Communication method, terminal, network device, communication system, and storage medium

Abstract

A communication method, a terminal, a network device, a communication system, and a storage medium. The method comprises: a terminal determining first information on the basis of a measurement configuration for performing measurement on a first cell, wherein the first information is used for indicating an application condition corresponding to an artificial intelligence (AI) model or function of the first cell; and the terminal sending the first information. The reliability of AI model or function prediction can be ensured.

Description

Communication method, terminal, network device, communication system and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to a communication method, a terminal, a network device, a communication system, and a storage medium. Background Art

[0002] In many fields, models trained through machine learning can achieve relatively accurate predictions. For example, in the communications field, artificial intelligence (AI) models or functions can predict future measurement results based on historical measurements, or they can predict the measurement results of other cells based on the measurement results of some cells.

[0003] Summary of the Invention

[0004] According to a first aspect of an embodiment of the present disclosure, a communication method is provided, the method comprising:

[0005] The terminal sends first information, where the first information includes a measurement configuration or measurement information for the terminal to measure a first cell, and the first information is used to indicate an application condition of an artificial intelligence (AI) model or function corresponding to the first cell. According to a second aspect of an embodiment of the present disclosure, a communication method is proposed, the method including:

[0006] The network device receives first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

[0007] According to a third aspect of an embodiment of the present disclosure, a terminal is provided, comprising:

[0008] A transceiver module is used to send first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell.

[0009] According to a fourth aspect of an embodiment of the present disclosure, a network device is provided, comprising:

[0010] A transceiver module is used to receive first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

[0011] According to a fifth aspect of an embodiment of the present disclosure, a terminal is provided, including:

[0012] one or more processors;

[0013] A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, causes the terminal to execute the communication method described in the first aspect.

[0014] According to a sixth aspect of an embodiment of the present disclosure, a network device is provided, including:

[0015] one or more processors;

[0016] A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, causes the network device to execute the communication method described in the second aspect.

[0017] According to the seventh aspect of an embodiment of the present disclosure, a communication system is proposed, comprising 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.

[0018] According to an eighth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the communication method as described in the first aspect or the second aspect.

[0019] In an embodiment of the present disclosure, the terminal can determine the corresponding application conditions based on the measurement configuration or measurement information of the first cell, and report the corresponding application conditions through the first information, so that the network device can configure the corresponding AI model or function for the terminal based on the first information, ensuring that the AI ​​model or function used by the terminal can meet the corresponding application conditions and ensure the reliability of the AI ​​model or function prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0021] FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0022] FIG2 is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.

[0023] FIG3A is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0024] FIG3B is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0025] FIG3C is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0026] FIG3D is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0027] FIG4A is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0028] FIG4B is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0029] FIG4C is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0030] FIG4D is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0031] FIG5 is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.

[0032] FIG6 is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

[0033] FIG7A is a schematic diagram of an exemplary structure of a terminal provided according to an embodiment of the present disclosure.

[0034] FIG7B is a schematic diagram of an exemplary structure of a network device provided according to an embodiment of the present disclosure.

[0035] FIG8A is a schematic diagram of an exemplary structure of a communication device provided according to an embodiment of the present disclosure.

[0036] FIG8B is a schematic diagram of an exemplary structure of a communication device provided according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] In a first aspect, an embodiment of the present disclosure provides a communication method, the method comprising:

[0038] The terminal sends first information, where the first information includes measurement configuration or measurement information for the terminal to measure the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

[0039] In the above embodiment, the terminal can determine the corresponding application conditions based on the measurement configuration or measurement information of the first cell, and report the corresponding application conditions through the first information, so that the network device can configure the corresponding AI model or function for the terminal based on the first information, ensuring that the AI ​​model or function used by the terminal can meet the corresponding application conditions and ensure the reliability of the AI ​​model or function prediction.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement configuration includes at least one of the following:

[0041] a synchronization signal sending period of the first cell;

[0042] a synchronization signal block measurement timing configuration (SSB Measurement Timing Configuration, SMTC) of the first cell;

[0043] a channel state information reference signal (CSI-RS) transmission period of the first cell;

[0044] a measurement interval configuration used by the terminal for the first cell;

[0045] Measurement interval priority;

[0046] Measurement interval allocation configuration;

[0047] Discontinuous Reception (DRX) configuration, where the DRX configuration includes at least one of a DRX cycle, a DRX offset, and a DRX duration;

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement information includes at least one of the following:

[0049] a measurement period for the terminal to measure the first cell;

[0050] A measurement delay measured by the terminal for the first cell.

[0051] In the above embodiments, the application conditions of the AI ​​model or function can be accurately determined through at least one of the above measurement configurations or measurement information.

[0052] In conjunction with some embodiments of the first aspect, in some embodiments, the method includes:

[0053] The terminal measures the first cell and determines at least one of a radio channel measurement result or measurement information;

[0054] The terminal sends the second information, where the second information includes at least one of a radio channel measurement result, a cell identifier of the first cell, the measurement configuration, or the measurement information.

[0055] In combination with some embodiments of the first aspect, in some embodiments, the wireless channel measurement result is used to train a first AI model or function, and the measurement information is used to indicate an application condition corresponding to the first AI model or function.

[0056] In the above embodiment, the terminal can report at least one of the wireless channel measurement results, the cell identity measurement configuration of the first cell, or the measurement information, so that the network device can not only train the AI ​​model or function based on the wireless channel measurement results obtained by actual measurement, but also accurately determine the application conditions corresponding to the trained AI model or function.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, the method includes:

[0058] The terminal receives third information, where the third information includes a second AI model or function, and the second AI model or function is determined based on the first information.

[0059] In the above embodiment, the terminal can reliably obtain an AI model or function that can match the measurement configuration for the first cell, that is, an AI model or function that can meet the corresponding application conditions by receiving the third information, and then can use the second AI model or function to accurately predict the measurement results or mobility events, thereby ensuring the accuracy of the measurement results or the accuracy of the mobility event judgment.

[0060] In conjunction with some embodiments of the first aspect, in some embodiments, the method includes:

[0061] The terminal receives fourth information, where the fourth information includes at least one candidate AI model or function and an application condition corresponding to the candidate AI model or function;

[0062] The candidate AI model or function is used to predict the measurement results or mobility events of the cell.

[0063] In conjunction with some embodiments of the first aspect, in some embodiments, the method includes:

[0064] The terminal determines a third AI model or function from the candidate AI models or functions according to an application condition corresponding to the AI ​​model or function of the first cell, and determines the third AI model or function from the candidate AI models or functions.

[0065] In the above embodiment, the terminal can receive one or more candidate AI models or functions, and based on the corresponding application conditions, select an AI model or function from the candidate AI models or functions that can accurately predict the measurement results or mobility events of the first cell, thereby ensuring the accuracy of the measurement results or the accuracy of the mobility event judgment.

[0066] In combination with some embodiments of the first aspect, in some embodiments, at least one of the second AI model or function and the third AI model or function is used by the terminal to predict the measurement results or mobility events of the first cell.

[0067] In combination with some embodiments of the first aspect, in some embodiments, the mobility event includes at least one of the following: measurement reporting conditions are met; handover failure; cell residence time; radio link failure.

[0068] In a second aspect, an embodiment of the present disclosure provides a communication method, the method comprising:

[0069] The network device receives first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

[0070] In conjunction with some embodiments of the second aspect, in some embodiments, the measurement configuration includes at least one of the following:

[0071] a synchronization signal sending period of the first cell;

[0072] a synchronization signal and physical broadcast channel block measurement timing configuration SMTC of the first cell;

[0073] a channel state information reference signal CSI-RS transmission period of the first cell;

[0074] a measurement interval configuration used by the first cell;

[0075] Measurement interval priority;

[0076] Measurement interval allocation configuration;

[0077] DRX configuration, the DRX configuration including at least one of a DRX cycle, a DRX offset, and a DRX duration;

[0078] In conjunction with some embodiments of the second aspect, in some embodiments, the measurement information includes at least one of the following:

[0079] a measurement period for the terminal to measure the first cell;

[0080] A measurement delay measured by the terminal for the first cell.

[0081] In conjunction with some embodiments of the second aspect, in some embodiments, the method includes:

[0082] The network device receives second information, where the second information includes a wireless channel measurement result, a cell identifier of the first cell, the measurement configuration, or at least one of the measurement information. At least one of the measurement information or the wireless channel measurement result is obtained by the terminal measuring the first cell.

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

[0084] The network device trains a first AI model or function according to the wireless channel measurement result;

[0085] The network device determines an application condition corresponding to the first AI model or function based on the measurement configuration or the measurement information.

[0086] In conjunction with some embodiments of the second aspect, in some embodiments, the method includes:

[0087] The network device determines a second AI model or function based on the first information;

[0088] The network device sends third information, where the third information includes the second AI model or function.

[0089] In combination with some embodiments of the second aspect, in some embodiments, the second AI model or function is used by the terminal to predict the measurement results or mobility events of the first cell.

[0090] In conjunction with some embodiments of the second aspect, in some embodiments, the method includes:

[0091] The network device sends fourth information, where the fourth information includes at least one candidate AI model or function and an application condition corresponding to the candidate AI model or function;

[0092] The candidate AI model or function is used to predict the measurement results or mobility events of the cell.

[0093] In conjunction with some embodiments of the second aspect, in some embodiments, the mobility event includes at least one of the following:

[0094] The measurement reporting conditions are met;

[0095] Switching failed;

[0096] Residential time in the community;

[0097] The wireless link has failed.

[0098] In a third aspect, an embodiment of the present disclosure provides a terminal, comprising:

[0099] A transceiver module is used to send first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell.

[0100] In a fourth aspect, an embodiment of the present disclosure provides a network device, comprising:

[0101] A transceiver module is used to: the first information includes the measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell.

[0102] In a fifth aspect, an embodiment of the present disclosure proposes a terminal comprising: one or more processors; a memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, enables the terminal to execute the communication method in the first aspect.

[0103] In the sixth aspect, an embodiment of the present disclosure proposes a network device, comprising: one or more processors; a memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, enables the network device to execute the communication method in the second aspect.

[0104] In the seventh aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

[0105] In an eighth aspect, an embodiment of the present disclosure proposes a storage medium, wherein the storage medium stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

[0106] In a ninth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

[0107] In a tenth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first and second aspects.

[0108] In an eleventh aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first and second aspects above.

[0109] It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0110] The embodiments of the present disclosure provide a communication method, a terminal, a network device, a communication system, and a storage medium. In some embodiments, the terms communication method and application condition reporting method are interchangeable, the terms communication device and application condition reporting device are interchangeable, and the terms information processing system and communication system are interchangeable.

[0111] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain 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 certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0112] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0113] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0114] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0115] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0116] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0117] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0118] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0119] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0120] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0121] In some embodiments, terms such as "time / frequency" and "time / frequency domain" refer to the time domain and / or the frequency domain.

[0122] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0123] In some embodiments, terms such as "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 less than", and "above" can be replaced with each other, and terms such as "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" can be replaced with each other.

[0124] 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", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0125] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0126] 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", "bandwidth part (BWP)" and the like may be used interchangeably.

[0127] 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, client, etc. can be used interchangeably.

[0128] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0129] 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, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

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

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

[0132] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0133] Figure 1 is a schematic diagram illustrating the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1 , communication system 100 includes a terminal 101 and a network device 102. In some embodiments, network device 102 may include at least one of an access network device or a core network device.

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

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

[0136] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0137] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0138] In some embodiments, the core network device may be a single device including a first network element, a second network element, etc., or may be a plurality of devices or a group of devices, each including all or part of the first network element, the second network element, etc. The network element may be virtual or physical. The core network may include, for example, at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0139] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0140] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0141] The embodiments of the present disclosure 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 (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (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 utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0142] In some embodiments, the UE may obtain a radio channel measurement result (such as RSRP or RSRQ) of the cell by measuring a reference signal of the cell, where the reference signal of the cell includes an SSB and a CSI-RS.

[0143] In some embodiments, the UE obtains the SSB location of the serving cell through system information. The serving cell can configure the SMTC (SSB measurement timing configuration) of other cells for the UE. The UE can determine the location of the SSB of other cells based on the SMTC and measure and obtain the radio channel quality of other cells. The SSB location can be determined by the SSB transmission period, the duration of each SSB transmission, and the offset in the time offset.

[0144] In some embodiments, due to radio frequency or hardware limitations, a UE may need to interrupt signal reception from its serving cell when measuring a cell. The network can configure a measurement gap for the UE, indicated by a period, duration, and offset. During the measurement gap, the UE can measure the cell without receiving signals from the serving cell. The cell and the serving cell may be co-frequency or off-frequency.

[0145] In some embodiments, the network can configure multiple measurement gaps for the UE, with each gap used to measure a frequency. When multiple measurement gaps overlap, the network can determine which gap to use based on the measurement gap priority.

[0146] In some embodiments, the network may configure a gap sharing ratio for the measurement gap, and the UE may allocate the measurement gap proportionally for intra-frequency or inter-frequency measurement based on the ratio.

[0147] In some embodiments, on the Uu interface, to save UE power, the network can configure discontinuous reception (DRX) for the UE. The UE calculates the activation time based on the DRX cycle, duration, and offset, and only wakes up to receive base station signals at the corresponding activation time.

[0148] In some embodiments, the UE determines an opportunity to measure reference signals based on all of the above factors. Each SSB measurement opportunity is called a measurement occasion. The UE obtains the reference signal results for N measurement opportunities and averages these reference signals to obtain an RSRP measurement value. This measurement value is an L1 measurement result that can be used to evaluate measurement reporting events and report to the network.

[0149] In some embodiments, the serving cell or neighbor cell measurement results used in evaluating measurement event satisfaction and reporting are the UE's Layer 1 measurement results after Layer 3 filtering. The Layer 3 filtering is as follows: Fn = (1 – a) * Fn-1 + a * Mn. Mn is the latest L1 measurement result; Fn is the filtered result; and Fn-1 is the last filtered measurement result. The quantity configuration determines the parameter a in the filtering. Different access technologies may use different filtering parameters.

[0150] It's no secret that machine learning algorithms are one of the most important approaches to artificial intelligence technology today. Machine learning uses large amounts of training data to generate models that can then be used to predict events. In many fields, models trained using machine learning can produce highly accurate predictions.

[0151] In some embodiments, the AI ​​model or function in the above communication architecture can predict future measurement results based on historical measurement results, and can also predict measurement results of other cells based on measurement results of some cells.

[0152] In some embodiments, the generalization performance of an AI model or function is limited, and a particular AI model or function can only achieve good performance under specific application conditions. Optionally, the application conditions are determined by the training data used to train the AI ​​model or function.

[0153] In some embodiments, due to limited performance, the UE may not be able to train the AI ​​model locally. The UE can collect data for training the AI ​​model and send it to the network, and the network can train the AI ​​model.

[0154] It is understandable that if AI models or functions are run under inappropriate application conditions, performance may be degraded.

[0155] FIG2 is a schematic diagram of an interaction of a communication method according to an embodiment of the present disclosure. As shown in FIG2 , an embodiment of the present disclosure relates to a communication method, and the method includes:

[0156] Step S2101: The terminal measures the first cell and determines measurement information and / or wireless channel measurement results.

[0157] In some embodiments, the first cell may be a candidate cell or a serving cell of the terminal, or may be any one or more cells within a certain range of the terminal's location, which is not limited in the embodiments of the present disclosure.

[0158] In some embodiments, the terminal performs a measurement on the first cell to obtain a radio channel measurement result. Optionally, the radio channel measurement result may be a layer 1 measurement result. Optionally, the terminal may perform a measurement on a reference signal transmitted by the first cell to obtain the radio channel measurement result. Optionally, the radio channel measurement result may be, for example, RSRP and / or RSRQ. Optionally, the reference signal may include an SSB and / or a CSI-RS.

[0159] In some embodiments, the terminal may measure the reference signal at multiple measurement occasions and average the multiple reference signals to obtain an RSRP measurement value. The RSRP measurement value may be a layer 1 (L1) measurement result.

[0160] In some embodiments, the measurement information includes at least one of the following: a measurement period of the terminal for measuring the first cell; and a measurement delay of the terminal for measuring the first cell.

[0161] In some embodiments, the measurement period during which the terminal measures the first cell may be a period during which the terminal can actually measure reference signal opportunities. The terminal may measure the first cell and determine a period during which the terminal can actually measure reference signal transmission opportunities of the network device as the measurement period.

[0162] Optionally, the measurement period may be an interval before the terminal performs multiple measurements on the first cell. Optionally, the measurement delay may be a time required for the terminal to perform one measurement on the first cell and obtain a measurement result.

[0163] In some embodiments, the measurement information may be determined based on a measurement configuration. Optionally, optional implementations of the measurement configuration will be described in detail in subsequent steps and are not described here. For example, the terminal may perform measurements based on the measurement configuration and simultaneously determine corresponding measurement information.

[0164] Step S2102: The terminal sends second information to the network device.

[0165] In some embodiments, the second information includes at least one of a wireless channel measurement result, a cell identity measurement configuration of the first cell, or measurement information.

[0166] For example, the second information may include only the radio channel measurement result and measurement configuration. In this way, the first information sent by the terminal to the network device in step S2105 may include the measurement configuration for the terminal's measurement of the first cell. Alternatively, the second information may include only the radio channel measurement result and measurement information. In this way, the first information sent by the terminal to the network device in step S2105 may include the measurement information for the terminal's measurement of the first cell. In this way, the performance of the AI ​​model or function ultimately used by the terminal can be further ensured.

[0167] In some embodiments, the second information is used for training and / or labeling of AI models or functions.

[0168] In some embodiments, the second information may be a measurement report. Optionally, the measurement report may include at least one of a radio channel measurement result, a cell identity measurement configuration of the first cell, or measurement information.

[0169] In some embodiments, the second information can be called "measurement report", "AI model or function training related information", "AI model annotation related information", "model training parameters", etc., which is not limited in the embodiments of the present disclosure.

[0170] In step S2103, the network device trains a first AI model or function based on the wireless channel measurement results.

[0171] In some embodiments, the network device trains the initial AI model or function based on the wireless channel measurement results in the second information to obtain a trained AI model or function, which can be referred to as a first AI model or function.

[0172] In some embodiments, the network device updates the parameters of the initial AI model or function based on the wireless channel measurement results to obtain a first AI model or function.

[0173] It is understood that the trained AI model can predict the measurement results or mobility events of a cell (e.g., the first cell) based on the input parameters. Optionally, the input parameters can be, for example, actual measurement results. Optionally, the input parameters can be measurement results of other cells.

[0174] For example, the terminal inputs a first measurement result obtained by actually measuring a first cell at a first time into a trained AI model or function to obtain a second measurement result. The second measurement result may be a measurement result corresponding to the first cell at a second time, where the second time may be a period of time after the first time. Alternatively, the terminal inputs a third measurement result obtained by actually measuring a second cell at a first time into a trained AI model or function to obtain a fourth measurement result. The fourth measurement result may be a measurement result corresponding to the first cell at the first time. The second cell may include one or more cells.

[0175] In some embodiments, the trained AI model or function can be deployed on a terminal or on a network device, which is not limited in the embodiments of the present disclosure.

[0176] In step S2104, the network device determines an application condition corresponding to the first AI model or function based on the measurement configuration or measurement information.

[0177] It is understood that if the second information includes a measurement configuration, the measurement configuration can be determined as the application condition corresponding to the first AI model or function. If the second information includes measurement information, the measurement information can be determined as the application condition corresponding to the first AI model or function.

[0178] Among them, the measurement configuration or measurement information may be the measurement configuration or measurement information corresponding to the wireless measurement result used by the network device when training the first AI model, such as the measurement period or measurement duration used when the terminal obtains the wireless measurement result, or the synchronization signal sending period of the first cell, etc.

[0179] It is worth noting that the generalization performance of AI models or functions is limited, and specific AI models or functions may only achieve better performance under specific application conditions.

[0180] In some embodiments, the network device determines the measurement configuration or measurement information as or as an application condition corresponding to the first AI model or function. For example, if the measurement period corresponding to the wireless measurement results used to train the first AI model or function is T1, the network device may determine the measurement period of T1 as the application condition for the first AI model or function. That is, when the first AI model or function predicts the measurement results of the cell with the measurement period of T1, it can obtain better performance.

[0181] In some embodiments, the network device may store the measurement configuration or measurement information in association with the first AI model or function. Optionally, the network device may maintain a mapping table of AI models or functions and corresponding measurement configurations or measurement information. The measurement configuration or measurement information corresponding to each AI model or function may be the application condition corresponding to the AI ​​model or function.

[0182] In some embodiments, the network device may further store the cell identifier of the first cell in association with the first AI model or function. For example, when the terminal needs to measure the first cell, the network device may send the first AI model or function to the terminal based on the cell identifier of the first cell, so that the terminal uses the first AI model or function to predict the measurement result or mobility event of the first cell.

[0183] Step S2105: The terminal sends first information to the network device.

[0184] In some embodiments, the first information includes a measurement configuration for the terminal to measure the first cell. Optionally, the first information includes measurement information for the terminal to measure the first cell.

[0185] In some embodiments, the measurement information may be determined based on step S2101 or determined by other means, which is not limited in the embodiments of the present disclosure.

[0186] In some embodiments, the first information is used to indicate an application condition of an AI model or function corresponding to the first cell. It is understood that the AI ​​model or function corresponding to the first cell may refer to an AI model or function that can more accurately predict measurement results or mobility events of the first cell.

[0187] In some embodiments, the terminal can obtain the SSB position of the serving cell through system information (SI). The serving cell can configure the SMTC of other cells (e.g., the first cell) for the terminal, and then the terminal can determine the SSB position of other cells based on the SMTC, and measure and obtain the radio channel quality of other cells. The SSB position can be determined by the SSB transmission period, the SSB transmission duration, and the SSB time offset.

[0188] In some embodiments, the terminal may measure a cell within a measurement interval, and the cell may be co-frequency or hetero-frequency with the serving cell of the terminal. The network device may configure one or more measurement gaps for the terminal, and each measurement gap may be used to measure a frequency. When multiple measurement gaps overlap, the measurement interval may be determined based on the measurement interval priority. Furthermore, the network device may indicate a measurement interval sharing configuration to the terminal, and the measurement interval sharing configuration may be used to indicate an interval sharing ratio of the measurement interval, and the terminal may allocate the measurement interval for inter-frequency or intra-frequency measurement based on the ratio.

[0189] In some embodiments, to save energy consumption of the terminal, the network device can configure DRX for the terminal. The terminal can determine the activation time based on the DRX cycle, duration, and offset, and wake up and receive signals sent by the network device, such as reference signals, only during the activation time.

[0190] In some embodiments, the network device may configure one or more measurement intervals for the terminal, and the terminal may select one of the measurement intervals to measure the first cell according to the measurement interval priority. Optionally, the network device indicates the period, duration, and offset of the measurement interval.

[0191] In some embodiments, the measurement configuration includes at least one of the following: the synchronization signal sending period of the first cell; the SMTC of the first cell; the CSI-RS sending period of the first cell; the measurement interval configuration used by the terminal for the first cell; the measurement interval priority; the measurement interval allocation configuration; and the DRX configuration.

[0192] Optionally, the DRX configuration includes at least one of a DRX cycle, a DRX offset, and a DRX duration.

[0193] Optionally, the transmission period of the synchronization signal may be the transmission period of a synchronization signal block (SSB). The synchronization signal block may include a synchronization signal and a PBCH. Optionally, the transmission period of the synchronization signal may be, for example, the transmission period of the PSS, the transmission period of the SSS, the transmission period of the PBCH, or the transmission period of the synchronization signal and the PBCH block. Optionally, the SMTC may be a synchronization signal and PBCH block measurement timing configuration.

[0194] It can be understood that the application condition of the AI ​​model or function corresponding to the first cell may correspond to the measurement configuration or measurement information of the terminal measuring the first cell. For example, if the measurement interval used by the terminal for the first cell is T2, the application condition of the AI ​​model or function corresponding to the first cell may be that the measurement interval is T2. Alternatively, if the measurement period of the terminal measuring the first cell is T3, the application condition of the AI ​​model or function corresponding to the first cell may be that the measurement period is T3.

[0195] In some embodiments, the first information is used to instruct the network device to send the third information or the fourth information.

[0196] In some embodiments, the first information is used by the network device to determine a second AI model or function, and the application condition corresponding to the second AI model or function matches the measurement configuration of the terminal for measuring the first cell.

[0197] In some embodiments, the first information may be referred to as “measurement configuration information”, “application condition information”, etc., and the embodiments of the present disclosure do not limit the names thereof.

[0198] In some embodiments, a network device receives first information.

[0199] In some embodiments, the network device determines the second AI model or function based on the first information. Optionally, the network device determines an application condition corresponding to the AI ​​model or function of the first cell based on the first information, and the network device determines the second AI model or function based on the application condition.

[0200] In some embodiments, the second AI model or function may be the first AI model or function trained in step S2103.

[0201] For example, after receiving the first information, the network device determines the application conditions of the AI ​​model or function corresponding to the first cell, and selects an AI model or function that matches the application conditions from multiple trained AI models or functions as the second AI model or function. The second AI model or function can achieve better performance when predicting the measurement results or mobility events of the first cell.

[0202] Step S2106: The network device sends the third information or the fourth information to the terminal.

[0203] In some embodiments, the network device sends third information after determining the second AI model or function based on the first information.

[0204] In some embodiments, the third information includes a second AI model or function, where the second AI model or function is determined based on the first information.

[0205] In some embodiments, the third information is used to indicate the second AI model or function. Optionally, the third information is used to indicate parameters of the second AI model or function.

[0206] In some embodiments, the terminal receives the third information. Optionally, the terminal determines the second AI model or function based on the third information. For example, the terminal determines the second AI model or function based on parameters of the second AI model or function indicated by the third information.

[0207] In some embodiments, after the terminal determines the second AI model or function, step S2108 is executed. Optionally, the terminal executes step S2108 in response to the third information.

[0208] In some embodiments, the third information may be referred to as "AI model or function indication information", "AI model or function configuration information", etc., and the embodiments of this disclosure do not limit its name.

[0209] In some embodiments, the network device sends the fourth information in response to the first information. Optionally, after receiving the first information, the network device may not perform the step of determining the second AI model or function and directly send the fourth information.

[0210] In some embodiments, the fourth information includes at least one candidate AI model or function, and an application condition corresponding to the candidate AI model or function.

[0211] In some embodiments, the fourth information includes parameters of at least one candidate AI model or function, and application conditions corresponding to the candidate AI model or function.

[0212] In some embodiments, the candidate AI model or function can be used to predict mobility events or measurement results of cells, such as the first cell and any other cells. For example, the candidate AI model or function may include an AI model or function corresponding to the first cell, and may also include AI models or functions corresponding to other cells. Optionally, each candidate AI model or function corresponds to a different application condition. For example, the candidate AI model may include an AI model or function whose application condition is a measurement period of T1, and an AI model or function whose application condition is a measurement period of T2.

[0213] In some embodiments, the candidate AI models or functions may be pre-trained by the network device. Optionally, each candidate AI module or function may be associated with and stored with corresponding application conditions. Optionally, the candidate AI models or functions may include the first AI model or function trained in step S2103.

[0214] In some embodiments, the terminal receives the fourth information. Optionally, the terminal executes step S2107 in response to the fourth information. Optionally, after receiving the fourth information, the terminal executes step S2107.

[0215] In some embodiments, the fourth information may be referred to as "candidate AI model or function indication information", "AI model or function configuration information", etc. The embodiments of this disclosure do not limit its name.

[0216] In step S2107, the terminal determines a third AI model or function from the candidate AI models or functions according to the application condition of the AI ​​model or function corresponding to the first cell.

[0217] Among them, the application conditions corresponding to the third AI model or function match the application conditions of the AI ​​model or function corresponding to the first cell, that is, the third AI model or function can accurately predict the measurement results or mobility events of the first cell, that is, the third AI model or function can be the AI ​​model or function corresponding to the first cell.

[0218] In some embodiments, the terminal determines a third AI model or function from candidate AI models or functions according to a measurement configuration for measuring the first cell.

[0219] In some embodiments, the third AI model or function may be the first AI model or function trained in step S2103.

[0220] For example, after receiving the fourth information, the terminal determines whether the application conditions of each candidate AI model or function match the measurement configuration of the first cell, and then selects an AI model or function that matches the application conditions of the AI ​​model or function corresponding to the first cell from one or more candidate AI models or functions as the third AI model or function. The third AI model or function can achieve better performance when predicting the measurement results or mobility events of the first cell.

[0221] In step S2108, the terminal predicts the measurement result or mobility event of the first cell according to the second AI model or function, or the third AI model or function.

[0222] In some embodiments, at least one of the second AI model or function and the third AI model or function is used by the terminal to predict the measurement result or mobility event of the first cell.

[0223] In some embodiments, the terminal may input input parameters (for example, historical measurement results for the first cell, or actual measurement results for other cells) into any one of the second AI model or function and the third AI model or function, and thereby obtain a corresponding prediction result, which may indicate the measurement result of the first cell, or whether the terminal triggers a mobility event corresponding to the first cell.

[0224] In some embodiments, the mobility event may include at least one of the following: measurement reporting conditions being met; handover failure; cell dwell time; and radio link failure.

[0225] For example, when the prediction result output by the second AI model or function indicates that a mobility event has been triggered and the measurement reporting condition is met, the terminal may send a measurement report to the network device.

[0226] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0227] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based" and the like may be used interchangeably.

[0228] In some embodiments, the terms "search space", "search space set", "search space configuration", "search space set configuration", "control resource set (CORESET)", "CORESET configuration" and the like may be used interchangeably.

[0229] In some embodiments, terms such as "synchronization signal (SS)", "synchronization signal block (SSB)", "reference signal (RS)", "pilot", and "pilot signal" can be used interchangeably.

[0230] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0231] In some embodiments, the terms "component carrier (CC)", "cell", "frequency carrier", "carrier frequency" and the like can be used interchangeably.

[0232] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0233] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0234] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0235] In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values ​​(for example, comparison with a predetermined value), but is not limited thereto.

[0236] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the recipient to respond to the content sent.

[0237] The communication method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2108. For example, step S2102 may be implemented as an independent embodiment, step S2104 may be implemented as an independent embodiment, step S2105 may be implemented as an independent embodiment, step S2106 may be implemented as an independent embodiment, steps S2102+S2103+S2104 may be implemented as an independent embodiment, steps S2105+S2106 may be implemented as an independent embodiment, and steps S2103+S2104+S2105+S2106 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0238] In some embodiments, step S2103 and step S2104 may be executed in an interchangeable order or simultaneously.

[0239] In some embodiments, steps S2101 to S2104 and steps S2106 to S2108 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0240] In some embodiments, steps S2101 to S2105 and steps S2107 to S2108 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0241] In some embodiments, steps S2101 to S2107 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0242] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2 .

[0243] FIG3A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a communication method (terminal side), the method comprising:

[0244] Step S3101: measure the first cell and determine measurement information.

[0245] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0246] Step S3102, sending the second information.

[0247] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0248] In some embodiments, the terminal sends the second information to the network device, but is not limited thereto, and the second information may also be sent to other entities.

[0249] Step S3103, sending the first information.

[0250] The optional implementation of step S3103 can refer to the optional implementation of step S2105 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0251] In some embodiments, the terminal sends the first information to the network device, but is not limited thereto, and the first information may also be sent to other entities.

[0252] Step S3104, receiving the third information or the fourth information.

[0253] The optional implementation of step S3104 can refer to the optional implementation of step S2106 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0254] In some embodiments, the terminal receives the third information or the fourth information sent by the network device, but is not limited thereto, and may also receive the third information or the fourth information sent by other entities.

[0255] Step S3105: Determine a third AI model or function from the candidate AI models or functions based on the application conditions of the AI ​​model or function corresponding to the first cell.

[0256] The optional implementation of step S3105 can refer to the optional implementation of step S2107 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0257] Step S3106: Predict the measurement result or mobility event of the first cell according to the second AI model or function, or the third AI model or function.

[0258] The optional implementation of step S3106 can refer to the optional implementation of step S2108 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0259] The communication method involved in the embodiments of the present disclosure may include at least one of steps S3101 to S3107. For example, step S3102 may be implemented as an independent embodiment, step S3103 may be implemented as an independent embodiment, step S3104 may be implemented as an independent embodiment, step S3105 may be implemented as an independent embodiment, steps S3103+S3104 may be implemented as an independent embodiment, steps S3104+S3105 may be implemented as an independent embodiment, and steps S3105+S3106 may be implemented as an independent embodiment, but are not limited thereto.

[0260] In some embodiments, steps S3101 to S3102 and steps S3103 to S3106 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0261] In some embodiments, steps S3101 to S3103 and steps S3105 to S3106 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0262] In some embodiments, steps S3101 to S3104 and step S3106 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0263] In some embodiments, steps S3101 to S3105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0264] FIG3B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a communication method (terminal side), which includes:

[0265] Step S3201, sending the first information.

[0266] The optional implementation of step S3201 can refer to step S2105 of FIG. 2 , the optional implementation of step S3103 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2 and FIG. 3A , which will not be described in detail here.

[0267] Step S3202, receiving third information.

[0268] The optional implementation of step S3202 can refer to the optional implementation of step S2106 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.

[0269] Step S3203: Predict the measurement result or mobility event of the first cell according to the second AI model or function.

[0270] The optional implementation of step S3203 can refer to the optional implementation of step S2108 in Figure 2, step S3106 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0271] The communication method involved in the embodiments of the present disclosure may include at least one of steps S3201 to S3203. For example, step S3201 may be implemented as an independent embodiment, step S3202 may be implemented as an independent embodiment, step S3203 may be implemented as an independent embodiment, and steps S3201+S3202 may be implemented as independent embodiments, but the present invention is not limited thereto.

[0272] In some embodiments, steps S3201 to S3202 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0273] In some embodiments, steps S3202 to S3203 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0274] FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a communication method (terminal side), which includes:

[0275] Step S3301, sending the first information.

[0276] The optional implementation of step S3301 can be found in step S2105 of Figure 2, step S3103 of Figure 3A, the optional implementation of step S3201 of Figure 3B, and other related parts in the embodiments involved in Figures 2, 3A, and 3B, which will not be repeated here.

[0277] Step S3302, receiving the fourth information.

[0278] The optional implementation of step S3302 can refer to step S2106 in Figure 2, the optional implementation of step S3104 in Figure 3A, and other related parts in the embodiments involved in Figures 2, 3A, and 3B, which will not be repeated here.

[0279] Step S3303: Determine a third AI model or function from the candidate AI models or functions based on the application conditions of the AI ​​model or function corresponding to the first cell.

[0280] The optional implementation of step S3303 can refer to step S2107 in Figure 2, the optional implementation of step S3105 in Figure 3A, and other related parts in the embodiments involved in Figures 2, 3A, and 3B, which will not be repeated here.

[0281] Step S3304: Predict the measurement result or mobility event of the first cell according to the third AI model or function.

[0282] The optional implementation of step S3304 can refer to step S2108 in Figure 2, the optional implementation of step S3106 in Figure 3A, and other related parts in the embodiments involved in Figures 2, 3A, and 3B, which will not be repeated here.

[0283] The communication method involved in the embodiments of the present disclosure may include at least one of steps S3301 to S3304. For example, step S3301 may be implemented as an independent embodiment, step S3303 may be implemented as an independent embodiment, step S3304 may be implemented as an independent embodiment, and steps S3302+S3303+S3304 may be implemented as independent embodiments, but are not limited thereto.

[0284] In some embodiments, steps S3301 to S3302 and step S3304 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0285] In some embodiments, steps S3301 to S3303 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0286] FIG3D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a communication method (terminal side), which includes:

[0287] Step S3402, sending the first information.

[0288] The optional implementation of step S3402 can be found in the optional implementation of step S2105 in Figure 2, step S3103 in Figure 3A, step S3201 in Figure 3B, step S3301 in Figure 3C, and other related parts in the embodiments involved in Figures 2, 3A, 3B, and 3C, which will not be repeated here.

[0289] In some embodiments, the terminal sends first information, the first information including measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell.

[0290] In some embodiments, the measurement configuration includes at least one of:

[0291] a synchronization signal transmission period of the first cell;

[0292] A synchronization signal block measurement timing configuration SMTC of the first cell;

[0293] a channel state information reference signal CSI-RS transmission period of the first cell;

[0294] a measurement interval configuration used by the first cell;

[0295] Measurement interval priority;

[0296] Measurement interval allocation configuration;

[0297] DRX configuration, where the DRX configuration includes at least one of a DRX cycle, a DRX offset, and a DRX duration;

[0298] In some embodiments, the measurement information includes at least one of the following:

[0299] a measurement period of the terminal for measuring the first cell;

[0300] The measurement delay measured by the terminal for the first cell.

[0301] In some embodiments, the method comprises:

[0302] The terminal measures the first cell and determines at least one of a radio channel measurement result or measurement information;

[0303] The terminal sends second information, where the second information includes at least one of a radio channel measurement result, a cell identifier of the first cell, a measurement configuration, or measurement information.

[0304] In some embodiments, the wireless channel measurement results are used to train the first AI model or function, and the measurement information or measurement configuration is used to indicate the application conditions corresponding to the first AI model or function.

[0305] In some embodiments, the method comprises:

[0306] The terminal receives third information, where the third information includes a second AI model or function, and the second AI model or function is determined based on the first information.

[0307] In some embodiments, the method comprises:

[0308] The terminal receives fourth information, where the fourth information includes at least one candidate AI model or function and an application condition corresponding to the candidate AI model or function;

[0309] The candidate AI models or functions are used to predict cell measurement results or mobility events.

[0310] In some embodiments, the method comprises:

[0311] The terminal determines a third AI model or function from the candidate AI models or functions according to the application condition of the AI ​​model or function corresponding to the first cell.

[0312] In some embodiments, at least one of the second AI model or function and the third AI model or function is used by the terminal to predict the measurement result or mobility event of the first cell.

[0313] In some embodiments, the mobility event includes at least one of:

[0314] The measurement reporting conditions are met;

[0315] Switching failed;

[0316] Residential time in the community;

[0317] The wireless link has failed.

[0318] FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

[0319] Step S4101, receiving second information.

[0320] The optional implementation of step S4101 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0321] In some embodiments, the network device receives the second information sent by the terminal, but is not limited thereto and may also receive the second information sent by other entities.

[0322] Step S4102: Train a first AI model or function based on the wireless channel measurement results.

[0323] The optional implementation of step S4102 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0324] Step S4103: Determine application conditions corresponding to the first AI model or function based on the measurement information or measurement configuration.

[0325] The optional implementation of step S4103 can refer to the optional implementation of step S2104 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0326] Step S4104, receiving the first information.

[0327] The optional implementation of step S4104 can refer to the optional implementation of step S2105 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0328] In some embodiments, the network device receives the first information sent by the terminal, but is not limited thereto and may also receive the first information sent by other entities.

[0329] Step S4105: Send the third information or the fourth information.

[0330] The optional implementation of step S4105 can refer to the optional implementation of step S2106 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0331] In some embodiments, the network device sends the third information or the fourth information to the terminal, but is not limited thereto, and the third information or the fourth information may also be sent to other entities.

[0332] The communication method involved in the embodiments of the present disclosure may include at least one of steps S4101 to S4105. For example, step S4101 can be implemented as an independent embodiment, step S4103 can be implemented as an independent embodiment, step S4104 can be implemented as an independent embodiment, step S4105 can be implemented as an independent embodiment, steps S4104+S4105 can be implemented as an independent embodiment, steps S4102+S4103 can be implemented as an independent embodiment, and steps S4103+S4104+S4105 can be implemented as independent embodiments, but are not limited thereto.

[0333] In some embodiments, step S4102 and step S4103 may be executed in an interchangeable order or simultaneously.

[0334] In some embodiments, steps S4101 to S4103 and step S4105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0335] In some embodiments, steps S4101 to S4104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0336] In some embodiments, steps S4101 to S4103 and step S4105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0337] FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

[0338] Step S4201, receiving first information.

[0339] The optional implementation of step S4201 can refer to the optional implementation of step S2105 in Figure 2, step S4204 in Figure 4A, and other related parts in the embodiments involved in Figures 2 and 4A, which will not be repeated here.

[0340] Step S4202, sending the third information.

[0341] The optional implementation of step S4202 can refer to the optional implementation of step S2107 in Figure 2, step S4205 in Figure 4A, and other related parts in the embodiments involved in Figures 2 and 4A, which will not be repeated here.

[0342] The communication method involved in the embodiment of the present disclosure may include at least one of steps S4201 and S4202. For example, step S4201 may be implemented as an independent embodiment, and step S4202 may be implemented as an independent embodiment.

[0343] FIG4C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4C , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

[0344] Step S4301, receiving first information.

[0345] The optional implementation of step S4301 can refer to step S2105 in Figure 2, the optional implementation of step S4204 in Figure 4A, and other related parts in the embodiments involved in Figures 2, 4A, and 4B, which will not be repeated here.

[0346] Step S4302, sending the fourth information.

[0347] The optional implementation of step S4302 can refer to step S2107 in Figure 2, the optional implementation of step S4205 in Figure 4A, and other related parts in the embodiments involved in Figures 2, 4A, and 4B, which will not be repeated here.

[0348] The communication method involved in the embodiment of the present disclosure may include at least one of steps S4301 and S4302. For example, step S4301 may be implemented as an independent embodiment, and step S4302 may be implemented as an independent embodiment.

[0349] FIG4D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4D , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

[0350] Step S4401, receiving first information.

[0351] The optional implementation of step S4401 can be found in the optional implementation of step S2105 in Figure 2, step S4104 in Figure 4A, step S4201 in Figure 4B, step S4301 in Figure 4C, and other related parts in the embodiments involved in Figures 2, 4A, 4B, and 4C, which will not be repeated here.

[0352] In some embodiments, the network device receives first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate application conditions of an artificial intelligence AI model or function corresponding to the first cell.

[0353] In some embodiments, the measurement configuration includes at least one of:

[0354] a synchronization signal transmission period of the first cell;

[0355] The synchronization signal and physical broadcast channel block measurement timing configuration SMTC of the first cell;

[0356] a channel state information reference signal CSI-RS transmission period of the first cell;

[0357] a measurement interval configuration used by the first cell;

[0358] Measurement interval priority;

[0359] Measurement interval allocation configuration;

[0360] DRX configuration, where the DRX configuration includes at least one of a DRX cycle, a DRX offset, and a DRX duration;

[0361] In some embodiments, the measurement information includes at least one of the following:

[0362] a measurement period of the terminal for measuring the first cell;

[0363] The measurement delay measured by the terminal for the first cell.

[0364] In some embodiments, the method comprises:

[0365] The network device receives second information, where the second information includes at least one of a wireless channel measurement result, a cell identifier of the first cell, a measurement configuration, or measurement information, where at least one of the measurement information or the wireless channel measurement result is obtained by the terminal measuring the first cell.

[0366] In some embodiments, the method comprises:

[0367] The network device trains a first AI model or function based on the wireless channel measurement results;

[0368] The network device determines an application condition corresponding to the first AI model or function based on the measurement configuration or measurement information.

[0369] In some embodiments, the method comprises:

[0370] The network device determines a second AI model or function based on the first information;

[0371] The network device sends third information, where the third information includes a second AI model or function.

[0372] In some embodiments, the second AI model or function is used by the terminal to predict the measurement results or mobility events of the first cell.

[0373] In some embodiments, the method comprises:

[0374] The network device sends fourth information, where the fourth information includes at least one candidate AI model or function and an application condition corresponding to the candidate AI model or function.

[0375] The candidate AI models or functions are used to predict cell measurement results or mobility events.

[0376] In some embodiments, the mobility event includes at least one of:

[0377] The measurement reporting conditions are met;

[0378] Switching failed;

[0379] Residential time in the community;

[0380] The wireless link has failed.

[0381] Figure 5 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 5, the embodiment of the present disclosure relates to a communication method, which includes:

[0382] Step S5101: The terminal sends first information to the network device.

[0383] For the optional implementation of step S5101, please refer to step S2105 of Figure 2, step S3103 of Figure 3A, step S3201 of Figure 3B, step S3301 of Figure 3C, step S3401 of Figure 3D, step S4204 of Figure 4A, step S4301 of Figure 4B, and the optional implementation of step S4301 of Figure 4C, as well as other related parts in the embodiments involved in Figures 2, 3A, 3B, 3C, 3D, 4A, 4B, 4C, and 4D, which will not be repeated here.

[0384] In some embodiments, the above method may include the method described in the above terminal side, network device side, etc. embodiments, which will not be repeated here.

[0385] FIG6 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG6 , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0386] In step S6101, the UE determines a configuration for measuring a target cell, determines the measurement configuration as an application condition, and reports the application condition to the network.

[0387] It can be understood that the target cell may be the first cell involved in some of the above embodiments.

[0388] In some embodiments, the network configures an AI model or function for the UE based on application conditions.

[0389] In some embodiments, the measurement configuration is any one of the following: SMTC configuration of the target cell; SSB transmission period of the target cell; CSI-RS transmission period of the target cell; measurement interval configuration, which is the measurement interval used for measuring the target cell; measurement interval priority; measurement interval sharing configuration; DRX configuration, including DRX cycle, offset, duration, etc.

[0390] In some embodiments, the UE determines a period for performing measurements on a target cell, and determines the period as a measurement configuration.

[0391] In some embodiments, the measurement period is a period during which the UE can actually measure reference signal opportunities.

[0392] In some embodiments, the UE determines a delay for obtaining an L1 measurement for the target cell, and uses the measurement delay as a measurement configuration.

[0393] In some embodiments, the UE measures a radio channel measurement result of a target cell, and reports the measurement configuration, the target cell identifier, and the radio channel measurement result to the network.

[0394] In some embodiments, the network may use the channel measurement results to train an AI model, and the measurement configuration serves as an application condition for the trained AI model.

[0395] In some embodiments, an AI model or function sent by a receiving network and corresponding measurement configuration application conditions are received.

[0396] In some embodiments, the AI ​​model or function can be used to predict the measurement results or mobility events of the cell, and the mobility events include measurement reporting condition satisfaction, handover failure, cell residence time, radio link failure, etc.

[0397] In some embodiments, the network may transmit multiple AI models or functions.

[0398] In some embodiments, the UE determines an AI model or function that matches the target cell based on the measurement configuration of the target cell.

[0399] In the embodiments of the present disclosure, 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 of other embodiments.

[0400] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, a terminal, a network device, etc.) in any of the above methods.

[0401] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0402] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution 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 relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by 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 implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be 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), a deep learning processing unit (DPU), etc.

[0403] Figure 7A is a structural diagram of the terminal proposed in an embodiment of the present disclosure. As shown in Figure 7A, the terminal 7100 may include: at least one of a transceiver module 7101, a processing module 7102, etc. In some embodiments, the processing module 7102 is used to determine the first information based on the measurement configuration for measuring the first cell, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell; the transceiver module 7101 is used to send the first information. Optionally, the transceiver module 7101 is used to execute at least one of the communication steps such as sending and / or receiving executed by the terminal in any of the above methods, which will not be repeated here. Optionally, the processing module 7102 is used to execute at least one of the other steps executed by the terminal in any of the above methods, which will not be repeated here.

[0404] Figure 7B is a structural diagram of the network device proposed in an embodiment of the present disclosure. As shown in Figure 7B, the network device 7200 may include: at least one of a transceiver module 7201, a processing module 7202, etc. In some embodiments, the above-mentioned transceiver module 7201 is used to receive first information, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell, and the first information is determined according to the measurement configuration of the terminal for the first cell. Optionally, the above-mentioned transceiver module 7201 is used to execute at least one of the communication steps such as sending and / or receiving performed by the network device in any of the above methods, which will not be repeated here. Optionally, the above-mentioned processing module 7202 is used to execute at least one of the other steps performed by the network device in any of the above methods, which will not be repeated here.

[0405] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0406] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0407] Figure 8A is a schematic diagram of the structure of a communication device 8100 proposed in an embodiment of the present disclosure. Communication device 8100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 8100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0408] As shown in Figure 8A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. Optionally, the communication device 8100 is used to perform any of the above methods. Optionally, one or more processors 8101 are used to call instructions to enable the communication device 8100 to perform any of the above methods.

[0409] 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-described method, and the processor 8101 performs at least one of the other steps. In an optional embodiment, the transceiver may include a receiver and / or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0410] In some embodiments, the communication device 8100 further includes one or more memories 8103 for storing data. Alternatively, all or part of the memories 8103 may be located outside the communication device 8100. In alternative embodiments, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuits 8104 are connected to the memories 8103 and may be configured to receive data from the memories 8103 or other devices, or to send data to the memories 8103 or other devices. For example, the interface circuits 8104 may read data stored in the memories 8103 and send the data to the processor 8101.

[0411] 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 the present disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG. 8A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0412] FIG8B is a schematic diagram of the structure of a chip 8200 according to an embodiment of the present disclosure. If the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 8200 shown in FIG8B , but the present disclosure is not limited thereto.

[0413] The chip 8200 includes one or more processors 8201. The chip 8200 is configured to execute any of the above methods.

[0414] In some embodiments, chip 8200 further includes one or more interface circuits 8202. Terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 8200 further includes one or more memories 8203 for storing data. Alternatively, all or part of memory 8203 may be located external to chip 8200. Optionally, interface circuit 8202 is connected to memory 8203 and may be used to receive data from memory 8203 or other devices, or may be used to send data to memory 8203 or other devices. For example, interface circuit 8202 may read data stored in memory 8203 and send the data to processor 8201.

[0415] In some embodiments, the interface circuit 8202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method. For example, the interface circuit 8202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 8202 performs data exchange 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 the other steps.

[0416] The modules and / or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0417] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 8100, causes the communication device 8100 to execute 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 is not limited thereto, and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a temporary storage medium.

[0418] The present disclosure also provides a program product, which, when executed by the communication device 8100, enables the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0419] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A communication method, characterized in that: The method comprises: The terminal sends first information, where the first information includes measurement configuration or measurement information for the terminal to measure the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

2. The method according to claim 1, characterized in that The measurement configuration includes at least one of the following: a synchronization signal sending period of the first cell; a synchronization signal block measurement timing configuration SMTC of the first cell; a channel state information reference signal CSI-RS transmission period of the first cell; a measurement interval configuration used by the first cell; Measurement interval priority; Measurement interval allocation configuration; The DRX configuration includes at least one of a DRX cycle, a DRX offset, and a DRX duration.

3. The method according to claim 1 or 2, characterized in that The measurement information includes at least one of the following: a measurement period, measured by the terminal for the first cell; A measurement delay measured by the terminal for the first cell.

4. The method according to any one of claims 1 to 3, characterized in that The method comprises: The terminal measures the first cell and determines at least one of a radio channel measurement result or measurement information; The terminal sends the second information, where the second information includes at least one of a radio channel measurement result, a cell identifier of the first cell, the measurement configuration, or the measurement information.

5. The method according to claim 4, characterized in that The wireless channel measurement result is used to train a first AI model or function, and the measurement information or the measurement configuration is used to indicate an application condition corresponding to the first AI model or function.

6. The method according to any one of claims 1 to 5, characterized in that The method comprises: The terminal receives third information, where the third information includes a second AI model or function, and the second AI model or function is determined based on the first information.

7. The method according to any one of claims 1 to 6, characterized in that The method comprises: The terminal receives fourth information, where the fourth information includes at least one candidate AI model or function and an application condition corresponding to the candidate AI model or function; The candidate AI model or function is used to predict the measurement results or mobility events of the cell.

8. The method according to claim 7, characterized in that The method comprises: The terminal determines a third AI model or function from the candidate AI models or functions according to an application condition of the AI ​​model or function corresponding to the first cell.

9. The method according to any one of items 6 to 8, characterized in that At least one of the second AI model or function and the third AI model or function is used by the terminal to predict a measurement result or a mobility event of the first cell.

10. The method according to claim 9, characterized in that The mobility event includes at least one of the following: The measurement reporting conditions are met; Switching failed; Residential time in the community; The wireless link has failed.

11. A communication method, characterized in that: The method comprises: The network device receives first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

12. The method according to claim 11, characterized in that The measurement configuration includes at least one of the following: a synchronization signal sending period of the first cell; a synchronization signal and physical broadcast channel block measurement timing configuration SMTC of the first cell; a channel state information reference signal CSI-RS transmission period of the first cell; a measurement interval configuration used by the first cell; Measurement interval priority; Measurement interval allocation configuration; The DRX configuration includes at least one of a DRX cycle, a DRX offset, and a DRX duration.

13. The method according to claim 10 or 11, characterized in that The measurement information includes at least one of the following: a measurement period, measured by the terminal for the first cell; A measurement delay measured by the terminal for the first cell.

14. The method according to any one of claims 10 to 13, characterized in that: The method comprises: The network device receives second information, where the second information includes a wireless channel measurement result, a cell identifier of the first cell, the measurement configuration, or at least one of the measurement information. At least one of the measurement information or the wireless channel measurement result is obtained by the terminal measuring the first cell.

15. The method according to claim 14, characterized in that The method comprises: The network device trains a first AI model or function according to the wireless channel measurement result; The network device determines an application condition corresponding to the first AI model or function based on the measurement configuration or the measurement information.

16. The method according to any one of claims 11 to 15, characterized in that The method comprises: The network device determines a second AI model or function based on the first information; The network device sends third information, where the third information includes the second AI model or function.

17. The method according to claim 16, characterized in that The second AI model or function is used by the terminal to predict the measurement result or mobility event of the first cell.

18. The method according to any one of claims 11 to 15, characterized in that: The method comprises: The network device sends fourth information, where the fourth information includes at least one candidate AI model or function and an application condition corresponding to the candidate AI model or function; The candidate AI model or function is used to predict the measurement results or mobility events of the cell.

19. The method according to any one of claims 17-18, characterized in that The mobility event includes at least one of the following: The measurement reporting conditions are met; Switching failed; Residential time in the community; The wireless link has failed.

20. A terminal, characterized in that: The terminal includes: A transceiver module is used to send first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate the application conditions of the artificial intelligence AI model or function corresponding to the first cell.

21. A network device, characterized in that: The network equipment includes: A transceiver module is used to receive first information, where the first information includes measurement configuration or measurement information of the terminal measuring the first cell, and the first information is used to indicate an application condition of an artificial intelligence AI model or function corresponding to the first cell.

22. A terminal, characterized in that: include: one or more processors; A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, enable the terminal to execute the communication method according to any one of claims 1 to 10.

23. A network device, characterized in that: include: one or more processors; A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, causes the network device to execute the communication method according to any one of claims 11 to 19.

24. A communication system, characterized in that: The invention comprises a terminal and a network device, wherein the terminal is configured to implement the communication method according to any one of claims 1 to 10, and the network device is configured to implement the communication method according to any one of claims 11 to 19.

25. A storage medium storing instructions, characterized in that: When the instruction is executed on a communication device, the communication device is caused to execute the communication method according to any one of claims 1 to 10 or claims 11 to 19.

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