[go: up one dir, main page]

WO2025227698A1 - Procédé de communication et appareil associé - Google Patents

Procédé de communication et appareil associé

Info

Publication number
WO2025227698A1
WO2025227698A1 PCT/CN2024/136003 CN2024136003W WO2025227698A1 WO 2025227698 A1 WO2025227698 A1 WO 2025227698A1 CN 2024136003 W CN2024136003 W CN 2024136003W WO 2025227698 A1 WO2025227698 A1 WO 2025227698A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
wireless
information
wireless data
communication device
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/136003
Other languages
English (en)
Chinese (zh)
Inventor
张公正
乔云飞
王坚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huawei Technologies Co Ltd
Original Assignee
Huawei Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huawei Technologies Co Ltd filed Critical Huawei Technologies Co Ltd
Publication of WO2025227698A1 publication Critical patent/WO2025227698A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/12—Wireless traffic scheduling
    • H04W72/1263—Mapping of traffic onto schedule, e.g. scheduled allocation or multiplexing of flows
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality

Definitions

  • This application relates to the field of communications, and more particularly to a communication method and related apparatus.
  • Wireless communication can be a transmission communication between two or more communication devices that does not propagate through conductors or cables. These communication devices generally include network devices and terminal devices.
  • communication equipment in wireless communication systems can also perform other new services, such as artificial intelligence (AI) services and sensing services.
  • AI artificial intelligence
  • sensing services Generally, the implementation of these services requires calling wireless data for related data processing.
  • This application provides a communication method and related apparatus, which enables the communication device to classify wireless data based on its own data characteristics, thereby achieving efficient and sufficient data classification. This facilitates downstream tasks to perform efficient data processing based on the data classification results, thus improving the performance of downstream tasks.
  • the first aspect of this application provides a communication method executed by a first communication device.
  • the first communication device may be a communication equipment (such as a terminal device or network device), or it may be a component of a communication equipment (such as a processor, chip, or chip system), or it may be a logic module or software capable of implementing all or part of the functions of the communication equipment.
  • the first communication device acquires first wireless data; the first communication device processes the data feature information of the first wireless data based on a classification model to obtain the data category of the first wireless data; wherein the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer.
  • the first communication device can process the data feature information of the first wireless data based on a classification model that corresponds to the clustering results of the data feature information associated with N wireless sample data, thereby obtaining the data category of the first wireless data.
  • the data category of the first wireless data obtained by the first communication device is based on the data feature information of the first wireless data. Therefore, compared with the method of classifying data solely based on the acquisition information of wireless data (such as acquisition time, acquisition scenario, etc.), in the above scheme, the communication device can classify based on the data features of the wireless data itself, achieving efficient and comprehensive data classification and improving the effectiveness of data classification.
  • the first communication device classifies the wireless data based on its own data characteristics.
  • the classification result can be used for the data processing of downstream tasks, which is beneficial for downstream tasks to achieve efficient data processing based on the data classification result, thereby improving the performance of downstream tasks.
  • the downstream task can be a task performed by the first communication device or a task performed by other communication devices; no limitation is made here.
  • the first communication device performs efficient and sufficient data classification based on the data characteristics of the wireless data itself to obtain wireless data related to the sensing service (such as one or more of the following: obstacle information, location information, Doppler information, speed information, distance information, and angle information in the environment). Subsequently, the downstream task can realize an efficient data processing process based on the wireless data related to the sensing service to improve the performance of the sensing task.
  • wireless data related to the sensing service such as one or more of the following: obstacle information, location information, Doppler information, speed information, distance information, and angle information in the environment.
  • the first communication device performs efficient and sufficient data classification based on the data characteristics of the wireless data itself to obtain wireless data related to the AI service (such as one or more of the following: time-domain configuration information, frequency-domain configuration information, spatial-domain configuration information, port information, periodic information, codebook configuration information related to the AI service for channel prediction; or, for example, one or more of the following: channel characteristic information of the cell related to the AI service for beam management, and the number of beams associated with the AI model for beam management).
  • the downstream task can realize an efficient data processing process based on the wireless data related to the AI service to improve the performance of the AI task.
  • the classification model can be a model that classifies data based on one or more of machine learning, mathematical algorithms, mathematical methods, AI, and neural networks.
  • classification model can be replaced with other terms, such as wireless data classification model, data classification model, wireless data classifier, data classifier, wireless data classification system, or data classification system.
  • clustering can be an unsupervised learning method that can spontaneously form category divisions by exploring the inherent structure and patterns of the data without prior knowledge of the category labels of the data points.
  • processing data or sample data, such as the N wireless sample data mentioned above
  • clustering a collection containing physical or abstract objects (usually referring to a dataset) can be divided into one or more subsets, each subset being called a cluster.
  • each cluster can be defined as a category, and each cluster can be assigned an index to identify the category corresponding to each cluster.
  • the clustering results may include information about the clusters obtained from the above clustering.
  • the clustering results may include at least one of the following: the number of clusters, the members of the clusters, the boundaries of the clusters, the centers of the clusters (i.e., the cluster centers), and the clustering effectiveness index.
  • clustering can be a process of organizing a dataset into meaningful, internally homogeneous, externally heterogeneous groups (e.g., clusters), while the clustering result is a detailed description of the specific cluster divisions and their related attributes produced by this process.
  • Clustering and clustering results are closely related; the former is the means of grouping data, while the latter is the actual product of applying this means to data. Both serve to explore and understand the inherent structure and patterns of the data.
  • the first communication device can collect data related to wireless communication (denoted as wireless data) through software modules or hardware modules.
  • the first wireless data may include the collected wireless data, and/or, the first wireless data may include wireless data obtained by further processing the collected data (e.g., data filtering, data cleaning, data deduplication, data enhancement, or one or more of these), and/or, the first wireless data may include simulated wireless data.
  • the first communication device can acquire the first wireless data through antennas, cameras, microphones, radar, sensors, etc.
  • acquiring first wireless data can be replaced with other descriptions, such as collecting, gathering, collecting, or capturing first wireless data.
  • the classification model includes the clustering results.
  • the classification model used to classify wireless data can include the clustering results corresponding to the data feature information of N wireless sample data.
  • the clustering results corresponding to N wireless sample data can be directly applied to the classification of the first wireless data to reduce complexity.
  • the clustering result includes one or more cluster centers, and the data feature information of the first wireless data is closest to the first cluster center of the one or more cluster centers; wherein, the data category of the first wireless data is the category corresponding to the first cluster center.
  • the classification model is a neural network model trained using the clustering result as label data and the wireless environment information corresponding to the N wireless sample data as input data.
  • the classification model used to classify wireless data can be a neural network model obtained through a training process.
  • This training process uses the wireless environment information corresponding to N wireless sample data as input data and the clustering results (such as the corresponding categories) of the N wireless sample data as label data. Therefore, this neural network model has the ability to determine the category of wireless data based on the wireless environment information. Since the data size of wireless data is generally larger than the data size of the corresponding wireless environment information, the above scheme can quickly determine the category of wireless data through the neural network model, thus reducing data processing latency.
  • the neural network model can be replaced with other implementations, such as mathematical models, machine learning models, or AI processing models.
  • the wireless environment information includes at least one of the following: configuration information for collecting wireless data, scenario type information for collecting wireless data, environmental map information for collecting wireless data, location information for collecting wireless data, wireless configuration information for collecting wireless data, or device configuration information for collecting wireless data.
  • the wireless environment information used to determine the neural network model can include at least one of the above items. Subsequently, the at least one item can be used as the input of the neural network model to obtain the category of wireless data, which can improve the flexibility of the scheme implementation.
  • the data feature information includes at least one of the following: data distribution information of the first wireless data, features extracted based on the first wireless data, and data distribution information of the features extracted based on the first wireless data.
  • the classification model can determine the data category of the first wireless data based on the data feature information of the first wireless data, wherein the data feature information may include at least one of the above, so as to improve the flexibility of the scheme implementation.
  • the data distribution information may include the mean, variance, empirical probability density function, empirical cumulative distribution function, standard deviation, median, mode, range, or one or more other information used to represent the data distribution.
  • the method further includes: the first communication device receiving first information, the first information being used to indicate the classification model.
  • the first communication device can receive first information for indicating the classification model, so that the first communication device, as a data collection node, can obtain the classification model based on the information sent by the task execution node (e.g., the second communication device), so as to execute the downstream task indicated by the task execution node based on the classification model.
  • the task execution node e.g., the second communication device
  • the first communication device can train/process the classification model locally, which can reduce overhead.
  • the method further includes: the first communication device transmitting second information, the second information being used to indicate the data category of the first wireless data.
  • the first communication device can send second information to indicate the data category of the first wireless data, so that the receiver of the second information (e.g., the second communication device) can know the data category corresponding to the data that the first communication device can provide, which is beneficial for the receiver to perform data processing on the data provided by the first communication device in accordance with the data category.
  • the receiver of the second information e.g., the second communication device
  • the method further includes: the first communication device transmitting the first wireless data, wherein the data category of the first wireless data is used for processing the first wireless data.
  • the first communication device can send first wireless data, enabling the receiver of the first wireless data to perform data processing on the first wireless data based on the data category of the first wireless data.
  • a second aspect of this application provides a communication method executed by a second communication device.
  • the second communication device can be a communication equipment (such as a terminal device or network device), or it can be a component of a communication equipment (such as a processor, chip, or chip system), or it can be a logic module or software capable of implementing all or part of the functions of the communication equipment.
  • the second communication device determines first information, which is used to instruct a classification model; wherein the classification model processes data feature information of first wireless data to obtain the data category of the first wireless data; the classification model is associated with clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer; and the second communication device transmits the first information.
  • the first information sent by the second communication device can be used to instruct the classification model, enabling the first communication device to process the data feature information of the first wireless data acquired by the first communication device based on the classification model corresponding to the clustering results of the data feature information associated with N wireless sample data, and obtain the data category of the first wireless data.
  • the data category of the first wireless data obtained by the first communication device is obtained based on the data feature information of the first wireless data. Therefore, compared with the method of classifying data solely based on the acquisition information of wireless data (such as acquisition time, acquisition scenario, etc.), in the above scheme, the communication device can classify based on the data features of the wireless data itself, achieving efficient and sufficient data classification to improve the data classification effect.
  • the first communication device classifies the wireless data based on its own data characteristics.
  • the classification result can be used for the data processing of downstream tasks, which is beneficial for downstream tasks to achieve efficient data processing based on the data classification result, thereby improving the performance of downstream tasks.
  • the first communication device serves as a data collection node
  • the second communication device can serve as a task execution node, enabling the data collection node to obtain a classification model based on the information sent by the task execution node, so that the data collection node can execute the downstream tasks indicated by the task execution node based on the classification model.
  • the classification model includes the clustering results.
  • the classification model used to classify wireless data can include the clustering results corresponding to the data feature information of N wireless sample data.
  • the clustering results corresponding to N wireless sample data can be directly applied to the classification of the first wireless data to reduce complexity.
  • the clustering result includes one or more cluster centers, and the data feature information of the first wireless data is closest to the first cluster center of the one or more cluster centers; wherein, the data category of the first wireless data is the category corresponding to the first cluster center.
  • the classification model is a neural network model trained using the clustering result as label data and the wireless environment information corresponding to the N wireless sample data as input data.
  • the classification model used to classify wireless data can be a neural network model obtained through a training process.
  • This training process uses the wireless environment information corresponding to N wireless sample data as input data and the clustering results corresponding to the N wireless sample data as label data. Therefore, this neural network model has the ability to determine the category of wireless data based on the wireless environment information. Since the data size of wireless data is generally larger than the data size of the corresponding wireless environment information, the above scheme can quickly determine the category of wireless data through the neural network model, thus reducing data processing latency.
  • the wireless environment information includes at least one of the following: configuration information for collecting wireless data, scenario type information for collecting wireless data, environmental map information for collecting wireless data, location information for collecting wireless data, wireless configuration information for collecting wireless data, and device configuration information for collecting wireless data.
  • the wireless environment information used to determine the neural network model can include at least one of the above items. Subsequently, the at least one item can be used as the input of the neural network model to obtain the category of wireless data, which can improve the flexibility of the scheme implementation.
  • the data feature information includes at least one of the following: data distribution information of the first wireless data, features extracted based on the first wireless data, and data distribution information of the features extracted based on the first wireless data.
  • the classification model can determine the data category of the first wireless data based on the data feature information of the first wireless data, wherein the data feature information may include at least one of the above, so as to improve the flexibility of the scheme implementation.
  • the method further includes: the second communication device receiving second information, the second information being used to indicate the data category of the first wireless data.
  • the second communication device can receive second information indicating the data category of the first wireless data, so that the second communication device can know the data category corresponding to the data that the first communication device can provide, which is beneficial for the second communication device to perform data processing on the data provided by the first communication device in accordance with the data category.
  • the method further includes: the second communication device receiving the first wireless data, wherein the data category of the first wireless data is used for processing the first wireless data.
  • the second communication device can receive the first wireless data, enabling the second communication device to perform data processing on the first wireless data based on the data category of the first wireless data.
  • a third aspect of this application provides a communication device, which is a first communication device, comprising a processing unit; the processing unit is configured to acquire first wireless data; the processing unit is further configured to process the data feature information of the first wireless data based on a classification model to obtain the data category of the first wireless data; wherein the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, and N is a positive integer.
  • the constituent modules of the communication device can also be used to execute the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects.
  • the constituent modules of the communication device can also be used to execute the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects.
  • a fourth aspect of this application provides a communication device, which is a second communication device.
  • the communication device includes a transceiver unit and a processing unit.
  • the processing unit is used to determine first information, which is used to instruct a classification model.
  • the classification model is used to process the data feature information of first wireless data to obtain the data category of the first wireless data.
  • the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer.
  • the transceiver unit is used to transmit the first information.
  • the constituent modules of the communication device can also be used to perform the steps executed in various possible implementations of the second aspect and achieve the corresponding technical effects.
  • the second aspect please refer to the second aspect, which will not be repeated here.
  • a fifth aspect of this application provides a communication device including at least one processor coupled to a memory; the memory is used to store a program or instructions; the at least one processor is used to execute the program or instructions to enable the communication device to implement the method described in any possible implementation of any of the first to second aspects.
  • the communication device may include the memory.
  • the sixth aspect of this application provides a communication device including at least one logic circuit and an input/output interface; the logic circuit is used to perform the method as described in any one of the possible implementations of the first to second aspects described above.
  • the seventh aspect of this application provides a communication system, which includes the first communication device and the second communication device described above.
  • An eighth aspect of this application provides a computer-readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, perform the method as described in any possible implementation of any of the first to second aspects described above.
  • the ninth aspect of this application provides a computer program product (or computer program) that, when executed by a processor, performs the method described in any possible implementation of any of the first to second aspects described above.
  • the tenth aspect of this application provides a chip system including at least one processor for supporting a communication device in implementing the method described in any possible implementation of any of the first to second aspects.
  • the chip system may further include a memory for storing program instructions and data necessary for the communication device.
  • the chip system may be composed of chips or may include chips and other discrete devices.
  • the chip system may also include interface circuitry that provides program instructions and/or data to the at least one processor.
  • FIGS 1a to 1c are schematic diagrams of the communication system provided in this application.
  • FIGS. 2a to 2e are schematic diagrams of the AI processing involved in this application.
  • FIG. 3 is an interactive schematic diagram of the communication method provided in this application.
  • FIGS. 4a to 4c are some schematic diagrams of the clustering process involved in this application.
  • FIGS 5 to 9 are schematic diagrams of the communication device provided in this application.
  • Terminal device can be a wireless terminal device that can receive network device scheduling and instruction information.
  • the wireless terminal device can be a device that provides voice and/or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.
  • Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN).
  • Terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones), computers, and data cards.
  • mobile phones or "cellular" phones
  • computers and data cards.
  • they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and/or data with the RAN.
  • Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets, and computers with wireless transceiver capabilities.
  • PCS personal communication service
  • SIP session initiation protocol
  • WLL wireless local loop
  • PDAs personal digital assistants
  • tablets and computers with wireless transceiver capabilities.
  • Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station (MS), remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.
  • the terminal device can also be a wearable device.
  • Wearable devices also known as wearable smart devices or smart wearable devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes.
  • Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories.
  • Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction.
  • wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.
  • Terminals can also be drones, robots, devices in device-to-device (D2D) communication, vehicles to everything (V2X) communication, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in telemedicine or telehealth services, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, and wireless terminals in smart homes, etc.
  • D2D device-to-device
  • V2X vehicles to everything
  • VR virtual reality
  • AR augmented reality
  • wireless terminals in industrial control wireless terminals in self-driving
  • wireless terminals in telemedicine or telehealth services wireless terminals in smart grids
  • wireless terminals in transportation safety wireless terminals in smart cities, and wireless terminals in smart homes, etc.
  • terminal devices can also be terminal devices in communication systems evolved from fifth-generation (5G) communication systems (such as 5G Advanced or sixth-generation (6G) communication systems), or terminal devices in future public land mobile networks (PLMNs).
  • 5G Advanced or 6G networks can further expand the form and function of 5G communication terminals; 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.
  • 5G fifth-generation
  • 6G sixth-generation
  • PLMNs public land mobile networks
  • 5G Advanced or 6G networks can further expand the form and function of 5G communication terminals
  • 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.
  • IoT Internet of Things
  • the terminal device can also obtain artificial intelligence (AI) services provided by the network device.
  • AI artificial intelligence
  • the terminal device can also have AI processing capabilities.
  • Network equipment This can be equipment in a wireless network.
  • network equipment can be a RAN node (or device) that connects terminal devices to the wireless network, and can also be called a base station.
  • RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in 5G communication systems, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point (AP), etc.
  • network equipment can include central unit (CU) nodes, distributed unit (DU) nodes, or RAN equipment including CU nodes and DU nodes.
  • CU central unit
  • DU distributed unit
  • RAN equipment including CU nodes and DU nodes.
  • RAN nodes can also be macro base stations, micro base stations or indoor stations, relay nodes or donor nodes, or radio controllers in cloud radio access network (CRAN) scenarios.
  • RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment.
  • the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).
  • V2X vehicle-to-everything
  • RSU roadside unit
  • RAN nodes collaborate to assist the terminal in achieving wireless access, with different RAN nodes each implementing some of the base station's functions.
  • RAN nodes can be CUs, DUs, CUs (control plane, CP), CUs (user plane, UP), or radio units (RUs).
  • CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU).
  • RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), radio heads (RHs), or remote radio heads (RRHs).
  • RRUs remote radio units
  • AAUs active antenna units
  • RHs radio heads
  • RRHs remote radio heads
  • CU or CU-CP and CU-UP
  • DU or RU
  • RU may have different names, but those skilled in the art will understand their meaning.
  • O-CU open CU
  • DU can also be called O-DU
  • CU-CP can also be called O-CU-CP
  • CU-UP can also be called O-CU-UP
  • RU can also be called O-RU.
  • this application uses CU, CU-CP, CU-UP, DU, and RU as examples.
  • Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.
  • This protocol layer may include a control plane protocol layer and a user plane protocol layer.
  • the control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc.
  • the user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.
  • SDAP service data adaptation protocol
  • Network devices can be other devices that provide wireless communication functions for terminal devices.
  • the embodiments of this application do not limit the specific technology or device form used in the network device. For ease of description, the embodiments of this application are not limited.
  • Network equipment may also include core network equipment, such as the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), Public Data Network Gateway (PDN Gateway, or P-GW) in 4G networks; and access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in 5G networks.
  • MME Mobility Management Entity
  • HSS Home Subscriber Server
  • S-GW Serving Gateway
  • PCRF Policy and Charging Rules Function
  • PDN Gateway Public Data Network Gateway
  • P-GW Public Data Network Gateway
  • P-GW Public Data Network Gateway
  • AMF access and mobility management function
  • UPF user plane function
  • SMF session management function
  • the network device may also have network nodes with AI capabilities, which can provide AI services to terminals or other network devices.
  • network nodes with AI capabilities can provide AI services to terminals or other network devices.
  • it may be an AI node, computing node, RAN node with AI capabilities, or core network element with AI capabilities on the network side (access network or core network).
  • the device for implementing the function of the network device can be the network device itself, or it can be a device capable of supporting the network device in implementing that function, such as a chip system, which can be installed in the network device.
  • a network device being used to implement the function of the network device is used to describe the technical solutions provided in this application embodiment.
  • Configuration and Pre-configuration In this application, both configuration and pre-configuration are used. Configuration refers to the network device/server sending configuration information or parameter values to the terminal via messages or signaling, so that the terminal can determine communication parameters or resources for transmission based on these values or information. Pre-configuration is similar to configuration; it can be parameter information or parameter values pre-negotiated between the network device/server and the terminal device, parameter information or parameter values specified by standard protocols for use by the base station/network device or terminal device, or parameter information or parameter values pre-stored in the base station/server or terminal device. This application does not limit this.
  • “send” and “receive” indicate the direction of signal transmission.
  • “send information to XX” can be understood as the destination of the information being XX, which may include sending directly through the air interface or sending indirectly through the air interface by other units or modules.
  • “Receive information from YY” can be understood as the source of the information being YY, which may include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by other units or modules.
  • “Send” can also be understood as the "output” of the chip interface, and “receive” can also be understood as the "input” of the chip interface.
  • sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, wiring, or interfaces.
  • Clustering is a data analysis method that aims to automatically divide a collection of physical or abstract objects (usually a dataset) into multiple subsets, each called a cluster.
  • the basic goal of clustering is to ensure that objects within the same cluster have high similarity, while objects in different clusters exhibit greater differences. This similarity or difference is typically measured based on specific criteria chosen (such as distance metrics, similarity coefficients, probabilistic models, etc.).
  • clustering can be implemented in various ways, such as the K-means algorithm, bottom-up hierarchical clustering, top-down hierarchical clustering, or Gaussian mixture models (GMM).
  • K-means algorithm bottom-up hierarchical clustering
  • top-down hierarchical clustering top-down hierarchical clustering
  • GMM Gaussian mixture models
  • clustering is an unsupervised learning method because it does not require prior knowledge of the category labels of data points. Instead, it spontaneously forms category divisions by exploring the inherent structure and patterns of the data itself.
  • Clustering has a wide range of applications, including but not limited to market segmentation, social network analysis, bioinformatics, image segmentation, text classification, and regional division in geographic information systems.
  • Clustering results refer to the specific cluster partitioning obtained after executing a clustering algorithm. Specifically, it includes one or more of the following:
  • Number of clusters This refers to how many different clusters the data is divided into. This can be predetermined (e.g., specifying the value of k in the k-means algorithm) or determined adaptively during the clustering process (e.g., selecting the optimal number of clusters using complexity metrics such as the silhouette coefficient). For subsequent data classification, each cluster can be defined as a category, and each cluster can be assigned an index to identify its corresponding category.
  • Cluster members For each cluster, its members consist of a set of objects from the original dataset. These objects are considered by the clustering algorithm to be closer to each other under the similarity metric used, and to have more common characteristics relative to members of other clusters.
  • Cluster boundaries Although they may not be explicitly given in the actual clustering output, each cluster has an implicit boundary that defines which data points belong to the cluster and which do not.
  • the boundary may be represented by a distance threshold, a hyperplane of the probability distribution, branches of a connection tree, etc., depending on the clustering algorithm used.
  • Cluster features or centers (or cluster centers): Some clustering methods generate a center or prototype representing the overall characteristics of each cluster. For example, in k-means, the cluster center is the mean vector of all data points within the cluster in the feature space; in hierarchical clustering, the cluster features may be indirectly represented by the connection rules in the merging process.
  • clustering validity metrics In some cases, clustering results may also include statistical measures or visualizations to evaluate the quality of cluster partitioning, such as silhouette coefficient, Calinski-Harabasz index, Davies-Bouldin index, etc. These can help users determine whether the obtained cluster partitioning is reasonable, stable, or meets expectations.
  • clustering is a process of organizing a dataset into meaningful, internally homogeneous, and externally heterogeneous groups (clusters), while the clustering result is a detailed description of the specific cluster divisions and related attributes produced by this process.
  • the clustering process and the result are closely related; the former is the means of grouping data, while the latter is the actual product of applying this means to the data. Both serve to explore and understand the inherent structure and patterns of the data.
  • "instruction” may include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction.
  • the information indicated by a certain piece of information (as described below, the instruction information) is called the information to be instructed.
  • the information to be instructed there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is an association between the other information and the information to be instructed; or it can only indicate a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon.
  • the instruction can be implemented by using a pre-agreed (e.g., protocol predefined) arrangement order of various information, thereby reducing the instruction overhead to a certain extent.
  • a pre-agreed e.g., protocol predefined
  • This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to indicate the information to be instructed; for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.
  • the communication system includes at least one network device and/or at least one terminal device.
  • Figure 1a is a schematic diagram of a communication system according to this application.
  • Figure 1a exemplarily shows one network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6.
  • terminal device 1 is a smart teacup
  • terminal device 2 is a smart air conditioner
  • terminal device 3 is a smart gas pump
  • terminal device 4 is a vehicle
  • terminal device 5 is a mobile phone
  • terminal device 6 is a printer.
  • the entity sending the AI configuration information can be a network device.
  • the entity receiving the AI configuration information can be terminal devices 1-6.
  • the network device and terminal devices 1-6 form a communication system.
  • terminal devices 1-6 can send data to the network device, and the network device needs to receive the data sent by terminal devices 1-6.
  • the network device can send configuration information to terminal devices 1-6.
  • terminal devices 4 and 6 can also form a communication system.
  • Terminal device 5 acts as a network device, i.e., the entity sending AI configuration information
  • terminal devices 4 and 6 act as terminal devices, i.e., the entities receiving AI configuration information.
  • V2X vehicle-to-everything
  • terminal device 5 sends AI configuration information to terminal devices 4 and 6 respectively, and receives data sent by terminal devices 4 and 6; correspondingly, terminal devices 4 and 6 receive the AI configuration information sent by terminal device 5 and send data back to terminal device 5.
  • V2X vehicle-to-everything
  • different devices may also perform AI-related services.
  • the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.
  • communication-related services and AI-related services can also be performed between televisions and mobile phones.
  • AI network elements can be introduced into the communication system provided in this application to implement some or all AI-related operations.
  • AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc.
  • the AI network element can be built into a network element within the communication system.
  • the AI network element can be an AI module built into: access network equipment, core network equipment, cloud server, or operation, administration, and maintenance (OAM) to implement AI-related functions.
  • OAM operation, administration, and maintenance
  • the OAM can act as the network management system for the core network equipment and/or the access network equipment.
  • the AI network element can also be an independently set network element in the communication system.
  • the terminal or its built-in chip can also include an AI entity to implement AI-related functions.
  • AI can endow machines with human-like intelligence, for example, allowing them to use computer hardware and software to simulate certain intelligent human behaviors.
  • machine learning methods can be employed.
  • machines learn (or train) a model using training data. This model represents the mapping between inputs and outputs.
  • the learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).
  • Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.
  • Supervised learning based on collected sample values and labels, uses machine learning algorithms to learn the mapping relationship between sample values and labels, and then expresses this learned mapping relationship using an AI model.
  • the process of training the machine learning model is the process of learning this mapping relationship.
  • sample values are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values and the sample labels (ideal values).
  • the mapping relationship learned in supervised learning can include linear or non-linear mappings.
  • the learning task can be divided into classification tasks and regression tasks.
  • Unsupervised learning relies on collected sample values to discover inherent patterns within the samples themselves.
  • One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning.
  • model parameters are optimized by calculating the error between the model's predictions and the samples themselves.
  • Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.
  • Reinforcement learning unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels.
  • the algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput.
  • the goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action.
  • the network cannot be optimized by calculating the error between the action and the "correct action.” Reinforcement learning training is achieved through iterative interaction with the environment.
  • Neural networks are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings.
  • Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.
  • each neuron performs a weighted summation of its input values and outputs the result through an activation function.
  • Figure 2a shows a schematic diagram of a neuron structure.
  • w ⁇ sub>i ⁇ /sub> is used as the weight for xi , and is used to weight xi .
  • the bias for the weighted summation of the input values based on the weights is, for example, b.
  • b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number.
  • the activation functions of different neurons in a neural network can be the same or different.
  • neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and/or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems.
  • the depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer.
  • a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network.
  • a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network.
  • a neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.
  • DNNs deep neural networks
  • DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
  • FNNs feedforward neural networks
  • CNNs convolutional neural networks
  • RNNs recurrent neural networks
  • Figure 2b is a schematic diagram of an FNN network.
  • a characteristic of FNN networks is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.
  • CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., two-dimensional discrete sampling) can both be considered grid-like data.
  • CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters.
  • each window can use different convolution kernels, allowing CNNs to better extract features from the input data.
  • RNNs are a type of distributed neural network (DNN) that utilizes feedback time-series information.
  • the input to an RNN includes the current input value and its own output value from the previous time step.
  • RNNs are well-suited for acquiring temporally correlated sequence features, and are particularly applicable to applications such as speech recognition and channel coding/decoding.
  • a loss function can be defined.
  • the loss function describes the difference between the model's output value and the ideal target value.
  • the loss function can be expressed in various forms, and there are no restrictions on its specific form.
  • the model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold or to meet the target requirement.
  • a model can also be called an AI model, a rule, or other names.
  • An AI model can be considered a specific method for implementing AI functions.
  • An AI model represents the mapping relationship or function between the model's input and output.
  • AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc.
  • AI functions can also be called AI (related) operations or AI-related functions.
  • a fully connected neural network is also called a multilayer perceptron (MLP).
  • MLP multilayer perceptron
  • an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle).
  • Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.
  • w is the weight matrix
  • b is the bias vector
  • f is the activation function
  • n is the index of the neural network layer
  • n is greater than or equal to 1 and less than or equal to N, where N is the total number of layers in the neural network.
  • a neural network can be understood as a mapping from an input data set to an output data set.
  • Neural networks are typically initialized randomly; the process of obtaining this mapping from random values w and b using existing data is called training the neural network.
  • the training method involves using a loss function to evaluate the output of the neural network.
  • the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches its minimum, which is the "better point (e.g., the optimal point)" in Figure 2d.
  • the neural network parameters corresponding to the "better point (e.g., the optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.
  • the gradient descent process can be represented as:
  • ⁇ represents the parameters to be optimized (including w and b)
  • L is the loss function
  • ⁇ is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of ⁇ with respect to L.
  • the backpropagation process can utilize the chain rule for partial derivatives.
  • the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:
  • w ⁇ sub>ij ⁇ /sub> is the weight connecting node j to node i
  • s ⁇ sub>i ⁇ /sub> is the weighted sum of the inputs at node i.
  • wireless communication systems such as the systems shown in Figure 1a, 1b, or 1c.
  • communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities.
  • the computing capabilities of the network device mainly provide computational support for signal transmission and reception capabilities (e.g., processing signals for transmission and reception) to realize the communication tasks between the network device and other communication nodes.
  • the communication device may also handle other communication tasks (such as channel prediction, beam management, resource scheduling, etc.).
  • the computing power of the communication node may not only provide computational support for the aforementioned communication tasks but also possess surplus computing power.
  • the services performed by communication devices may include not only traditional communication services but also other new services, such as AI services and sensing services. Generally, the implementation of these services requires calling wireless data for related data processing.
  • communication devices may access different categories of wireless data.
  • One possible approach is to classify the data based on its acquisition information (e.g., acquisition time, acquisition scenario, simulation scenario for data generation, etc.).
  • acquisition information e.g., acquisition time, acquisition scenario, simulation scenario for data generation, etc.
  • this classification method does not consider the nature of the wireless data itself, potentially leading to overly coarse or insufficient classification. Therefore, how to efficiently classify wireless data is a pressing technical problem that needs to be solved.
  • Figure 3 is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.
  • the communication device can be a communication device (such as a terminal device or a network device), or a chip, baseband chip, modem chip, system-on-chip (SoC) chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, logic module, or software in the communication device.
  • a communication device such as a terminal device or a network device
  • SoC system-on-chip
  • SIP system-in-package
  • the second communication device sends first information, and correspondingly, the first communication device receives the first information.
  • the first information is used to instruct a classification model; the classification model is used to process the data feature information of the first wireless data to obtain the data category of the first wireless data; the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer.
  • step S301 is an optional step.
  • the first communication device it can determine the classification model through the first information in step S301.
  • the first information may include model parameters of the classification model, the identifier of the classification model, or other methods to indicate the classification model.
  • one or more first communication devices may be data collection nodes, and the second communication device sending the first information may be a task execution node.
  • the first communication device can receive the first information indicating the classification model, and the data collection nodes can obtain the classification model based on the information sent by the task execution node, so as to execute subsequent downstream tasks indicated by the task execution node based on the classification model.
  • the first communication device can obtain the classification model through other means.
  • the first communication device can train/process the classification model locally, thereby reducing overhead.
  • the first communication device processes the data feature information of the first wireless data based on a classification model to obtain the data category of the first wireless data; wherein, the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, and N is a positive integer.
  • the first communication device can acquire first wireless data.
  • the first communication device can process the first wireless data acquired by the first communication device based on a classification model to obtain the data category of the first wireless data.
  • the first communication device can collect data related to wireless communication (denoted as wireless data) through software or hardware modules.
  • the first wireless data may include the collected wireless data, and/or, the first wireless data may include wireless data obtained by further processing the collected data (e.g., data filtering, data cleaning, data deduplication, data enhancement, or one or more of these), and/or, the first wireless data may include simulated wireless data.
  • the first communication device can acquire the first wireless data through antennas, cameras, microphones, radar, sensors, etc.
  • acquiring first wireless data can be replaced with other descriptions, such as collecting, gathering, collecting, or capturing first wireless data.
  • the wireless data involved in this application may include wireless-related data, such as one or more of the following: wireless channel information, MIMO precoding matrix information, multipath component (MPC) information, channel impulse response information, channel frequency domain response information, beam information, scheduling information, data rate information, and sensing information.
  • wireless-related data such as one or more of the following: wireless channel information, MIMO precoding matrix information, multipath component (MPC) information, channel impulse response information, channel frequency domain response information, beam information, scheduling information, data rate information, and sensing information.
  • the classification model can be a model that classifies data based on one or more of machine learning, mathematical algorithms, mathematical methods, AI, and neural networks.
  • classification model can be replaced with other terms, such as wireless data classification model, data classification model, wireless data classifier, data classifier, wireless data classification system, or data classification system.
  • clustering can be an unsupervised learning method that can spontaneously form category divisions by exploring the inherent structure and patterns of the data without prior knowledge of the category labels of the data points.
  • processing data or sample data, such as the N wireless sample data mentioned above
  • clustering a collection containing physical or abstract objects (usually referring to a dataset) can be divided into one or more subsets, each subset being called a cluster.
  • each cluster can be defined as a category, and each cluster can be assigned an index to identify the category corresponding to each cluster.
  • the clustering results may include information about the clusters obtained from the above clustering.
  • the clustering results may include at least one of the following: the number of clusters, the members of the clusters, the boundaries of the clusters, the centers of the clusters (i.e., the cluster centers), and the clustering effectiveness index.
  • clustering can be a process of organizing a dataset into meaningful, internally homogeneous, externally heterogeneous groups (e.g., clusters), while the clustering result is a detailed description of the specific cluster divisions and their related attributes produced by this process.
  • Clustering and clustering results are closely related; the former is the means of grouping data, while the latter is the actual product of applying this means to data. Both serve to explore and understand the inherent structure and patterns of the data.
  • the data feature information includes at least one of the following: data distribution information of the first wireless data, features extracted based on the first wireless data, and data distribution information of the features extracted based on the first wireless data.
  • the classification model can determine the data category of the first wireless data based on the data feature information of the first wireless data, wherein the data feature information may include at least one of the above, to improve the flexibility of the solution implementation.
  • data distribution information may include the mean, variance, empirical probability density functions (PDF), empirical cumulative distribution function (CDF), standard deviation, median, mode, range, or one or more other information used to represent the data distribution.
  • PDF empirical probability density functions
  • CDF empirical cumulative distribution function
  • each of the N wireless sample data can correspondingly include one or more of the above.
  • the input of the K-means algorithm can include the data feature information of the N wireless sample data (e.g., the data itself, mean, standard deviation, etc.), and the clustering result can be obtained after processing by the K-means algorithm.
  • the first communication device can process the data feature information of the first wireless data based on a classification model of clustering results corresponding to the data feature information of N wireless sample data to obtain the data category of the first wireless data.
  • the data category of the first wireless data obtained by the first communication device is based on the data feature information of the first wireless data. Therefore, compared with the method of classifying data solely based on the acquisition information of wireless data (such as acquisition time, acquisition scenario, etc.), in the above scheme, the communication device can classify based on the data features of the wireless data itself, achieving efficient and sufficient data classification to improve the effect of data classification.
  • the first communication device classifies the wireless data based on its own data characteristics.
  • the classification result can be used for the data processing of downstream tasks, which is beneficial for downstream tasks to achieve efficient data processing based on the data classification result, thereby improving the performance of downstream tasks.
  • the classification model used to determine the classification result is associated with the clustering results corresponding to the data feature information of N wireless sample data. This association may be achieved in various ways, which will be described below with some examples.
  • Implementation method one The classification model includes the clustering results.
  • the classification model used to classify wireless data can include the clustering results corresponding to the data feature information of N wireless sample data. In this way, the clustering results corresponding to N wireless sample data can be directly applied to the classification of the first wireless data to reduce complexity.
  • the clustering result includes one or more cluster centers, and the data feature information of the first wireless data is closest to the first cluster center of the one or more cluster centers; wherein, the data category of the first wireless data is the category corresponding to the first cluster center.
  • Figure 4b illustrates one implementation example of Method 1.
  • data distribution information for N wireless sample data (optionally including wireless environment information from the N wireless sample data) is obtained through processing. This information is then processed by clustering to obtain clustering results, which can serve as part or all of the classification model.
  • the input first wireless data and/or its data distribution information can be processed to obtain the data category of the first wireless data. For example, for the collected first wireless data, the data distribution information can be obtained and compared with one or more cluster centers included in the classification model. The category containing the nearest cluster center is selected as the category of the collected first wireless data.
  • the second implementation method is a neural network model that uses clustering results as label data and wireless environment information corresponding to N wireless sample data as input data for training.
  • the classification model used to classify wireless data can be a neural network model obtained through a training process.
  • This training process uses the wireless environment information corresponding to N wireless sample data as input data and the clustering results (such as the corresponding categories) of the N wireless sample data as label data. Therefore, this neural network model has the ability to determine the category of wireless data based on the wireless environment information. Since the data size of wireless data is generally larger than the data size of the corresponding wireless environment information, the above scheme can quickly determine the category of wireless data through the neural network model, thus reducing data processing latency.
  • the neural network model can be replaced with other implementations, such as mathematical models, machine learning models, or AI processing models.
  • Figure 4c illustrates one implementation example of Method 2.
  • the data distribution information of N wireless sample data (optionally including wireless environment information of N wireless sample data) is obtained through processing.
  • clustering processing is performed to obtain the clustering result, which can be used as a label.
  • the wireless environment information of the N wireless sample data is used as input to train the first model (e.g., the first model is a pre-configured model, an initialized model, etc.) to obtain the classification model.
  • the wireless environment information of the input first wireless data can be processed based on this model to obtain the data category of the first wireless data.
  • the wireless environment information of the first wireless data can be obtained, input into the classification model, and processed by the classification model to obtain the category of the first wireless data.
  • the wireless environment information described above includes at least one of the following: configuration information for collecting wireless data, scenario type information for collecting wireless data, environmental map information for collecting wireless data, location information for collecting wireless data, wireless configuration information for collecting wireless data, or device configuration information for collecting wireless data.
  • the wireless environment information used to determine the neural network model may include at least one of the above, and subsequently, this at least one can be used as input to the neural network model to obtain the category of wireless data, thereby improving the flexibility of the solution implementation.
  • the method further includes: the first communication device sending second information, which indicates the data category of the first wireless data.
  • the receiver of the second information e.g., the second communication device
  • the receiver of the second information can know the data category corresponding to the data provided by the first communication device, which is beneficial for the receiver to subsequently perform data processing on the data provided by the first communication device in accordance with that data category.
  • the second information may be triggered based on a request from the second communication device.
  • one or more first communication devices may be data collection nodes, and the second communication device may be a task execution node.
  • the first communication device may receive first information for instructing the classification model, and the data collection node may also instruct each data collection node to send the data category corresponding to the wireless data it acquires (or collects).
  • the method further includes: the first communication device transmitting the first wireless data, wherein the data category of the first wireless data is used for processing the first wireless data.
  • the receiver of the first wireless data e.g., a second communication device
  • the receiver of the first wireless data to perform data processing on the first wireless data based on the data category of the first wireless data.
  • the first wireless data may be triggered based on a request from the second communication device.
  • one or more first communication devices may be data collection nodes, and the second communication device may be a task execution node.
  • the first communication device may receive first information instructing the classification model, and the data collection nodes may also instruct each data collection node to transmit the wireless data it has acquired (or collected).
  • the task execution node can subsequently process downstream tasks based on the wireless data transmitted by the data collection nodes and the corresponding data categories.
  • the first communication device may also act as a task execution node. After determining the data category of the first wireless data in step S302, the first communication device performs downstream task processing locally based on the acquired first wireless data and the data category corresponding to the first wireless data.
  • the task execution node can process wireless data of a specific data category based on the task objective and/or task requirements of the AI task.
  • the first communication device can classify data based on its own data characteristics, it can achieve efficient and sufficient data classification, thereby improving the effectiveness of data classification.
  • This application embodiment provides a communication device 500, which can realize the functions of the first communication device (or second communication device) in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments.
  • the communication device 500 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip, baseband chip, modem chip, SoC chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, etc.
  • SIP system-in-package
  • the transceiver unit 502 may include a transmitting unit and a receiving unit, which are used to perform transmitting and receiving respectively.
  • the device 500 when the device 500 is used to execute the method performed by the first communication device in FIG3 and related embodiments, the device 500 includes a processing unit 501; the processing unit 501 is used to acquire first wireless data; the processing unit 501 is also used to process the data feature information of the first wireless data based on a classification model to obtain the data category of the first wireless data; wherein, the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, and N is a positive integer.
  • the device 500 when the device 500 is used to execute the method performed by the second communication device in FIG3 and related embodiments, the device 500 includes a processing unit 501 and a transceiver unit 502; the processing unit 501 is used to determine first information, which is used to indicate a classification model; wherein, the classification model is used to process the data feature information of the first wireless data to obtain the data category of the first wireless data; the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer; the transceiver unit 502 is used to send the first information.
  • the processing unit 501 is used to determine first information, which is used to indicate a classification model
  • the classification model is used to process the data feature information of the first wireless data to obtain the data category of the first wireless data
  • the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer
  • the transceiver unit 502 is used to send the first information.
  • the function of the processing unit 501 can be implemented by one or more processors.
  • the processor may include a modem chip, or a SoC chip or SIP chip containing a modem core.
  • the function of the transceiver unit 502 can be implemented by transceiver circuitry.
  • the function of the processing unit 501 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores.
  • the function of the transceiver unit 502 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.
  • the communication device 600 includes a logic circuit 601 and an input/output interface 602.
  • the communication device 600 can be a chip or an integrated circuit.
  • the transceiver unit 502 can be a communication interface, which can be the input/output interface 602 in Figure 6, and the input/output interface 602 can include an input interface and an output interface.
  • the communication interface can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • the logic circuit 601 when the device 600 is used to execute the method performed by the first communication device in FIG3 and related embodiments, the logic circuit 601 is used to acquire first wireless data; the logic circuit 601 is also used to process the data feature information of the first wireless data based on a classification model to obtain the data category of the first wireless data; wherein, the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, and N is a positive integer.
  • the logic circuit 601 is used to determine first information, which is used to indicate a classification model; wherein, the classification model is used to process the data feature information of the first wireless data to obtain the data category of the first wireless data; the classification model is associated with the clustering results corresponding to the data feature information of N wireless sample data, where N is a positive integer; the input/output interface 602 is used to send the first information.
  • the logic circuit 601 and the input/output interface 602 can also perform other steps performed by the first or second communication device in any embodiment and achieve corresponding beneficial effects, which will not be elaborated here.
  • the processing unit 501 shown in FIG5 can be the logic circuit 601 in FIG6.
  • the logic circuit 601 can be a processing device, the functions of which can be partially or entirely implemented in software.
  • the processing apparatus may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and/or steps in any of the method embodiments.
  • the processing device may consist of only a processor.
  • a memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuitry/wires to read and execute the computer programs stored in the memory.
  • the memory and processor may be integrated together or physically independent of each other.
  • the processing device may be one or more chips, or one or more integrated circuits.
  • the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic controllers (PLDs), or other integrated chips, or any combination of the above chips or processors.
  • FPGAs field-programmable gate arrays
  • ASICs application-specific integrated circuits
  • SoCs system-on-chips
  • CPUs central processing units
  • NPs network processors
  • DSPs digital signal processors
  • MCUs microcontroller units
  • PLDs programmable logic controllers
  • the communication device 700 can be the communication device that serves as a terminal device in the above embodiments.
  • the present invention provides a possible logical structure diagram of the communication device 700, which may include, but is not limited to, at least one processor 701 and a communication port 702.
  • the transceiver unit 502 can be a communication interface, which can be the communication port 702 in Figure 7.
  • the communication port 702 can include an input interface and an output interface.
  • the communication port 702 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • the device may also include at least one of a memory 703 and a bus 704.
  • the at least one processor 701 is used to control the operation of the communication device 700.
  • the processor 701 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application.
  • the processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc.
  • the communication device 700 shown in Figure 7 can be used to implement the steps implemented by the terminal device in the aforementioned method embodiments and to achieve the corresponding technical effects of the terminal device.
  • the specific implementation of the communication device shown in Figure 7 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.
  • Figure 8 is a schematic diagram of the structure of the communication device 800 involved in the above embodiments provided in the embodiments of this application.
  • the communication device 800 can be a communication device as a network device in the above embodiments.
  • the communication device 800 includes at least one processor 811 and at least one network interface 814.
  • the communication device further includes at least one memory 812, at least one transceiver 813, and one or more antennas 815.
  • the processor 811, memory 812, transceiver 813, and network interface 814 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited thereto.
  • the antenna 815 is connected to the transceiver 813.
  • the network interface 814 enables the communication device to communicate with other communication devices through a communication link.
  • the network interface 814 may include a network interface between the communication device and core network equipment, such as an S1 interface, or a network interface between the communication device and other communication devices (e.g., other network devices or core network equipment), such as an X2 or Xn interface.
  • core network equipment such as an S1 interface
  • other communication devices e.g., other network devices or core network equipment
  • the transceiver unit 502 can be a communication interface, which can be the network interface 814 in Figure 8.
  • the network interface 814 can include an input interface and an output interface.
  • the network interface 814 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • the processor 811 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from these programs, for example, to support the actions described in the embodiments of the communication device.
  • the communication device may include a baseband processor and a central processing unit (CPU).
  • the baseband processor is primarily used to process communication protocols and communication data, while the CPU is primarily used to control the entire terminal device, execute software programs, and process data from these programs.
  • the processor 811 in Figure 8 can integrate the functions of both a baseband processor and a CPU. Those skilled in the art will understand that the baseband processor and CPU can also be independent processors interconnected via technologies such as buses.
  • a terminal device may include multiple baseband processors to adapt to different network standards, and multiple CPUs to enhance its processing capabilities.
  • the various components of the terminal device can be connected via various buses.
  • the baseband processor can also be described as a baseband processing circuit or a baseband processing chip.
  • the CPU can also be described as a central processing circuit or a central processing chip.
  • the function of processing communication protocols and communication data can be built into the processor or stored in memory as a software program, which is then executed by the processor to implement the baseband processing function.
  • the memory is primarily used to store software programs and data.
  • the memory 812 can exist independently or be connected to the processor 811.
  • the memory 812 can be integrated with the processor 811, for example, integrated into a single chip.
  • the memory 812 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 811.
  • the various types of computer program code being executed can also be considered as drivers for the processor 811.
  • Figure 8 shows only one memory and one processor. In actual terminal devices, there may be multiple processors and multiple memories. Memory can also be called storage medium or storage device, etc. Memory can be a storage element on the same chip as the processor, i.e., an on-chip storage element, or it can be a separate storage element; this application does not limit this.
  • Transceiver 813 can be used to support the reception or transmission of radio frequency (RF) signals between a communication device and a terminal.
  • Transceiver 813 can be connected to antenna 815.
  • Transceiver 813 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 815 can receive RF signals.
  • the receiver Rx of transceiver 813 receives the RF signals from the antennas, converts the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provides the digital baseband signals or IF signals to processor 811 so that processor 811 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding.
  • IF intermediate frequency
  • the transmitter Tx in transceiver 813 is also used to receive modulated digital baseband signals or IF signals from processor 811, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 815.
  • the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal.
  • IF digital intermediate frequency
  • the order of these downmixing and IF conversion processes is adjustable.
  • the transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal.
  • the order of these upmixing and IF conversion processes is also adjustable.
  • the digital baseband signal and the digital IF signal can be collectively referred to as digital signals.
  • the transceiver 813 can also be called a transceiver unit, transceiver, transceiver device, etc.
  • the device in the transceiver unit that performs the receiving function can be regarded as the receiving unit
  • the device in the transceiver unit that performs the transmitting function can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit.
  • the receiving unit can also be called a receiver, input port, receiving circuit, etc.
  • the transmitting unit can be called a transmitter, transmitter, or transmitting circuit, etc.
  • the communication device 800 shown in Figure 8 can be used to implement the steps implemented by the network device in the aforementioned method embodiments and achieve the corresponding technical effects of the network device.
  • the specific implementation of the communication device 800 shown in Figure 8 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.
  • Figure 9 is a schematic diagram of the structure of the communication device involved in the above embodiments provided in the embodiments of this application.
  • the communication device 900 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to execute the technical solutions provided in this application.
  • the communication device 900 may be the terminal device or network device described above, or a component (e.g., a chip) within these devices, used to implement the methods described in the following method embodiments.
  • the communication device 900 includes one or more processors 901.
  • the processor 901 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit.
  • the baseband processor can be used to process communication protocols and communication data
  • the central processing unit can be used to control the communication device (e.g., a RAN node, terminal, or chip), execute software programs, and process data from the software programs.
  • processor 901 may include program 903 (sometimes also referred to as code or instructions), which may be executed on processor 901 to cause communication device 900 to perform the methods described in the embodiments below.
  • communication device 900 includes circuitry (not shown in FIG9).
  • the communication device 900 may include one or more memories 902 storing a program 904 (sometimes referred to as code or instructions), which can be run on the processor 901 to cause the communication device 900 to perform the methods described in the above method embodiments.
  • a program 904 sometimes referred to as code or instructions
  • the processor 901 and/or memory 902 may include AI modules 907 and 908, which are used to implement AI-related functions.
  • the AI modules can be implemented through software, hardware, or a combination of both.
  • the AI module may include a radio intelligence control (RIC) module.
  • the AI module may be a near real-time RIC or a non-real-time RIC.
  • processor 901 and/or memory 902 may also store data.
  • the processor and memory may be configured separately or integrated together.
  • the communication device 900 may further include a transceiver 905 and/or an antenna 906.
  • the processor 901 sometimes referred to as a processing unit, controls the communication device (e.g., a RAN node or terminal).
  • the transceiver 905, sometimes referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver, is used to implement the transmission and reception functions of the communication device via the antenna 906.
  • the processing unit 501 can be a processor 901.
  • the transceiver unit 502 shown in Figure 5 can be a communication interface, which can be the transceiver 905 in Figure 9.
  • the transceiver 905 can include an input interface and an output interface.
  • the transceiver 905 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • This application also provides a computer-readable storage medium for storing one or more computer-executable instructions.
  • the processor When the computer-executable instructions are executed by a processor, the processor performs the method described in the possible implementations of the first or second communication device in the foregoing embodiments.
  • This application also provides a computer program product (or computer program) that, when executed by a processor, executes the method described above for the possible implementation of the first or second communication device.
  • This application also provides a chip system including at least one processor for supporting a communication device in implementing the functions involved in the possible implementations of the communication device described above.
  • the chip system further includes an interface circuit that provides program instructions and/or data to the at least one processor.
  • the chip system may also include a memory for storing the program instructions and data necessary for the communication device.
  • the chip system may be composed of chips or may include chips and other discrete devices, wherein the communication device may specifically be the first communication device or the second communication device in the aforementioned method embodiments.
  • This application also provides a communication system, the network system architecture of which includes the first communication device and/or the second communication device in any of the above embodiments.
  • the disclosed systems, apparatuses, and methods can be implemented in other ways.
  • the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods.
  • multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
  • the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Whether a function is implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
  • the units described as separate components may or may not be physically separate.
  • the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
  • the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
  • the integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.
  • the aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé de communication et un appareil associé. Dans ce procédé, après qu'un premier appareil de communication a acquis des premières données sans fil, le premier appareil de communication peut traiter des informations caractéristiques des premières données sans fil sur la base d'un modèle de classification associé à un résultat de regroupement correspondant à des informations caractéristiques de N échantillons de données sans fil, afin d'obtenir la catégorie de données des premières données sans fil. En d'autres termes, la catégorie de données des premières données sans fil, qui sont obtenues au moyen du premier appareil de communication, est obtenue sur la base des informations relatives aux caractéristiques des premières données sans fil. Par conséquent, par rapport au procédé dans lequel la classification des données est mise en œuvre uniquement à l'aide des informations de collecte (par exemple, l'heure et le scénario de collecte) des données sans fil, la solution ci-dessus, dans laquelle l'appareil de communication peut mettre en œuvre une classification sur la base d'une caractéristique des données sans fil elles-mêmes, permet de réaliser une classification efficace et suffisante des données, améliorant ainsi l'efficacité de la classification des données ; et une tâche en aval est facilitée dans la mise en œuvre d'un processus de traitement des données efficace sur la base d'un résultat de classification des données, améliorant ainsi les performances de la tâche en aval.
PCT/CN2024/136003 2024-04-28 2024-12-02 Procédé de communication et appareil associé Pending WO2025227698A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202410525507.2 2024-04-28
CN202410525507.2A CN120857270A (zh) 2024-04-28 2024-04-28 一种通信方法及相关装置

Publications (1)

Publication Number Publication Date
WO2025227698A1 true WO2025227698A1 (fr) 2025-11-06

Family

ID=97415414

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/136003 Pending WO2025227698A1 (fr) 2024-04-28 2024-12-02 Procédé de communication et appareil associé

Country Status (2)

Country Link
CN (1) CN120857270A (fr)
WO (1) WO2025227698A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016122346A1 (fr) * 2015-01-30 2016-08-04 Nokia Technologies Oy Procédé et appareil pour classer des informations de capteur
CN109993234A (zh) * 2019-04-10 2019-07-09 百度在线网络技术（北京）有限公司 一种无人驾驶训练数据分类方法、装置及电子设备
CN112580327A (zh) * 2019-09-30 2021-03-30 国际商业机器公司 增强自然语言分类器的多分类方法
CN117036834A (zh) * 2023-10-10 2023-11-10 腾讯科技（深圳）有限公司 基于人工智能的数据分类方法、装置及电子设备
US20240070658A1 (en) * 2022-08-23 2024-02-29 Plaid Inc. Parsing event data for clustering and classification

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016122346A1 (fr) * 2015-01-30 2016-08-04 Nokia Technologies Oy Procédé et appareil pour classer des informations de capteur
CN109993234A (zh) * 2019-04-10 2019-07-09 百度在线网络技术（北京）有限公司 一种无人驾驶训练数据分类方法、装置及电子设备
CN112580327A (zh) * 2019-09-30 2021-03-30 国际商业机器公司 增强自然语言分类器的多分类方法
US20240070658A1 (en) * 2022-08-23 2024-02-29 Plaid Inc. Parsing event data for clustering and classification
CN117036834A (zh) * 2023-10-10 2023-11-10 腾讯科技（深圳）有限公司 基于人工智能的数据分类方法、装置及电子设备

Also Published As

Publication number Publication date
CN120857270A (zh) 2025-10-28

Similar Documents

Publication Publication Date Title
WO2025227698A1 (fr) Procédé de communication et appareil associé
WO2025227699A1 (fr) Procédé de communication et appareil associé
WO2025227700A1 (fr) Procédé de communication et appareil associé
WO2025190244A1 (fr) Procédé de communication et appareil associé
WO2025167443A1 (fr) Procédé de communication et dispositif associé
WO2025189831A1 (fr) Procédé de communication et appareil associé
WO2025190252A1 (fr) Procédé de communication et appareil associé
WO2025175756A1 (fr) Procédé de communication et dispositif associé
WO2025019990A1 (fr) Procédé de communication et dispositif associé
WO2025189860A1 (fr) Procédé de communication et appareil associé
CN120935033A (zh) 一种通信方法以及相关装置
WO2025190248A1 (fr) Procédé de communication et appareil associé
WO2025140282A1 (fr) Procédé de communication et dispositif associé
WO2025179919A1 (fr) Procédé de communication et appareil associé
CN120568497A (zh) 一种通信方法及相关装置
WO2025189861A1 (fr) Procédé de communication et appareil associé
WO2025107835A1 (fr) Procédé de communication et dispositif associé
WO2025190246A1 (fr) Procédé de communication et appareil associé
CN120128300A (zh) 一种通信方法及相关设备
WO2025232164A1 (fr) Procédé de communication et appareil associé
WO2025227701A1 (fr) Procédé de communication et appareil associé
WO2025208880A1 (fr) Procédé de communication et appareil associé
CN120128935A (zh) 一种通信方法及相关设备
WO2025019989A1 (fr) Procédé de communication et dispositif associé
WO2025025193A1 (fr) Procédé de communication et dispositif associé

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24937727

Country of ref document: EP

Kind code of ref document: A1