Communication method, terminal, network device, system, medium, and computer program product

Abstract

The present disclosure relates to a communication method, a terminal, a network device, a system, a medium and a computer program product. The method executed by the terminal comprises: determining an artificial intelligence (AI) function; and acquiring usage requirement information of the AI function, wherein the usage requirement information is used for indicating characteristics needing to be satisfied by a first-type cell to which input data of an AI model belongs and a second-type cell to which output data of the AI model belongs, the AI model being a model corresponding to the AI function. The present disclosure facilitates the use, based on the usage requirement information, of the AI model corresponding to the AI function to perform prediction, such that prediction errors of the AI model can be reduced, thereby improving the prediction accuracy of the AI function.

Description

Communication methods, terminals, network equipment, systems, media, and computer program products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, terminal, network device, system, medium, and computer program product. Background Technology

[0002] Machine learning algorithms are one of the most important methods for implementing artificial intelligence (AI) technology. Machine learning can obtain models from large amounts of training data, and these models can then be used to predict events. In many fields, machine learning models can achieve very accurate predictions. In the field of communication technology, models can also be applied to event prediction.

[0003] Summary of the Invention

[0004] This disclosure provides a communication method, terminal, network device, system, medium, and computer program product.

[0005] According to a first aspect of the present disclosure, a communication method is proposed, executed by a terminal, the method comprising: determining an artificial intelligence (AI) function; obtaining usage requirement information of the AI ​​function, the usage requirement information being used to indicate the features that a first type of cell to which the input data of the AI ​​model belongs and a second type of cell to which the output data of the AI ​​model belongs must satisfy, the AI ​​model being a model corresponding to the AI ​​function.

[0006] According to a second aspect of the present disclosure, a communication method is proposed, executed by a network device, the method comprising: determining usage requirement information for an AI function, the usage requirement information being used to indicate features that must be satisfied by a first type of cell to which the input data of the AI ​​model belongs and a second type of cell to which the output data of the AI ​​model belongs, the AI ​​model being a model corresponding to the AI ​​function.

[0007] According to a third aspect of the present disclosure, a terminal is provided, comprising: a processing module, configured to determine an artificial intelligence (AI) function; and to acquire usage requirement information of the AI ​​function, wherein the usage requirement information is used to indicate the features that a first type of cell to which the input data of the AI ​​model belongs and a second type of cell to which the output data of the AI ​​model belongs must satisfy, wherein the AI ​​model is a model corresponding to the AI ​​function.

[0008] According to a fourth aspect of the present disclosure, a network device is provided, comprising: a transceiver module, configured to determine usage requirement information of an AI function, wherein the usage requirement information is used to indicate the characteristics that a first type of cell to which the input data of the AI ​​model belongs and a second type of cell to which the output data of the AI ​​model belongs must satisfy, and the AI ​​model is a model corresponding to the AI ​​function.

[0009] According to a fifth aspect of the present disclosure, a terminal is provided, comprising: one or more processors; and a memory coupled to the processors, the memory storing executable instructions that, when executed by the processors, cause the terminal to perform the communication method described in the first aspect.

[0010] According to a sixth aspect of the present disclosure, a network device is provided, comprising: one or more processors; and a memory coupled to the processors, the memory storing executable instructions that, when executed by the processors, cause the network device to perform the communication method described in the second aspect.

[0011] According to a seventh 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 an eighth aspect of the present disclosure, a storage medium is provided that stores instructions that, when executed on a communication device, cause the communication device to perform the communication method described in the first or second aspect.

[0013] According to a ninth aspect of the present disclosure, a computer program product is provided, comprising a computer program and / or instructions that, when executed by a communication device, implement the communication method described in the first or second aspect.

[0014] By adopting the above technical solution, at least the following beneficial technical effects can be achieved:

[0015] By identifying the AI ​​function and obtaining its usage requirements, and since these requirements indicate the features that the AI ​​model must meet for the first-class cells to which the AI ​​model's input data belongs and the second-class cells to which the AI ​​model's output data belongs, the AI ​​model is a model corresponding to the AI ​​function. Therefore, using the AI ​​model corresponding to the AI ​​function for prediction based on the usage requirements can reduce the AI ​​model's prediction error and improve the AI ​​function's prediction accuracy. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are merely some embodiments of this disclosure and do not impose specific limitations on the scope of protection of this disclosure.

[0017] Figure 1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0018] Figure 2 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure.

[0019] Figure 3A is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0020] Figure 3B is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0021] Figure 3C is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0022] Figure 3D is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0023] Figure 4A is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0024] Figure 4B is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0025] Figure 5 is a schematic diagram of the structure of a terminal according to an embodiment of the present disclosure.

[0026] Figure 6 is a schematic diagram of the structure of a network device according to an embodiment of the present disclosure.

[0027] Figure 7 is a schematic diagram of the structure of a communication device according to an embodiment of the present disclosure.

[0028] Figure 8 is a schematic diagram of the structure of a chip according to an embodiment of the present disclosure. Detailed Implementation

[0029] This disclosure provides a communication method, terminal, network device, system, medium, and computer program product.

[0030] In a first aspect, embodiments of this disclosure propose a communication method executed by a terminal, the method comprising: determining an artificial intelligence (AI) function; obtaining usage requirement information of the AI ​​function, wherein the usage requirement information is used to indicate the features that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must satisfy, and the AI ​​model is a model corresponding to the AI ​​function.

[0031] In the above embodiments, by determining the AI ​​function and obtaining its usage requirement information, and since this usage requirement information indicates the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet, the AI ​​model is a model corresponding to the AI ​​function. Therefore, using the AI ​​model corresponding to the AI ​​function for prediction based on the usage requirement information can reduce the prediction error of the AI ​​model and improve the prediction accuracy of the AI ​​function.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​model is used to predict the predicted data of the second type of cell based on the measured data of the first type of cell.

[0033] In the above embodiments, the input data and prediction targets of the AI ​​model can be determined based on the usage requirements information. For example, a first type of cell and a second type of cell can be determined based on the usage requirements information, and the predicted data for the second type of cell can be predicted based on the measured data of the first type of cell, which can improve the accuracy of the predicted data for the second type of cell.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, the usage requirement information includes a frequency offset range, which is used to indicate the range of values ​​required for the difference between the deployment frequencies of the first type of cell and the second type of cell.

[0035] In the above embodiments, when the measured cell and the predicted cell are on different frequencies, if the frequency offsets are different, then the adjacent channel interference may be different. If the frequency offsets of the measured cell and the predicted cell are inconsistent with the data used to train the AI ​​model, then the AI ​​prediction may be inaccurate. Therefore, by specifying the required frequency offset values ​​for the first type of cell and the second type of cell in the usage requirements information, and using the AI ​​function or AI model according to the usage requirements information, the problem of inaccurate prediction due to the frequency offset not meeting the requirements can be avoided, thereby improving the prediction effect of the AI ​​function or AI model.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, obtaining the usage requirement information of the AI ​​function includes: determining the usage requirement information based on training data used by the terminal to train the AI ​​model; or, obtaining the usage requirement information from a first device, wherein the first device is a device that provides the AI ​​function.

[0037] Optionally, the first device includes at least one of a network device, a server, and other terminals.

[0038] In the above embodiments, since the usage requirement information can be determined through the model training data on the terminal's local machine, or the usage requirement information can be obtained from the first device that provides the AI ​​function, the usage requirement information can accurately guide the use of the AI ​​function and improve the effectiveness of the AI ​​function.

[0039] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending the usage requirement information associated with the AI ​​function to a network device.

[0040] In the above embodiments, by reporting the usage requirement information associated with the AI ​​function to the network device, it is possible to keep the network device and the terminal synchronized and facilitate the interaction between the network device and the terminal.

[0041] In conjunction with some embodiments of the first aspect, in some embodiments, the number of AI functions is one or more, and one AI function is associated with one usage requirement information.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​function is at least one of the following:

[0043] Activated AI functionality;

[0044] Available AI features;

[0045] AI functionality stored.

[0046] In conjunction with some embodiments of the first aspect, in some embodiments, the number of AI functions is one or more; one AI function is associated with at least one usage requirement information; the method further includes: determining a first AI function from one or more AI functions; determining a first cell and a second cell based on the first usage requirement information associated with the first AI function, wherein the first cell belongs to the first type of cell and the second cell belongs to the second type of cell.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, determining the first cell and the second cell based on the first usage requirement information associated with the first AI function includes: determining a first candidate cell and a second candidate cell based on the first usage requirement information, wherein the first candidate cell and the second candidate cell satisfy the features indicated by the first usage requirement information; determining the first cell from the first candidate cells; and determining the second cell from the second candidate cells.

[0048] In some embodiments, in conjunction with the first aspect, the method further includes: inputting the measured data of the first cell into the first AI model corresponding to the first AI function to predict the predicted data of the second cell.

[0049] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: determining a first AI function from one or more AI functions; and performing data prediction using a first AI model corresponding to the first AI function based on first usage requirement information associated with the first AI function.

[0050] In the above embodiments, a first AI function to be used can be determined from multiple AI functions, and a corresponding first AI model can be used to predict data based on the first usage requirement information associated with the first AI function to obtain highly accurate prediction data.

[0051] In conjunction with some embodiments of the first aspect, in some embodiments, the step of performing data prediction using a first AI model corresponding to the first AI function based on first usage requirement information associated with the first AI function includes: determining a first candidate cell and a second candidate cell based on the first usage requirement information, wherein the first candidate cell and the second candidate cell satisfy the features indicated by the first usage requirement information; determining a first cell from the first candidate cells and determining a second cell from the second candidate cells; inputting the measured data of the first cell into the first AI model to predict the predicted data of the second cell.

[0052] In the above embodiments, the cell combination that serves as the input and output of the first AI function can be determined based on the first usage requirement information. Making predictions based on the determined cell combination improves prediction accuracy.

[0053] In conjunction with some embodiments of the first aspect, in some embodiments, determining a first cell from the first candidate cells and determining a second cell from the second candidate cells includes: sending a first message to a network device, the first message indicating the first candidate cell and the second candidate cell; receiving a second message sent by the network device, the second message indicating the first cell and the second cell; and determining the first cell and the second cell based on the second message.

[0054] In the above embodiments, it is specified that the network device can determine the first cell and the second cell from the first candidate cell and the second candidate cell, so as to be applicable to the scenario where the network device indicates the cell to be measured and predicts the cell, thereby achieving efficient and accurate prediction of the cell to be measured.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, determining the first AI function includes: receiving a third message sent by a network device, the third message including an identifier of the first AI function; and determining the first AI function based on the third message.

[0056] In conjunction with some embodiments of the first aspect, in some embodiments, determining the first cell from the first candidate cells includes: determining at least one of the following from the first candidate cells as the first cell:

[0057] The terminal's serving cell;

[0058] Cells equipped with measurement configurations;

[0059] The cell indicated by the network device.

[0060] In the above embodiments, the serving cell of the terminal, the cell configured with measurement settings, and the cell indicated by the network device can be used as the first cell, thereby enabling accurate prediction of cells that cannot be measured or cannot be accurately measured based on the measurable cells.

[0061] In conjunction with some embodiments of the first aspect, in some embodiments, determining the second cell from the second candidate cells includes: sending a fourth message to a network device, the fourth message indicating the second candidate cell; receiving a fifth message sent by the network device, the fifth message indicating the second cell determined by the network device from the second candidate cells; and determining the second cell based on the fifth message.

[0062] In the above embodiments, it is specified that when the terminal determines the first cell, the network device can indicate a second cell that meets the usage requirements.

[0063] In conjunction with some embodiments of the first aspect, in some embodiments, the step of predicting the second cell based on the first cell and the first AI model includes: inputting the measured data of the first cell into the first AI model to predict the predicted data of the second cell.

[0064] In the above embodiments, the predicted data of the second cell is predicted based on the measured data of the first cell, which can improve the accuracy of the predicted data of the second cell.

[0065] Secondly, embodiments of this disclosure propose a communication method executed by a network device, the method comprising: determining usage requirement information for an AI function, the usage requirement information being used to indicate the characteristics that a first type of cell to which the input data of the AI ​​model belongs and a second type of cell to which the output data of the AI ​​model belongs must satisfy, the AI ​​model being a model corresponding to the AI ​​function.

[0066] In conjunction with some embodiments of the second aspect, in some embodiments, determining the usage requirement information of the AI ​​function includes: receiving the usage requirement information associated with the AI ​​function sent by the terminal.

[0067] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: sending a third message to the terminal, the third message being used to indicate a first AI function selected by the network device.

[0068] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving a first message sent by the terminal, the first message including a first candidate cell and a second candidate cell determined by the terminal based on the first usage requirement information of the first AI function; determining a first cell from the first candidate cells; determining a second cell from the second candidate cells; and sending a second message to the terminal based on the first cell and the second cell, the second message including the identifier of the first cell and the identifier of the second cell.

[0069] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving a fourth message sent by the terminal, the fourth message including a second candidate cell determined by the terminal based on the first usage requirement information of the first AI function; determining a second cell from the second candidate cells; and sending a fifth message to the terminal based on the second cell, the fifth message including an identifier of the second cell.

[0070] Thirdly, embodiments of this disclosure propose a terminal, which includes at least one of a transceiver module and a processing module; wherein the terminal is used to execute an optional implementation of the first aspect.

[0071] Fourthly, embodiments of this disclosure propose a network device, which includes at least one of a transceiver module and a processing module; wherein the network device is used to perform an optional implementation of the second aspect.

[0072] Fifthly, embodiments of this disclosure provide a terminal, which includes one or more processors; a memory coupled to the processors, the memory storing executable instructions, which, when executed by the processors, cause the terminal to perform an optional implementation of the first aspect.

[0073] In a sixth aspect, embodiments of this disclosure provide a network device comprising one or more processors; and a memory coupled to the processors, the memory storing executable instructions which, when executed by the processors, cause the network device to perform an optional implementation of the second aspect.

[0074] In a seventh aspect, embodiments of this disclosure provide a communication system comprising a terminal and a network device, wherein the terminal is configured to perform a communication method as described in the optional implementation of the first aspect, and the network device is configured to perform a communication method as described in the optional implementation of the second aspect.

[0075] Eighthly, 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 as described in the optional implementations of the first and second aspects.

[0076] Ninthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method as described in the optional implementations of the first and second aspects.

[0077] In a tenth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the methods described in alternative implementations of the first and second aspects.

[0078] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the methods described according to optional implementations of the first and second aspects above.

[0079] It is understood that the aforementioned terminals, network devices, communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems 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.

[0080] This disclosure provides a communication method, terminal, network device, system, medium, and computer program product. In some embodiments, the terms "communication method" and "information processing method," "AI function usage method," and "AI function selection method" can be used interchangeably; the terms "communication device" and "information processing device," "AI function usage device," and "AI function selection device" can be used interchangeably; and the terms "communication system" and "information processing system," "AI function usage system," and "AI function selection system" can be used interchangeably.

[0081] 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.

[0082] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0083] 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.

[0084] 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.

[0085] In the embodiments of this disclosure, "multiple" refers to two or more.

[0086] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0087] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0088] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0089] 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.

[0090] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0091] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0092] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0093] 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”.

[0094] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0095] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0101] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0102] 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.

[0103] 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 101 and a network device 102.

[0104] In some embodiments, terminal 101 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, 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.

[0105] In some embodiments, network device 102 may include at least one of access network device and core network device.

[0106] Optionally, network device 102 is an access network device. Optionally, 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.

[0107] In some embodiments, network device 102 is a base station. Optionally, a base station may be, for example, a macro base station, micro base station (also called a small station), relay station, access point, 5G base station or future base station, satellite, Transmitting and Receiving Point (TRP), Transmitting Point (TP), mobile switching center, or other equipment that performs base station functions in a communication system, etc., and this disclosure does not specifically limit this type of device. For ease of description, in all embodiments of this disclosure, the apparatus that provides wireless communication functions for terminal devices is collectively referred to as a network device or base station.

[0108] In some embodiments, network device 102 is a core network device. Optionally, the core network device can be a single device, including a first network element, a second network element, etc., or it can be multiple devices or a group of devices, each including all or part of the first network element, the second network element, etc. Network elements can be virtual or physical. The core network includes, for example, at least one of Evolved Packet Core (EPC), 5G Core Network (5GCN), and Next Generation Core (NGC).

[0109] 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.

[0110] 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 of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0111] 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.

[0112] 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. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0113] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G New Radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation Radio Access (FX), Global System for Mobile Communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0114] In some embodiments, 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 may include mobile communication system processes such as beam management, CSI (Channel Status Information) reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0115] In some embodiments, during mobility operations, the UE can predict cell measurement results, handover target cells, or mobility events. The UE's ability to predict future cell measurement results can be termed temporal prediction. Alternatively, predicting the measurement results of cells that have not yet been measured can be termed spatial prediction. Mobility events include the fulfillment of measurement reporting conditions, handover failure, cell dwell time, radio link failure, etc.

[0116] In some embodiments, the UE can perform measurements on one or more cells and use AI to predict the measurement results of other cells, thereby reducing the power consumption of the measurement.

[0117] In some embodiments, the use and reasoning of AI may require multiple AI models or AI functions for reasoning and prediction. An AI function implements a specific function and may correspond to or include one or more AI models.

[0118] In some embodiments, the inference of the AI ​​model or function can be run on the UE side or on the network side.

[0119] In some embodiments, when the measured cell and the predicted cell are on different frequencies, if the frequency offsets are different, then the adjacent channel interference may be different. If the frequency offsets of the measured cell and the predicted cell are inconsistent with the data used to train the AI ​​model, then the AI's prediction may be inaccurate. In view of this, embodiments of this disclosure propose a communication method, terminal, network device, system, medium, and computer program product to reduce or avoid the problem of inaccurate predictions caused by frequency offsets and other non-compliance with requirements.

[0120] Figure 2 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2, the embodiment of the present disclosure relates to a communication method executed by a communication system 100, and the method includes the following steps S201 to S208.

[0121] In step S201, terminal 101 determines the AI ​​function and the usage requirements information of the AI ​​function.

[0122] In some embodiments, the number of AI functions is one or more. Optionally, an AI function corresponds to or is associated with at least one usage requirement information. Optionally, an AI function corresponds to one or more AI models, which are used to implement the AI ​​function.

[0123] In some embodiments, the AI ​​functionality may be used to predict cell measurement results, predict handover target cells, or predict other mobility events.

[0124] In some embodiments, the AI ​​function is a preset AI function, a specified AI function, or a special AI function that corresponds to relevant usage requirement information.

[0125] In some embodiments, the AI ​​function is at least one of the following:

[0126] AI functions activated on the terminal;

[0127] AI functions available on the device;

[0128] AI functions stored on the terminal.

[0129] In some embodiments, the implementation of the terminal determining AI functions includes: determining which functions on the terminal are active AI functions.

[0130] In some embodiments, the implementation of the terminal determining AI functions includes: determining which AI functions are available on the terminal.

[0131] In some embodiments, the implementation of the terminal determining AI functions includes: determining which AI functions are currently stored on the terminal.

[0132] In some embodiments, the implementation of the terminal determining the AI ​​function includes: acquiring the AI ​​function. Optionally, the implementation of the terminal acquiring the AI ​​function may be that the terminal obtains the AI ​​function from a first device. For example, the terminal obtains relevant configuration information of the AI ​​function from the first device, thereby acquiring the AI ​​function. For example, the terminal obtains relevant permissions to use the AI ​​function from the first device, thereby enabling the implementation of the AI ​​function. For example, the terminal obtains a model application with AI function from the first device, thereby acquiring the AI ​​function.

[0133] Alternatively, the terminal can acquire AI functions by training an AI model to obtain a trained AI model, thereby acquiring the functions of an AI model.

[0134] In some embodiments, the terminal may acquire the usage requirement information of the AI ​​function at the same time as or after acquiring the AI ​​function. In some embodiments, the usage requirement information of the AI ​​function is used to ensure or improve the effectiveness of the AI ​​function.

[0135] In some embodiments, requirement information is used to indicate the conditions, scenarios, and methods of using the AI ​​function. For example, requirement information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet. These characteristics include, but are not limited to, time-domain characteristics, frequency-domain characteristics, spatial-domain characteristics, and time-frequency characteristics. Here, the AI ​​model is a model corresponding to the AI ​​function, and the AI ​​model is used to implement the AI ​​function.

[0136] In some embodiments, requirement information is used to instruct the terminal to predict the predicted data for a second type of cell based on measured data of a first type of cell when using an AI model. Here, the first type of cell refers to the cell corresponding to the input data of the AI ​​model, and the second type of cell refers to the cell corresponding to the output data of the AI ​​model. That is, the relevant information of the first type of cell can be used by the AI ​​model to predict the relevant information of the second type of cell.

[0137] In some embodiments, requirement information is used to instruct the terminal to input measured data of the first type of cell into the AI ​​model to predict the predicted data of the second type of cell.

[0138] In some embodiments, the name of the usage requirement information is not limited, and it may be, for example, usage conditions, usage requirements, usage methods, guidance information, application manual, model application specifications, etc.

[0139] In some embodiments, the usage requirement information includes frequency domain requirements. Frequency domain requirements may include a frequency offset range, which indicates the required range of the difference between the deployment frequencies of the first-class cells and the second-class cells. Using AI functions or AI models based on this usage requirement information can avoid inaccurate predictions due to unmet frequency offset requirements, thereby improving the predictive performance of the AI ​​functions or AI models.

[0140] Optionally, the frequency offset range includes an upper limit and / or a lower limit.

[0141] For example, assuming the frequency offset range includes an upper limit, then when the frequency difference between cell A and cell B is less than or equal to that upper limit, cell A and cell B can be considered to meet the requirements indicated by the AI ​​model's usage requirements information. Cell A can be classified as a first-class cell and cell B as a second-class cell. Alternatively, cell A can be classified as a second-class cell and cell B as a first-class cell.

[0142] For example, assuming the frequency offset range includes a lower limit, then when the frequency difference between cell A and cell B is greater than or equal to that lower limit, cell A and cell B can be considered to meet the requirements indicated by the usage requirements information of the AI ​​model. Cell A can be classified as a first-class cell and cell B as a second-class cell. Alternatively, cell A can be classified as a second-class cell and cell B as a first-class cell.

[0143] For example, assuming the frequency offset range includes a lower limit and an upper limit, then when the frequency difference between cell A and cell B is greater than or equal to the lower limit and less than or equal to the upper limit, cell A and cell B can be considered to meet the requirements indicated by the AI ​​model's usage requirements. Cell A can be classified as a first-class cell and cell B as a second-class cell. Alternatively, cell A can be classified as a second-class cell and cell B as a first-class cell.

[0144] In other embodiments, the required information includes frequency domain requirements. For example, a frequency domain requirement may be that the first type of cell and the second type of cell are inter-frequency cells. For example, a frequency domain requirement may be a range requirement for the frequency domain difference between the first type of cell and the second type of cell.

[0145] In some embodiments, the requirement information may include airspace requirements. For example, an airspace requirement may indicate that a Class 1 cell is a neighboring cell of a Class 2 cell. For example, an airspace requirement may indicate an overlap between the coverage areas of the Class 1 cell and the Class 2 cell. For example, an airspace requirement may indicate that the Class 1 cell and the Class 2 cell are adjacent and have a handover relationship. For example, an airspace requirement may indicate a distance requirement between the Class 1 cell and the Class 2 cell.

[0146] In some embodiments, the requirement information may include time-domain requirements. For example, time-domain requirements may indicate the required value of the difference between the maximum time advance (TA) of Category 1 cells and Category 2 cells.

[0147] In some embodiments, obtaining the usage requirement information of an AI function can be implemented as follows: if the AI ​​model is trained by a terminal, the usage requirement information of the AI ​​model is determined based on the training data used by the terminal to train the AI ​​model, and the usage requirement information of the AI ​​model is the usage requirement information of the corresponding AI function. For example, the usage requirement information of the model is determined based on the model input samples and the corresponding model output samples, and the usage requirement information is associated or bound to the corresponding AI function. Optionally, when the usage requirement information of an AI function is determined, the terminal can report the AI ​​functions it possesses and / or the usage requirement information associated with the AI ​​functions to the network device.

[0148] In some embodiments, obtaining the usage requirement information of an AI function can be implemented as follows: if the AI ​​function is provided by a first device, the corresponding usage requirement information is obtained from the first device, and the usage requirement information is associated or bound to the AI ​​function. Optionally, the first device determines the usage requirement information based on the training data of the AI ​​model corresponding to the AI ​​function.

[0149] In some embodiments, the first device is an electronic device or network node that provides AI functionality. Optionally, the first device is a network device, a server, or other terminal.

[0150] In some embodiments, step S201 is performed according to instructions from the network device. For example, the network device instructs the terminal to determine the artificial intelligence (AI) function and instructs the terminal to obtain information on the usage requirements of the AI ​​function. The terminal then performs step S201.

[0151] In step S202, network device 102 instructs terminal 101 to perform the first AI function.

[0152] In some embodiments, the network device may instruct the terminal to one or more first AI functions. The first AI function is one or more of the AI ​​functions supported by the terminal.

[0153] In some embodiments, the terminal determines one or more first AI functions according to instructions from the network device.

[0154] For example, the network device sends a third message to the terminal, which includes the identifier of one or more first AI functions. The terminal receives the third message and identifies one or more first AI functions.

[0155] In some embodiments, the name of the third message is not limited, and it may be, for example, a function indication, a function activation command, etc.

[0156] In some embodiments, step S202 can be omitted, and the terminal can determine the first AI function on its own. For example, the terminal determines the AI ​​function to be used as the first AI function based on the functional application requirements.

[0157] In some embodiments, after determining the first AI function, the terminal may determine the first usage requirement information associated with the first AI function, and use the corresponding first AI model to perform data prediction based on the first usage requirement information.

[0158] In step S203, terminal 101 determines the first candidate cell and the second candidate cell based on the first usage requirement information associated with the first AI function.

[0159] In some embodiments, if the terminal needs to use a first AI model for data prediction, then a first candidate cell and a second candidate cell can be determined based on first usage requirement information. The first candidate cell and the second candidate cell satisfy the characteristics indicated by the first usage requirement information. The number of both the first candidate cell and the second candidate cell is not limited.

[0160] In some embodiments, the terminal determines one or more groups of candidate cells based on first usage requirement information. A group of candidate cells includes at least one first candidate cell and at least one second candidate cell. The first and second candidate cells in the same group can be arbitrarily combined for data prediction.

[0161] In some embodiments, a first cell can be determined from a first candidate cell, and a second cell can be determined from a second candidate cell. The number of first and second cells is not limited. After determining the first and second cells, step S207 can be executed.

[0162] In some embodiments, the terminal may autonomously determine a first cell from a first candidate cell and autonomously determine a second cell from a second candidate cell, and then execute step S207.

[0163] In some embodiments, the terminal may request the network device to determine a first cell from the first candidate cells, and / or request the network device to determine a second cell from the second candidate cells, and then execute step S207. Specific implementation steps can be found in steps S204 to S206.

[0164] In step S204, terminal 101 sends the first candidate cell and / or the second candidate cell to network device 102.

[0165] In step S205, network device 102 determines a first cell based on a first candidate cell, and / or determines a second cell based on a second candidate cell.

[0166] In step S206, network device 102 indicates the first cell and / or the second cell to terminal 101.

[0167] In some embodiments, a terminal may request a network device to determine a first cell and a second cell.

[0168] Optionally, the terminal sends a first candidate cell and a second candidate cell to the network device. The first candidate cell is used by the network device to determine a first cell, and the second candidate cell is used by the network device to determine a second cell. For example, the terminal sends a first message to the network device, which indicates the first and second candidate cells. The first message may include identifiers of the first and second candidate cells. The name of the first message may be other names.

[0169] Optionally, the network device receives a first candidate cell and a second candidate cell sent by the terminal. For example, the network device receives a first message and determines the first candidate cell and the second candidate cell based on the first message.

[0170] Optionally, the network device determines a first cell from the first candidate cells indicated by the first message, and determines a second cell from the second candidate cells.

[0171] Optionally, the network device indicates a first cell and a second cell to the terminal. For example, the network device sends a second message to the terminal, which indicates the first cell and the second cell. The second message includes the identifiers of the first cell and the second cell. The terminal receives the second message and determines the first cell and the second cell. The name of the second message can be other names.

[0172] In some embodiments, the terminal may autonomously determine the first cell from the first candidate cells. For example, the terminal may determine at least one of the following as the first cell:

[0173] The terminal's service area;

[0174] Cells equipped with measurement configurations;

[0175] The cell indicated by the network device.

[0176] Furthermore, if the terminal autonomously determines the first cell, it can request the network device to determine the second cell.

[0177] Optionally, the terminal sends a second candidate cell to the network device. The second candidate cell and the determined first cell conform to the characteristics indicated by the first usage requirement information. For example, the terminal sends a fourth message to the network device, which indicates the second candidate cell. The fourth message may include the identifier of the second candidate cell. The name of the fourth message may be other names.

[0178] Optionally, the network device receives a second candidate cell sent by the terminal. For example, the network device receives a fourth message sent by the terminal, and determines the second candidate cell based on the fourth message.

[0179] Optionally, the network device determines the second cell from the second candidate cells.

[0180] Optionally, the network device indicates a second cell to the terminal. For example, the network device sends a fifth message to the terminal, which includes the identifier of the second cell determined by the network device from the second candidate cells. The terminal receives the fifth message and determines the second cell. The name of the fifth message can be other names.

[0181] In some embodiments, the terminal may autonomously determine the second cell from the second candidate cells. For example, the terminal may determine at least one of the following as the second cell:

[0182] Unmeasurable area;

[0183] The neighboring communities of the currently served community;

[0184] Cells without measurement configuration;

[0185] Cells to be predicted as indicated by network equipment.

[0186] Furthermore, if the terminal autonomously determines the second cell, the terminal can request the network device to determine the first cell.

[0187] Optionally, the terminal sends a first candidate cell to the network device. For example, the terminal sends a sixth message to the network device, which indicates the first candidate cell. The sixth message may include the identifier of the first candidate cell. The name of the sixth message may be other than the given name.

[0188] Optionally, the network device receives the first candidate cell sent by the terminal. For example, the network device receives the sixth message sent by the terminal, and determines the first candidate cell based on the sixth message.

[0189] Optionally, the network device determines the first cell from the first candidate cells.

[0190] Optionally, the network device indicates a first cell to the terminal. For example, the network device sends a seventh message to the terminal, which includes the identifier of the first cell determined by the network device from the first candidate cells. The terminal receives the seventh message and determines the first cell. The name of the seventh message can be other names.

[0191] In step S207, terminal 101 performs actual measurements on the first cell and obtains the measured data of the first cell.

[0192] In some embodiments, after determining the first cell, the terminal can perform actual measurements on the first cell to obtain the measured data of the first cell.

[0193] In some embodiments, after determining the first cell and the second cell, the second cell can be predicted based on the first cell and the first AI model.

[0194] For example, conducting a test on the first cell could involve performing cell measurements on the first cell. For example, conducting a test on the first cell could involve determining whether the first cell is a target cell for handover. For example, conducting a test on the first cell could involve determining whether a handover to the first cell would fail. For example, conducting a test on the first cell could involve determining the dwell time in the first cell. For example, conducting a test on the first cell could involve determining whether a radio link failure occurred when accessing the first cell. For example, conducting a test on the first cell could involve determining whether an RRC reconstruction was initiated in the first cell. The measured data for the first cell can be the actual determination result in any of the above examples. Of course, this disclosure includes, but is not limited to, the methods for conducting tests on the first cell as described in the above examples, and may also include actually measuring other mobility events of the first cell.

[0195] In step S208, terminal 101 inputs the measured data of the first cell into the first AI model to predict the predicted data of the second cell.

[0196] In some embodiments, after obtaining the measured data of the first cell, the terminal can input the measured data of the first cell into the first AI model to predict the predicted data of the second cell.

[0197] Among them, the measured data of the first community is one of the output data of the first AI model, and the predicted data of the second community is the data output by the first AI model.

[0198] In some embodiments, step S208 can be triggered immediately after the first cell and the second cell are determined.

[0199] In some embodiments, step S208 may be temporarily suspended after determining the first cell and the second cell, and executed when the triggering condition is detected.

[0200] The triggering conditions include, but are not limited to, receiving a cell measurement command or handover command sent by a network device, the terminal detecting a wireless link failure, or the timeout of a preset timer (e.g., T304, T310).

[0201] In some embodiments, the number of first cells is one or more. In some embodiments, the number of second cells is one or more.

[0202] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0203] In some embodiments, “get,” “obtain,” “receive,” “transmit,” “bidirectional transmission,” and “send and / or receive” can be used interchangeably and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining through self-processing, or autonomous implementation, among other meanings.

[0204] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0205] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0206] The communication method involved in the embodiments of this disclosure may include at least one of steps S201 to S208. For example, step S201 may be implemented as a standalone embodiment, steps S207 and S208 may be implemented as standalone embodiments, and steps S204 and S206 may be implemented as standalone embodiments, but are not limited thereto.

[0207] In some embodiments, the order of any two steps S201 to S208 can be interchanged or they can be performed simultaneously. For example, the order of steps S201 and S202 can be interchanged or they can be performed simultaneously.

[0208] In some embodiments, steps S202 to S208 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0209] In some embodiments, steps S201 to S206 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0210] In some embodiments, steps S201 to S203, S205, S207, and S208 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0211] In some embodiments, other optional implementations may be described before or after the specification corresponding to FIG2.

[0212] Figure 3A is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3A, the embodiment of the present disclosure relates to a communication method executed by a terminal side, the method including:

[0213] Step S3101: Determine the AI ​​function and the usage requirements for the AI ​​function.

[0214] The optional implementation of step S3101 can be found in the optional implementation of step S201 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0215] Step S3102: Receive the third message and determine the first AI function based on the third message.

[0216] The optional implementation of step S3102 can be found in the optional implementation of step S202 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0217] In some embodiments, terminal 101 receives a third message sent by network device 102, but is not limited thereto; it may also receive a third message sent by other entities.

[0218] In some embodiments, terminal 101 obtains a third message defined by the protocol.

[0219] In some embodiments, terminal 101 obtains a third message from an upper layer(s).

[0220] In some embodiments, terminal 101 processes the data to obtain a third message.

[0221] In some embodiments, step S3102 is omitted, and the terminal 101 autonomously implements the function indicated by the third message, or the above function is default or default.

[0222] Step S3103: Determine the first candidate cell and the second candidate cell.

[0223] The optional implementation of step S3103 can be found in the optional implementation of step S203 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0224] Step S3104: Report the first candidate cell and / or the second candidate cell.

[0225] The optional implementation of step S3104 can be found in the optional implementation of step S204 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0226] In some embodiments, terminal 101 reports a first candidate cell and / or a second candidate cell to network device 102, but is not limited thereto, and may also send the first candidate cell and / or the second candidate cell to other entities.

[0227] Step S3105: Determine the first cell and the second cell.

[0228] The optional implementations of step S3105 can be found in the optional implementations of steps S205 and S206 in Figure 2, as well as other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0229] Step S3106: Measure the first cell.

[0230] The optional implementation of step S3106 can be found in the optional implementation of step S207 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0231] Step S3107: Input the measured data of the first cell into the first AI model to obtain the predicted data of the second cell.

[0232] The optional implementation of step S3107 can be found in the optional implementation of step S208 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0233] The communication method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3107. For example, step S3101 may be implemented as a standalone embodiment, steps S3104 and S3105 may be implemented as standalone embodiments, and steps S3106 and S3107 may be implemented as standalone embodiments, but is not limited thereto.

[0234] In some embodiments, the order of any two steps S3101 to S3107 can be interchanged or they can be performed simultaneously. For example, the order of steps S3101 and S3102 can be interchanged or they can be performed simultaneously.

[0235] In some embodiments, steps S3102 to S3107 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0236] In some embodiments, steps S3101 to S3103, S3106 and S3107 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0237] In some embodiments, steps S3101 to S3105 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0238] Figure 3B is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3B, the embodiment of the present disclosure relates to a communication method executed by a terminal side, the method including:

[0239] Step S3201: Determine the AI ​​function.

[0240] The optional implementation of step S3201 can be found in the optional implementation of step S201 in Figure 2, step S3101 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0241] Step S3202: Obtain the usage requirements information for the AI ​​function.

[0242] The optional implementation of step S3202 can be found in the optional implementation of step S201 in Figure 2, step S3101 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0243] In some embodiments, terminal 101 obtains the usage requirement information of AI function from network device 102, but is not limited thereto, and may also obtain the usage requirement information of AI function from other entities.

[0244] In some embodiments, terminal 101 obtains usage requirement information for AI functions as specified in the protocol.

[0245] In some embodiments, terminal 101 obtains the usage requirement information of AI function from upper layer(s).

[0246] In some embodiments, the terminal 101 processes information to obtain usage requirements for AI functions.

[0247] In some embodiments, step S3202 is omitted, and the terminal 101 autonomously implements the function indicated by the AI ​​function usage requirement information, or the above function is defaulted or set to default.

[0248] The communication method involved in the embodiments of this disclosure may include at least one of steps S3201 and S3202. For example, step S3201 may be implemented as a separate embodiment, and step S3202 may be implemented as a separate embodiment, and is not limited thereto.

[0249] In some embodiments, steps S3201 and S3202 may be performed in an alternate order or simultaneously.

[0250] In some embodiments, step S3201 is optional and may be omitted or replaced in different embodiments.

[0251] In some embodiments, step S3202 is optional and may be omitted or replaced in different embodiments.

[0252] In this embodiment of the disclosure, step S3201 can be combined with one or more of steps S3102 to S3107 in FIG3A, and step S3202 can be combined with one or more of steps S3102 to S3107 in FIG3A.

[0253] Figure 3C is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3C, the embodiment of the present disclosure relates to a communication method executed by a terminal side, the method including:

[0254] Step S3301: Determine and send the first candidate cell and the second candidate cell based on the first usage requirement information of the first AI function.

[0255] The optional implementations of step S3301 can be found in steps S203 and S204 in Figure 2, steps S3103 and S3104 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0256] Step S3302: Receive the first cell and the second cell.

[0257] The optional implementations of step S3302 can be found in steps S205 and S206 in Figure 2, the optional implementations of step S3105 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0258] Step S3303: Use the measurement data of the first cell to predict the second cell, and obtain the predicted data of the second cell.

[0259] The optional implementations of step S3303 can be found in steps S207 and S208 in Figure 2, steps S3106 and S3107 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0260] The communication method involved in the embodiments of this disclosure may include at least one of steps S3301 to S3303. For example, step S3303 may be implemented as a separate embodiment, and steps S3301 and S3302 may be implemented as separate embodiments, but are not limited thereto.

[0261] In some embodiments, the order of any two steps in steps S3301 to S3303 can be interchanged or they can be performed simultaneously.

[0262] In some embodiments, steps S3301 and S3302 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0263] In some embodiments, step S3303 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0264] In this embodiment of the disclosure, step S3301 can be combined with step S3101 or step S3102 of FIG3A, and step S3303 can be combined with step S3106 of FIG3A.

[0265] Figure 3D is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3D, the embodiment of the present disclosure relates to a communication method executed by a terminal side, the method including:

[0266] Step S3401: Determine the first candidate cell and the second candidate cell based on the first usage requirement information of the first AI model.

[0267] The optional implementation of step S3401 can be found in the optional implementation of step S203 in Figure 2, step S3103 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0268] Step S3402: Determine the first cell from the first candidate cells.

[0269] The optional implementation of step S3402 can be found in the optional implementation of step S204 in Figure 2, step S3104 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0270] Step S3403: Send the second candidate cell.

[0271] The optional implementation of step S3403 can be found in the optional implementation of step S204 in Figure 2, step S3104 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0272] Step S3404: Receive the second cell.

[0273] The optional implementation of step S3404 can be found in the optional implementations of steps S205 and S206 in Figure 2, step S3105 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0274] Step S3405: ​​Use the first AI model to predict the second cell based on the measurement data of the first cell to obtain the predicted data of the second cell.

[0275] The optional implementations of step S3405 can be found in steps S207 and S208 in Figure 2, steps S3106 and S3107 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0276] The communication method involved in the embodiments of this disclosure may include at least one of steps S3401 to S3405. For example, steps S3402, S3403, and S3404 may be implemented as independent embodiments, but are not limited thereto.

[0277] In some embodiments, the order of any two steps in steps S3401 to S3405 can be interchanged or they can be performed simultaneously.

[0278] In some embodiments, steps S3401 to S3404 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0279] In some embodiments, steps S3401 and S3405 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0280] In this embodiment of the disclosure, step S3401 can be combined with step S3101 or step S3102 of FIG3A, and step S3405 can be combined with step S3106 of FIG3A.

[0281] Figure 4A is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4A, the embodiment of the present disclosure relates to a communication method executed by a network device, the method comprising:

[0282] Step S4101: Send information about the AI ​​function and the requirements for using the AI ​​function.

[0283] The optional implementation of step S4101 can be found in the optional implementation of step S201 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0284] In some embodiments, network device 102 sends AI functions and AI function usage requirements information to terminal 101, but is not limited thereto, and may also send AI functions and AI function usage requirements information to other entities.

[0285] Step S4102, instruct the first AI function.

[0286] The optional implementation of step S4102 can be found in the optional implementation of step S202 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0287] In some embodiments, network device 102 indicates a first AI function to terminal 101, but is not limited thereto; it may also indicate the first AI function to other entities.

[0288] Step S4103: Receive the first candidate cell and / or the second candidate cell.

[0289] The optional implementation of step S4103 can be found in the optional implementation of step S204 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0290] In some embodiments, network device 102 receives a first candidate cell and / or a second candidate cell sent by terminal 101, but is not limited thereto, and may also receive a first candidate cell and / or a second candidate cell sent by other entities.

[0291] In some embodiments, network device 102 acquires a first candidate cell and / or a second candidate cell as specified by a protocol.

[0292] In some embodiments, network device 102 obtains a first candidate cell and / or a second candidate cell from upper layer(s).

[0293] In some embodiments, network device 102 performs processing to obtain a first candidate cell and / or a second candidate cell.

[0294] In some embodiments, step S4103 is omitted, and the network device 102 autonomously implements the functions indicated by the first candidate cell and / or the second candidate cell, or the above functions are default or default.

[0295] Step S4104: Determine and send the first cell and / or the second cell.

[0296] The optional implementations of step S4104 can be found in the optional implementations of steps S205 and S206 in Figure 2, as well as other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0297] In some embodiments, network device 102 sends a first cell and / or a second cell to terminal 101, but is not limited thereto, and may also send the first cell and / or the second cell to other entities.

[0298] The communication method involved in the embodiments of this disclosure may include at least one of steps S4101 to S4104. For example, step S4101 may be implemented as a standalone embodiment, step S4102 may be implemented as a standalone embodiment, and steps S4103 and S4104 may be implemented as standalone embodiments, but are not limited thereto.

[0299] In some embodiments, the order of any two steps S4101 to S4104 can be interchanged or they can be performed simultaneously. For example, the order of steps S4101 and S4102 can be interchanged or they can be performed simultaneously.

[0300] In some embodiments, steps S4102 to S4104 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0301] In some embodiments, steps S4101, S4103, and S4104 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0302] In some embodiments, steps S4101 and S4102 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0303] Figure 4B is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4B, the embodiment of the present disclosure relates to a communication method executed by a network device, the method including:

[0304] Step S4201: Determine the usage requirements information for the AI ​​function.

[0305] The optional implementation of step S4101 can be found in step S201 of Figure 2, the optional implementation of step S4101 of Figure 4A, and other related parts in the embodiments involved in Figures 2 and 4A, which will not be repeated here.

[0306] In some embodiments, this disclosure provides a communication method executed by a communication system 100, the method comprising:

[0307] Step S501: The terminal requests the AI ​​function and the usage requirements information of the AI ​​function from the network device.

[0308] The optional implementation of step S501 can be found in the optional implementation of step S201 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0309] In step S502, the network device sends the AI ​​function and the usage requirements information of the AI ​​function to the terminal.

[0310] The optional implementation of step S502 can be found in the optional implementation of step S201 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0311] In some embodiments, the above methods may include the methods described in the embodiments on the communication system side, terminal side, network device side, etc., which will not be repeated here.

[0312] In some embodiments, this disclosure provides Embodiment 1, which determines first information for an AI function. This first information indicates the frequency offset of the deployment frequencies of a first type of cell and a second type of cell. The first type of cell serves as the input to the AI, and the second type of cell serves as the output of the AI. The AI ​​uses the measurement results of the first type of cell to predict the measurement results of the second type of cell. The first information is the same as or similar to the usage requirement information in the foregoing embodiments.

[0313] Optionally, each AI function is bound to at least one piece of primary information, which can be obtained when the UE acquires the AI ​​function. The AI ​​function can be provided via a network or a server.

[0314] Optionally, the UE determines the first information based on the data used to train the AI ​​model.

[0315] This disclosure provides Embodiment 2, which is based on Embodiment 1. The UE determines a first AI function and selects a first candidate cell and a second candidate cell based on the first information of the first AI function. The frequency offset of the deployment frequency of the selected first candidate cell and the second candidate cell meets the frequency offset requirement indicated by the first information.

[0316] Optionally, meeting the requirement can be understood as the frequency offset of the deployment frequency of the selected first candidate cell and the second candidate cell being greater than or equal to the frequency offset indicated by the first information, or it can be understood as the frequency offset of the deployment frequency of the selected first candidate cell and the second candidate cell being less than or equal to the frequency offset indicated by the first information.

[0317] Optionally, the first candidate cell and the second candidate cell can be one or more.

[0318] Optionally, the first AI function can be an activated AI function, a currently available AI function, or a currently stored AI function.

[0319] This disclosure provides Embodiment 3, which, based on Embodiment 1, involves the UE reporting the first information of the AI ​​function to the network.

[0320] Optionally, the UE can report initial information for multiple AI functions.

[0321] This disclosure provides Embodiment 4, which, based on Embodiment 2, involves the UE reporting a first candidate cell and a second candidate cell to the network.

[0322] Optionally, the UE can report multiple combinations of first candidate cells and second candidate cells.

[0323] This disclosure provides Embodiment 5, which, based on Embodiment 2, involves the UE determining a first cell and, according to a first AI function, determining a second candidate cell. The second candidate cell is then reported to the network. The network determines the second cell. The first cell and the second cell can serve as the first type of cell and the second type of cell for the first AI function.

[0324] Optionally, the first cell may be a serving cell, a cell configured with measurement settings, or determined according to network instructions.

[0325] Optionally, the second cell can be one or more cells.

[0326] This disclosure provides embodiment 6, based on embodiment 3 or 4, receiving second information sent by the network, the second information indicating a first cell identifier and a second cell identifier, the first cell and the second cell serving as a first type of cell and a second type of cell for the first AI function.

[0327] Optionally, the first cell and the second cell can be one or more.

[0328] This disclosure provides embodiment 7, which, based on embodiments 2 and 6, receives third information sent from the network, the third information indicating a first AI function.

[0329] Optionally, the third information may carry an AI function identifier or an AI model identifier.

[0330] In the embodiments disclosed herein, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations in other embodiments.

[0331] This disclosure also provides an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0332] 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.

[0333] 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).

[0334] Figure 5 is a schematic diagram of the structure of a terminal according to an embodiment of the present disclosure. As shown in Figure 5, the terminal 500 may include at least one of a transceiver module 501 and a processing module 502. In some embodiments, the processing module 502 is used to determine an artificial intelligence (AI) function; obtain usage requirement information of the AI ​​function, wherein the usage requirement information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet, and the AI ​​model is a model corresponding to the AI ​​function. Optionally, the transceiver module 501 is used to perform at least one of the communication steps (e.g., steps S202, S204, S206, but not limited thereto) performed by the terminal 101 in any of the above methods, which will not be described in detail here. Optionally, the processing module 502 is used to perform at least one of the other steps (e.g., steps S201, S203, S205, S207, S208, but not limited thereto) performed by the terminal 101 in any of the above methods, which will not be described in detail here.

[0335] Figure 6 is a schematic diagram of a network device according to an embodiment of the present disclosure. As shown in Figure 6, the network device 600 may include at least one of a transceiver module 601 and a processing module 602. In some embodiments, the transceiver module 601 is used to determine usage requirement information for an AI function. The usage requirement information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet. The AI ​​model is a model corresponding to the AI ​​function. Optionally, the transceiver module 601 is used to perform at least one of the communication steps (e.g., steps S202, S204, and S206, but not limited thereto) performed by the network device 102 in any of the above methods. Optionally, the processing module 602 is used to perform at least one of the other steps (e.g., steps S201, S203, S205, S207, and S208, but not limited thereto) performed by the network device 102 in any of the above methods.

[0336] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0337] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0338] Figure 7 is a schematic diagram of the structure of a communication device 8100 according to an embodiment of this disclosure. The communication device 8100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 8100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0339] As shown in Figure 7, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 8100 can be used to execute any of the above methods. Optionally, one or more processors 8101 can be used to invoke instructions to cause the communication device 8100 to execute any of the above methods.

[0340] In some embodiments, the communication device 8100 further includes one or more transceivers 8102. When the communication device 8100 includes one or more transceivers 8102, the transceiver 8102 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S202, S204, S206, but not limited thereto), and the processor 8101 performs at least one of other steps (e.g., steps S201, S203, S205, S207, S208, but not limited thereto). 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; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0341] In some embodiments, the communication device 8100 further includes one or more memories 8103 for storing data. Optionally, all or part of the memories 8103 may be located outside the communication device 8100. In an optional embodiment, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuits 8104 are connected to the memories 8103 and can be used to receive data from the memories 8103 or other devices, and to send data to the memories 8103 or other devices. For example, the interface circuits 8104 can read data stored in the memories 8103 and send that data to the processor 8101.

[0342] The communication device 8100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 8100 described in this disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG. 7. 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 and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0343] Figure 8 is a schematic diagram of the structure of chip 8200 according to an embodiment of the present disclosure. For cases where the communication device 8100 can be a chip or a chip system, the schematic diagram of chip 8200 shown in Figure 8 can be referenced, but is not limited thereto.

[0344] Chip 8200 includes one or more processors 8201. Chip 8200 is used to perform any of the methods described above.

[0345] In some embodiments, chip 8200 further includes one or more interface circuits 8202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 8200 further includes one or more memories 8203 for storing data. Optionally, all or part of the memories 8203 may be located outside of chip 8200. Optionally, interface circuit 8202 is connected to memory 8203, and interface circuit 8202 can be used to receive data from memory 8203 or other devices, and interface circuit 8202 can be used to send data to memory 8203 or other devices. For example, interface circuit 8202 can read data stored in memory 8203 and send the data to processor 8201.

[0346] In some embodiments, the interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S202, S204, and S206, but not limited thereto). The interface circuit 8202 performing the communication steps such as sending and / or receiving in the above method refers, for example, to the interface circuit 8202 performing data interaction between the processor 8201, the chip 8200, the memory 8203, or the transceiver device. In some embodiments, the processor 8201 performs at least one of other steps (e.g., steps S201, S203, S205, S207, and S208, but not limited thereto).

[0347] 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.

[0348] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device 8100, cause the communication device 8100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0349] This disclosure also provides a program product that, when executed by the communication device 8100, causes the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0350] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method, characterized in that, The method, executed by a terminal, includes: Determine the artificial intelligence (AI) functions; Obtain the usage requirement information of the AI ​​function. The usage requirement information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet. The AI ​​model is a model corresponding to the AI ​​function.

2. The method according to claim 1, characterized in that, The AI ​​model is used to predict the data for the second type of cell based on the measured data of the first type of cell.

3. The method according to claim 1 or 2, characterized in that, The usage requirements information includes a frequency offset range, which indicates the range of values ​​required for the difference between the deployment frequencies of the first type of cell and the second type of cell.

4. The method according to any one of claims 1-3, characterized in that, The process of obtaining the usage requirements information for the AI ​​function includes: The usage requirements information is determined based on the training data used by the terminal to train the AI ​​model; or, The usage requirement information is obtained from a first device, wherein the first device is a device that provides the AI ​​function.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Send the usage requirement information associated with the AI ​​function to the network device.

6. The method according to any one of claims 1-5, characterized in that, The AI ​​function is at least one of the following: Activated AI functionality; Available AI features; AI functionality stored.

7. The method according to any one of claims 1-6, characterized in that, The number of AI functions may be one or more; each AI function is associated with at least one usage requirement information; the method further includes: Identify the first AI function from one or more AI functions; Based on the first usage requirement information associated with the first AI function, a first cell and a second cell are determined, wherein the first cell belongs to the first type of cell and the second cell belongs to the second type of cell.

8. The method according to claim 7, characterized in that, The step of determining the first cell and the second cell based on the first usage requirement information associated with the first AI function includes: A first candidate cell and a second candidate cell are determined based on the first usage requirement information, wherein the first candidate cell and the second candidate cell satisfy the features indicated by the first usage requirement information; The first cell is determined from the first candidate cells, and the second cell is determined from the second candidate cells.

9. The method according to claim 8, characterized in that, The step of determining the first cell from the first candidate cells and determining the second cell from the second candidate cells includes: Send a first message to the network device, the first message being used to indicate the first candidate cell and the second candidate cell; Receive a second message sent by the network device, the second message being used to indicate the first cell and the second cell; The first cell and the second cell are determined based on the second message.

10. The method according to any one of claims 7-9, characterized in that, The step of determining the first AI function from one or more AI functions includes: Receive a third message sent by a network device, the third message being used to indicate the first AI function; The first AI function is determined from one or more AI functions based on the third message.

11. The method according to claim 8, characterized in that, Determining the first cell from the first candidate cells includes: identifying at least one of the following from the first candidate cells as the first cell: The terminal's serving cell; There are cells with measurement configurations. The cell indicated by the network device.

12. The method according to any one of claims 8, 10, and 11, characterized in that, Determining the second cell from the second candidate cells includes: Send a fourth message to the network device, the fourth message being used to indicate the second candidate cell; The network device receives a fifth message, which indicates the second cell determined by the network device from the second candidate cells. The second cell is determined based on the fifth message.

13. The method according to any one of claims 7-12, characterized in that, The method further includes: The measured data of the first cell is input into the first AI model corresponding to the first AI function to predict the predicted data of the second cell.

14. A communication method, characterized in that, Performed by a network device, the method includes: The usage requirements information for the AI ​​function is determined. The usage requirements information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet. The AI ​​model is a model corresponding to the AI ​​function.

15. The method according to claim 14, characterized in that, The information determining the usage requirements of AI functions includes: Receive the usage requirement information associated with the AI ​​function sent by the terminal.

16. The method according to claim 14 or 15, characterized in that, The method further includes: A third message is sent to the terminal, the third message being used to instruct the network device to select a first AI function.

17. The method according to claim 16, characterized in that, The method further includes: Receive a first message sent by the terminal, the first message including a first candidate cell and a second candidate cell determined by the terminal based on the first usage requirement information of the first AI function; The first cell is determined from the first candidate cells; The second cell is determined from the second candidate cells; A second message is sent to the terminal based on the first cell and the second cell, the second message being used to indicate the first cell and the second cell.

18. The method according to claim 16, characterized in that, The method further includes: The terminal receives a fourth message, the fourth message including a second candidate cell determined by the terminal based on the first usage requirement information of the first AI function; The second cell is determined from the second candidate cells; The second cell sends a fifth message to the terminal, the fifth message including the identifier of the second cell.

19. A terminal, characterized in that, include: The processing module is used to determine the artificial intelligence (AI) function; obtain the usage requirement information of the AI ​​function, wherein the usage requirement information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet, and the AI ​​model is a model corresponding to the AI ​​function.

20. A network device, characterized in that, include: The transceiver module is used to determine the usage requirements information of the AI ​​function. The usage requirements information is used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet. The AI ​​model is a model corresponding to the AI ​​function.

21. A terminal, characterized in that, include: One or more processors; A memory coupled to the processor, the memory storing executable instructions, which, when executed by the processor, cause the terminal to perform the communication method according to any one of claims 1-13.

22. A network device, characterized in that, include: One or more processors; A memory coupled to the processor, the memory storing executable instructions, which, when executed by the processor, cause the network device to perform the communication method of any one of claims 14-18.

23. A communication system, characterized in that, The system includes a terminal and a network device, wherein the terminal is configured to determine an artificial intelligence (AI) function; acquire usage requirement information for the AI ​​function, the usage requirement information being used to indicate the characteristics that the first type of cell to which the input data of the AI ​​model belongs and the second type of cell to which the output data of the AI ​​model belongs must meet, and the AI ​​model is a model corresponding to the AI ​​function; The network device is configured to determine the usage requirements information for AI functions.

24. A storage medium storing instructions, characterized in that, When the instruction is executed on the communication device, the communication device performs the communication method according to any one of claims 1-18.

25. A computer program product comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by the communication device, the communication method of any one of claims 1-18 is implemented.

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