internet service 230. The network architecture 200 may interconnect with other access networks, but these entities / interfaces are not shown for simplicity.

[0373] As attached Figure 2As shown, the network architecture 200 provides packet switching services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. The RAN 202 includes Node B 203 and other nodes 204. Node B 203 provides user and control plane protocol termination toward the UE 201. Node B 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul). Node B 203 may also be referred to as eNB (evolved Node B), gNB, base station, base transceiver station, wireless base station, wireless transceiver, transceiver function, Basic Service Set (BSS), Extended Service Set (ESS), TRP (Transmitter Receiver Point), or some other suitable term. Node B 203 provides UE 201 with an access point to the core network 210; the core network 210 is a 5GC (5G Core network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC (6G Core network). Examples of the UE 201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, GPS devices, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network devices, machine-type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the UE 201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, radio terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. The Node B 203 is connected to the core network 210 via an S1 / NG interface.The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes carrier-compliant Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched streaming services.

[0374] As an example, the first node in this application includes the UE 201.

[0375] As an example, the second node in this application includes node B 203.

[0376] As an example, node B 203 is a macrocell base station.

[0377] As an example, node B 203 is a microcell base station.

[0378] As an example, node B 203 is a pico cell base station.

[0379] As an example, node B 203 is a femtocell.

[0380] As an example, node B 203 is a base station device that supports large latency differences.

[0381] As an example, node B 203 is a flight platform device.

[0382] As an example, node B 203 is a satellite device.

[0383] As an example, node B 203 is a test device (e.g., a transceiver device simulating part of the functions of a base station, a signaling tester).

[0384] As an example, the UE 201 includes a mobile phone.

[0385] As an example, the UE 201 is a vehicle including a car.

[0386] As an example, the wireless link from the UE 201 to the node B 203 is an uplink, which is used to perform uplink transmissions.

[0387] As an example, the radio link from the node B 203 to the UE 201 is a downlink, which is used to perform downlink transmissions.

[0388] As an example, the wireless link between the node B 203 and the UE 201 includes a cellular link.

[0389] As an example, the node B 203 and the UE 201 are connected via the Uu air interface.

[0390] As an example, the sender of the first information block in this application includes the node B 203.

[0391] As an example, the recipient of the first information block in this application includes the UE 201.

[0392] As an example, the sender of the second information block in this application includes the UE 201.

[0393] As an example, the recipient of the second information block in this application includes the node B 203.

[0394] As an example, the node B 203 supports the deployment of network-side (NW-side) AI / ML models.

[0395] As an example, the UE 201 supports the deployment of UE-side AI / ML models.

[0396] As an example, the UE 201 supports AI / ML-based operations.

[0397] As an example, node B 203 supports AI / ML-based operations.

[0398] As an example, the UE 201 supports UE-side data collection.

[0399] As an example, the UE 201 supports a 5G system.

[0400] As an example, the node B 203 supports a 5G system.

[0401] As an example, the UE 201 supports at least a 6G system.

[0402] As an example, the node B 203 supports at least a 6G system.

[0403] Example 3

[0404] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in the attached diagram. Figure 3 As shown.

[0405] Figure 3 This is a schematic diagram illustrating an embodiment of a wireless protocol architecture for the user plane 350 and the control plane 300. Figure 3The wireless protocol architecture for the control plane 300 between the first communication node device (UE or RSU in V2X, onboard equipment or onboard communication module) and the second node device (gNB, UE or RSU in V2X, onboard equipment or onboard communication module), or between two UEs, is illustrated using three layers: Layer 1 (L1), Layer 2 (L2), and Layer 3 (L3). L1 is the lowest layer and implements various PHY (Physical layer) signal processing functions. L1 will be referred to as PHY 301 in this document. L2305 sits above PHY 301 and is responsible for the link between the first and second node devices, or between two UEs, via PHY 301. L2305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. It also provides security through encrypted data packets and supports cross-cell mobility between the second communication node devices and the first communication node device. The RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ (Hybrid Automatic Repeat Quest). The MAC sublayer 302 provides multiplexing between logical and transport channels. It is also responsible for allocating various radio resources (e.g., resource blocks) within a cell among the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in L3 of the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and using RRC signaling between the second communication node device and the first communication node device to configure the lower layer.The wireless protocol architecture of user plane 350 includes Layer 1 (L1) and Layer 2 (L2). The wireless protocol architecture for the first and second communication node devices in user plane 350 is largely the same as the corresponding layers and sublayers in control plane 300 for Physical Layer 351, PDCP sublayer 354 in L2355, RLC sublayer 353 in L2355, and MAC sublayer 352 in L2355. However, PDCP sublayer 354 also provides header compression for upper-layer packets to reduce wireless transmission overhead. L2355 in user plane 350 also includes SDAP (Service Data Adaptation Protocol) sublayer 356. SDAP sublayer 356 is responsible for mapping between QoS (Quality of Service) streams and Data Radio Bearer (DRB) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above L2355, including a network layer (e.g., IP (Internet Protocol) layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., remote UE, server, etc.).

[0406] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the first node in this application.

[0407] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the second node in this application.

[0408] As an example, in this application, the first information block is generated in the RRC 306.

[0409] As an example, in this application, the first information block is generated in MAC 302 or MAC 352.

[0410] As an example, the second information block in this application is generated in the RRC 306.

[0411] As an example, the second information block in this application is generated in MAC 302 or MAC 352.

[0412] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0413] As an example, the higher layer described in this application includes the RRC layer.

[0414] As an example, the higher-layer signaling described in this application includes RRC IE.

[0415] As an example, the higher-level signaling described in this application includes RRC messages.

[0416] As an example, the higher layer described in this application includes the MAC layer.

[0417] As an example, the higher-layer signaling described in this application includes MAC CE.

[0418] Example 4

[0419] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in the attached diagram. Figure 4 As shown. (Attached) Figure 4 This is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0420] The first communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0421] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0422] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper-layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements L2 functionality. In the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operation, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for L1 (i.e., the physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and mapping of signal clusters based on various modulation schemes (e.g., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), M-PSK, and M-Quadrature Amplitude Modulation (M-QAM)). Multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, generating one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time and / or frequency domains, and then uses an inverse fast fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmit processor 471 into an RF stream, which is then provided to a different antenna 420.

[0423] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various L1 signal processing functions. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any parallel stream destined for the second communication device 450. Symbols on each parallel stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted by the first communication device 410 over the physical channel. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements L2 functionality. The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the DL, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer packets from the core network. The upper-layer packets are then provided to all protocol layers above L2. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0424] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper-layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmission functions at the first communication device 410 described in the DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, implementing L2 functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0425] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the receiving function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 function. The controller / processor 475 implements the L2 function. The controller / processor 475 may be associated with a memory 476 storing program code and data. The memory 476 may be referred to as computer-readable media. The controller / processor 475 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer data packets from the second communication device 450. The upper-layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0426] As one embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 450 apparatus at least receives the first information block in this application, the first information block indicating the first RS resource set in this application, the first RS resource set being for data collection; executes a process for the data collection; executes the process for the data collection including measurements in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for the data collection depends on at least one of the data type included in the data collection and the inference configuration associated with the data collection.

[0427] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that, when executed by at least one processor, produces actions including: receiving the first information block in this application; and executing a process for data collection as described in this application.

[0428] As one embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 means to transmit at least the first information block in this application, the first information block indicating the first RS resource set in this application, the first RS resource set for data collection; the recipient of the first information block is the second communication device 450, the second communication device 450 executing a process for the data collection; executing the process for the data collection includes measurements by the second communication device 450 in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources occupied by the process for the data collection depends on at least one of the data type included in the data collection and the inference configuration associated with the data collection.

[0429] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that produces an action when executed by at least one processor, the action including: sending the first information block in this application.

[0430] As an example, the first node in this application includes the second communication device 450.

[0431] As an example, the second node in this application includes the first communication device 410.

[0432] As an example, at least one of {the antenna 420, the transmitter 418, the transmitter processor 416, the multi-antenna transmitter processor 471, the controller / processor 475, and the memory 476} is used to transmit the first information block in this application; at least one of {the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the first information block in this application.

[0433] As an example, at least one of the following is used to perform the process for data collection as described in this application: {antenna 452, transmitter / receiver 454, transmitter processor 468, receiver processor 456, multi-antenna transmitter processor 457, multi-antenna receiver processor 458, controller / processor 459, memory 460, data source 467}.

[0434] As an example, at least one of {the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to perform the process for data collection as described in this application.

[0435] As an example, at least one of {the antenna 452, the transmitter / receiver 454, the transmission processor 468, the multi-antenna transmission processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the second information block in this application; at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the second information block in this application.

[0436] Example 5

[0437] Example 5 illustrates a flowchart of the transmission between a first node and a second node according to an embodiment of this application, as shown in the attached diagram. Figure 5 As shown. In the appendix Figure 5 In this embodiment, the first node U1 and the second node N2 communicate via a wireless link; the steps in block 51 are optional. It should be noted that the order in this embodiment does not limit the signal transmission order or the order of implementation in this application.

[0438] For the first node U1, the first information block is received in step S510; the data collection process is executed in step S511; and the second information block is sent in step S5110.

[0439] For the second node N2, the first information block is sent in step S520; the second information block is received in step S5210.

[0440] In Embodiment 5, the first information block indicates a first RS resource set for data collection; the process for performing the data collection includes measurements in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0441] As an example, the first node U1 is the first node in this application.

[0442] As an example, the second node N2 is the second node in this application.

[0443] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the base station equipment and the user equipment.

[0444] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the relay node device and the user equipment.

[0445] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between user equipment and user equipment.

[0446] As one example, the second node N2 and the first node U1 communicate via the Uu interface.

[0447] As one example, the second node N2 is the maintenance base station of the serving cell of the first node U1.

[0448] As an example, the transmission channel occupied by the first information block includes DL-SCH (DownLink-Shared Channel).

[0449] As an example, the physical layer channel occupied by the first information block includes PDSCH (Physical Downlink Shared Channel).

[0450] As an example, step S510 occurs before step S511.

[0451] As an example, Appendix Figure 5 The steps in box 51 exist, and the method applied to the first node in this application includes: sending a second information block, the second information block including the result of the data collection.

[0452] As one embodiment, the second information block includes first channel information, which depends on measurements on the first RS resource set.

[0453] As an example, the first channel information is generated by measurements on the first RS resource set.

[0454] As an example, measurements on the first RS resource set generate at least the first channel information.

[0455] As one example, the first channel information includes CSI.

[0456] As an example, the first channel information includes candidates including RSRP, RSRQ (Reference Signal Receiving Quality), SINR (Signal-to-Interference and Noise Ratio), RSSI (Received Signal Strength Indication), CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), and RI (Rank Indicator).

[0457] As an example, the second information block is carried by a UEInformationResponse message.

[0458] As an example, the second information block is carried by a UEAssistanceInformation message.

[0459] As an example, the second information block is carried by a MeasurementReport message.

[0460] As an example, the second information block is carried by a MeasurementReportAppLayer message.

[0461] As an example, the second information block is carried by SRBn (Signalling Radio Bearer n), where n is a positive integer not less than 4.

[0462] As one example, the second information block passes through NAS (Non-Access Stratum) signaling transmission.

[0463] As one example, the transmission channel occupied by the second information block includes UL-SCH (UpLink-SharedCHannel).

[0464] As one embodiment, the physical layer channel occupied by the second information block includes PUSCH (Physical Uplink Shared Channel).

[0465] As an example, step S5110 is after step S511; step S5210 is after step S520.

[0466] As an example, Appendix Figure 5 The step in box 51 does not exist.

[0467] Example 6

[0468] Example 6 illustrates a schematic diagram of a data type according to an embodiment of this application, as shown in the attached diagram. Figure 6 As shown. In the appendix Figure 6 In this context, the amount of inference resources used to execute the process for the data collection depends on the data type included in the data collection; candidates for the included data type include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0469] As an example, the candidates for the included data types include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0470] As an example, the data type included is CSI.

[0471] As an example, the CSI includes compressed CSI.

[0472] As an example, the CSI includes uncompressed CSI.

[0473] As one example, the CSI includes a pair of {compressed CSI, uncompressed CSI}.

[0474] As an example, the CSI includes a pair of {CSI, predicted CSI}.

[0475] As an example, the CSI includes power information.

[0476] As an example, the CSI includes RSRP.

[0477] As an example, the CSI includes L1 (Layer 1)-RSRP.

[0478] As one example, the CSI includes a higher-level RSRP.

[0479] As an example, the CSI includes SINR.

[0480] As an example, the CSI includes L1-SINR.

[0481] As an example, the CSI includes SNR (Signal to Noise Ratio).

[0482] As an example, the CSI includes RSSI.

[0483] As an example, the CSI includes CLI (Cross Link Interference)-RSSI.

[0484] As an example, the CSI includes CQI.

[0485] As an example, the CSI includes a wideband CQI.

[0486] As an example, the CSI includes at least one subband CQI.

[0487] As an example, the CSI includes PMI.

[0488] As an example, the CSI includes a broadband PMI.

[0489] As an example, the CSI includes at least one sub-band PMI.

[0490] As an example, the CSI includes RI.

[0491] As an example, the CSI includes LI (Layer Indicator).

[0492] As an example, the CSI includes CRI (CSI-RS Resource Indicator).

[0493] As an example, the CSI includes SSBRI (SSB Resource Indicator).

[0494] As an example, the CSI includes at least one of CQI, PMI, CRI, RI, LI, SSBRI, L1-RSRP, L1-SINR, capability index, or capability set index.

[0495] As one embodiment, the included data type is location-related information of the first node.

[0496] As an example, the location-related information of the first node includes at least one of the longitude and latitude of the first node.

[0497] As an example, the location-related information of the first node includes at least one of the longitude, latitude, and altitude of the first node.

[0498] As an example, the location-related information of the first node includes the longitude and latitude region corresponding to the longitude and latitude of the first node.

[0499] As an example, the location-related information of the first node includes the spatial region corresponding to the longitude, latitude, and altitude of the first node.

[0500] As one example, the location-related information of the first node is related to the region identifier corresponding to the first node.

[0501] As a sub-implementation of this embodiment, the area identifier is a cell ID.

[0502] As a sub-implementation of this embodiment, the region identifier is a non-negative integer.

[0503] As a sub-implementation of this embodiment, the region identifier corresponds to a pair of integers, which are used to represent the horizontal and vertical positions of the first node relative to a reference point.

[0504] As a sub-implementation of this embodiment, the region identifier corresponds to a pair of integers, which are used to represent the longitude and latitude positions of the first node relative to a reference point.

[0505] As a sub-implementation of this embodiment, the region identifier corresponds to a pair of integers, which are used to represent the lateral and longitudinal positions of the first node relative to a reference point, respectively.

[0506] As a sub-implementation of this embodiment, the region identifier corresponds to three integers, which are used to represent the horizontal position, vertical position and height of the first node relative to a reference point, respectively.

[0507] As a sub-implementation of this embodiment, the region identifier corresponds to three integers, which are used to represent the horizontal position, vertical position and height of the first node relative to a reference point, respectively.

[0508] As one example, the location-related information of the first node includes the distance of the first node relative to a reference point.

[0509] As an example, the location-related information of the first node includes the position of the first node relative to a reference point.

[0510] As an example, the location-related information of the first node includes the AoD (Angle of Departure) corresponding to the first node when receiving the RS in the first RS resource set.

[0511] As an example, the reference point described in Example 6 is fixed.

[0512] As an example, in Example 6, a reference point is the serving base station of the first node.

[0513] As an example, the reference point described in Example 6 is the second node in this application.

[0514] As an example, one of the reference points described in Example 6 is a relay node.

[0515] As an example, one reference point described in Example 6 is RIS (Reconfigurable Intelligent Surface).

[0516] As one example, the data type included is decoded information.

[0517] As one example, the decoding information includes the decoder of the first node.

[0518] As one example, the decoding information includes whether the first node uses AI decoding.

[0519] As one example, the decoding information includes whether the first node successfully decoded.

[0520] As one example, the decoding information includes the type of decoder used by the first node.

[0521] As one embodiment, the decoding information includes the identifier of the decoder used by the first node.

[0522] As one example, the decoding information includes whether the first node is based on AI decoding.

[0523] As one example, the decoding information includes the bit error rate (BER).

[0524] As an example, the decoding information includes BLER (BLockErrorRate, BLER).

[0525] As one example, the decoding information includes HARQ.

[0526] As one example, the decoded information includes HARQ-ACK.

[0527] As one example, the decoding information includes HARQ-NACK.

[0528] As one example, the decoding information includes a channel matrix.

[0529] As an example, the data type included is mobility management-related information.

[0530] As one example, the mobility management-related information includes inter-system mobility.

[0531] As one example, the mobility management-related information includes inter-RAT mobility management.

[0532] As one example, the mobility management-related information includes inter-cell mobility management.

[0533] As one example, the mobility management-related information includes the results of cell reselection.

[0534] As one example, the mobility management-related information includes the target cell identifier.

[0535] As one example, the mobility management-related information includes candidate cell identifiers.

[0536] As one example, the mobility management-related information includes LTM (L1 / L2 Triggered Mobility) candidate cell identifiers.

[0537] As one example, the mobility management-related information includes beam-level mobility management.

[0538] As one example, the mobility management-related information includes beam indication.

[0539] As one example, the mobility management-related information includes a measurement report.

[0540] As one example, the mobility management-related information includes neighboring cell power information.

[0541] As one example, the mobility management related information includes neighboring cell RSRP.

[0542] As one example, the mobility management-related information includes neighboring cell SINR.

[0543] As one example, the mobility management related information includes neighboring cell RSRQ.

[0544] As one example, the mobility management-related information includes HOF (Handover Failure).

[0545] As one example, the mobility management-related information includes RLF (Radio Link Failure).

[0546] As an example, the amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection.

[0547] As an example, the candidate data type includes K1 candidate data types, each of which corresponds to K1 candidate integers. Each of the K1 candidate integers is a non-negative integer. The data type included in the data collection is the first candidate data type among the K1 candidate data types. The first candidate data type corresponds to the first candidate integer among the K1 candidate integers. The amount of processing resources occupied by the process executing the data collection depends on the first candidate integer.

[0548] As a sub-example of this embodiment, the amount of processing resources occupied by the process executing the data collection is linearly related to the first candidate integer.

[0549] As a sub-example of this embodiment, the K1 candidate data types include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0550] As an example, the candidate data type includes a set of K2 candidate data types, the set of K1 candidate data types corresponds to K2 candidate values, the K2 candidate values ​​are K2 non-negative integers, the data type included in the data collection is the first candidate data type set among the set of K2 candidate data types, the first candidate data type set corresponds to the first candidate value among the K2 candidate values, and the amount of processing resources occupied by the process executing the data collection depends on the first candidate value.

[0551] As a sub-example of this embodiment, the amount of processing resources occupied by the process executing the data collection is linearly related to the first candidate value.

[0552] As a sub-example of this embodiment, the K2 candidate data type set includes at least two of CSI, location-related information, decoding information, and mobility management-related information.

[0553] As a sub-example of this embodiment, the K2 candidate data type set includes at least one of CSI, location-related information, decoding information, and mobility management-related information, as well as at least one of data types other than CSI, location-related information, decoding information, and mobility management-related information.

[0554] As one embodiment, the candidates for the data type include a first type and types other than the first type; the amount of processing resources used by the process executing the data collection depends on whether the data type included in the data collection belongs to the first type; or, the amount of processing resources used by the process executing the data collection depends on whether the data type included in the data collection includes the first type.

[0555] As a sub-implementation of this embodiment, the first type is CSI.

[0556] As a sub-implementation of this embodiment, the first type is location-related information.

[0557] As a sub-example of this embodiment, the first type is mobility management related information.

[0558] As a sub-implementation of this embodiment, the first type is decoded information.

[0559] As a sub-example of this embodiment, the first type is a data type used for L1-RSRP calculation.

[0560] As a sub-example of this embodiment, the first type is a data type used for CSI calculation.

[0561] As a sub-example of this embodiment, the first type is a data type used for channel measurement.

[0562] As a sub-implementation of this embodiment, the first type is a reasoning-based data type.

[0563] As a sub-implementation of this embodiment, the first type is a data type generated at the physical layer.

[0564] As a sub-implementation of this embodiment, the first type is a data type generated at the physical layer.

[0565] As a sub-example of this embodiment, the first type includes data types generated based on inference output.

[0566] As a sub-implementation of this embodiment, the data types included in the data collection all belong to the first type, and the amount of processing resources occupied by the process executing the data collection depends on a first integer; the data types included in the data collection all belong to types other than the first type, and the amount of processing resources occupied by the process executing the data collection depends on a second integer; the first integer and the second integer are both non-negative integers.

[0567] As a supplementary embodiment of this sub-example, the data types included in the data collection include the first type and data types other than the first type, and the amount of processing resources occupied by the process executing the data collection depends on the first integer.

[0568] As a supplementary embodiment of this sub-example, the data types included in the data collection include the first type and data types other than the first type, and the amount of processing resources occupied by the process executing the data collection depends on the second integer.

[0569] As a supplementary embodiment of this sub-example, the data types included in the data collection include the first type and data types other than the first type, and the amount of processing resources occupied by the process executing the data collection depends on the first integer and the second integer.

[0570] As a supplementary embodiment of this sub-example, the data types included in the data collection include the first type and data types other than the first type, and the amount of processing resources occupied by the process executing the data collection depends on the larger of the first integer and the second integer, or the smaller value, or the weighted value.

[0571] Example 7

[0572] Example 7 illustrates a schematic diagram of the number of data types according to an embodiment of this application, as shown in the attached diagram. Figure 7 As shown. In the appendix Figure 7 In this context, the amount of processing resources used by the process executing the data collection depends on the number of data types included in the data collection.

[0573] As an example, the quantity of the data types included in the data collection is indicated by datacollectionQuantity.

[0574] As an example, the quantity of the data types included in the data collection is indicated by trainingdatacollectionQuantity.

[0575] As an example, the quantity of the data types included in the data collection is indicated by trainingdataQuantity.

[0576] As an example, the quantity of the data types included in the data collection is indicated by dataQuantity.

[0577] As an example, the quantity of the data types included in the data collection is indicated by collectionQuantity.

[0578] As an example, the quantity of the data types included in the data collection is indicated by collectedQuantity.

[0579] As an example, the first information block explicitly indicates the quantity of the data types included in the data collection.

[0580] As an example, the first information block directly configures the quantity of the data types included in the data collection.

[0581] As an example, the first information block indicates the quantity of the data types included in the data collection by directly configuring the data types included in the data collection.

[0582] As an example, the amount of processing resources used to execute the process for the data collection depends on the number of data types included in the data collection.

[0583] As a sub-example of this embodiment, the amount of processing resources used by the process executing the data collection is linearly related to the number of data types included in the data collection.

[0584] As a sub-example of this embodiment, the amount of processing resources used by the process executing the data collection increases as the number of data types included in the data collection increases.

[0585] As an example, the amount of processing resources used to execute the process for the data collection depends on the relationship between the number of data types included in the data collection and a first threshold; the first threshold is a positive integer; the first threshold is predefined, or the first threshold is configurable, or the first threshold depends on the capability of the first node.

[0586] As a sub-implementation of this embodiment, the number of data types included in the data collection is not less than or greater than the first threshold, and the amount of processing resources occupied by the process executing the data collection depends on a third integer; the number of data types included in the data collection is less than or greater than the first threshold, and the amount of processing resources occupied by the process executing the data collection depends on a fourth integer; the third integer and the fourth integer are both non-negative integers.

[0587] As a supplementary embodiment of this sub-example, the third integer depends on the first threshold.

[0588] As a supplementary embodiment of this sub-example, the third integer is equal to the first threshold.

[0589] As a supplementary embodiment of this sub-example, the fourth integer depends on the first threshold.

[0590] As a supplementary embodiment of this sub-example, the fourth integer is equal to the first threshold.

[0591] Example 8

[0592] Example 8 illustrates a schematic diagram of data collection associated with inference configuration according to one embodiment of this application, as shown in the attached diagram. Figure 8 As shown. In the appendix Figure 8 In this context, the data collection is associated with an inference configuration.

[0593] In Example 8, the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0594] As an example, the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0595] As an example, the inference configuration associated with the data collection is a first candidate inference configuration of M1 candidate inference configurations, each of the M1 candidate inference configurations corresponding to M1 candidate parameters, each of the M1 candidate parameters being a non-negative integer. The first candidate inference configuration is used to determine from the M1 candidate parameters a candidate parameter equal to the amount of processing resources occupied by the process executing the data collection.

[0596] As an example, the processing resources used by the process for the data collection include inference resources, and the amount of inference resources used by the process for the data collection depends on the inference configuration associated with the data collection.

[0597] As a sub-implementation of this embodiment, the inference configuration associated with the data collection is a first candidate inference configuration of M2 candidate inference configurations, each of the M2 candidate inference configurations corresponding to M2 candidate parameters, each of the M2 candidate parameters being M2 non-negative integers. The first candidate inference configuration is used to determine from the M2 candidate parameters the candidate parameter equal to the amount of inference resources occupied by the process for executing the data collection.

[0598] As a sub-implementation of this embodiment, the inference configuration associated with the data collection belongs to the first candidate function among M3 candidate functions. The M3 candidate inference configurations correspond to M3 candidate parameters, which are M3 non-negative integers. The first candidate function is used to determine from the M3 candidate parameters the candidate parameter equal to the amount of inference resources occupied by the process for executing the data collection.

[0599] As a sub-implementation of this embodiment, the inference configuration associated with the data collection belongs to the first candidate AI model among M4 candidate AI models. The M4 candidate inference configurations correspond to M4 candidate parameters, which are M4 non-negative integers. The first candidate AI model is used to determine from the M4 candidate parameters the candidate parameter equal to the amount of inference resources occupied by the process for executing the data collection.

[0600] As one example, the inference resource includes an AI model.

[0601] As one embodiment, the inference resource includes a vector processing unit.

[0602] As an example, the inference resource corresponds to an NPU (Neural network Processing Unit).

[0603] As an example, the inference resource corresponds to an IPU (Inference Processing Unit).

[0604] As one example, the inference resource includes an APU.

[0605] As an example, the APU mentioned in this application refers to: Accelerated Processing Unit.

[0606] As an example, the APU mentioned in this application refers to: AI / ML Processing Unit, AI / ML processor.

[0607] As an example, the processing resources used by the process for the data collection include inference resources, and the amount of inference resources used by the process for the data collection depends on the number of inference configurations associated with the data collection.

[0608] As a sub-example of this embodiment, the number of inference configurations associated with the data collection is Y, and the amount of inference resources used to execute the process for the data collection depends on the value of Y.

[0609] As a sub-implementation of this embodiment, the number of associated inference configurations includes the number of associated AI models.

[0610] As a sub-implementation of this embodiment, the number of associated inference configurations includes the number of AI models included in the associated functionality.

[0611] Example 9

[0612] Example 9 illustrates a schematic diagram of the RS resources occupied by a first RS resource set according to an embodiment of this application, as shown in the attached diagram. Figure 9 As shown. In the appendix Figure 9 In the diagram, the horizontal axis represents time, and the rectangles with different fills represent a transmission of a different RS resource in the first RS resource set.

[0613] In Example 9, the amount of processing resources used to execute the process for the data collection depends on the number of RS resources used by the first RS resource set.

[0614] As an example, the amount of processing resources used by the process executing the data collection depends on the number of RS resources used by the first RS resource set.

[0615] As an example, the amount of processing resources used by the process executing the data collection is linearly related to the number of RS resources included in the first RS resource set.

[0616] As an example, the amount of processing resources used by the process executing the data collection is equal to the number of RS resources included in the first RS resource set.

[0617] As an example, the amount of processing resources used to execute the process for the data collection depends on the relationship between the number of RS resources included in the first RS resource set and a second threshold; the second threshold is a positive integer; the second threshold is predefined, or the second threshold is configurable, or the second threshold depends on the capabilities of the first node.

[0618] As a sub-implementation of this embodiment, the number of RS resources included in the first RS resource set is not less than or greater than the second threshold, and the amount of processing resources occupied by the process executing the data collection depends on a fifth integer; the number of RS resources included in the first RS resource set is less than or greater than the second threshold, and the amount of processing resources occupied by the process executing the data collection depends on a fourth integer; the fifth integer and the sixth integer are both positive integers.

[0619] As a supplementary embodiment of this sub-example, the fifth integer depends on the second threshold.

[0620] As a supplementary embodiment of this sub-example, the fifth integer is equal to the second threshold.

[0621] As a supplementary embodiment of this sub-example, the sixth integer depends on the second threshold.

[0622] As a supplementary embodiment of this sub-example, the sixth integer is equal to the second threshold.

[0623] As an example, the amount of processing resources used to execute the process for the data collection depends on the number of RS resources included in the first RS resource set that are used for the data collection.

[0624] As an example, the amount of processing resources used to execute the process for data collection is linearly related to the number of RS resources in the first RS resource set that are used for data collection.

[0625] As an example, the amount of processing resources used to execute the process for the data collection is equal to the number of RS resources in the first RS resource set that are used for the data collection.

[0626] As an example, the amount of processing resources used to execute the process for data collection depends on the relationship between the number of RS resources used for data collection among the RS resources included in the first RS resource set and a third threshold; the third threshold is a positive integer; the third threshold is predefined, or the third threshold is configurable, or the third threshold depends on the capabilities of the first node.

[0627] As a sub-implementation of this embodiment, the number of RS resources used for data collection in the first RS resource set is not less than or greater than a third threshold, and the amount of processing resources occupied by the process executing the data collection depends on a seventh integer; the number of RS resources used for data collection in the first RS resource set is less than or greater than the third threshold, and the amount of processing resources occupied by the process executing the data collection depends on an eighth integer; the seventh integer and the eighth integer are both positive integers.

[0628] As a supplementary embodiment of this sub-example, the seventh integer depends on the third threshold.

[0629] As a supplementary embodiment of this sub-example, the seventh integer is equal to the third threshold.

[0630] As a supplementary embodiment of this sub-example, the eighth integer depends on the third threshold.

[0631] As a supplementary embodiment of this sub-example, the eighth integer is equal to the third threshold.

[0632] Example 10

[0633] Example 10 illustrates a schematic diagram of the configuration cycle of RS resources in a first RS resource set according to an embodiment of this application, as shown in the attached diagram. Figure 10 As shown. In the appendix Figure 10 In the diagram, the horizontal axis represents time, and the multiple unfilled rectangles represent multiple transmissions of the same RS resource in the first RS resource set.

[0634] In Example 10, the amount of processing resources used to execute the process for the data collection depends on the configuration cycle of the first RS resource set.

[0635] As an example, the first information block indicates the configuration period of the first RS resource set.

[0636] As an example, all RS resource sets in the first RS resource set have the same configuration period.

[0637] As an example, the RS resources in the first RS resource set have different configuration periods, and the configuration period of the first RS resource set is the largest of the different configuration periods.

[0638] As an example, the RS resources in the first RS resource set have different configuration periods, and the configuration period of the first RS resource set is the smallest of the different configuration periods.

[0639] As an example, the RS resources in the first RS resource set have different configuration periods, and the configuration period of the first RS resource set is the greatest common multiple of the different configuration periods.

[0640] As an example, the configuration period is X, and the amount of processing resources used by the process executing the data collection depends on the value of X.

[0641] As a sub-example of this embodiment, the unit of X is slot.

[0642] As a sub-example of this embodiment, the unit of X is ms (milliseconds).

[0643] As a sub-example of this embodiment, the unit of X is symbol.

[0644] As an example, the configuration period includes the density of the first RS resource set in the time domain per unit time.

[0645] As an example, the configuration period includes the time-domain behavior of the first RS resource set.

[0646] As one example, the temporal behavior includes periodic and semi-persistent behavior.

[0647] As one example, the time-domain behavior includes fixed-period and adaptive-period behaviors.

[0648] As an example, the amount of processing resources used by the process performing the data collection depends on the temporal behavior of the first RS resource set.

[0649] Example 11

[0650] Example 11 illustrates a schematic diagram of the relationship between UE capabilities and processing resource usage according to an embodiment of this application, as shown in the attached diagram. Figure 11 As shown. In the appendix Figure 11 In this context, the amount of processing resources used to execute the process for data collection depends on the capabilities of the first node.

[0651] As an example, the first node is the first node in this application.

[0652] As one example, the first node is a user equipment.

[0653] As one example, the first node is a terminal.

[0654] As an example, the amount of processing resources used to execute the process for the data collection depends on the capabilities of the first node.

[0655] As an example, the amount of processing resources used to perform the process for the data collection depends on N, the value of which is a capability indicator reported by the first node.

[0656] As a sub-example of this embodiment, the amount of processing resources used to execute the process for the data collection is linearly related to the value of N.

[0657] As an example, the amount of processing resources used to execute the process for the data collection depends on whether the first node has the first capability.

[0658] As a sub-implementation of this embodiment, the first capability includes whether the first node supports AI-based data collection.

[0659] As a sub-implementation of this embodiment, the first capability includes the number of RS resources supported by the first node.

[0660] As a sub-implementation of this embodiment, the first capability includes whether the first node supports simultaneous CSI-based reporting and data collection.

[0661] Example 12

[0662] Example 12 illustrates a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of this application, as shown in the attached diagram. Figure 12 As shown. In the appendix Figure 12 In this context, gNB can be replaced with network equipment such as eNB or 6G base stations.

[0663] In Example 12, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1202, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1201 (as shown in the attached figure). Figure 12 (As shown by the dashed arrow in the diagram). The RAN domain ML training function 1203 is located in the RAN domain management function 1202; while the ML inference function is located in the base station, that is, the AI / ML inference function 1204 is located in gNB 1205, the AI / ML inference function 1206 is located in gNB 1207, and so on.

[0664] AI / ML related functions include ML training (also known as AI training or AI / ML training), ML testing, and ML inference (also known as AI inference or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[0665] ML training functionality can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, ML training functionality for MDA (Management Data Analytics) can be deployed in MDAF (Management Data Analytics Function); ML training for network data analytics can be deployed in NWDAF (Network Data Analytics Function), meaning the ML training functionality is an MTLF (Model Training Logical Function).

[0666] The ML inference function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics Logical Function) located in NWDAF.

[0667] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0668] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1201.

[0669] It should be noted that Example 12 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed at the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0670] As an example, one of the gNBs (or base stations) in Example 12 is the second node of this application.

[0671] As an example, the dataset carried by the second information block in this application is used for ML training function 1203.

[0672] As an example, the dataset carried by the second information block in this application is available for ML testing.

[0673] Example 13

[0674] Example 13 illustrates a schematic diagram of the deployment of AI / ML functions in a UE according to an embodiment of this application, as shown in the attached diagram. Figure 13 As shown. In the appendix Figure 13 In this context, the RAN domain ML training function 1304 is optional.

[0675] UE function 1303 is deployed in the first node of this application, and the UE function 1303 includes AI / ML inference function 1305; the AI / ML inference function 1305 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0676] As an example, the UE function 1303 includes a RAN domain ML training function 1304, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0677] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0678] Optionally, the UE function 1303 also includes a CN domain ML training function ( Figure 13 (Not included in the text).

[0679] Optionally, the UE function 1303 also includes an AI / ML deployment function. Figure 13 It is not included in the list, which is used to load ML models and data.

[0680] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0681] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0682] Optionally, the UE function 1303 is an MnS producer that provides data to the CN domain MnF (Management Function) and / or the RAN domain MnF and / or the cross-domain management system 1301 for management or analysis (as shown by the double arrow 1302).

[0683] Optionally, the UE function 1303 is an MnS consumer that loads data from the CN domain MnF and / or RAN domain MnF and / or cross-domain management system 1301 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1302).

[0684] As an example, the data collection described in this application is used for RAN domain ML training function 1304.

[0685] As an example, the data collection described in this application generates the relevant metadata.

[0686] As an example, the UE function 1303 is an MnS producer, and the first node performs the data collection and data generation described in this application.

[0687] As an example, the first node is an MnS producer, and the first node provides data to the second node in this application by sending the second information block in this application.

[0688] As an example, the ML model is based on NN (Neural Networks).

[0689] As an example, the ML model is based on ANN (Artificial Neural Networks).

[0690] As an example, the ML model is based on CNN (Convolutional Neural Networks).

[0691] As an example, the ML model is based on the LLM (Large Language Model) architecture.

[0692] As an example, the ML model is based on the Transformer architecture.

[0693] As an example, the ML model is based on LSTM (Long Short-Term Memory network).

[0694] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0695] As an example, the ML model is based on GAN (Generative Adversarial Networks).

[0696] As an example, the ML model is based on a lightweight neural network.

[0697] As a sub-example of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0698] Example 14

[0699] Example 14 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application, as shown in the attached diagram. Figure 14 As shown. In the appendix Figure 14 In this context, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0700] In Example 14, the first processor sends a first dataset to the second processor and a second dataset to the third processor; the second processor generates a target first-class parameter set based on the first dataset, and sends the generated target first-class parameter set to the third processor; the third processor processes the second dataset using the target first-class parameter set to obtain a first-class output, optionally, the third processor sends the first-class output to the fourth processor. (See Appendix...) Figure 14 In this configuration, the first type of feedback and the second type of feedback are optional; the second processor includes ML training functionality; and the third processor includes ML inference functionality.

[0701] As one embodiment, the fourth processor includes ML testing functionality.

[0702] As one embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0703] As an example, the third processor sends a first type of feedback to the second processor; the first type of feedback is used to trigger the recalculation or update of the target first type of parameter set, that is, to trigger ML initial training or ML retraining.

[0704] As one embodiment, the fourth processor sends a second type of feedback to the first processor; the second type of feedback is used to generate the first dataset or the second dataset, or the second type of feedback is used to trigger the sending of the first dataset or the sending of the second dataset.

[0705] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0706] As one embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0707] As an example, the third processor belongs to the first node.

[0708] As an example, the first dataset includes training data.

[0709] As one example, the first dataset includes the dataset generated by the first node performing data collection.

[0710] As an example, the dataset generated by the first node performing data collection includes the first dataset.

[0711] As one embodiment, the second processor is used to train an ML model, and the trained model is described by the target first class of parameter sets.

[0712] As an example, the second processor belongs to the first node; the above method avoids passing the first dataset to the second node.

[0713] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[0714] As an example, the second processor belongs to the core network; the above method supports network-wide joint training, further optimizing system performance.

[0715] As an example, the second dataset includes inference data.

[0716] As an example, the third processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[0717] As an example, the third processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[0718] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the second processing opportunity will recalculate the target first type of parameter set.

[0719] As an example, when the error is too large or the update has not been performed for too long, the performance of the trained model is considered to be unsatisfactory.

[0720] As an example, the target first type of parameter group includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0721] As an example, the target first type of parameter group includes one or more of the following: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0722] Example 15

[0723] Example 15 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application, as shown in the attached diagram. Figure 15 As shown. In the appendix Figure 15 In this process, the first and second operations belong to the first stage, the third operation belongs to the second stage, the fourth operation belongs to the third stage, and the fifth operation belongs to the fourth stage; the arrowed lines indicate the sequence of the process.

[0724] As an example, the first operation includes AI / ML training, the second operation includes AI / ML testing, the third operation includes AI / ML emulation, the fourth operation includes AI / ML entity loading, and the fifth operation includes AI / ML inference.

[0725] As one embodiment, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an inference phase.

[0726] As an example, the first stage includes AI / ML model training.

[0727] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0728] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0729] As an example, the training of the AI / ML model depends on training data.

[0730] As an example, the training of the AI / ML model depends on the first node performing the data collection.

[0731] As an example, the AI / ML model training includes AI / ML entity validation.

[0732] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0733] As an example, the AI / ML entity verification relies on verification data.

[0734] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0735] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0736] As an example, if the AI / ML test results meet expectations, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0737] As an example, the AI / ML test relies on test data.

[0738] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0739] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0740] As one embodiment, the second stage is optional.

[0741] As an example, the third stage includes AI / ML entity loading, which is to obtain trained AI / ML entities to obtain the desired AI / ML inference function.

[0742] As an example, the third stage is optional.

[0743] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0744] As an example, the fourth stage includes AI / ML inference.

[0745] Example 16

[0746] Example 16 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of this application, as shown in the attached diagram. Figure 16 As shown. In the appendix Figure 16 In the first node, the processing device 1600 includes a first receiver 1601 and a first transmitter 1602, wherein the first transmitter 1602 is optional.

[0747] In embodiment 16, the first receiver 1601 receives a first information block indicating a first RS resource set for data collection; the first receiver 1601 executes a process for the data collection.

[0748] In Example 16, the process of performing the data collection includes measurements in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process of performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0749] As an example, the amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection; candidates for the included data type include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0750] As an example, the amount of processing resources used to execute the process for the data collection depends on the number of data types included in the data collection.

[0751] As an example, the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0752] As an example, the amount of processing resources used by the process executing the data collection depends on the number of RS resources used by the first RS resource set.

[0753] As an example, the amount of processing resources used to execute the process for the data collection depends on the configuration cycle of the first RS resource set.

[0754] As an example, the amount of processing resources used to execute the process for the data collection depends on the capabilities of the first node.

[0755] As an example, the first transmitter 1602 transmits a second information block, the second information block including the results of the data collection.

[0756] As an example, the first information block instructs the first RS resource set for the data collection.

[0757] As an example, the first information block configures the data collection for the first RS resource set.

[0758] As an example, the dataset collected by the data collection is used to train the AI ​​model associated with the inference configuration.

[0759] As an example, the dataset collected by the data collection is used for model training, the model is configured for inference, and the model is associated with the inference configuration.

[0760] As an example, the dataset collected by the data collection is configured to a function.

[0761] As an example, the dataset collected by the data collection includes the results of inference.

[0762] As an example, the first information block includes an inference configuration that instructs the first node to perform a process for the data collection.

[0763] As an example, the data collected includes data associated with the inference configuration.

[0764] As an example, the data collected includes data generated by performing inference according to the inference configuration.

[0765] As an example, the first information block indicates the inference configuration.

[0766] As one embodiment, the candidates for the data type include a first type and types other than the first type; the amount of processing resources used by the process executing the data collection depends on whether the data type included in the data collection belongs to the first type; or, the amount of processing resources used by the process executing the data collection depends on whether the data type included in the data collection includes the first type.

[0767] As an example, the amount of processing resources used to execute the process for the data collection depends on the relationship between the number of data types included in the data collection and a first threshold; the first threshold is a positive integer; the first threshold is predefined, or the first threshold is configurable, or the first threshold depends on the capabilities of the first node.

[0768] As an example, the processing resources used by the process for the data collection include inference resources, and the amount of inference resources used by the process for the data collection depends on the inference configuration associated with the data collection.

[0769] As an example, the amount of processing resources used to execute the process for the data collection depends on the number of RS resources included in the first RS resource set that are used for the data collection.

[0770] As an example, the configuration period includes the time-domain behavior of the first RS resource set.

[0771] Typically, the processing resources used by the process for collecting the data include only computing resources.

[0772] Typically, the processing resources used by the process for collecting the data include only computing and storage resources.

[0773] Typically, the amount of inference resources used by the process executing the data collection depends on the inference configuration associated with the data collection, corresponding to a fifth coefficient; the amount of inference resources used by the process executing the data collection depends on the number of RS resources used by the first RS resource set, corresponding to a third coefficient; the capability of the first node corresponds to a fourth coefficient; and the amount of inference resources used by the process executing the data collection is linearly related to the fifth, third, and fourth coefficients.

[0774] Typically, the amount of inference resources used to execute the process for the data collection depends on the data types included in the data collection, including CSI and location-related information, with CSI and location-related information corresponding to a first coefficient and a second coefficient, respectively; the amount of inference resources used to execute the process for the data collection depends on the number of RS resources used by the first RS resource set, corresponding to a third coefficient; the capability of the first node corresponds to a fourth coefficient; and the amount of inference resources used by the process for the data collection is linearly related to the first coefficient, the second coefficient, the third coefficient, and the fourth coefficient.

[0775] As an example, the first node 1600 is a user equipment.

[0776] As an example, the first node 1600 is a terminal.

[0777] As an example, the first node 1600 is a relay node device.

[0778] As an example, the first receiver 1601 includes at least one of the following in embodiment 4: the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467.

[0779] As an example, the first transmitter 1602 includes at least one of the following in embodiment 4: the antenna 452, the transmitter 454, the transmission processor 468, the multi-antenna transmission processor 457, the controller / processor 459, the memory 460, and the data source 467.

[0780] Example 17

[0781] Example 17 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of this application, as shown in the attached diagram. Figure 17 As shown. In the appendix Figure 17 In the second node, the processing device 1700 includes a second transmitter 1701 and a second receiver 1702, wherein the second receiver 1702 is optional.

[0782] In embodiment 17, the second transmitter 1701 transmits a first information block, the first information block indicating a first RS resource set, the first RS resource set being used for data collection.

[0783] In Example 17, the recipient of the first information block executes a process for the data collection; the execution of the process for the data collection includes measurements of the first node in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

[0784] As an example, the amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection; candidates for the included data type include at least one of CSI, location-related information, decoding information, and mobility management-related information.

[0785] As an example, the amount of processing resources used to execute the process for the data collection depends on the number of data types included in the data collection.

[0786] As an example, the amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

[0787] As an example, the amount of processing resources used by the process executing the data collection depends on the number of RS resources used by the first RS resource set.

[0788] As an example, the amount of processing resources used to execute the process for the data collection depends on the configuration cycle of the first RS resource set.

[0789] As an example, the amount of processing resources used to execute the process for the data collection depends on the capabilities of the receiver of the first information block.

[0790] As one embodiment, the second receiver 1702 receives a second information block, the second information block including the results of the data collection.

[0791] As an example, the first information block instructs the first RS resource set for the data collection.

[0792] As an example, the first information block configures the data collection for the first RS resource set.

[0793] As an example, the dataset collected by the data collection is used to train the AI ​​model associated with the inference configuration.

[0794] As an example, the dataset collected by the data collection is used for model training, the model is configured for inference, and the model is associated with the inference configuration.

[0795] As an example, the dataset collected by the data collection is configured to a function.

[0796] As an example, the dataset collected by the data collection includes the results of inference.

[0797] As one embodiment, the first information block includes an inference configuration that instructs the recipient of the first information block to perform a process for the data collection.

[0798] As an example, the data collected includes data associated with the inference configuration.

[0799] As an example, the data collected includes data generated by performing inference according to the inference configuration.

[0800] As an example, the first information block indicates the inference configuration.

[0801] As one embodiment, the candidates for the data type include a first type and types other than the first type; the amount of processing resources used by the process executing the data collection depends on whether the data type included in the data collection belongs to the first type; or, the amount of processing resources used by the process executing the data collection depends on whether the data type included in the data collection includes the first type.

[0802] As an example, the amount of processing resources used to execute the process for the data collection depends on the relationship between the number of data types included in the data collection and a first threshold; the first threshold is a positive integer; the first threshold is predefined, or the first threshold is configurable, or the first threshold depends on the capabilities of the receiver of the first information block.

[0803] As an example, the processing resources used by the process for the data collection include inference resources, and the amount of inference resources used by the process for the data collection depends on the inference configuration associated with the data collection.

[0804] As an example, the amount of processing resources used to execute the process for the data collection depends on the number of RS resources included in the first RS resource set that are used for the data collection.

[0805] As an example, the configuration period includes the time-domain behavior of the first RS resource set.

[0806] Typically, the processing resources used by the process for collecting the data include only computing resources.

[0807] Typically, the processing resources used by the process for collecting the data include only computing and storage resources.

[0808] Typically, the amount of inference resources used by the process executing the data collection depends on the inference configuration associated with the data collection, corresponding to a fifth coefficient; the amount of inference resources used by the process executing the data collection depends on the number of RS resources used by the first RS resource set, corresponding to a third coefficient; the capability of the receiver of the first information block corresponds to a fourth coefficient; and the amount of inference resources used by the process executing the data collection is linearly related to the fifth, third, and fourth coefficients.

[0809] Typically, the amount of inference resources used by the process executing the data collection depends on the data types included in the data collection, including CSI and location-related information, with CSI and location-related information corresponding to a first coefficient and a second coefficient, respectively; the amount of inference resources used by the process executing the data collection depends on the number of RS resources used by the first RS resource set, corresponding to a third coefficient; the capability of the receiver of the first information block corresponds to a fourth coefficient; and the amount of inference resources used by the process executing the data collection is linearly related to the first coefficient, the second coefficient, the third coefficient, and the fourth coefficient.

[0810] As one example, the second node 1700 is a base station device.

[0811] As one embodiment, the second node 1700 is a user equipment.

[0812] As an example, the second node 1700 is a TRP.

[0813] As one embodiment, the second transmitter 1701 includes at least one of the following in embodiment 4: the antenna 420, the transmitter 416, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476.

[0814] As one embodiment, the second receiver 1702 includes at least one of the following in embodiment 4: the antenna 420, the receiver 416, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476.

[0815] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, vehicles, RSUs, wireless sensors, internet cards, IoT terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, and other wireless communication devices. The base stations or system equipment in this application include, but are not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, airborne base stations, RSUs, unmanned aerial vehicles, and test equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[0816] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A first node used for wireless communication data collection, characterized in that, include: A first receiver receives a first information block, the first information block indicating a first RS resource set, the first RS set being used for data collection; Execute the process for the data collection; The process of performing the data collection includes measurements in the first RS resource set, and the data collection is associated with an inference configuration; the amount of processing resources used by the process of performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

2. The first node according to claim 1, characterized in that, The amount of processing resources used to execute the process for the data collection depends on the data type included in the data collection; candidates for the included data type include at least one of CSI, location-related information, decoding information, and mobility management-related information.

3. The first node according to claim 2, characterized in that, The amount of processing resources used to execute the process for the data collection depends on the number of data types included in the data collection.

4. The first node according to any one of claims 1 to 3, characterized in that, The amount of processing resources used to execute the process for the data collection depends on the inference configuration associated with the data collection.

5. The first node according to any one of claims 1 to 4, characterized in that, The amount of processing resources used to execute the process for the data collection depends on the number of RS resources used by the first RS resource set.

6. The first node according to any one of claims 1 to 5, characterized in that, The amount of processing resources used to execute the process for the data collection depends on the configuration cycle of the first RS resource set.

7. The first node according to any one of claims 1 to 6, characterized in that, The amount of processing resources used to execute the process for the data collection depends on the capabilities of the first node.

8. The first node according to any one of claims 1 to 7, characterized in that, include: The first transmitter sends a second information block, which includes the results of the data collection.

9. A second node used for wireless communication data collection, characterized in that, include: The second transmitter sends a first information block, the first information block indicating a first RS resource set, the first RS resource set being used for data collection; Wherein, the recipient of the first information block executes a process for the data collection; the execution of the process for the data collection includes measurements of the first node in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

10. A method for a first node used for wireless communication data collection, characterized in that, include: Receive a first information block, the first information block indicating a first RS resource set, the first RS resource set being used for data collection; Execute the process for the data collection; The process of performing the data collection includes measurements in the first RS resource set, and the data collection is associated with an inference configuration; the amount of processing resources used by the process of performing the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

11. A method for a second node used for wireless communication data collection, characterized in that, include: Send a first information block, the first information block indicating a first RS resource set, the first RS resource set being used for data collection; Wherein, the recipient of the first information block executes a process for the data collection; the execution of the process for the data collection includes the measurement of the first node in the first RS resource set, the data collection being associated with an inference configuration; the amount of processing resources used by the process for the data collection depends on at least one of the data types included in the data collection and the inference configuration associated with the data collection.

Images