Communication method, first node, second node, system, storage medium and product

Abstract

The present disclosure relates to a communication method, a first node, a second node, a system, a storage medium and a product. The method comprises: a first node determining that it is necessary for an AI model to be trained, and sending first information to a second node, wherein the first information is used for requesting training data for the AI model. Thus, when it is determined that it is necessary for an AI model to be trained, a training data request is sent to a second node, so as to request the second node to feed back model training data for the AI model. Therefore, a training data request process is standardized, such that the second node can send to a first node training data that meets requirements.

Description

Communication methods, first node, second node, system, storage media and products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, a first node, a second node, a system, a storage medium, and a product. Background Technology

[0002] Machine learning algorithms are one of the most important methods for implementing artificial intelligence (AI) technology. Machine learning can obtain models from large amounts of training data, and these models can then predict events. In many fields, machine learning models can achieve highly accurate predictions. Wireless communication networks can use AI for prediction and reasoning, improving system performance.

[0003] Summary of the Invention

[0004] To overcome the technical problem of unclear training data acquisition process in related technologies, this disclosure provides a communication method, a first node, a second node, a system, a storage medium, and a product.

[0005] According to a first aspect of the embodiments of this disclosure, a communication method is provided, executed by a first node, the method comprising:

[0006] Once it is determined that an AI model needs to be trained, a first message is sent to the second node, which requests the training data for the AI ​​model.

[0007] According to a second aspect of the embodiments of this disclosure, a communication method is provided, executed by a second node, the method comprising:

[0008] Receive the first message sent by the first node, which is used to request training data for the AI ​​model.

[0009] According to a third aspect of the embodiments of this disclosure, a first node is provided, comprising:

[0010] The transceiver module is used to determine that an AI model needs to be trained and to send first information to the second node. The first information is used to request training data for the AI ​​model.

[0011] According to a fourth aspect of the embodiments of this disclosure, a second node is proposed, comprising:

[0012] The transceiver module is used to receive the first information sent by the first node, which is used to request training data for the AI ​​model.

[0013] According to a fifth aspect of the embodiments of this disclosure, a first node is provided, comprising:

[0014] One or more processors;

[0015] The first node is used to execute the communication method described in any one of the first aspects of this disclosure.

[0016] According to a sixth aspect of the embodiments of this disclosure, a second node is provided, comprising:

[0017] One or more processors;

[0018] The second node is used to execute the communication method described in any one of the second aspects of this disclosure.

[0019] According to a seventh aspect of the embodiments of this disclosure, a communication system is provided, comprising:

[0020] The first node is used to perform the method as described in any one of the first aspects of this disclosure;

[0021] The second node is used to perform the method as described in any one of the second aspects of this disclosure.

[0022] According to an eighth aspect of the present disclosure, a storage medium is provided that stores instructions which, when executed on a communication device, cause the communication device to perform a communication method as described in any one of the first aspects of the present disclosure, or cause the communication device to perform a communication method as described in any one of the second aspects of the present disclosure.

[0023] According to a ninth aspect of the present disclosure, a computer program product is provided, comprising a computer program and / or instructions, wherein the computer program and / or instructions, when executed by a communication device, implement the communication method as described in any one of the first aspects of the present disclosure, or the computer program and / or instructions, when executed by a communication device, implement the communication method as described in any one of the second aspects of the present disclosure.

[0024] By adopting the above technical solution, at least the following beneficial technical effects can be achieved:

[0025] The first node, upon determining that AI model training is needed, sends a first message to the second node requesting training data for the AI ​​model. Thus, when AI model training is required, the first node initiates a training data request to the second node, requesting the second node to provide the AI ​​model's training data. This standardizes the training data request process, enabling the second node to send qualified training data to the first node. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0027] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0028] Figure 1B is a schematic diagram of the architecture of a data service system according to an embodiment of the present disclosure.

[0029] Figure 2A is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure.

[0030] Figure 2B is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0031] Figure 2C is a schematic flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0032] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0033] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0034] Figure 5 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0035] Figure 6 is a structural schematic diagram of the first node according to an embodiment of the present disclosure.

[0036] Figure 7 is a schematic diagram of the structure of the second node according to an embodiment of the present disclosure.

[0037] Figure 8 is a structural schematic diagram of a communication device 8100 according to an embodiment of the present disclosure.

[0038] Figure 9 is a schematic diagram of the structure of chip 8200 according to an embodiment of the present disclosure. Detailed Implementation

[0039] This disclosure provides a communication method, a first node, a second node, a system, a storage medium, and a product.

[0040] In a first aspect, embodiments of this disclosure propose a communication method, executed by a first node, the method comprising:

[0041] Once it is determined that an AI model needs to be trained, a first message is sent to the second node, which requests the training data for the AI ​​model.

[0042] In the above embodiment, when it is determined that AI model training is needed, a training data request is initiated to the second node, requesting the second node to provide the AI ​​model's training data. This standardizes the training data request process, enabling the second node to send qualified training data to the first node.

[0043] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0044] Receive second information sent by the second node, the second information being used to indicate information about the training data provided by the second node.

[0045] In the above embodiment, the second node sends information about the training data it can provide to the first node. This allows the first node to determine whether the second node possesses training data for the AI ​​model. When training the AI ​​model is needed, the first node requests training data from the second node, thus improving the training data request process and avoiding signaling overhead caused by invalid requests.

[0046] In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following:

[0047] Data type;

[0048] Application scenarios of data;

[0049] Terminal-side conditions during data collection;

[0050] Network-side conditions during data collection.

[0051] In the above embodiments, the second information may include one or more different types of information, thereby helping the first node to accurately determine whether training data exists in the second node based on the second information, thus improving the efficiency of training data request.

[0052] In conjunction with some embodiments of the first aspect, in some embodiments, before sending the first information to the second node, the method further includes:

[0053] Based on the second information, the training data provided by the second node is determined to meet the model training requirements of the AI ​​model.

[0054] In the above embodiments, the first node determines, based on the second information, whether the second node can provide training data that meets the training requirements of the corresponding AI model. This facilitates the first node in obtaining training data from the second node, thereby improving the efficiency of model training.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0056] The system receives a third message sent by the second node, the third message indicating whether the second node can transmit training data.

[0057] In the above embodiment, the second node provides feedback to the first node based on the third information, indicating whether training data can be transmitted, thereby improving the transmission process of training data and increasing the model training efficiency of the AI ​​model in the first node.

[0058] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

[0059] The identifier of the AI ​​model;

[0060] Identification of AI functions;

[0061] The expected performance metrics of the AI ​​model;

[0062] Data volume;

[0063] Application scenarios of the AI ​​model;

[0064] The application conditions of the AI ​​model;

[0065] Data transmission service requirements;

[0066] The AI ​​task identifier of the AI ​​model.

[0067] In the above embodiments, the first information includes one or more types of information, so that the second node can determine whether the training data required by the first node for AI model training exists based on the first information. This makes the training data request process more accurate, thereby improving the training efficiency of model training in the first node.

[0068] Secondly, embodiments of this disclosure propose a communication method executed by a second node, the method comprising:

[0069] Receive the first message sent by the first node, which is used to request training data for the AI ​​model.

[0070] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0071] Send a second message to the first node, the second message being used to indicate information about the training data provided by the second node.

[0072] In conjunction with some embodiments of the second aspect, in some embodiments, the second information includes at least one of the following:

[0073] Data type;

[0074] Application scenarios of data;

[0075] Terminal-side conditions during data collection;

[0076] Network-side conditions during data collection.

[0077] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0078] Determine whether the second node meets the set conditions, and generate third information, which is used to indicate whether the second node can transmit training data;

[0079] Send the third information to the first node.

[0080] In conjunction with some embodiments of the second aspect, in some embodiments, the setting conditions include:

[0081] The second node does not have training data that meets the requirements; and,

[0082] The data transmission of training data in the second node does not meet the service requirements.

[0083] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0084] It is determined that the second node satisfies the set conditions;

[0085] The training data of the AI ​​model is sent to the first node.

[0086] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following:

[0087] The identifier of the AI ​​model;

[0088] Identification of AI functions;

[0089] The expected performance metrics of the AI ​​model;

[0090] Data volume;

[0091] Application scenarios of the AI ​​model;

[0092] The application conditions of the AI ​​model;

[0093] Data transmission service requirements;

[0094] The AI ​​task identifier of the AI ​​model.

[0095] Thirdly, embodiments of this disclosure provide a first node, including:

[0096] The transceiver module is used to determine that an AI model needs to be trained and to send first information to the second node. The first information is used to request training data for the AI ​​model.

[0097] Fourthly, embodiments of this disclosure provide a second node, comprising:

[0098] The transceiver module is used to receive the first information sent by the first node, which is used to request training data for the AI ​​model.

[0099] Fifthly, embodiments of this disclosure provide a first node, comprising:

[0100] One or more processors;

[0101] The first node is used to execute the communication method described in any one of the first aspects of this disclosure.

[0102] Sixthly, embodiments of this disclosure provide a second node, comprising:

[0103] One or more processors;

[0104] The second node is used to execute the communication method described in any one of the second aspects of this disclosure.

[0105] In a seventh aspect, embodiments of this disclosure provide a communication system, comprising:

[0106] The first node is used to perform the method as described in any one of the first aspects of this disclosure;

[0107] The second node is used to perform the method as described in any one of the second aspects of this disclosure.

[0108] Eighthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform a communication method as described in any one of the first aspects of this disclosure, or cause the communication device to perform a communication method as described in any one of the second aspects of this disclosure.

[0109] In a ninth aspect, embodiments of this disclosure provide a computer program product, including a computer program and / or instructions, wherein when the computer program and / or instructions are executed by a communication device, they implement the communication method as described in any one of the first aspects of this disclosure, or when the computer program and / or instructions are executed by a communication device, they implement the communication method as described in any one of the second aspects of this disclosure.

[0110] In a tenth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the methods described in optional implementations of the first and / or second aspects.

[0111] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the methods described according to optional implementations of the first and / or second aspects above.

[0112] It is understood that the aforementioned first node, second node, communication system, storage medium, program product, computer program, chip, or chip system are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0113] This disclosure provides a communication method, a first node, a second node, a system, a storage medium, and a product. In some embodiments, terms such as information processing method and communication method can be used interchangeably, as can terms such as information processing device and communication device, and terms such as information processing system and communication system.

[0114] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0115] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0116] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0117] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0118] In the embodiments disclosed herein, "multiple" refers to two or more.

[0119] In some embodiments, the terms “at least one (at least one item, at least one)”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0120] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0121] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0122] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0123] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0124] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0125] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0126] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0127] In some embodiments, "network" can be interpreted as devices included in a network, such as access network devices, core network devices, etc.

[0128] In some embodiments, "Access Network device (AN device)" may also be referred to as "Radio Access Network device (RAN device)," "Base Station (BS)," "Radio Base Station," or "Fixed Station." In some embodiments, it may also be understood as "node," "access point," "Transmission Point (TP)," "Reception Point (RP)," "Transmission / Reception Point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," or "Band Width Part (BWP)," etc.

[0129] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0130] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0131] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0132] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0133] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1A, the communication system 100 includes a first node 101 and a second node 102.

[0134] In some embodiments, the first node 101 includes, for example, at least one of the following: a mobile phone, a wearable device, an Internet of Things device, a car with communication capabilities, a smart car, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and a wireless terminal device in a smart home, but is not limited thereto.

[0135] In some embodiments, the first node 101 may also be, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation evolved Node B (ng-eNB), next-generation Node B (gNB), Node B (NB), Home Node B (HNB), Home evolved Node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0136] In some embodiments, the second node 102 includes, for example, at least one of the following: a mobile phone, a wearable device, an Internet of Things device, a car with communication capabilities, a smart car, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and a wireless terminal device in a smart home, but is not limited thereto.

[0137] In some embodiments, the second node may also be, for example, a node or device that connects the terminal to the wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), Node B (NB), Home Node B (HNB), Home evolved Node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0138] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0139] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some protocol layer functions are centrally controlled by the CU, while the remaining part or all protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0140] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0141] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0142] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G New Radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation Radio Access (FX), Global System for Mobile Communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0143] In some embodiments, training an AI model requires collecting a large amount of data, and the data requirements vary depending on the application scenario. Application scenarios may include mobile communication system processes such as beam management, CSI (Channel State Information) reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0144] In some embodiments, the use and inference of AI may require multiple AI models or AI functions for reasoning and prediction. For example, the implementation of a specific AI function may correspond to one or more AI models.

[0145] In some embodiments, data is crucial for AI, and this data may include: training data for training and testing the AI ​​model; inference data for using the AI ​​model; and performance monitoring data for monitoring the performance of the AI ​​model, thereby controlling the AI ​​model. AI model control based on performance monitoring data includes at least one of the following: AI model activation, AI model deactivation, and AI model switching.

[0146] In some embodiments, in beam management, the model training data may include at least one of the following: beam measurement results, beam identifiers, measurement results of the K strongest beams, beam identifiers, and the acquisition time of the beam measurement results. The measured beams can be network-configurable. The model training data differs in different application scenarios. For example, in CSI compression, the model training data may include at least one of the following: CSI measurement results and the acquisition time of the CSI measurement results; in a positioning scenario, the model training data may include at least one of the following: channel impulse response measurement results, UE (User Equipment) location information, and PRS (Positioning Reference Signal) measurement results; in mobility management, the model training data may include at least one of the following: measurement results of the serving cell, measurement results of the target cell, time, UE location, source cell, and target cell.

[0147] In some embodiments, the training data may further include the conditions under which the data was collected, which can be divided into network-side conditions and UE-side conditions. For example, UE-side conditions may include at least one of the following:

[0148] UE speed;

[0149] UE power consumption;

[0150] UE power;

[0151] The computing power of a UE can be measured by FLOPs (floating-point operations per second).

[0152] UE location can be a geographical location or the UE's location within the cell;

[0153] The business type can be audio, video, multimedia, voice, etc.

[0154] Antenna configuration, including the number of ports;

[0155] Rotational speed;

[0156] Storage space can be measured in bits.

[0157] The network-side conditions may include at least one of the following:

[0158] Community types, such as macro, micro, and dense urban communities;

[0159] Network deployment scenarios, such as indoors and outdoors;

[0160] Wireless channel quality can be determined by RSRP (Reference Signal Receiving Power / Reference Signal Received Power), RSRQ (Reference Signal Received Quality), or SINR (Signal Interference Noise Ratio).

[0161] The frequency of the cell;

[0162] Location of the residential area;

[0163] Distance between base stations;

[0164] Antenna configuration, including the number of ports and the number of MIMO (Multiple-Input Multiple-Output) layers;

[0165] Transmission power;

[0166] Numerology (parameter set).

[0167] Figure 1B is a schematic diagram of the architecture of a data service system according to an embodiment of the present disclosure. As shown in Figure 1B, the data management node can collect, store, and distribute data. The model training node can request training data from the data management node. The model training node can be a UE, a base station, an OAM (Operation Administration and Maintenance) node, other network nodes, or a server. The data management node can be a UE, a base station, an OAM node, other network nodes, or a service.

[0168] In some embodiments, how the model training node requests training data from the data management node is currently unclear, and a reasonable process and signaling need to be defined so that the data management node can send qualified training data to the model training node. Therefore, this embodiment provides a method for requesting data, enabling the model training node to request training data from the data management node for model training.

[0169] Figure 2A is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2A, the embodiments of the present disclosure relate to a communication method, which includes:

[0170] In step S2101, the first node determines that an AI model needs to be trained and sends the first information to the second node.

[0171] In some embodiments, the second node receives the first information.

[0172] In some embodiments, the first information is used to request training data for the AI ​​model.

[0173] In some embodiments, the name of the first information is not limited, and it may be, for example, "training data request information", "data request information", "model training request information", etc.

[0174] In some embodiments, the first node is a network node capable of performing AI model training. The first node may be configured with an initial neural network, which is trained based on the requested model training data to obtain the corresponding AI model. The first node may also be configured with a pre-trained AI model, which is trained based on the model training data requested from the second node to obtain an AI model that meets the current service requirements.

[0175] For example, the first node can be a model training node, which can be a UE, access network device, OAM, other network node, or server. The first node performs the AI ​​model training process based on the training data requested from the second node.

[0176] In some embodiments, the second node is used to manage the model training data of the AI ​​model. For example, this management process may include at least one of the following: configuring a database cluster, performing data backup and recovery, managing data security and transmission permissions, data resource isolation, and changing data node configuration. In this embodiment, the second node is used to verify data requests sent by the first node to determine whether it authorizes the transmission of AI model training data to the first node.

[0177] In some embodiments, the second node may be a data management node for managing data during AI model training and application. This data management node may include at least one of the following: UE, access network device, OAM, other network nodes, or a server. For example, the second node is used to schedule training data for the AI ​​model. When the second node receives a first message from the first node and determines that it is authorized to request the training data, it can schedule the training data from a third node storing the training data and send it to the first node. Optionally, the second node may also include a storage unit for storing the AI ​​model's training data. When the second node verifies the training data request sent by the first node and determines that it is authorized to obtain the training data, it sends the training data stored in the storage unit to the first node.

[0178] In some embodiments, the first information includes at least one of the following:

[0179] Identification of AI models;

[0180] Identification of AI functions;

[0181] Expected performance metrics for AI models;

[0182] Data volume;

[0183] Application scenarios of AI models;

[0184] Conditions for applying AI models;

[0185] Data transmission service requirements;

[0186] AI task identifiers for AI models.

[0187] It should be noted that AI model training data includes various types, and different AI models require different types of training data for different application scenarios. When the first node determines that AI model training is needed, it sends first information to the second node. This first information includes one or more data pieces that can indicate the training data required by the first node. Based on this data, the second node determines the training data requested by the first node. This training data information may include at least one of the following: training data type, training data volume, training data quality requirements, and training data application scenario.

[0188] For example, the model identifier of the AI ​​model indicates the data type of the training data requested by the first node;

[0189] The data type of the training data requested by the first node is indicated by the AI ​​function identifier of the model corresponding to the first node.

[0190] The data quality of the training data requested by the first node is indicated by the expected performance metric of the AI ​​model. The expected performance metric can be accuracy or generalization. The data management node determines the amount of data required based on the performance metric.

[0191] The data volume indicates the amount of training data requested by the first node, where the data volume can be the number of data items.

[0192] The application scenarios of the AI ​​model indicate the data type of the training data requested by the first node. The application scenarios may include at least one of the following: beam management, CSI reporting, CSI compression, positioning, handover, mobility management, radio resource management and other mobile communication system processes.

[0193] The application conditions of the AI ​​model indicate the data type of the training data requested by the first node. The application conditions can be UE-side conditions or network-side conditions during model inference.

[0194] The service requirements for data transmission indicate the quality requirements of the training data requested by the first node. These service requirements can be transmission latency, indicated by duration or time; the requested data must be sent to the model training node within the specified latency. Optionally, the service requirements can also be data integrity, indicated by an error threshold (the data error / error rate must be below a certain threshold). Alternatively, the service requirements can also be data freshness, indicated by duration (the time between the transmission and the data's acquisition must be less than a certain amount of time).

[0195] The AI ​​task identifier in the AI ​​model indicates the data type of the training data requested by the first node. The AI ​​task is the task the AI ​​model is tasked with completing. Tasks can include reducing latency, increasing throughput, and minimizing link failures.

[0196] Optionally, in some embodiments, before step S2101 described above, the method further includes:

[0197] The first node receives the second message sent by the second node.

[0198] In some embodiments, the second node sends second information.

[0199] In some embodiments, the second information is used to indicate information about the training data provided by the second node.

[0200] For example, to ensure the accuracy of the training data request process and improve the model training efficiency of the AI ​​model in the first node, before the first node determines that the AI ​​model needs to be trained, the second node sends a second message to the first node. This second message indicates the training data that the second node can provide. Based on this second message, the first node can determine which training data exists in the second node, and thus request training data from the second node when the first node needs to train the AI ​​model. This improves the accuracy of data requests in the first node.

[0201] In some embodiments, the name of the second information is not limited, and may be, for example, "training data information", "training data available information", "training data type information", "training data indication information", etc.

[0202] It is understandable that there are multiple second nodes communicating with the first node, each storing training data of different types, purposes, and amounts. These multiple second nodes report second information to the first node, allowing the first node to determine the information of the training data stored in each second node. Therefore, when the first node determines that it needs to train an AI model, it can send first information to certain second nodes storing the corresponding training data to request the relevant training data. This ensures the accuracy of the training data request process.

[0203] In some embodiments, the second information is used to indicate data information of the training data that the second node can provide to the first node. This data information may include at least one of the following: data type information, data volume information, data application scenario information, and data quality information. For example, the second information includes at least one of the following:

[0204] Data type;

[0205] Application scenarios of data;

[0206] Terminal-side conditions during data collection;

[0207] Network-side conditions during data collection.

[0208] For example, the data type may include at least one of the following: beam measurement results, beam identifiers, measurement results and beam identifiers of the K strongest beams, and the acquisition time of the beam measurement results. The beams being measured may be configured on the network side. CSI measurement results, acquisition time of the CSI measurement results. Channel impulse response measurement results, UE location information, PRS measurement results. Measurement results of the serving cell, measurement results of the target cell, time, UE location, source cell and target cell, etc.;

[0209] The application scenarios for the data may include at least one of the following: beam management, CSI reporting, CSI compression, positioning, handover, mobility management, radio resource management and other mobile communication system processes;

[0210] The conditions for data collection can be either UE-side conditions or network-side conditions.

[0211] Optionally, in some embodiments, the method further includes:

[0212] The first node determines the training data provided by the second node based on the second information, thus meeting the training requirements of the AI ​​model.

[0213] For example, based on the second information sent by the second node, the first node determines whether the training data provided by the second node meets the training requirements of the AI ​​model. Once it determines that the training data provided by the second node meets the model training requirements, the first node sends the first information to the second node, requesting the training data. Specifically, when determining whether the training data meets the AI ​​model's training requirements, the first node can first identify the application conditions of the AI ​​model. If the data application scenario included in the second information matches these application conditions, then it can be determined that the training data provided by the second node meets the AI ​​model's training requirements. The first node can also determine whether the data type included in the second information is the same as the training data type corresponding to the AI ​​model that needs to be trained, thereby determining whether the training data provided by the second node meets the AI ​​model's training requirements. The method by which the first node determines whether the training data provided by the second node meets the AI ​​model's training requirements based on the second information is not limited.

[0214] In step S2102, the second node sends the third information to the first node based on the first information.

[0215] In some embodiments, the first node receives third information.

[0216] In some embodiments, the third information is used to indicate whether the second node can transmit training data.

[0217] In some embodiments, the name of the third information is not limited, and it may be, for example, "transmission indication information", "transmission response information", "request feedback information", etc.

[0218] For example, after receiving a data request from the first node, the second node determines whether to authorize the first node to request the corresponding training data and generates third information to feed back to the first node. The second node can determine whether it can transmit training data to the first node based on factors such as the first node's ID, the first node's training data management rules based on the first information request, and training data transmission conditions.

[0219] Optionally, in some embodiments, step S2102 above includes:

[0220] The second node determines whether the second node meets the set conditions and generates the third information;

[0221] The second node sends the third message to the first node.

[0222] For example, in this embodiment, the second node determines whether a set condition is met, which is used to determine whether the second node can transmit training data to the first node. Then, it generates third information and sends it to the first node.

[0223] In some embodiments, the setting conditions include:

[0224] The second node does not contain training data that meets the requirements; and,

[0225] The data transmission of training data in the second node does not meet the service requirements.

[0226] For example, if the second node determines that all of the above-mentioned conditions are met, it is determined that the second node is currently able to transmit training data. If the second node determines that any of the above-mentioned conditions are not met, it is determined that the second node is currently unable to transmit training data. The conditions include: the second node currently does not have training data that meets the training requirements of the corresponding AI model; and the data transmission between the second node and the first node does not meet the service requirements.

[0227] Optionally, in some embodiments, the method further includes:

[0228] The second node is determined to meet the set conditions;

[0229] The second node sends the training data of the AI ​​model to the first node.

[0230] For example, after the second node sends the third information, when the second node determines that the above-mentioned conditions are met, it sends the training data of the AI ​​model to the first node.

[0231] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0232] In some embodiments, the terms "codebook," "codeword," and "precoding matrix" can be used interchangeably. For example, a codebook can be a collection of one or more codewords / precoding matrices.

[0233] In some embodiments, the terms "uplink", "uplink", and "physical uplink" can be used interchangeably, as can the terms "downlink", "downlink", and "physical downlink", as well as the terms "sidelink", "sidelink", "sidelink communication", "sidelink communication", "direct connection", "direct link", "direct communication", and "direct link communication".

[0234] In some embodiments, the terms “Downlink Control Information (DCI),” “Downlink (DL) assignment,” “DL DCI,” “Uplink (UL) grant,” and “UL DCI” can be used interchangeably.

[0235] In some embodiments, terms such as "Physical Downlink Shared Channel (PDSCH)" and "DL data" can be used interchangeably, as can terms such as "Physical Uplink Shared Channel (PUSCH)" and "UL data".

[0236] In some embodiments, the terms “radio”, “wireless”, “Radio Access Network (RAN)”, “Access Network (AN)”, and “RAN-based” can be used interchangeably.

[0237] In some embodiments, the terms "search space", "search space set", "search space configuration", "search space set configuration", "control resource set (CORESET)", and "CORESET configuration" can be used interchangeably.

[0238] In some embodiments, the terms “Synchronization Signal (SS)”, “Synchronization Signal Block (SSB)”, “Reference Signal (RS)”, “pilot”, and “pilot signal” can be used interchangeably.

[0239] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0240] In some embodiments, the terms "component carrier (CC)," "cell," "frequency carrier," and "carrier frequency" can be used interchangeably.

[0241] In some embodiments, the terms “Resource Block (RB)”, “Physical Resource Block (PRB)”, “Sub-Carrier Group (SCG)”, “Resource Element Group (REG)”, “PRB Pair”, “RB Pair”, “Resource Element (RE)”, and “sub-carrier” can be used interchangeably.

[0242] In some embodiments, terms such as wireless access scheme and waveform can be used interchangeably.

[0243] In some embodiments, the terms "precoding", "precoder", "weight", "precoding weight", "quasi-co-location (QCL)", "transmission configuration indication (TCI) status", "spatial relation", "spatial domain filter", "transmission power", "phase rotation", "antenna port", "antenna port group", "layer", "the number of layers", "rank", "resource", "resource set", "resource group", "beam", "beam width", "beam angular degree", "antenna", "antenna element", and "panel" can be used interchangeably.

[0244] In some embodiments, the terms “frame”, “radio frame”, “subframe”, “slot”, “sub-slot”, “mini-slot”, “symbol”, “symbol”, and “transmission time interval (TTI)” can be used interchangeably.

[0245] In some embodiments, “get,” “obtain,” “receive,” “transmit,” “bidirectional transmission,” and “send and / or receive” can be used interchangeably and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining through self-processing, or autonomous implementation, among other meanings.

[0246] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transmit,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0247] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or indication, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0248] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (boolean), or by a comparison of numerical values ​​(e.g., a comparison with a predetermined value), but is not limited thereto.

[0249] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the receiver to respond to the sent content.

[0250] The communication method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2102. For example, step S2101 may be implemented as a separate embodiment, and step S2102 may be implemented as a separate embodiment, but are not limited thereto.

[0251] In some embodiments, steps S2101 and S2102 may be performed in an alternate order or simultaneously.

[0252] In some embodiments, steps S2101 and S2102 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0253] In some embodiments, other alternative implementations may be described before or after the specification corresponding to FIG2A.

[0254] Figure 2B is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 2B, the present disclosure relates to a communication method, which includes:

[0255] Step S2201: The second node sends the second information to the first node.

[0256] In some embodiments, the second information is used to indicate information about the training data provided by the second node.

[0257] For example, the definitions of the first node, the second node, and the second information in this embodiment are the same as those in the above embodiments, and can be referred to the above embodiments, which will not be repeated here. The second node sends the second information to the first node to report information about the training data that the second node can provide. This information may include at least one of the following: data type, application of the data, terminal-side conditions during data collection, and network-side conditions during data collection. The first node can request the training data required for the current AI model training from the second node based on the second information.

[0258] In some embodiments, the second nodes that have a communication connection with the first node may include multiple nodes. These multiple second nodes report second information to the first node, indicating the training data that can be provided to the first node. Based on the second information reported by the multiple second nodes, the first node selects a target second node from the multiple second nodes. The target second node is a node that can provide the training data required for training the current AI model to the first node.

[0259] In step S2202, the first node determines that an AI model needs to be trained and sends the first information to the second node.

[0260] In some embodiments, the definition of the first information is the same as that in the above embodiments, and can be referred to the above embodiments, which will not be repeated here.

[0261] For example, after determining the target second node from one or more second nodes through the second information, the first node sends the first information to the target second node, which is a node that can provide training data corresponding to the AI ​​model.

[0262] The optional implementation of step S2202 can be found in the optional implementation of step S2101 in Figure 2A, and other related parts in the embodiments involved in Figure 2A, which will not be repeated here.

[0263] In step S2203, the second node sends the third information to the first node based on the first information.

[0264] For example, after receiving the first information, the second node determines whether it can provide training data for the AI ​​model to the first node, generates third information, and sends the third information to the first node. The definition of the third information is the same as in the above embodiments and will not be repeated here.

[0265] The communication method involved in the embodiments of this disclosure may include at least one of steps S2201 to S2203. For example, step S2201 may be implemented as a separate embodiment, and step S2202 may be implemented as a separate embodiment, but are not limited thereto.

[0266] In some embodiments, steps S2201, S2202, and S2203 may be performed in an alternate order or simultaneously.

[0267] In some embodiments, steps S2201 and S2203 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0268] In some embodiments, other alternative implementations may be described before or after the specification corresponding to FIG2B.

[0269] Figure 2C is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 2C, the embodiments of the present disclosure relate to a communication method, which includes:

[0270] In step S2301, the first node determines that an AI model needs to be trained and sends the first information to the second node.

[0271] For example, the definitions of the first node, the second node, and the first information in this embodiment are the same as those in the above embodiments, and can be referred to the above embodiments, which will not be repeated here.

[0272] The optional implementation of step S2301 can be found in the optional implementation of step S2101 in Figure 2A, and other related parts in the embodiments involved in Figure 2A, which will not be repeated here.

[0273] In step S2302, the second node responds to the first information by sending the second information to the first node.

[0274] For example, in this embodiment, the first node requests training data for the AI ​​model from the second node using first information. After receiving the first information, the second node sends second information back to the first node. This second information indicates the training data that the second node can provide to the first node. In other words, after receiving the first information, the second node indicates to the first node what training data it can provide. This second information helps the first node filter out the training data needed for training the current AI model from the training data that the second node can provide.

[0275] Optionally, in some embodiments, the method further includes:

[0276] The first node sends the fourth message to the second node.

[0277] In some embodiments, the fourth information is used to instruct the second node to send target training data for training the current AI model to the first node from a plurality of training data that can be provided.

[0278] In step S2303, the second node sends the third information to the first node based on the first information.

[0279] For example, the second information mentioned above is used to indicate the data information that the second node can provide to the first node for training data. In this embodiment, the second node determines whether it can provide training data to the first node based on the received first information and generates third information. The definition of the third information is the same as that in the above embodiments, and can be referred to the above embodiments, so it will not be repeated here.

[0280] The communication method involved in the embodiments of this disclosure may include at least one of steps S2301 to S2303. For example, step S2301 may be implemented as a separate embodiment, and step S2302 may be implemented as a separate embodiment, but are not limited thereto.

[0281] In some embodiments, steps S2301, S2302, and S2303 may be performed in an alternate order or simultaneously.

[0282] In some embodiments, steps S2301, S2302, and S2303 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0283] In some embodiments, other alternative implementations may be described before or after the specification corresponding to FIG2C.

[0284] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3, the embodiment of the present disclosure relates to a communication method executed by a first node, the method including:

[0285] Step S3101: Determine that an AI model needs to be trained, and send the first information to the second node.

[0286] In some embodiments, the first information is used to request training data for the AI ​​model.

[0287] The optional implementation of step S3101 can be found in the optional implementation of step S2101 in Figure 2A and other related parts in the embodiments involved in Figure 2A, which will not be repeated here.

[0288] In some embodiments, the method further includes:

[0289] Receive the second information sent by the second node, which is used to indicate the information of the training data provided by the second node.

[0290] In some embodiments, the second information includes at least one of the following:

[0291] Data type;

[0292] Application scenarios of data;

[0293] Terminal-side conditions during data collection;

[0294] Network-side conditions during data collection.

[0295] In some embodiments, before sending the first information to the second node, the method further includes:

[0296] Based on the second piece of information, determine the training data provided by the second node to meet the model training requirements of the AI ​​model.

[0297] In some embodiments, the method further includes:

[0298] Receive the third information sent by the second node, which indicates whether the second node can transmit training data.

[0299] In some embodiments, the first information includes at least one of the following:

[0300] Identification of AI models;

[0301] Identification of AI functions;

[0302] Expected performance metrics for AI models;

[0303] Data volume;

[0304] Application scenarios of AI models;

[0305] Conditions for applying AI models;

[0306] Data transmission service requirements;

[0307] AI task identifiers for AI models.

[0308] In some embodiments, other optional implementations may be described before or after the specification corresponding to FIG3.

[0309] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4, this embodiment relates to a communication method executed by a second node, the method comprising:

[0310] Step S4101: Receive the first information sent by the first node.

[0311] In some embodiments, the first information is used to request training data for the AI ​​model.

[0312] The optional implementation of step S4101 can be found in the optional implementation of step S2101 in Figure 2A and other related parts in the embodiments involved in Figure 2A, which will not be repeated here.

[0313] In some embodiments, the method further includes:

[0314] Send a second message to the first node, which is used to indicate the information of the training data provided by the second node.

[0315] In some embodiments, the second information includes at least one of the following:

[0316] Data type;

[0317] Application scenarios of data;

[0318] Terminal-side conditions during data collection;

[0319] Network-side conditions during data collection.

[0320] In some embodiments, the method further includes:

[0321] Determine whether the second node meets the set conditions, and generate third information. The third information is used to indicate whether the second node can transmit training data.

[0322] Send the third message to the first node.

[0323] In some embodiments, the set conditions include:

[0324] The second node does not contain training data that meets the requirements; and,

[0325] The data transmission of training data in the second node does not meet the service requirements.

[0326] In some embodiments, the method further includes:

[0327] Determine that the second node meets the set conditions;

[0328] Send the training data of the AI ​​model to the first node.

[0329] In some embodiments, the first information includes at least one of the following:

[0330] Identification of AI models;

[0331] Identification of AI functions;

[0332] Expected performance metrics for AI models;

[0333] Data volume;

[0334] Application scenarios of AI models;

[0335] Conditions for applying AI models;

[0336] Data transmission service requirements;

[0337] AI task identifiers for AI models.

[0338] In some embodiments, other optional implementations may be described before or after the specification corresponding to Figure 4.

[0339] Figure 5 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 5, the present disclosure relates to a communication method, which includes:

[0340] In step S5101, the model training node determines that the model needs to be trained and sends the first message to the data management node, indicating a request for training data.

[0341] In some embodiments, the model training node receives second information sent by the data management node, the second information indicating data information that the data management node can provide, wherein the data information includes at least one of the following:

[0342] a. Data type, which may include beam measurement results, beam identifiers, measurement results and identifiers of the K strongest beams, and the acquisition time of the beam measurement results. The measured beams may be network-configurable. CSI measurement results, acquisition time of CSI measurement results. Channel impulse response measurement results, UE location information, PRS measurement results. Serving cell measurement results, target cell measurement results, time, UE location, source cell, and target cell, etc.

[0343] b. Application scenarios of the data, which may include mobile communication system processes such as beam management, CSI reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0344] c. The conditions for data collection can be either UE-side conditions or network-side conditions.

[0345] In some embodiments, the model training node determines that the available data indicated in the second information meets the requirements for model training, and sends the first information to the data management node.

[0346] In some embodiments, the first information includes at least one of the following:

[0347] a. Identifiers of the model or function that needs to be trained;

[0348] b. The model's expected performance metrics, which can be accuracy or generalization ability. The data management node determines the required amount of data based on the performance metrics;

[0349] c. Data volume, which can be the number of data items;

[0350] d. Application scenarios of the model, which may include mobile communication system processes such as beam management, CSI reporting, CSI compression, positioning, handover, mobility management, and radio resource management;

[0351] e. The application conditions of the model, which can be the UE-side conditions or network-side conditions during model inference;

[0352] f. Service requirements (QoS), where the service requirement can be transmission latency, which can be indicated by duration or time, i.e., the requested data needs to be sent to the model training node within the specified latency; the service requirement can also be data integrity, which can be indicated by an error threshold, i.e., the data error / error rate is below a certain threshold; the service requirement can also be data freshness, which can be indicated by duration, i.e., the time between the transmission of the data and the time when the data was collected is less than a certain duration.

[0353] g. The AI ​​task identifier corresponding to this model, which is the task that the AI ​​model is to complete. The AI ​​task can be to reduce latency, increase throughput, reduce link failures, etc.

[0354] In some embodiments, after receiving the first information, the data management node determines whether it can send data to the model training node, and sends a third information to the model training node to indicate whether data can be transmitted.

[0355] In some embodiments, data can be sent to the model training node if all of the following conditions are met, and cannot be sent to the model training node if any one of the conditions is not met. The conditions include:

[0356] a. There is no data in the data management node that meets the requirements;

[0357] b. The transmission of training data cannot meet the indicated service requirements.

[0358] In some embodiments, if the data management node can send data, the requested data is sent to the model training node.

[0359] In some embodiments, other optional implementations described before or after the specification corresponding to Figure 5 may be referred to.

[0360] The above method enables the model training node to request training data from the data management node, thereby enabling model training.

[0361] This disclosure also provides an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0362] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an Application-Specific Integrated Circuit (ASIC), and the functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), which can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0363] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a Graphics Processing Unit (GPU) (which can be understood as a microprocessor), or a Digital Signal Processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an Application-Specific Integrated Circuit (ASIC) or a Programmable Logic Device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be hardware circuits designed for artificial intelligence, which can be understood as ASICs, such as Neural Network Processing Units (NPUs), Tensor Processing Units (TPUs), and Deep Learning Processing Units (DPUs).

[0364] Figure 6 is a schematic diagram of the structure of a first node according to an embodiment of the present disclosure. As shown in Figure 6, the first node 6100 may include a transceiver module 6101. In some embodiments, the transceiver module 6101 is used to determine that an AI model needs to be trained and send first information to a second node, the first information being used to request training data for the AI ​​model. Optionally, the transceiver module 6101 is used to perform at least one of the communication steps such as determination and / or acquisition performed by the first node in any of the above methods, which will not be elaborated here.

[0365] In some embodiments, the transceiver module may include a receiving module and a transmitting module, which may be separate or integrated. Optionally, the transmitting module may be interchangeable with a transmitter. The receiving module may be interchangeable with a receiver.

[0366] Figure 7 is a schematic diagram of the structure of a second node according to an embodiment of the present disclosure. As shown in Figure 7, the second node 7100 may include a transceiver module 7101. In some embodiments, the transceiver module 7101 is used to receive first information sent by the first node, the first information being used to request training data for an AI model. Optionally, the transceiver module 7101 is used to perform at least one of the communication steps such as determination and / or acquisition performed by the network device 102 in any of the above methods, which will not be elaborated here.

[0367] In some embodiments, the transceiver module may include a receiving module and a transmitting module, which may be separate or integrated. Optionally, the transmitting module may be interchangeable with a transmitter. The receiving module may be interchangeable with a receiver.

[0368] Figure 8 is a schematic diagram of the structure of a communication device 8100 according to an embodiment of this disclosure. The communication device 8100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 8100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0369] As shown in Figure 8, the communication device 8100 includes one or more third processors 8101. The third processor 8101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 8100 can be used to execute any of the above methods. Optionally, one or more third processors 8101 can be used to invoke instructions to cause the communication device 8100 to execute any of the above methods.

[0370] In some embodiments, the communication device 8100 further includes one or more third transceivers 8102. When the communication device 8100 includes one or more third transceivers 8102, the third transceiver 8102 performs at least one of the communication steps such as sending and / or receiving in the above method, and the third processor 8101 performs at least one of the other steps. In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, sending unit, transmitter, sending circuit, etc., can be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0371] In some embodiments, the communication device 8100 further includes one or more third memories 8103 for storing data. Optionally, all or part of the third memories 8103 may be located outside the communication device 8100. In optional embodiments, the communication device 8100 may include one or more first interface circuits 8104. Optionally, the first interface circuit 8104 is connected to the third memory 8103, and the first interface circuit 8104 can be used to receive data from the third memory 8103 or other devices, and can be used to send data to the third processor 8101 or other devices. For example, the first interface circuit 8104 can read data stored in the third memory 8103 and send the data to the third processor 8101.

[0372] The communication device 8100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 8100 described in this disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG8. The communication device may be a standalone device or may be part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0373] Figure 9 is a schematic diagram of the structure of chip 8200 according to an embodiment of the present disclosure. For cases where the communication device 8100 can be a chip or a chip system, the schematic diagram of chip 8200 shown in Figure 9 can be referenced, but is not limited thereto.

[0374] Chip 8200 includes one or more fourth processors 8201. Chip 8200 is used to perform any of the above methods.

[0375] In some embodiments, chip 8200 further includes one or more second interface circuits 8202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 8200 further includes one or more fourth memories 8203 for storing data. Optionally, all or part of the fourth memories 8203 may be located outside chip 8200. Optionally, the second interface circuit 8202 is connected to the fourth memories 8203, and the second interface circuit 8202 can be used to receive data from the fourth memories 8203 or other devices, and the second interface circuit 8202 can be used to send data to the fourth memories 8203 or other devices. For example, the second interface circuit 8202 can read data stored in the fourth memories 8203 and send the data to the fourth processor 8201.

[0376] In some embodiments, the second interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above-described method. For example, the second interface circuit 8202 performing the communication steps such as sending and / or receiving in the above-described method refers to the second interface circuit 8202 performing data interaction between the fourth processor 8201, the chip 8200, the fourth memory 8203, or the transceiver device. In some embodiments, the fourth processor 8201 performs at least one of the other steps.

[0377] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0378] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device 8100, cause the communication device 8100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0379] This disclosure also provides a program product that, when executed by the communication device 8100, causes the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0380] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method characterized by comprising: Executed by the first node, the method includes: Once it is determined that an AI model needs to be trained, a first message is sent to the second node, which requests the training data for the AI ​​model.

2. The method of claim 1, wherein, The method further includes: Receive second information sent by the second node, the second information being used to indicate information about the training data provided by the second node.

3. The method of claim 2, wherein, The second information includes at least one of the following: Data type; Application scenarios of data; Terminal-side conditions during data collection; Network-side conditions during data collection.

4. The method according to claim 2 or 3, characterized in that, Before sending the first information to the second node, the method further includes: Based on the second information, the training data provided by the second node is determined to meet the model training requirements of the AI ​​model.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: The system receives a third message sent by the second node, the third message indicating whether the second node can transmit training data.

6. The method according to any one of claims 1-5, characterized in that, The first information includes at least one of the following: The identifier of the AI ​​model; Identification of AI functions; The expected performance metrics of the AI ​​model; Data volume; Application scenarios of the AI ​​model; The application conditions of the AI ​​model; Data transmission service requirements; The AI ​​task identifier of the AI ​​model.

7. A communication method characterized by comprising: Executed by the second node, the method includes: Receive the first message sent by the first node, which is used to request training data for the AI ​​model.

8. The method of claim 7, wherein, The method further includes: Send a second message to the first node, the second message being used to indicate information about the training data provided by the second node.

9. The method of claim 8, wherein, The second information includes at least one of the following: Data type; Application scenarios of data; Terminal-side conditions during data collection; Network-side conditions during data collection.

10. The method according to any one of claims 7-9, characterized in that, The method further includes: Determine whether the second node meets the set conditions, and generate third information, which is used to indicate whether the second node can transmit training data; Send the third information to the first node.

11. The method of claim 10, wherein, The setting conditions include: The second node does not have training data that meets the requirements; and, The data transmission of training data in the second node does not meet the service requirements.

12. The method according to claim 10 or 11, characterized in that, The method further includes: It is determined that the second node satisfies the set conditions; The training data of the AI ​​model is sent to the first node.

13. The method according to any one of claims 7-12, characterized in that, The first information includes at least one of the following: The identifier of the AI ​​model; Identification of AI functions; The expected performance metrics of the AI ​​model; Data volume; Application scenarios of the AI ​​model; The application conditions of the AI ​​model; Data transmission service requirements; The AI ​​task identifier of the AI ​​model.

14. A first node, characterized by include: The transceiver module is used to determine that an AI model needs to be trained and to send first information to the second node. The first information is used to request training data for the AI ​​model.

15. A second node, comprising: include: The transceiver module is used to receive the first information sent by the first node, which is used to request training data for the AI ​​model.

16. A first node, comprising: include: One or more processors; The first node is used to execute the communication method according to any one of claims 1-6.

17. A second node, comprising: include: One or more processors; The second node is used to execute the communication method according to any one of claims 7-13.

18. A communication system, characterized by include: The first node is used to perform the method as described in any one of claims 1-6; The second node is used to perform the method as described in any one of claims 7-13.

19. A storage medium, the storage medium storing instructions, wherein, When the instruction is executed on the communication device, it causes the communication device to perform the communication method as described in any one of claims 1-6, or causes the communication device to perform the communication method as described in any one of claims 7-13.

20. A computer program product comprising computer programs and / or instructions, characterized in that, When the computer program and / or instructions are executed by the communication device, they implement the communication method as described in any one of claims 1-6, or when the computer program and / or instructions are executed by the communication device, they implement the communication method as described in any one of claims 7-13.

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