Communication method, communication device, communication system and storage medium

Abstract

Provided in the present disclosure are a communication method, a communication device, a communication system and a storage medium. The method comprises: sending first information to a network device, wherein the first information is configured to indicate that a terminal no longer needs a first configuration, and the first configuration is configured to collect training data of an artificial intelligence (AI) model. The method of the present disclosure can avoid resource waste.

Description

Communication methods, communication equipment, communication systems, storage media Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to communication methods, communication devices, communication systems, and storage media. Background Technology

[0002] With the continuous development of artificial intelligence (AI) and machine learning technologies, a variety of AI functions have been introduced into communication systems. These AI functions may include, for example, AI-based beam management, AI-based channel state information (CSI) reporting, AI-based CSI compression, AI-based positioning, AI-based handover, AI-based mobility management, and AI-based radio resource management. Summary of the Invention

[0003] This disclosure provides communication methods, communication devices, communication systems, and storage media.

[0004] According to a first aspect of the present disclosure, a communication method is proposed, executed by a terminal, comprising: sending first information to a network device, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data of an artificial intelligence (AI) model.

[0005] According to a second aspect of the present disclosure, a communication method is proposed, performed by a network device, the method comprising: receiving first information sent by a terminal, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data of an artificial intelligence (AI) model.

[0006] According to a third aspect of the present disclosure, a terminal is provided, comprising: a transceiver module, configured to send first information to a network device, the first information being configured to indicate that the terminal no longer requires a first configuration, wherein the first configuration is configured to collect training data of an artificial intelligence (AI) model.

[0007] According to a fourth aspect of the present disclosure, a network device is provided, comprising: a transceiver module, configured to receive first information sent by a terminal, the first information being configured to indicate that the terminal no longer needs a first configuration, wherein the first configuration is configured to collect training data of an artificial intelligence (AI) model.

[0008] According to a fifth aspect of the embodiments of this disclosure, a communication device is provided, comprising:

[0009] One or more processors;

[0010] The processor is configured to invoke instructions to cause the communication device to execute any of the communication methods described in the first aspect to the second aspect.

[0011] According to a sixth aspect of the present disclosure, a communication system is provided, including a terminal and a network device, wherein the terminal is configured to implement the communication method described in the first aspect, and the network device is configured to implement the communication method described in the second aspect.

[0012] According to a seventh 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 of the first to second aspects.

[0013] According to an eighth aspect of the present disclosure, the present disclosure provides a program product including a computer program that, when executed by a communication device, implements the communication method as described in any of the first to second aspects.

[0014] According to a ninth aspect of the present disclosure, the present disclosure provides a computer program that, when run on a computer, causes the computer to perform the communication method as described in any of the first to second aspects.

[0015] It is understood that the aforementioned terminals, network devices, communication devices, communication systems, storage media, program products, and computer programs are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here. Attached Figure Description

[0016] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0017] Figure 1 is a schematic diagram of the architecture of some communication systems provided in the embodiments of this disclosure;

[0018] Figure 2 is an interactive schematic diagram of a communication method provided in an embodiment of this disclosure;

[0019] Figure 3 is a flowchart illustrating a communication method provided in another embodiment of this disclosure;

[0020] Figure 4 is a flowchart illustrating a communication method provided in another embodiment of this disclosure;

[0021] Figure 5A is a schematic diagram of the structure of a terminal provided in an embodiment of this disclosure;

[0022] Figure 5B is a schematic diagram of the structure of a network device provided in an embodiment of this disclosure;

[0023] Figure 6A is a schematic diagram of the structure of a communication device provided in an embodiment of this disclosure;

[0024] Figure 6B is a schematic diagram of the structure of a chip provided in an embodiment of this disclosure. Detailed Implementation

[0025] This disclosure provides embodiments of a communication method, a communication device, a communication system, and a storage medium.

[0026] In a first aspect, embodiments of this disclosure propose a communication method executed by a terminal, the method comprising: sending first information to a network device, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data of an artificial intelligence (AI) model.

[0027] In the above embodiments, the terminal can indicate to the network device that it no longer needs the first configuration. The first configuration can be used to collect training data for an AI model; for example, it may include the wireless signal required for collecting training data. Therefore, when the network device receives the first information, it can determine that the terminal no longer needs to collect training data. The network device can then promptly stop transmitting the wireless signal, or promptly release the transmission resources corresponding to the wireless signal. This prevents scenarios where "the terminal no longer needs the first configuration, but the network device continues to transmit the wireless signal related to the first configuration," thus avoiding resource waste.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following: a first resource location corresponding to the first signal; input characteristic requirements of the AI ​​model; output characteristic requirements of the AI ​​model; measurement configuration information of the terminal, wherein the measurement configuration information is used to determine the measurement result of the first signal; wherein the training data is collected based on the measurement result of the first signal, and the training data satisfies the input characteristic requirements and the output characteristic requirements of the AI ​​model.

[0029] In the above embodiments, the contents of the first configuration are described, and how the terminal collects training data of the AI ​​model based on the first configuration is explained, so that the terminal can accurately collect training data of the AI ​​model based on the first configuration, and train the AI ​​model based on the training data, thereby improving the accuracy of the AI ​​model.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: the first request being used to indicate one or more of the following: the terminal needs to collect the training data; one or more first configurations required by the terminal; and an AI model corresponding to one or more first configurations required by the terminal.

[0031] In the above embodiments, the terminal sends a first request to the network device so that the network device can determine the first configuration required by the terminal based on the first request and send the first configuration to the terminal. Thus, the terminal can accurately collect training data of the AI ​​model based on the first configuration and train the AI ​​model based on the training data, thereby improving the accuracy of the AI ​​model.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: receiving an acknowledgment indication or a rejection indication sent by the network device; wherein the acknowledgment indication is used to indicate one or more of the following: the network device acknowledges sending the first configuration; an identifier corresponding to one or more of the first configurations acknowledged by the network device; an AI model corresponding to one or more of the first configurations acknowledged by the network device; and the rejection indication is used to indicate one or more of the following: the network device rejects sending the first configuration; an identifier corresponding to one or more of the first configurations rejected by the network device; an AI model corresponding to one or more of the first configurations rejected by the network device.

[0033] In the above embodiments, the network device sends an acknowledgment or rejection instruction to the terminal. Based on the acknowledgment or rejection instruction sent by the network device, the terminal can know which first configurations the network device can provide to the terminal. Thus, the terminal can collect training data for the corresponding AI model based on these first configurations and train the AI ​​model based on the training data, thereby improving the accuracy of the AI ​​model.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, sending the first information to the network device includes: the terminal determining to stop collecting the training data and sending the first information to the network device.

[0035] In conjunction with some embodiments of the first aspect, in some embodiments, sending the first information to the network device includes: the terminal has sent a first request to the network device, the terminal determines to stop collecting the training data, and sends the first information to the network device.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, sending the first information to the network device includes: the terminal has sent a first request to the network device, the terminal receives an acknowledgment instruction, the terminal determines to stop collecting the training data, and sends the first information to the network device.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, sending the first information to the network device includes: the terminal has sent a first request to the network device, the terminal determines that a first condition is met, the terminal determines to stop collecting the training data, and sends the first information to the network device;

[0038] The first condition includes any of the following: the network device has already sent the first configuration; the network device is sending the first configuration; the network device has sent the first configuration, but the network device has not sent the first configuration at the current moment.

[0039] In conjunction with some embodiments of the first aspect, in some embodiments, sending the first information to the network device includes: the terminal has sent a first request to the network device, the terminal determines to stop collecting training data of at least one AI model, and sends the first information to the network device.

[0040] In the above embodiments, the preconditions for the terminal to send the first information are explained. That is, the first information is not sent blindly by the terminal, but can only be sent after certain conditions are met, thereby ensuring the accuracy of the first information when it is sent.

[0041] In conjunction with some embodiments of the first aspect, in some embodiments, the first information is further used to indicate one or more of the following: one or more first configurations that the terminal does not need; and the AI ​​model corresponding to one or more first configurations that the terminal does not need.

[0042] In the above embodiments, the first information can also indicate first configurations that the terminal does not need. Thus, based on the first information, the network device can know which first configurations the terminal does not need. The network device can then selectively stop sending the wireless signals corresponding to these first configurations and selectively release the transmission resources of the wireless signals corresponding to these first configurations. This improves the accuracy of the network device operation, prevents the network device from mistakenly stopping the first configurations needed by the terminal, which could lead to the failure of AI model training and ensures communication stability.

[0043] In conjunction with some embodiments of the first aspect, in some embodiments, sending the first information to the network device includes: receiving third information sent by the network device, the third information being used to indicate whether the terminal is allowed to send the first information; the third information indicating that the terminal is allowed to send the first information, and sending the first information to the network device.

[0044] In the above embodiments, the terminal sends the first information only when the network device allows the terminal to send the first information, rather than the terminal freely determining whether to send the first information. This unifies the understanding of "whether to send the first information" between the network device and the terminal, ensuring communication stability.

[0045] Secondly, embodiments of this disclosure propose a communication method executed by a network device, the method comprising: receiving first information sent by a terminal, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data of an artificial intelligence (AI) model.

[0046] In conjunction with some embodiments of the second aspect, in some embodiments, the first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following: a first resource location corresponding to the first signal; input characteristic requirements of the AI ​​model; output characteristic requirements of the AI ​​model; measurement configuration information of the terminal, wherein the measurement configuration information is used to determine the measurement result of the first signal; wherein the training data is collected based on the measurement result of the first signal, and the training data satisfies the input characteristic requirements and the output characteristic requirements of the AI ​​model.

[0047] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes one or more of the following: stopping the transmission of the first signal; releasing the first resource location.

[0048] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving a first request sent by the terminal, the first request indicating one or more of the following: the terminal needs to collect the training data; one or more first configurations required by the terminal; and an AI model corresponding to one or more first configurations required by the terminal.

[0049] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: the network device being able to provide the first configuration to the terminal and sending an acknowledgment indication to the terminal; or, the network device being unable to provide the first configuration to the terminal and sending a rejection indication to the terminal; wherein the acknowledgment indication is used to indicate one or more of the following: the network device acknowledging the sending of the first configuration; an identifier corresponding to one or more of the first configurations acknowledged by the network device; an AI model corresponding to one or more of the first configurations acknowledged by the network device; and the rejection indication is used to indicate one or more of the following: the network device rejecting the sending of the first configuration; an identifier corresponding to one or more of the first configurations rejected by the network device; an AI model corresponding to one or more of the first configurations rejected by the network device.

[0050] In conjunction with some embodiments of the second aspect, in some embodiments, the first information is further used to indicate one or more of the following: one or more first configurations that the terminal does not need; and the AI ​​model corresponding to one or more first configurations that the terminal does not need.

[0051] In some embodiments, in conjunction with the second aspect, the method further includes: sending third information to the terminal, the third information being used to indicate whether the terminal is allowed to send the first information.

[0052] Thirdly, this disclosure provides a terminal, including: a transceiver module, used to send first information to a network device, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data of an artificial intelligence (AI) model.

[0053] Fourthly, this disclosure provides a network device, including: a transceiver module, configured to receive first information sent by a terminal, the first information indicating that the terminal no longer needs a first configuration, wherein the first configuration is configured to collect training data of an artificial intelligence (AI) model.

[0054] Fifthly, embodiments of this disclosure provide a communication device, which includes: one or more processors; one or more memories for storing instructions; wherein the processors are configured to invoke the instructions to cause the communication device to perform the methods described in the first aspect, optional implementations of the first aspect, the second aspect, optional implementations of the second aspect, the third aspect, and optional implementations of the third aspect.

[0055] In a sixth aspect, embodiments of this disclosure provide a communication system comprising: a terminal and a network device; wherein the terminal is configured to perform the method described in the first aspect and optional implementations thereof, and the network device is configured to perform the method described in the second aspect and optional implementations thereof.

[0056] In a seventh aspect, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method described in the first aspect, an optional implementation of the first aspect, the second aspect, and an optional implementation of the second aspect.

[0057] Eighthly, embodiments of this disclosure provide a program product including a computer program that, when executed by a processor, implements the methods described in the first aspect, optional implementations of the first aspect, the second aspect, and optional implementations of the second aspect.

[0058] In a ninth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the methods described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.

[0059] It is understood that the aforementioned terminals, network devices, communication devices, communication systems, storage media, program products, and computer programs are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0060] This disclosure provides communication methods, communication devices, communication systems, and storage media. In some embodiments, the terms resource selection method, information processing method, and communication method can be used interchangeably.

[0061] 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. In all embodiments of this disclosure, unless otherwise specified or logically conflicting, the terminology and / or descriptions between the embodiments are consistent and can be mutually referenced. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0062] 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.

[0063] 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.

[0064] In the embodiments disclosed herein, "multiple" refers to two or more.

[0065] In some embodiments, the terms “at least one of A or B, at least one of A and B”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0066] 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 whether there is a branch B); in some embodiments, B (execute B regardless of whether there is a branch A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, both A and B are executed. The same applies when there are more branches such as A, B, C, etc.

[0067] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execute A regardless of whether a branch B exists); in some embodiments, B (execute B regardless of whether a branch A exists); 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, and C.

[0068] 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.

[0069] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0070] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0071] In some embodiments, terms such as “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc. can be used interchangeably. These descriptions all refer to the device taking corresponding actions under certain objective circumstances. They do not necessarily limit the time, nor do they require the device to have a judgment action when implementing it, nor do they mean that there must be other limitations.

[0072] 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”.

[0073] In some embodiments, devices, etc., may be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as “device,” “equipment,” “circuit,” “network element,” “network function,” “network device,” “function,” “node,” “unit,” “section,” “system,” “network,” “chip,” “chip system,” “entity,” and “subject” are interchangeable.

[0074] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0075] In some embodiments, the terms "access network device (AN device)," "radio access network device (RAN device)," "base station (BS)," "radio base station," "fixed station," "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," and "bandwidth part (BWP)" can be used interchangeably.

[0076] In some embodiments, the terms "terminal", "terminal device", "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", and "client" can be used interchangeably.

[0077] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0078] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0079] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0080] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0081] 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.

[0082] Machine learning algorithms are one of the most important methods for implementing artificial intelligence technology. Models can be trained using large amounts of data, and these models can then be used to predict events. In many fields, machine learning models can achieve highly accurate predictions.

[0083] Data is crucial for AI. Data can be divided into three categories: training data, used for training and testing models; inference data, used for model usage; and performance monitoring data, used to monitor model performance and thus control the model, including activation, deactivation, and model switching.

[0084] An AI function is a collection of AI models that can be used for a specific function.

[0085] Wireless communication networks can use AI for prediction and inference to improve system performance. Training AI models requires collecting a large amount of data, and the data requirements vary depending on the application scenario. Application scenarios can include mobile communication system processes such as beam management, CSI reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0086] During beam management, the UE can reduce the number of beams measured, and the UE or base station can obtain the optimal beam through AI inference. Beam prediction includes spatial beam prediction and temporal beam prediction. In spatial beam prediction, the UE measures a small number of beams and predicts the measurement results of other beams. In temporal beam prediction, the UE predicts future beam measurement results based on historical beam measurement results.

[0087] During beam management, the UE can predict the beam measurement results of set B based on the measurement results of beams in beam set A. The AI ​​model output can be the identifiers of the k strongest beams in set B, along with the measurement results of these beams. The radio resource locations for beam transmission in set A and set B include time and frequency locations. The radio resource locations for beam transmission can be indicated using CSI-ReportConfig.

[0088] By using big data related to handover to train the AI ​​model, the AI ​​can predict the success rate of handover to a certain cell, the probability of handover failure, or the failure of the radio link based on the UE's real-time network environment.

[0089] AI models or functions can achieve good performance under specific application conditions, which can be divided into network-side conditions and UE-side conditions.

[0090] Network-side conditions may include

[0091] Community types, such as macro, micro, and dense urban communities.

[0092] Network deployment scenarios, such as indoor and outdoor.

[0093] Wireless channel quality can be determined using RSRP, RSRQ, or SINR.

[0094] Cell frequency

[0095] Location of the community

[0096] Distance between base stations

[0097] Antenna configuration, including the number of ports and the number of MIMO layers.

[0098] Transmit power

[0099] Numerology

[0100] The network-side conditions can be bound to an ID, and the network indicates the network-side conditions by providing the ID. The UE does not know which specific network-side conditions the ID represents. When the UE uses AI for inference, it determines whether the current network-indicated ID is consistent with the network ID used when collecting AI function / model training data. If they are inconsistent, it determines that the current AI function / model does not meet the network-side conditions.

[0101] In traditional handover, the trigger measurement reporting and CHO measurement results are L3 cell measurements, which are obtained by filtering the L1 / L2 measurement results of multiple beams.

[0102] The measurement results of the serving cell or neighboring cell used in the evaluation and reporting of measurement events are obtained after the UE's Layer 1 measurement results are filtered by Layer 3. The Layer 3 filtering is shown below F. n = (1-a)*F n-1 +a*M

[0103] in,

[0104] Mn is the latest measurement result;

[0105] Fn is the filtered result;

[0106] Fn-1 is the measurement result after the previous filtering.

[0107] 'a' represents the layer 3 filtering factor.

[0108] Therefore, switching control based on Layer 3 measurements results in significant delays, while switching triggering based on L1 / L2 measurement results can trigger switching quickly.

[0109] In L1 / L2 measurements, different beams will produce different measurement results. Therefore, target beam information will be added to the CHO.

[0110] When an AI model is trained on the UE side, the UE needs to collect data for model training. In the scenarios mentioned above, including beam management, CSI compression, positioning, handover, and mobility management, the network needs to provide corresponding radio resource configurations. Only under these configurations can the UE collect the necessary data. The UE can send a request to the network, indicating a specific radio resource configuration. The network then sends radio signals according to the configuration, and the UE collects the corresponding data for model training. However, the network doesn't know whether the UE has collected enough data. If the UE has already received enough data, and the network still sends radio signals according to the UE's requested configuration, it will result in a waste of radio resources.

[0111] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 may include a terminal and network devices. Optionally, the network devices may include at least one of access network devices and core network devices.

[0112] In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0113] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include 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, but is not limited thereto.

[0114] 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.

[0115] 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.

[0116] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of the following: Evolved Packet Core (EPC), 5G Core Network (5GCN), and Next Generation Core (NGC).

[0117] 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.

[0118] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​The number and form of each main body are arbitrary. 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.

[0119] 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 resource selection 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).

[0120] Optionally, the AI ​​functions mentioned above are implemented based on AI models. In some embodiments, when the AI ​​model is deployed on the terminal side, the terminal typically needs to train the AI ​​model so that it can accurately implement the AI ​​functions. Optionally, before training the AI ​​model, the terminal usually needs to collect training data for the AI ​​model. In some embodiments, the terminal can collect training data by measuring the wireless signals sent by the network device. Optionally, when the terminal needs to train the AI ​​model, it can first determine the wireless signal required to collect the training data. Then, the terminal can send a request message to the network device. This request message can indicate the relevant configuration of the wireless signal required by the terminal. Based on the request message, the network device can send the corresponding wireless signal to the terminal so that the terminal can measure the wireless signal to collect the training data for the AI ​​model. Furthermore, when the terminal has collected enough training data, the network device no longer needs to send wireless signals. However, if the network device cannot know whether the terminal has collected enough training data, a situation may occur where "the terminal has collected enough training data, but the network device still sends wireless signals to the terminal," resulting in a waste of resources.

[0121] Figure 2 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2, this embodiment of the disclosure relates to a communication method for a communication system 100; the method includes:

[0122] Step 2101: The terminal sends a first request to the network device.

[0123] Optionally, the first request can be used to indicate one or more of the following: the terminal needs to collect training data, the terminal needs one or more first configurations, or the AI ​​model corresponding to one or more first configurations required by the terminal. Optionally, the first request can indicate one or more first configurations required by the terminal by using an identifier indicating one or more first configurations required by the terminal.

[0124] Optionally, the training data described above can be used for at least one of the following: training an AI model, testing an AI model, or validating an AI model. Optionally, when the terminal needs to train an AI model and determines that it needs to collect training data, the terminal can send a first request to the network device.

[0125] Optionally, the AI ​​model described above can be used to implement AI functions, which may include at least one of the following: AI-based beam management, AI-based channel state information (CSI) reporting, AI-based CSI compression, AI-based positioning, AI-based handover, AI-based mobility management, AI-based radio resource management, etc.

[0126] Optionally, taking the above-mentioned "AI-based beam management and AI-based switching" as examples, we can introduce how the above-mentioned AI model specifically implements AI functions.

[0127] Optionally, the aforementioned "AI-based beam management" may include, for example, beam prediction, i.e., predicting the optimal beams based on an AI model. Optionally, beam prediction may include temporal domain prediction and / or spatial domain prediction. In some embodiments, beam set A and beam set B may be defined, wherein beam set A and beam set B may each include at least one beam, and the number of beams in beam set A may be less than the number of beams in beam set B. Furthermore, the aforementioned spatial domain prediction may be understood, for example, as follows: the terminal measures the beam measurement results of beam set A, and inputs the beam measurement results of beam set A into an AI model to infer and predict the optimal k beams in beam set B, where k is a positive integer. The aforementioned temporal domain prediction may be understood, for example, as follows: the historical beam measurement results of beam set A are input into an AI model to infer and predict the predicted beam measurement results of beam set B and / or beam set A at future times.

[0128] Optionally, the aforementioned "AI-based handover" can refer to, for example, obtaining cell handover results based on AI model inference. These cell handover results may include at least one of handover success rate, handover failure probability, and radio link failure rate. Optionally, cell set A and cell set B can be defined. The terminal can measure the cell measurement results in cell set A and cell set B, and input these cell measurement results into the AI ​​model to infer the terminal's optimal target cell. This target cell can be understood as the cell to which the terminal is about to handover.

[0129] Optionally, the first configuration described above can be used to collect training data, and the first configuration may include at least one of the second information and the first signal. Optionally, the second information described above can be used to configure one or more of the following: the first resource location corresponding to the first signal, the input characteristic requirements of the AI ​​model, the output characteristic requirements of the AI ​​model, and the measurement configuration information of the terminal.

[0130] Optionally, the first signal mentioned above can be a signal transmitted by a beam in beamset A and / or a beam in beamset B. In some embodiments, the first resource location corresponding to the first signal can include at least one of the following: the time-domain location of a beam in beamset A, the frequency-domain location of a beam in beamset A, the time-domain location of a beam in beamset B, and the frequency-domain location of a beam in beamset B. Optionally, the first resource location corresponding to the first signal can be indicated, for example, by CSI-ReportConfig.

[0131] In some embodiments, the first signal mentioned above may be related to the AI ​​function implemented by the AI ​​model. Optionally, when the AI ​​function implemented by the AI ​​model is AI-based positioning, the first signal may be a positioning reference signal. Then, the terminal can determine the training data required by the AI ​​model by measuring the positioning reference signal. When the AI ​​function implemented by the AI ​​model is AI-based CSI reporting, the first signal may be a channel-state information reference signal (CSI-RS). Then, the terminal can determine the training data required by the AI ​​model by measuring the CSI-RS.

[0132] Optionally, the aforementioned "input characteristic requirements of the AI ​​model" may include: terminal-side conditions required for the AI ​​model to run, network-side conditions required for the AI ​​model to run, and the input content of the AI ​​model.

[0133] Optionally, the "terminal-side conditions required for the AI ​​model to run" mentioned above may include at least one of the following:

[0134] The terminal speed required for AI model execution;

[0135] The power consumption of the terminal required for the AI ​​model to run;

[0136] The terminal power required for AI model operation;

[0137] The terminal computing power required for AI models to run can be measured in FLOPS.

[0138] The terminal location required for the AI ​​model to run may include at least one of the following: the terminal's geographic location and the terminal's location in the serving cell.

[0139] The types of terminal services required for the AI ​​model to run may include at least one of the following: audio services, video services, multimedia services, voice services, etc.

[0140] The terminal antenna configuration required for AI model operation may include, for example, the number of ports;

[0141] The terminal rotation speed required for AI model operation

[0142] The terminal storage space required for an AI model to run can be measured in bits.

[0143] Optionally, the aforementioned "network-side conditions required for the AI ​​model to run" may include at least one of the following:

[0144] The cell types required for the AI ​​model to run may include at least one of the following: macro cell, micro cell, dense urban cell, etc.

[0145] The network deployment scenarios required for AI models to run can be, for example, at least one of the following: indoor or outdoor.

[0146] The wireless channel quality required for the AI ​​model to run can be determined, for example, by at least one of Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Interference plus Noise Ratio (SNR).

[0147] The cell frequency required for AI model operation;

[0148] The cell locations required for the AI ​​model to run;

[0149] The distance between network devices required for AI models to run;

[0150] The antenna configuration required for AI model operation may include, for example, the number of ports, the number of Multiple Input Multiple Output (MIMO) layers, etc.

[0151] The transmission power required for the AI ​​model to run;

[0152] The numerology required for the AI ​​model to run may optionally include, for example, a cyclic prefix (CP) and / or sub-carrier spacing (SCS).

[0153] Optionally, in some embodiments, different network-side conditions may correspond to different condition identifiers (IDs), and the second information can indicate the network-side conditions that the AI ​​model needs to meet to run by indicating the identifier corresponding to the network-side conditions.

[0154] Optionally, the "input content of the AI ​​model" mentioned above may include at least one of the following: the number of beam measurement results required by the AI ​​model, the resource location (e.g., time domain location and / or frequency domain location) of the beam corresponding to the beam measurement results required by the AI ​​model, and the historical time corresponding to the historical beam measurement results required by the AI ​​model.

[0155] Optionally, the above-mentioned "output characteristic requirements of the AI ​​model" may include the output content of the AI ​​model, such as at least one of the following: the identifiers of the k strongest beams, the beam measurement results to be output by the AI ​​model, the cell measurement results to be output by the AI ​​model, and the future time corresponding to the predicted beam measurement results to be output by the AI ​​model.

[0156] Optionally, the measurement configuration information described above can be used to determine the measurement result of the first signal. In some embodiments, the measurement configuration information may include at least one of the following: measurement frequency, period of measurement interval, offset of measurement interval, and filter factor.

[0157] Optionally, the "offset of measurement interval" mentioned above may refer to, for example, the offset between the starting position of the measurement interval and a predefined reference position.

[0158] Optionally, the filtering factor can be used to process measurement results (e.g., beam measurement results and / or cell measurement results) to obtain filtered measurement results (e.g., filtered beam measurement results and / or filtered cell measurement results). Optionally, the filtered measurement results can be used as input to an AI model. For example, the measurement results of terminal layer 1 (L1) or layer 2 (L2) can be filtered based on the filtering factor to obtain the filtered measurement results of layer 3 (L3). Optionally, the measurement results can be filtered using the following formula: F n = (1-a)*F n-1 +a*M n

[0159] Among them, M n It is the latest measurement result of L1 or L2, F n This is the filtered measurement result, F n-1 This is the measurement result after the last filtering, where 'a' is the filtering factor.

[0160] Optionally, the training data required for different AI models will vary, and correspondingly, the required initial configuration will also differ. Optionally, the training data for the AI ​​model may include at least one of the following: beam identifier, beam measurement results, cell identifier, and cell measurement results. Optionally, this training data can be collected based on the measurement results of a first signal, and the training data can meet the input and output characteristic requirements of the AI ​​model.

[0161] Step 2102: The network device sends an acknowledgment or rejection instruction to the terminal.

[0162] Optionally, the network device can determine the first configuration required by the terminal when collecting training data based on the first request sent by the terminal. In some embodiments, after the network device determines the first configuration required by the terminal, the network device can determine whether it can provide the first configuration required by the terminal to the terminal. When the network device can provide the first configuration required by the terminal to the terminal, the network device can send an acknowledgment instruction to the terminal. When the network device cannot provide the first configuration required by the terminal to the terminal, the network device can send a rejection instruction to the terminal.

[0163] Optionally, the above confirmation instructions may be used to indicate one or more of the following:

[0164] The network device confirms the sending of the first configuration;

[0165] The network device acknowledges the identifier corresponding to one or more of the first configurations sent;

[0166] The network device confirms the AI ​​model corresponding to one or more of the first configurations sent;

[0167] Optionally, a rejection instruction may be used to indicate one or more of the following:

[0168] The network device refused to send the first configuration.

[0169] The network device refuses to send one or more identifiers corresponding to the first configuration;

[0170] The network device refuses to send one or more AI models corresponding to the first configuration.

[0171] In some embodiments, when a first request requests multiple first configurations, the network device can independently send an acknowledgment or rejection indication for the different first configurations requested by the first request.

[0172] Optionally, in some embodiments, when the network device sends an acknowledgment indication, the terminal may prepare to receive the first configuration confirmed by the network device and begin collecting the corresponding training data. When the network device sends a rejection indication, the terminal may stop receiving the first configuration rejected by the network device and stop collecting the corresponding training data.

[0173] Step 2103: The network device sends the first configuration to the terminal.

[0174] Optionally, when the network device can send the first configuration required by the terminal to the terminal, the network device may send the first configuration to the terminal. Optionally, when the network device sends the first configuration to the terminal, it may also send second information to the terminal, and may send the first signal at the first resource location corresponding to the first signal. For a detailed description of the first configuration, the second information, and the first signal, please refer to the description in step 2101 above.

[0175] Step 2104: The terminal collects training data based on the first configuration.

[0176] Optionally, the terminal can determine the second information based on the first configuration, and determine the first resource location corresponding to the first signal based on the second information. The terminal can receive and measure the first signal at the first resource location to obtain the measurement result of the first signal. Optionally, when measuring the first signal, the terminal can measure the first signal based on the measurement configuration information configured in the second information. Optionally, the terminal can determine training data that meets the input and output characteristic requirements of the AI ​​model based on the measurement result of the first signal.

[0177] Step 2105: The terminal sends the first information to the network device.

[0178] Optionally, the first information can be used to indicate that the terminal no longer needs the first configuration.

[0179] In some embodiments, when the terminal determines to stop collecting training data, it may send a first message to the network device. For example, when the terminal has collected a predetermined amount of training data, it may determine to stop collecting training data and send the first message to the network device.

[0180] In some embodiments, when the terminal has already sent a first request to the network device, if the terminal decides to stop collecting training data, the terminal may send a first message to the network device.

[0181] In some embodiments, when the terminal has sent a first request to the network device and received an acknowledgment, if the terminal decides to stop collecting training data, the terminal may send a first message to the network device.

[0182] In some embodiments, when the terminal has sent a first request to the network device and determines that the first condition is met, if the terminal decides to stop collecting training data, the terminal may send first information to the network device; optionally, the first condition may include any of the following:

[0183] The network device has sent the initial configuration;

[0184] The network device is sending the first configuration;

[0185] The network device has sent the first configuration, but the network device has not sent the first configuration at the current moment.

[0186] In some embodiments, when the terminal has already sent a first request to the network device, if the terminal determines to stop collecting training data corresponding to at least one AI model, the terminal may send a first message to the network device.

[0187] Optionally, in some embodiments, the first information may also indicate one or more of the following: one or more first configurations that the terminal does not need, and the AI ​​model corresponding to one or more first configurations that the terminal does not need.

[0188] In some embodiments, the terminal may also receive third information, which may be used to indicate whether the terminal is allowed to send the first information. Optionally, when the third information indicates that the terminal is allowed to send the first information, if the terminal determines to stop collecting training data, the terminal may send the first information to the network device.

[0189] Step 2106: The network device stops sending the first signal and releases the first resource location.

[0190] Optionally, after receiving the first information, the network device may stop sending the first signal and release the first resource location. Optionally, in some embodiments, when the first information indicates one or more first configurations that the terminal does not need, the network device may stop sending specific first signals and release specific first resource locations. Optionally, the "specific first signal" here may refer to, for example, the first signal corresponding to the first configuration that the terminal does not need, and the "specific first resource location" here may refer to, for example, the first resource location corresponding to the first configuration that the terminal does not need.

[0191] Optionally, "releasing the first resource location" here may include, for example, sending a first signal without using the first resource location, or sending other signals using the first resource location.

[0192] In summary, in the above embodiments, the terminal can indicate to the network device that it no longer needs the first configuration. The first configuration can be used to collect training data for the AI ​​model; for example, it may include the wireless signal required for collecting training data. Therefore, when the network device receives the first information, it can determine that the terminal no longer needs to collect training data. The network device can then promptly stop transmitting the wireless signal, or promptly release the transmission resources corresponding to the wireless signal. This prevents scenarios where "the terminal no longer needs the first configuration, but the network device continues to transmit the wireless signal related to the first configuration," thus avoiding resource waste.

[0193] The communication method involved in the embodiments of this disclosure may include at least one of steps 2101 to 2106. For example, step 2104 may be implemented as a separate embodiment, step 2105 may be implemented as a separate embodiment, and steps 2104+2105 may be implemented as a separate embodiment.

[0194] In some embodiments, step 2102 is optional and may be omitted or replaced in different embodiments.

[0195] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0196] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3, the present disclosure relates to a communication method for a terminal, the method comprising:

[0197] Step 3101: Send the first message to the network device.

[0198] Optionally, the first information is used to indicate that the terminal no longer needs the first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

[0199] Optionally, the first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following:

[0200] The first resource location corresponding to the first signal;

[0201] The input characteristics required for the AI ​​model;

[0202] The output characteristics of the AI ​​model are required;

[0203] The measurement configuration information of the terminal is used to determine the measurement result of the first signal;

[0204] The training data is collected based on the measurement results of the first signal, and the training data meets the input characteristic requirements and output characteristic requirements of the AI ​​model.

[0205] Optionally, the method further includes:

[0206] The first request is used to indicate one or more of the following:

[0207] The terminal needs to collect the training data;

[0208] One or more first configurations required by the terminal;

[0209] The terminal requires one or more AI models corresponding to the first configuration.

[0210] Optionally, the method further includes:

[0211] Receive confirmation or rejection instructions sent by the network device;

[0212] The confirmation indication is used to indicate one or more of the following:

[0213] The network device confirms the transmission of the first configuration;

[0214] The network device confirms the identification corresponding to one or more of the first configurations sent;

[0215] The network device confirms the AI ​​model corresponding to one or more first configurations sent;

[0216] Furthermore, the rejection indication is used to indicate one or more of the following:

[0217] The network device refuses to send the first configuration;

[0218] The network device refuses to send one or more identifiers corresponding to the first configuration;

[0219] The network device refuses to send one or more AI models corresponding to the first configuration.

[0220] Optionally, sending the first information to the network device includes:

[0221] The terminal determines to stop collecting the training data and sends the first information to the network device.

[0222] Optionally, sending the first information to the network device includes:

[0223] The terminal has sent a first request to the network device, and the terminal has determined to stop collecting the training data and sent the first information to the network device.

[0224] Optionally, sending the first information to the network device includes:

[0225] The terminal has sent a first request to the network device. Upon receiving a confirmation instruction, the terminal determines to stop collecting the training data and sends the first information to the network device.

[0226] Optionally, sending the first information to the network device includes:

[0227] The terminal has sent a first request to the network device. The terminal determines that the first condition is met, determines to stop collecting the training data, and sends the first information to the network device.

[0228] The first condition includes any of the following:

[0229] The network device has sent the first configuration;

[0230] The network device is sending the first configuration;

[0231] The network device sent the first configuration, but the network device has not sent the first configuration at the current moment.

[0232] Optionally, sending the first information to the network device includes:

[0233] The terminal has sent a first request to the network device, and the terminal has determined to stop collecting training data for at least one AI model and sent the first information to the network device.

[0234] Optionally, the first information is also used to indicate one or more of the following:

[0235] One or more first configurations that are not required by the terminal;

[0236] The terminal does not require one or more AI models corresponding to the first configuration.

[0237] Optionally, sending the first information to the network device includes:

[0238] The terminal receives third information sent by the network device, the third information being used to indicate whether the terminal is allowed to send the first information;

[0239] The third information indicates that the terminal is permitted to send the first information to the network device.

[0240] For a detailed description of step 3101, please refer to the above embodiment.

[0241] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0242] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4, the present disclosure relates to a communication method for a network device, the method comprising:

[0243] Step 4101: Receive the first information sent by the terminal.

[0244] Optionally, the first information is used to indicate that the terminal no longer needs the first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

[0245] Optionally, the first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following:

[0246] The first resource location corresponding to the first signal;

[0247] The input characteristics required for the AI ​​model;

[0248] The output characteristics of the AI ​​model are required;

[0249] The measurement configuration information of the terminal is used to determine the measurement result of the first signal;

[0250] The training data is collected based on the measurement results of the first signal, and the training data meets the input characteristic requirements and output characteristic requirements of the AI ​​model.

[0251] Optionally, the method further includes one or more of the following:

[0252] Stop sending the first signal;

[0253] Release the first resource location.

[0254] Optionally, the method further includes:

[0255] Receive a first request sent by the terminal, the first request being used to indicate one or more of the following:

[0256] The terminal needs to collect the training data;

[0257] One or more first configurations required by the terminal;

[0258] The terminal requires one or more AI models corresponding to the first configuration.

[0259] Optionally, the method further includes:

[0260] The network device can provide the first configuration to the terminal and send a confirmation instruction to the terminal; or

[0261] The network device cannot provide the first configuration to the terminal and sends a rejection indication to the terminal.

[0262] The confirmation indication is used to indicate one or more of the following:

[0263] The network device confirms the transmission of the first configuration;

[0264] The network device confirms the identification corresponding to one or more of the first configurations sent;

[0265] The network device confirms the AI ​​model corresponding to one or more first configurations sent;

[0266] Furthermore, the rejection indication is used to indicate one or more of the following:

[0267] The network device refuses to send the first configuration;

[0268] The network device refuses to send one or more identifiers corresponding to the first configuration;

[0269] The network device refuses to send one or more AI models corresponding to the first configuration.

[0270] Optionally, the first information is also used to indicate one or more of the following:

[0271] One or more first configurations that are not required by the terminal;

[0272] The terminal does not require one or more AI models corresponding to the first configuration.

[0273] Optionally, the method further includes:

[0274] A third message is sent to the terminal, the third message indicating whether the terminal is allowed to send the first message.

[0275] For a detailed description of step 4101, please refer to the above embodiment.

[0276] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0277] The following is an exemplary description of the above method.

[0278] This disclosure provides a method for controlling data collection configuration to avoid wasting radio resources because the network continues to send radio signals according to the configuration requested by the UE even when the UE has already collected enough data.

[0279] In an optional embodiment, the UE sends a first indication to the network, indicating that the configuration for data collection or the transmission of wireless signals is no longer needed. After receiving the indication, the network can release the corresponding configuration or stop the transmission of wireless signals.

[0280] In optional embodiment 1, the UE determines to stop data collection and sends a first instruction to the network.

[0281] In optional embodiment 1-1, the UE has sent a data collection request to the network, and the UE decides to stop data collection and sends a first instruction to the network.

[0282] When a UE determines that data collection is necessary, it may send a data collection request to the network. In the data collection request, the UE indicates to the network that data collection is required, and optionally, indicates the necessary data collection configuration, which can be any of the following.

[0283] 1. Beam combination of Set A and Set B. The location of radio resources transmitted in Set A and Set B is indicated by the radio resource set, including time and frequency locations. This is indicated by CSI-ReportConfig.

[0284] 2. Input and output characteristics of AI inference, including any one or more of the following: network-side conditions, information of beam set A or B (number of beams in the beam set, radio resource location of the beams), content reported by the UE (identification of the k strongest beams, measurement results of the beams, measurement results of the cell), measurement time and location information, and prediction time and location information.

[0285] 3. Measurement frequency, wherein the frequency is used to obtain the measurement results of the cell at the corresponding frequency.

[0286] 4. Measurement interval period and offset

[0287] 5. Filtering factor, which is used to process the measurement results and obtain filtered measurement results.

[0288] The UE determines that data collection can be stopped based on the data already collected for model training.

[0289] After receiving a data collection request, the network can provide the requested configuration to the UE and transmit radio signals according to the requested configuration. The UE measures the radio signals and collects data. The data includes beam identifiers and measurement results, cell identifiers and measurement results.

[0290] Network-side conditions may include

[0291] - Community type, such as macro, micro, dense urban.

[0292] -Network deployment scenarios, such as indoor and outdoor.

[0293] - Wireless channel quality can be determined using RSRP, RSRQ, or SINR.

[0294] -Frequency of the cell

[0295] -Location of the neighborhood

[0296] - Distance between base stations

[0297] - Antenna configuration, including the number of ports and the number of MIMO layers.

[0298] -Transmit power

[0299] -Numerology

[0300] The network-side conditions can be bound to an ID, which the network uses to indicate the network-side conditions. The UE does not know which specific network-side conditions the ID represents.

[0301] In optional embodiments 1-2, the UE has sent a data collection request to the network and received a confirmation instruction from the network. The UE then decides to stop data collection and sends a first instruction to the network.

[0302] After receiving a data request, the network can provide the requested configuration to the UE and send an acknowledgment instruction to the UE, allowing the UE to begin preparing for data collection.

[0303] After receiving a data request, the network is unable to provide the requested configuration to the UE, sends a rejection indication to the UE, and the UE stops data collection.

[0304] In optional embodiments 1-3, the UE has sent a data collection request to the network, and the network has provided or is providing the requested configuration. The UE determines to stop data collection and sends a first instruction to the network.

[0305] In optional embodiment 2, the UE has sent a data collection request to the network. The UE determines to stop collecting data corresponding to a certain model and sends a first instruction to the network, wherein the instruction indicates the identifier of the configuration corresponding to the data of the model.

[0306] The data collection request can specify multiple configurations for data collection. Each configuration corresponds to a type of data that needs to be collected to train the corresponding model. The network sends an acknowledgment or rejection indication independently for each configuration.

[0307] In optional embodiment 3, the UE receives a zeroth indication sent by the network, indicating whether the UE is allowed to report the first indication. If reporting the first indication is allowed, the UE can report it. Otherwise, the UE does not report the first indication.

[0308] This disclosure also proposes an apparatus (also referred to as a communication device, etc.) for implementing any of the above methods. For example, an apparatus is proposed that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Furthermore, another apparatus is proposed 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.

[0309] 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). 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). Taking a field-programmable gate array (FPGA) as an example, it 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.

[0310] 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. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0311] Figure 5A is a schematic diagram of the structure of a terminal proposed in an embodiment of this disclosure. The terminal is used to execute any of the above methods. In some embodiments, as shown in Figure 5A, the terminal may include at least one of a transceiver module, a processing module, etc. The transceiver module is used to send first information to a network device, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

[0312] Optionally, the transceiver module described above is used to perform at least one of the communication steps such as sending and / or receiving performed by the terminal in any of the above methods, which will not be elaborated here. Optionally, the processing module described above is used to perform at least one of the other steps performed by the terminal in any of the above methods, which will not be elaborated here.

[0313] Optionally, the first information is used to indicate that the terminal no longer needs the first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

[0314] Optionally, the first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following:

[0315] The first resource location corresponding to the first signal;

[0316] The input characteristics required for the AI ​​model;

[0317] The output characteristics of the AI ​​model are required;

[0318] The measurement configuration information of the terminal is used to determine the measurement result of the first signal;

[0319] The training data is collected based on the measurement results of the first signal, and the training data meets the input characteristic requirements and output characteristic requirements of the AI ​​model.

[0320] Optionally, the method further includes:

[0321] The first request is used to indicate one or more of the following:

[0322] The terminal needs to collect the training data;

[0323] One or more first configurations required by the terminal;

[0324] The terminal requires one or more AI models corresponding to the first configuration.

[0325] Optionally, the method further includes:

[0326] Receive confirmation or rejection instructions sent by the network device;

[0327] The confirmation indication is used to indicate one or more of the following:

[0328] The network device confirms the transmission of the first configuration;

[0329] The network device confirms the identification corresponding to one or more of the first configurations sent;

[0330] The network device confirms the AI ​​model corresponding to one or more first configurations sent;

[0331] Furthermore, the rejection indication is used to indicate one or more of the following:

[0332] The network device refuses to send the first configuration;

[0333] The network device refuses to send one or more identifiers corresponding to the first configuration;

[0334] The network device refuses to send one or more AI models corresponding to the first configuration.

[0335] Optionally, sending the first information to the network device includes:

[0336] The terminal determines to stop collecting the training data and sends the first information to the network device.

[0337] Optionally, sending the first information to the network device includes:

[0338] The terminal has sent a first request to the network device, and the terminal has determined to stop collecting the training data and sent the first information to the network device.

[0339] Optionally, sending the first information to the network device includes:

[0340] The terminal has sent a first request to the network device. Upon receiving a confirmation instruction, the terminal determines to stop collecting the training data and sends the first information to the network device.

[0341] Optionally, sending the first information to the network device includes:

[0342] The terminal has sent a first request to the network device. The terminal determines that the first condition is met, determines to stop collecting the training data, and sends the first information to the network device.

[0343] The first condition includes any of the following:

[0344] The network device has sent the first configuration;

[0345] The network device is sending the first configuration;

[0346] The network device sent the first configuration, but the network device has not sent the first configuration at the current moment.

[0347] Optionally, sending the first information to the network device includes:

[0348] The terminal has sent a first request to the network device, and the terminal has determined to stop collecting training data for at least one AI model and sent the first information to the network device.

[0349] Optionally, the first information is also used to indicate one or more of the following:

[0350] One or more first configurations that are not required by the terminal;

[0351] The terminal does not require one or more AI models corresponding to the first configuration.

[0352] Optionally, sending the first information to the network device includes:

[0353] The terminal receives third information sent by the network device, the third information being used to indicate whether the terminal is allowed to send the first information;

[0354] The third information indicates that the terminal is permitted to send the first information to the network device.

[0355] Figure 5B is a schematic diagram of the structure of a network device proposed in an embodiment of this disclosure. The network device is used to perform any of the above methods. In some embodiments, as shown in Figure 5B, the network device may include at least one of a transceiver module, a processing module, etc. The transceiver module is used to receive first information sent by a terminal, the first information being used to indicate that the terminal no longer needs a first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

[0356] Optionally, the transceiver module is used to perform at least one of the communication steps such as sending and / or receiving performed by the network device in any of the above methods, which will not be elaborated here. Optionally, the processing module is used to perform at least one of the other steps performed by the network device in any of the above methods, which will not be elaborated here.

[0357] Optionally, the first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following:

[0358] The first resource location corresponding to the first signal;

[0359] The input characteristics required for the AI ​​model;

[0360] The output characteristics of the AI ​​model are required;

[0361] The measurement configuration information of the terminal is used to determine the measurement result of the first signal;

[0362] The training data is collected based on the measurement results of the first signal, and the training data meets the input characteristic requirements and output characteristic requirements of the AI ​​model.

[0363] Optionally, the method further includes one or more of the following:

[0364] Stop sending the first signal;

[0365] Release the first resource location.

[0366] Optionally, the method further includes:

[0367] Receive a first request sent by the terminal, the first request being used to indicate one or more of the following:

[0368] The terminal needs to collect the training data;

[0369] One or more first configurations required by the terminal;

[0370] The terminal requires one or more AI models corresponding to the first configuration.

[0371] Optionally, the method further includes:

[0372] The network device can provide the first configuration to the terminal and send a confirmation instruction to the terminal; or

[0373] The network device cannot provide the first configuration to the terminal and sends a rejection indication to the terminal.

[0374] The confirmation indication is used to indicate one or more of the following:

[0375] The network device confirms the transmission of the first configuration;

[0376] The network device confirms the identification corresponding to one or more of the first configurations sent;

[0377] The network device confirms the AI ​​model corresponding to one or more first configurations sent;

[0378] Furthermore, the rejection indication is used to indicate one or more of the following:

[0379] The network device refuses to send the first configuration;

[0380] The network device refuses to send one or more identifiers corresponding to the first configuration;

[0381] The network device refuses to send one or more AI models corresponding to the first configuration.

[0382] Optionally, the first information is also used to indicate one or more of the following:

[0383] One or more first configurations that are not required by the terminal;

[0384] The terminal does not require one or more AI models corresponding to the first configuration.

[0385] Optionally, the method further includes:

[0386] A third message is sent to the terminal, the third message indicating whether the terminal is allowed to send the first message.

[0387] Figure 6A is a schematic diagram of the structure of the communication device 6100 proposed in an embodiment of this disclosure. The communication device 6100 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 6100 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.

[0388] As shown in Figure 6A, the communication device 6100 is used to execute any of the above methods. In some embodiments, the communication device 6100 includes one or more processors 6101. The processor 6101 may be a general-purpose processor or a special-purpose processor, such as a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may 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 6100 is used to execute any of the above methods. Optionally, one or more processors 6101 are used to invoke instructions to cause the communication device 6100 to execute any of the above methods.

[0389] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps such as sending and / or receiving in the above-described method, and the processor 6101 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, transmitting unit, transmitter, transmitting circuit, etc., can be used interchangeably; the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0390] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data and / or instructions. Optionally, one or more processors 6101 are used to invoke instructions stored in the memory 6103 to cause the communication device 6100 to perform any of the above methods. Optionally, all or part of the memory 6103 may also be located outside the communication device 6100. In an optional embodiment, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6102 and can be used to receive data and / or instructions from the memory 6102 or other devices, and can be used to send data and / or instructions to the memory 6102 or other devices. For example, the interface circuit 6104 can read data and / or instructions stored in the memory 6102 and send the data and / or instructions to the processor 6101.

[0391] The communication device 6100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 6100 described in this disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited by FIG. 6A. The communication device may be a standalone device or a 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, programs and / or instructions; (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.

[0392] Figure 6B is a schematic diagram of the structure of chip 6200 according to an embodiment of this disclosure. For cases where the communication device 6100 can be a chip or a chip system, please refer to the schematic diagram of chip 6200 shown in Figure 6B, but it is not limited thereto.

[0393] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0394] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data and / or instructions. Optionally, all or part of the memories 6203 may be located outside of chip 6200. Optionally, interface circuit 6202 is connected to memory 6203, and interface circuit 6202 can be used to receive data and / or instructions from memory 6203 or other devices, and interface circuit 6202 can be used to send data and / or instructions to memory 6203 or other devices. For example, interface circuit 6202 can read data and / or instructions stored in memory 6203 and send the data and / or instructions to processor 6201.

[0395] In some embodiments, the interface circuit 6202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method. For example, the interface circuit 6202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 6202 performs data and / or instruction interaction between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs at least one of the other steps.

[0396] 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.

[0397] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device, cause the communication device 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.

[0398] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by a communication device, cause the communication device to perform any of the above methods. Optionally, the program product is a computer program product. Optionally, the program product is stored on the storage medium.

[0399] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0400] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program can be transferred from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0401] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are 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 disclosure.

[0402] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0403] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A communication method, characterized in that, The method, executed by a terminal, includes: Send a first message to the network device, the first message being used to indicate that the terminal no longer needs the first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

2. The method as described in claim 1, characterized in that, The first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following: The first resource location corresponding to the first signal; The input characteristics required for the AI ​​model; The output characteristics of the AI ​​model are required; The measurement configuration information of the terminal is used to determine the measurement result of the first signal; The training data is collected based on the measurement results of the first signal, and the training data meets the input characteristic requirements and output characteristic requirements of the AI ​​model.

3. The method as described in claim 1 or 2, characterized in that, The method further includes: Send a first request to the network device, the first request being used to instruct one or more of the following: The terminal needs to collect the training data; One or more first configurations required by the terminal; The terminal requires one or more AI models corresponding to the first configuration.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Receive confirmation or rejection instructions sent by the network device; The confirmation indication is used to indicate one or more of the following: The network device confirms the transmission of the first configuration; The network device confirms the identification corresponding to one or more of the first configurations sent; The network device confirms the AI ​​model corresponding to one or more first configurations sent; Furthermore, the rejection indication is used to indicate one or more of the following: The network device refuses to send the first configuration; The network device refuses to send one or more identifiers corresponding to the first configuration; The network device refuses to send one or more AI models corresponding to the first configuration.

5. The method according to any one of claims 1-4, characterized in that, Sending the first information to the network device includes: The terminal determines to stop collecting the training data and sends the first information to the network device.

6. The method according to any one of claims 1-4, characterized in that, Sending the first information to the network device includes: The terminal has sent a first request to the network device, and the terminal has determined to stop collecting the training data and sent the first information to the network device.

7. The method according to any one of claims 1-4, characterized in that, Sending the first information to the network device includes: The terminal has sent a first request to the network device. Upon receiving a confirmation instruction, the terminal determines to stop collecting the training data and sends the first information to the network device.

8. The method according to any one of claims 1-4, characterized in that, Sending the first information to the network device includes: The terminal has sent a first request to the network device. The terminal determines that the first condition is met, decides to stop collecting the training data, and sends the first information to the network device.

9. The method as described in claim 8, characterized in that, The first condition includes any of the following: The network device has sent the first configuration; The network device is sending the first configuration; The network device sent the first configuration, but the network device has not sent the first configuration at the current moment.

10. The method according to any one of claims 1-4, characterized in that, Sending the first information to the network device includes: The terminal has sent a first request to the network device, and the terminal has determined to stop collecting training data for at least one AI model and sent the first information to the network device.

11. The method according to any one of claims 1-10, characterized in that, The first information is also used to indicate one or more of the following: One or more first configurations that are not required by the terminal; The terminal does not require one or more AI models corresponding to the first configuration.

12. The method according to any one of claims 1-11, characterized in that, Sending the first information to the network device includes: The terminal receives third information sent by the network device, the third information being used to indicate whether the terminal is allowed to send the first information; The third information indicates that the terminal is permitted to send the first information to the network device.

13. A communication method, characterized in that, Performed by a network device, the method includes: The receiving terminal sends first information, which indicates that the terminal no longer needs the first configuration, wherein the first configuration is used to collect training data for an artificial intelligence (AI) model.

14. The method as described in claim 13, characterized in that, The first configuration is provided by the network device, and the first configuration includes at least one of second information and a first signal, wherein the second information is used to configure one or more of the following: The first resource location corresponding to the first signal; The input characteristics required for the AI ​​model; The output characteristics of the AI ​​model are required; The measurement configuration information of the terminal is used to determine the measurement result of the first signal; The training data is collected based on the measurement results of the first signal, and the training data meets the input characteristic requirements and output characteristic requirements of the AI ​​model.

15. The method as described in claim 14, characterized in that, The method also includes one or more of the following: Stop sending the first signal; Release the first resource location.

16. The method according to any one of claims 13-15, characterized in that, The method further includes: Receive a first request sent by the terminal, the first request being used to indicate one or more of the following: The terminal needs to collect the training data; One or more first configurations required by the terminal; The terminal requires one or more AI models corresponding to the first configuration.

17. The method according to any one of claims 13-16, characterized in that, The method further includes: The network device can provide the first configuration to the terminal and send a confirmation instruction to the terminal; or The network device cannot provide the first configuration to the terminal and sends a rejection indication to the terminal.

18. The method as described in claim 17, characterized in that, The confirmation instruction is used to indicate one or more of the following: The network device confirms the transmission of the first configuration; The network device confirms the identification corresponding to one or more of the first configurations sent; The network device confirms the AI ​​model corresponding to one or more first configurations sent; Furthermore, the rejection indication is used to indicate one or more of the following: The network device refuses to send the first configuration; The network device refuses to send one or more identifiers corresponding to the first configuration; The network device refuses to send one or more AI models corresponding to the first configuration.

19. The method according to any one of claims 13-18, characterized in that, The first information is also used to indicate one or more of the following: One or more first configurations that are not required by the terminal; The terminal does not require one or more AI models corresponding to the first configuration.

20. The method according to any one of claims 13-19, characterized in that, The method further includes: A third message is sent to the terminal, the third message indicating whether the terminal is allowed to send the first message.

21. A communication device, characterized in that, The communication device is used to perform the communication method according to any one of claims 1-12 and 13-20.

22. A communication system, characterized in that, The method includes a network device and a terminal, wherein the terminal is configured to implement the method according to any one of claims 1 to 12, and the network device is configured to implement the method according to any one of claims 13 to 20.

23. A storage medium storing instructions, characterized in that, When the instructions are executed on a communication device, the communication device performs the method as claimed in any one of claims 1 to 12 or claims 13 to 20.

24. A program product, characterized in that, It includes a computer program that, when executed by a communication device, implements the method as claimed in any one of claims 1 to 12 or 13 to 20.

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