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

Abstract

The present disclosure relates to a communication method, a network device, a communication system, and a storage medium. The method comprises: a first network device sends a first instruction to a second network device, the first instruction being used for instructing the second network device to perform a first artificial intelligence (AI) operation on an AI model, and inputs to the AI model being from the second network device and at least one third network device. By means of the embodiments of the present disclosure, management of the performance of an AI model can be performed in a scenario involving centralized deployment of the AI model, thereby improving communication efficiency.

Description

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

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

[0002] With the development of communication technology, artificial intelligence (AI) technology has made continuous breakthroughs in many fields, including but not limited to AI-based positioning.

[0003] There are currently multiple deployment options for AI-based positioning. For example, the AI ​​model can be deployed on the base station side, with the location management function (LMF) assisting in positioning.

[0004] Summary of the Invention

[0005] In a centralized deployment model based on AI positioning, how to manage the performance of AI models is a technical problem that needs to be solved.

[0006] The embodiments of the present disclosure provide a communication method, a network device, a communication system, and a storage medium.

[0007] According to a first aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: a first network device sending a first instruction to a second network device, wherein the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, wherein the input of the AI ​​model comes from the second network device and at least one third network device.

[0008] According to a second aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: a second network device receiving a first instruction sent by a first network device, wherein the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and at least one third network device.

[0009] According to a third aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: a third network device receiving a second instruction, wherein the second instruction is used to instruct the third network device to perform a second AI operation, the second instruction being determined based on a first AI operation indicated by a first instruction, the first instruction being used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, the input of the AI ​​model coming from the second network device and the third network device.

[0010] According to a fourth aspect of an embodiment of the present disclosure, a first network device is proposed, comprising: a transceiver module for sending a first instruction to a second network device, wherein the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and at least one third network device.

[0011] According to a fifth aspect of an embodiment of the present disclosure, a second network device is proposed, including: a transceiver module, used to receive a first instruction sent by a first network device, the first instruction being used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and at least one third network device.

[0012] According to a sixth aspect of an embodiment of the present disclosure, a second network device is proposed, including: a transceiver module for receiving a second instruction, wherein the second instruction is used to instruct the third network device to perform a second AI operation, and the second instruction is determined based on the first AI operation indicated by the first instruction, and the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and the third network device.

[0013] According to a seventh aspect of an embodiment of the present disclosure, a first network device is proposed, comprising: one or more processors; wherein the processor is configured to execute the communication method of the first aspect.

[0014] According to an eighth aspect of an embodiment of the present disclosure, a second network device is proposed, comprising: one or more processors; wherein the processor is configured to execute the communication method of the second aspect.

[0015] According to a ninth aspect of an embodiment of the present disclosure, a third network device is proposed, comprising: one or more processors; wherein the processor is configured to execute the communication method of the third aspect.

[0016] According to the tenth aspect of an embodiment of the present disclosure, a communication system is proposed, comprising a first network device, a second network device, and a third network device, wherein the first network device is configured to implement the communication method of the first aspect, the second network device is configured to implement the communication method of the second aspect, and the third network device is configured to implement the communication method of the third aspect.

[0017] According to an eleventh aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions, and is characterized in that when the instructions are executed on a communication device, the communication device executes the method of the first aspect, the second aspect, or the third aspect.

[0018] Through the embodiments of the present disclosure, the input of the AI ​​model comes from a second network device and at least one third network device. The first network device sends a first instruction to the second network device on which the AI ​​model is deployed. The first instruction is used to instruct the second network device to perform a first AI operation on the AI ​​model, thereby realizing performance management of the AI ​​model in a centralized deployment scenario of the AI ​​model, thereby improving communication efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] FIG1A is a schematic diagram of the architecture of an AI function.

[0021] FIG1B is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure.

[0022] FIG1C is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure.

[0023] FIG1D is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure.

[0024] FIG1E is a schematic diagram of a communication system architecture according to an embodiment of the present disclosure.

[0025] FIG2A is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

[0026] FIG2B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0027] FIG3A is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0028] FIG3B is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0029] FIG4A is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0030] FIG4B is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0031] FIG5A is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0032] FIG5B is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0033] FIG6 is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0034] FIG7A is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0035] FIG7B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0036] FIG7C is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0037] FIG7D is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0038] FIG8A is a schematic structural diagram of a first network device proposed in an embodiment of the present disclosure.

[0039] FIG8B is a schematic structural diagram of a second network device proposed in an embodiment of the present disclosure.

[0040] FIG8C is a schematic structural diagram of a third network device proposed in an embodiment of the present disclosure.

[0041] FIG9A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0042] FIG9B is a schematic diagram of the structure of the chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] The embodiments of the present disclosure provide a communication method, a network device, a communication system, and a storage medium.

[0044] In a first aspect, an embodiment of the present disclosure proposes a communication method, comprising: a first network device sends a first instruction to a second network device, wherein the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and at least one third network device.

[0045] In the above embodiment, the input of the AI ​​model comes from the second network device and at least one third network device. The first network device sends a first instruction to the second network device on which the AI ​​model is deployed. The first instruction is used to instruct the second network device to perform a first AI operation on the AI ​​model, thereby realizing performance management of the AI ​​model in a centralized deployment scenario of the AI ​​model, thereby improving communication efficiency.

[0046] In combination with some embodiments of the first aspect, in some embodiments, the first AI operation includes at least one of the following: an activation operation of the AI ​​model or AI function; a deactivation operation of the AI ​​model or AI function; a switching operation of the AI ​​model or AI function; and an operation of falling back to a non-AI mode.

[0047] In the above embodiment, the first AI operation includes the above content, which can instruct the AI ​​model to perform corresponding operations, thereby improving the processing efficiency of the AI ​​model.

[0048] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: the first network device sends a second instruction to the third network device, where the second instruction is used to instruct the third network device to perform a second AI operation.

[0049] In the above embodiment, the second instruction is used to instruct the third network device to perform the second AI operation to achieve performance management in a centralized deployment scenario of the AI ​​model, thereby improving communication efficiency.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the second AI operation includes at least one of the following: the third network device sends a first measurement parameter to the second network device; the third network device sends a second measurement parameter to the first network device.

[0051] In the above embodiment, the second AI operation includes the above content, which can instruct the third network device to perform corresponding operations, thereby improving communication efficiency.

[0052] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: the first network device receives first information sent by the second network device, where the first information is used to determine the first AI operation.

[0053] In combination with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following: performance monitoring indicator information of the AI ​​model; environmental information of the terminal; and a recommended first AI operation.

[0054] In the above embodiment, the first information includes the above content, so that the first network device can determine the first AI operation according to the first information, thereby improving the processing efficiency of the AI ​​model.

[0055] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: the first network device sending auxiliary data to the second network device, where the auxiliary data is used to determine the first information.

[0056] In the above embodiment, the second network device can determine the first information according to the auxiliary data sent by the first network device, thereby improving the accuracy of the first information.

[0057] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: the first network device receives auxiliary data sent by the second network device and / or the third network device, where the auxiliary data is used to determine the first AI operation.

[0058] In the above embodiment, the first network device may determine the first AI operation according to the auxiliary data sent by the second network device and / or the third network device, thereby improving the accuracy of the first AI operation.

[0059] In combination with some embodiments of the first aspect, in some embodiments, the auxiliary data includes at least one of the following: the coordinates of the terminal; the measurement value of the terminal under the network device; the channel information of the network device; wherein the network device includes at least one of the first network device, the second network device and the third network device.

[0060] In a second aspect, an embodiment of the present disclosure proposes a communication method, comprising: a second network device receives a first instruction sent by a first network device, the first instruction being used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, the input of the AI ​​model coming from the second network device and at least one third network device.

[0061] In the above embodiment, the input of the AI ​​model comes from the second network device and at least one third network device. The first network device sends a first instruction to the second network device on which the AI ​​model is deployed. The first instruction is used to instruct the second network device to perform a first AI operation on the AI ​​model, thereby realizing performance management of the AI ​​model in a centralized deployment scenario of the AI ​​model, thereby improving communication efficiency.

[0062] In combination with some embodiments of the second aspect, in some embodiments, the first AI operation includes at least one of the following: an activation operation of the AI ​​model or AI function; a deactivation operation of the AI ​​model or AI function; a switching operation of the AI ​​model or AI function; and an operation of falling back to a non-AI mode.

[0063] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: the second network device sends a third instruction to the third network device, where the third instruction is used to instruct the third network device to perform a second AI operation.

[0064] In combination with some embodiments of the second aspect, in some embodiments, the second AI operation includes at least one of the following: the third network device sends a first measurement parameter to the second network device; the third network device sends a second measurement parameter to the first network device.

[0065] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: the second network device sending first information to the second network device, where the first information is used to determine the first AI operation.

[0066] In combination with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following: performance monitoring indicator information of the AI ​​model; environmental information of the terminal; and a recommended first AI operation.

[0067] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: the second network device receives auxiliary data sent by the first network device and / or the third network device, and the auxiliary data is used to determine the first information.

[0068] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: the second network device sending auxiliary data to the first network device, where the auxiliary data is used to determine the first AI operation.

[0069] In combination with some embodiments of the second aspect, in some embodiments, the auxiliary data includes at least one of the following: the coordinates of the terminal; the measurement value of the terminal under the network device; the channel information of the network device; wherein the network device includes at least one of the first network device, the second network device and the third network device.

[0070] In a third aspect, an embodiment of the present disclosure proposes a communication method, comprising: a third network device receives a second instruction, the second instruction is used to instruct the third network device to perform a second AI operation, the second instruction is determined based on the first AI operation indicated by the first instruction, the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and the third network device.

[0071] In combination with some embodiments of the third aspect, in some embodiments, the first AI operation includes at least one of the following: an activation operation of the AI ​​model or AI function; a deactivation operation of the AI ​​model or AI function; a switching operation of the AI ​​model or AI function; and an operation of falling back to a non-AI mode.

[0072] In combination with some embodiments of the third aspect, in some embodiments, the second instruction is sent by the second network device or the first network device.

[0073] In combination with some embodiments of the third aspect, in some embodiments, the second AI operation includes at least one of the following: the third network device sends a first measurement parameter to the second network device; the third network device sends a second measurement parameter to the first network device.

[0074] In combination with some embodiments of the third aspect, in some embodiments, the method further includes: the third network device sending auxiliary data to the second network device, where the auxiliary data is used to determine the first information.

[0075] In combination with some embodiments of the third aspect, in some embodiments, the first information includes at least one of the following: performance monitoring indicator information of the AI ​​model; environmental information of the terminal; and a recommended first AI operation.

[0076] In combination with some embodiments of the third aspect, in some embodiments, the method further includes: the third network device sending auxiliary data to the first network device, where the auxiliary data is used to determine the first AI operation.

[0077] In combination with some embodiments of the third aspect, in some embodiments, the auxiliary data includes at least one of the following: coordinates of the terminal; measurement values ​​of the terminal under the third network device; channel information of the third network device.

[0078] In a fourth aspect, an embodiment of the present disclosure proposes a first network device, comprising: a transceiver module, used to send a first instruction to a second network device, wherein the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and at least one third network device.

[0079] In a fifth aspect, an embodiment of the present disclosure proposes a second network device, comprising: a transceiver module for receiving a first instruction sent by a first network device, wherein the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, and the input of the AI ​​model comes from the second network device and at least one third network device.

[0080] In a sixth aspect, an embodiment of the present disclosure proposes a second network device, comprising: a transceiver module for receiving a second instruction, the second instruction being used to instruct the third network device to perform a second AI operation, the second instruction being determined based on the first AI operation indicated by the first instruction, the first instruction being used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, the input of the AI ​​model coming from the second network device and the third network device.

[0081] In a seventh aspect, an embodiment of the present disclosure proposes a first network device, comprising: one or more processors; wherein the processor is used to execute the communication method of the first aspect.

[0082] In an eighth aspect, an embodiment of the present disclosure proposes a second network device, comprising: one or more processors; wherein the processor is used to execute the communication method of the second aspect.

[0083] In a ninth aspect, an embodiment of the present disclosure proposes a third network device, comprising: one or more processors; wherein the processor is used to execute the communication method of the third aspect.

[0084] In the tenth aspect, an embodiment of the present disclosure proposes a communication system, including a first network device, a second network device and a third network device, wherein the first network device is configured to implement the communication method of the first aspect, the second network device is configured to implement the communication method of the second aspect, and the third network device is configured to implement the communication method of the third aspect.

[0085] In the eleventh aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, wherein the storage medium is characterized in that when the instructions are executed on a communication device, the communication device executes the method of the first aspect, the second aspect, or the third aspect.

[0086] In the twelfth aspect, an embodiment of the present disclosure proposes a program product, including: a computer program, which, when executed by a communication device, enables the communication device to execute the method described in the optional implementation of the first aspect, the second aspect, or the third aspect.

[0087] Thirteenthly, 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 aspect, the second aspect, or the third aspect.

[0088] In a fourteenth 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 aspect, the second aspect, or the third aspect.

[0089] It is understandable that the above-mentioned 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.

[0090] The embodiments of the present disclosure provide a communication method, a first network device, a second network device, a third network device, a communication system, and a storage medium. In some embodiments, the terms communication method, information transmission method, information reporting method, and information receiving method are interchangeable.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0096] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0110] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0111] 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.

[0112] The widespread adoption of 5G technology is bringing tremendous changes to every aspect of our lives. According to the International Telecommunication Union (ITU), 5G will permeate every aspect of future society, building a comprehensive, user-centric information ecosystem. 5G user experience rates can reach 100 megabits per second (Mbit / s) to 1 gigabit per second (Gbit / s), enabling premium services like mobile virtual reality. 5G peak rates can reach 10 Gbit / s to 20 Gbit / s, with traffic density reaching 10 megabits per second per square meter (Mbit / s / m²), supporting a thousand-fold increase in mobile traffic. 5G connection density can reach 1 million per square meter (m²), effectively supporting massive IoT devices. 5G transmission latency can reach milliseconds, meeting the stringent requirements of connected vehicles and industrial control. 5G can support speeds of 500 kilometers per hour (km / h), ensuring a superior user experience even in high-speed rail environments. It is foreseeable that 5G, as a representative of new infrastructure, will reshape the future information society.

[0113] In recent years, AI technology has achieved continuous breakthroughs in numerous fields. The continued development of fields like intelligent voice and computer vision has not only brought a rich variety of applications to smart terminals, but has also found widespread application in education, transportation, home furnishings, healthcare, retail, security, and other fields. While bringing convenience to people's lives, it is also promoting industrial upgrading across various industries. AI technology is also rapidly interpenetrating with other disciplines. Its development integrates knowledge from different disciplines while also providing new directions and methods for their development.

[0114] In wireless AI research, application cases of artificial intelligence include: AI-based CSI enhancement; AI-based beam management; and AI-based positioning.

[0115] Figure 1A is a schematic diagram of the AI ​​architecture. As shown in Figure 1A, the AI ​​architecture includes functions such as data collection, model management, model training, and model inference.

[0116] AI-based positioning includes but is not limited to the following five deployment methods:

[0117] For example, AI / machine learning (ML) directly targets:

[0118] 1) UE-based positioning: the model is deployed on the UE side, and AI / ML directly locates;

[0119] 2) UE-assisted LMF-based positioning, the model is deployed on the LMF side, and AI / ML directly locates;

[0120] 3) Next generation radio access network (NG-RAN) node assisted positioning, the model is deployed on the LMF side, and AI / ML directly locates.

[0121] Another example is AI / ML-assisted positioning:

[0122] 4) UE-assisted LMF-based positioning, the model is deployed on the UE side, and AI / ML assists positioning;

[0123] 5) NG-RAN node-assisted positioning: the model is deployed on the gNB side, and AI / ML assists in positioning.

[0124] AI / ML direct positioning can be understood as the AI ​​or ML model outputting location information, that is, obtaining positioning results. AI / ML assisted positioning can be understood as the AI ​​or ML model outputting intermediate parameters for positioning, which can be used to calculate location information. In other words, the AI ​​or ML model does not directly obtain positioning results.

[0125] In some embodiments, for the above deployment method 5), when the AI ​​model is deployed on the base station side, there are three AI-based positioning architectures that can be considered:

[0126] First, each transmission point (transmission and receiving point, TRP) corresponds to an AI model, or each TRP corresponds to an ML model. Figure 1B is a schematic diagram of an AI-based positioning architecture shown in an exemplary embodiment of the present disclosure. As shown in Figure 1B, 18 TRPs (TRP0~TRP17) are taken as an example for illustration, but the present disclosure does not limit the number of TRPs. Each TRP corresponds to an ML model, for example, TRP0~TRP17 correspond to ML model 0~ML model 17 respectively, and the time domain path delay profile (PDP) (for example, PDP 0~PDP 17) measured by each TRP can be input into the corresponding ML model, and the output unknown direct path (unobserved direct path) arrival time (time of arrival, ToA) is passed to LMF. LMF performs positioning based on unobserved direct path ToA and traditional positioning methods.

[0127] Second, multiple TRPs correspond to the same AI model, or multiple TRPs correspond to the same ML model. Figure 1C is a schematic diagram of an AI-based positioning architecture shown in an exemplary embodiment of the present disclosure. As shown in Figure 1C, multiple TRPs correspond to the same ML model. For example, 18 TRPs (TRP0~TRP17) correspond to one ML model, and the time domain carrier to interference ratio (CIR) (for example, CIR0~CIR17) measured by each TRP is input into the same ML model, and the output unobserved direct path ToA is passed to LMF. LMF performs positioning based on unobserved direct path ToA and traditional positioning methods. LMF outputs location information.

[0128] Third, multiple TRPs correspond to the same AI model, or multiple TRPs correspond to the same ML model. Some of the multiple TRPs belong to the gNB that deploys the AI ​​model or ML model, while others belong to other gNBs. Figure 1D is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure. As shown in Figure 1D , the gNB that deploys the ML model is gNB1. The time-domain CIR measured by gNB1's TRP and the time-domain CIR measured by gNB2's TRP are both input into the ML model on gNB1's side, and the output unobserved direct path ToA is passed to the LMF. The LMF performs positioning based on the unobserved direct path ToA and traditional positioning methods. The LMF outputs location information. gNB2 is any gNB different from gNB1.

[0129] It can be understood that each TRP has its corresponding subordinate gNB, and each gNB can have multiple TRPs. In non-AI-based positioning, the positioning intermediate parameters corresponding to each TRP are calculated by the corresponding gNB and transmitted to the LMF by the corresponding gNB. Among them, the positioning intermediate parameters represent the intermediate parameters used to calculate the location information during the positioning process. For example, it can be a time-related parameter, that is, the ToA of the unknown direct path shown in Figures 1B to 1D; line of sight (LoS) or non-line of sight (NLoS) indicator, etc.

[0130] In AI-based operations, it is necessary to monitor the performance of the AI ​​model. For example, you can monitor whether the AI ​​model input matches the distribution trend of the model input during training, whether the AI ​​model output matches the distribution trend of the model output during training, or monitor the difference between the AI ​​model output and the ideal model output. Based on the monitoring results, you can take corresponding measures for the AI ​​model, such as starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, and using non-AI operations.

[0131] In a centralized deployment model based on AI positioning, how to manage the performance of AI models is a technical problem that needs to be solved.

[0132] Therefore, an embodiment of the present disclosure provides a communication method, in which the input of the AI ​​model comes from a second network device and at least one third network device, and the first network device sends a first instruction to the second network device on which the AI ​​model is deployed, and the first instruction is used to instruct the second network device to perform a first AI operation on the AI ​​model, thereby realizing performance management of the AI ​​model in a centralized deployment scenario of the AI ​​model, thereby improving communication efficiency.

[0133] FIG1E is a schematic diagram of a communication system architecture according to an embodiment of the present disclosure.

[0134] As shown in Figure 1E , communication system 100 includes a first network device 101, a second network device 102, and a third network device 103. First network device 101 may be a Local Mobile Infrastructure (LMF), second network device 102 may be a gNB deploying an AI model, and third network device 103 may be a gNB different from second network device 102. This disclosure is merely illustrative, and the first, second, and third network devices may also be other types of nodes in the communication system. Although this disclosure uses LMF and gNB as examples, it is not limited thereto.

[0135] In some embodiments, the network node may include at least one of an access network device and a core network device.

[0136] 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) in a fifth generation mobile communication technology (5G) communication system, 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.

[0137] 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.

[0138] 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.

[0139] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. 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).

[0140] 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.

[0141] 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.

[0142] 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).

[0143] An embodiment of the present disclosure provides a communication method, in which the input of an AI model comes from a second network device and at least one third network device. The first network device sends a first instruction to the second network device on which the AI ​​model is deployed. The first instruction is used to instruct the second network device to perform a first AI operation on the AI ​​model, thereby realizing performance management of the AI ​​model in a centralized deployment scenario of the AI ​​model, thereby improving communication efficiency.

[0144] FIG2A is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG2A , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0145] Step S2101: A first network device sends auxiliary data to a second network device.

[0146] In some embodiments, the second network device receives the auxiliary data sent by the first network device.

[0147] In some embodiments, the first network device may be a core network element used for positioning.

[0148] For example, the first network device may be a Location Management Function (LMF).

[0149] In some embodiments, the second network device may be a base station on which an AI model is deployed.

[0150] For example, the second network device is a gNB deployed with an AI model.

[0151] The input of the AI ​​model comes from the second network device and at least one third network device.

[0152] In some embodiments, the third network device may be a base station on which no AI model is deployed.

[0153] For example, the third network device is a gNB that does not deploy an AI model.

[0154] In some embodiments, the AI ​​model may be a model for positioning.

[0155] In some embodiments, the input of the AI ​​model may be the Channel Impulse Response (CIR) or PDP of each gNB, and the output of the AI ​​model may be ToA.

[0156] In some embodiments, the third network device may send the CIR corresponding to the third network device to the second network device, and the second network device may input the CIR corresponding to the second network device and the CIR corresponding to the third network device into the AI ​​model to predict the output parameters of the AI ​​model (e.g., ToA). The second network device may send the output parameters of the AI ​​model to the first network device, and the first network device may locate the terminal based on the output parameters of the AI ​​model.

[0157] In some embodiments, the assistance data sent by the first network device may be measurement data of the first network device.

[0158] In some embodiments, the assistance data sent by the first network device is used to determine the first information.

[0159] In some embodiments, the auxiliary data sent by the first network device includes at least one of the following:

[0160] The coordinates of the terminal;

[0161] The measurement value of the terminal under the first network device;

[0162] Channel information of the first network device.

[0163] The coordinates of the terminal may be relative coordinates of the terminal and the first network device.

[0164] The measurement value of the terminal under the first network device may also be referred to as a channel measurement value of the first network device for the terminal.

[0165] In some embodiments, the auxiliary data sent by the first network device may further include: receiving beam information of the first network device.

[0166] In some embodiments, the auxiliary data sent by the first network device may be data used for AI model input, or may be data output by the AI ​​model, so as to detect the performance of the AI ​​model. For example, it is possible to monitor whether the input of the AI ​​model is consistent with the distribution trend of the model input during training, or whether the output of the AI ​​model is consistent with the distribution trend of the model output during training, or the difference between the output of the AI ​​model and the ideal model output, etc. Based on the monitoring results, corresponding measures can be taken for the AI ​​model, such as starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, using non-AI operations, etc.

[0167] Step S2102: The third network device sends auxiliary data to the second network device.

[0168] In some embodiments, the second network device receives the auxiliary data sent by the third network device.

[0169] In some embodiments, the assistance data sent by the third network device may be measurement data of the third network device.

[0170] In some embodiments, the auxiliary data sent by the third network device is used to determine the first information.

[0171] In some embodiments, the auxiliary data sent by the third network device includes at least one of the following:

[0172] The coordinates of the terminal;

[0173] The measurement value of the terminal under the third network device;

[0174] Channel information of the third network device.

[0175] The coordinates of the terminal may be relative coordinates of the terminal and the third network device.

[0176] The measurement value of the terminal under the third network device may also be referred to as a channel measurement value of the terminal by the third network device.

[0177] In some embodiments, the auxiliary data sent by the third network device may further include: receiving beam information of the third network device.

[0178] In some embodiments, the auxiliary data sent by the third network device may be data used for AI model input, or may be data output by the AI ​​model, so as to detect the performance of the AI ​​model. For example, it is possible to monitor whether the input of the AI ​​model is consistent with the distribution trend of the model input during training, or whether the output of the AI ​​model is consistent with the distribution trend of the model output during training, or the difference between the output of the AI ​​model and the ideal model output, etc. Based on the monitoring results, corresponding measures can be taken for the AI ​​model, such as starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, using non-AI operations, etc.

[0179] Step S2103: The second network device determines the first information.

[0180] In some embodiments, the second network device may determine the first information according to the auxiliary data sent by the first network device and / or the third network device.

[0181] In some embodiments, the auxiliary data may be data used for AI model input, and the first information may be the distribution of AI model input data (e.g., mean, variance); the auxiliary data may be data output by the AI ​​model, and the first information may be the distribution of AI model output data (e.g., mean, variance).

[0182] In some embodiments, the first information includes at least one of the following:

[0183] Performance monitoring indicator information of AI models;

[0184] Environmental information of the terminal;

[0185] Recommended first AI action.

[0186] Among them, the performance monitoring indicator information can be, for example, the distribution of model input information (such as mean, variance), the distribution of model output information (such as mean, variance), or the difference from a certain threshold; the environmental information of the terminal can be, for example, the channel quality, the scene (such as indoors, outdoors, factory, office).

[0187] Step S2104: The second network device sends first information to the first network device.

[0188] In some embodiments, the first network device receives first information sent by the second network device.

[0189] In some embodiments, the first information is used to determine a first AI operation.

[0190] Step S2105: The first network device determines a first AI operation according to the first information.

[0191] In some embodiments, the first AI operation includes at least one of the following:

[0192] Activation of AI models or AI functions;

[0193] Deactivation of AI models or AI functions;

[0194] Switching operations of AI models or AI functions;

[0195] Fall back to non-AI mode operation.

[0196] Among them, the activation operation can also be understood as starting the AI ​​model or AI function, the deactivation operation can also be understood as turning off the AI ​​model or AI function, and falling back to the non-AI mode can also be understood as using non-AI operations.

[0197] In some embodiments, different conditions may be set, and when the first information satisfies the corresponding conditions, a first AI operation corresponding to the conditions is determined.

[0198] For example, when the first information meets the preset conditions, the first information includes turning on the AI ​​model; when the first information does not meet the preset conditions, the first information includes turning off the AI ​​model or using non-AI operations; when the first information does not meet the preset conditions and other AI models meet the preset conditions, the first information includes switching the AI ​​model.

[0199] In some embodiments, when the first information includes the distribution of AI model input data, the AI ​​model input can be monitored to see if it matches the distribution trend of the model input during training. When the first information includes the distribution of AI model output data, the AI ​​model output can be monitored to see if it matches the distribution trend of the model output during training, or the difference between the AI ​​model output and the ideal model output can be monitored. Based on the monitoring results, corresponding measures can be taken for the AI ​​model, such as starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, or using non-AI operations.

[0200] Step S2106: The first network device sends a first instruction to the second network device.

[0201] In some embodiments, the second network device receives the first instruction sent by the first network device.

[0202] In some embodiments, the first instruction is used to instruct the second network device to perform a first AI operation on the AI ​​model.

[0203] Step S2107: The first network device sends a second instruction to the third network device.

[0204] In some embodiments, the third network device receives the second instruction sent by the first network device.

[0205] In some embodiments, the second instruction is used to instruct the third network device to perform a second AI operation.

[0206] In some embodiments, the second instruction is used to instruct the third network device whether to send the first measurement parameter to the second network device.

[0207] In some embodiments, the second instruction is used to instruct the third network device whether to send the second measurement parameter to the first network device.

[0208] In some embodiments, the second AI operation includes at least one of the following:

[0209] The third network device sends the first measurement parameter to the second network device;

[0210] The third network device sends the second measurement parameter to the first network device.

[0211] In some embodiments, the second AI operation may further include at least one of the following:

[0212] The third network device does not send the first measurement parameter to the second network device;

[0213] The third network device does not send the second measurement parameter to the first network device.

[0214] In some embodiments, the first measurement parameter may include any one of measured time information, power information, phase information, angle information, and transmission path information.

[0215] In some embodiments, the second measurement parameter may include any one of measured time information, power information, phase information, angle information, and transmission path information.

[0216] In some embodiments, the second instruction may be determined based on the first AI operation.

[0217] For example, when the first AI operation includes activating the AI ​​model (i.e., turning on the AI ​​model), this indicates that the input or output of the AI ​​model is consistent with that during training, i.e., the AI ​​model has high accuracy. The second instruction may instruct the third network device to send a first measurement parameter to the second network device to use the AI ​​model for prediction and obtain the output result of the AI ​​model. The first measurement parameter may be, for example, CIR or PDP.

[0218] For example, when the first AI operation includes deactivating the AI ​​model (i.e., shutting down the AI ​​model), this indicates that the input or output of the AI ​​model does not match that during training, i.e., the AI ​​model has poor accuracy. The second instruction may instruct the third network device to send a second measurement parameter to the first network device. The third network device may calculate the second measurement parameter based on the measurement data. The second measurement parameter may be an intermediate positioning parameter (e.g., ToA), and then send the second measurement parameter to the first network device.

[0219] Step S2108: The second network device sends a third instruction to the third network device.

[0220] In some embodiments, the third network device receives a third instruction sent by the second network device.

[0221] In some embodiments, the third instruction is used to instruct the third network device to perform a second AI operation.

[0222] In some embodiments, the first network device may send a second instruction to the third network device to instruct the third network device to perform the second AI operation; or the second network device may send a third instruction to the third network device to instruct the third network device to perform the second AI operation.

[0223] The communication method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2108. For example, step S2103 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, and step S2106 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0224] In some embodiments, steps S2101 and S2102 may be executed in an exchanged order or simultaneously, steps S2106 and S2107 may be executed in an exchanged order or simultaneously, and steps S2106 and S2108 may be executed in an exchanged order or simultaneously.

[0225] In some embodiments, steps S2101, S2102, S2103, S2104, S2105, S2107, and S2108 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0226] In some embodiments, steps S2101 and S2108 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0227] In some embodiments, steps S2101 and S2107 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0228] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

[0229] FIG2B is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG2B , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0230] Step S2201: The second network device sends auxiliary data to the first network device.

[0231] In some embodiments, the first network device receives the auxiliary data sent by the second network device.

[0232] In some embodiments, the assistance data sent by the second network device may be measurement data of the second network device.

[0233] In some embodiments, the assistance data sent by the second network device is used to determine the first information.

[0234] In some embodiments, the auxiliary data sent by the second network device includes at least one of the following:

[0235] The coordinates of the terminal;

[0236] The measurement value of the terminal under the second network device;

[0237] Channel information of the second network device.

[0238] The coordinates of the terminal may be relative coordinates of the terminal and the second network device.

[0239] In some embodiments, the auxiliary data sent by the second network device includes at least one of the following:

[0240] a channel measurement value of the second network device for the terminal;

[0241] The second network device receives beam information.

[0242] In some embodiments, the auxiliary data sent by the second network device may be data used for AI model input, or may be data output by the AI ​​model, so as to detect the performance of the AI ​​model. For example, it is possible to monitor whether the input of the AI ​​model is consistent with the distribution trend of the model input during training, or whether the output of the AI ​​model is consistent with the distribution trend of the model output during training, or the difference between the output of the AI ​​model and the ideal model output, etc. Based on the monitoring results, corresponding measures can be taken for the AI ​​model, such as starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, using non-AI operations, etc.

[0243] Step S2202: The third network device sends auxiliary data to the first network device.

[0244] In some embodiments, the first network device receives auxiliary data sent by the third network device.

[0245] In some embodiments, the assistance data sent by the third network device may be measurement data of the third network device.

[0246] In some embodiments, the auxiliary data sent by the third network device is used to determine the first information.

[0247] In some embodiments, the auxiliary data sent by the third network device includes at least one of the following:

[0248] The coordinates of the terminal;

[0249] The measurement value of the terminal under the third network device;

[0250] Channel information of the third network device.

[0251] In some embodiments, the auxiliary data sent by the third network device includes at least one of the following:

[0252] a channel measurement value of the terminal by the third network device;

[0253] The third network device receives beam information.

[0254] In some embodiments, the auxiliary data packets provided by the second network device and the third network device may be different.

[0255] In some embodiments, the auxiliary data sent by the third network device may be data used for AI model input, or may be data output by the AI ​​model, so as to detect the performance of the AI ​​model. For example, it is possible to monitor whether the input of the AI ​​model is consistent with the distribution trend of the model input during training, or whether the output of the AI ​​model is consistent with the distribution trend of the model output during training, or the difference between the output of the AI ​​model and the ideal model output, etc. Based on the monitoring results, corresponding measures can be taken for the AI ​​model, such as starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, using non-AI operations, etc.

[0256] Step S2203: The first network device determines the first information.

[0257] In some embodiments, the first network device may determine the first information according to the auxiliary data sent by the second network device and / or the third network device.

[0258] In some embodiments, the auxiliary data may be data used for AI model input, and the first information may be the distribution of AI model input data (e.g., mean, variance); the auxiliary data may be data output by the AI ​​model, and the first information may be the distribution of AI model output data (e.g., mean, variance).

[0259] In some embodiments, the first information includes at least one of the following:

[0260] Performance monitoring indicator information of AI models;

[0261] Environmental information of the terminal;

[0262] Recommended first AI action.

[0263] Among them, the performance monitoring indicator information can be, for example, the distribution of model input information (such as mean, variance), the distribution of model output information (such as mean, variance), or the difference from a certain threshold; the environmental information of the terminal can be, for example, the channel quality, the scene (such as indoors, outdoors, factory, office).

[0264] Step S2204: The first network device determines a first AI operation according to the first information.

[0265] The optional implementation of step S2204 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0266] Step S2205: The first network device sends a first instruction to the second network device.

[0267] The optional implementation of step S2205 can refer to the optional implementation of step S2106 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0268] Step S2206: The first network device sends a second instruction to the third network device.

[0269] The optional implementation of step S2206 can refer to the optional implementation of step S2107 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0270] Step S2207: The second network device sends a third instruction to the third network device.

[0271] The optional implementation of step S2207 can refer to the optional implementation of step S2108 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0272] The communication method involved in the embodiment of the present disclosure may include at least one of steps S2201 to S2207. For example, step S2203 may be implemented as an independent embodiment, step S2204 may be implemented as an independent embodiment, and step S2205 may be implemented as an independent embodiment, but are not limited thereto.

[0273] In some embodiments, steps S2201 and S2202 may be executed in an exchanged order or simultaneously, steps S2205 and S2206 may be executed in an exchanged order or simultaneously, and steps S2205 and S2207 may be executed in an exchanged order or simultaneously.

[0274] In some embodiments, steps S2201, S2202, S2203, S2204, S2205, and S2207 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0275] In some embodiments, step S2207 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0276] In some embodiments, step S2106 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0277] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2B .

[0278] 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.

[0279] 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.

[0280] 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.

[0281] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0282] 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.

[0283] 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 (bool)) represented by true (true) or false (false), or by comparison of numerical values ​​(for example, comparison with a predetermined value), but is not limited thereto.

[0284] 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.

[0285] 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, which includes:

[0286] Step S3101: Send auxiliary data.

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

[0288] In some embodiments, the first network device sends the assistance data to the second network device.

[0289] Step S3102, obtaining first information.

[0290] The optional implementation of step S3102 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0291] In some embodiments, the first network device receives first information sent by the second network device.

[0292] Step S3103: Determine a first AI operation according to the first information.

[0293] The optional implementation of step S3103 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0294] In some embodiments, the first network device determines the first AI operation according to the first information.

[0295] Step S3104: Send the first instruction.

[0296] The optional implementation of step S3104 can refer to the optional implementation of step S2106 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0297] In some embodiments, the first network device sends a first instruction to the second network device.

[0298] Step S3105: Send the second instruction.

[0299] The optional implementation of step S3105 can refer to the optional implementation of step S2107 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0300] In some embodiments, the first network device sends a second instruction to the third network device.

[0301] The communication method involved in the embodiment of the present disclosure may include at least one of steps S3101 to S3105. For example, step S3102 may be implemented as an independent embodiment, step S3103 may be implemented as an independent embodiment, and step S3104 may be implemented as an independent embodiment, but are not limited thereto.

[0302] In some embodiments, steps S3101 and S3105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0303] 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, which includes:

[0304] Step S3201: Acquire auxiliary data sent by the second network device.

[0305] The optional implementation of step S3201 can refer to the optional implementation of step S2201 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0306] In some embodiments, the first network device receives the auxiliary data sent by the second network device.

[0307] Step S3202: Acquire auxiliary data sent by the third network device.

[0308] The optional implementation of step S3202 can refer to the optional implementation of step S2202 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0309] In some embodiments, the first network device receives auxiliary data sent by the third network device.

[0310] Step S3203, determine the first information.

[0311] The optional implementation of step S3203 can refer to the optional implementation of step S2203 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0312] In some embodiments, the first network device determines the first information.

[0313] Step S3204: Determine a first AI operation according to the first information.

[0314] The optional implementation of step S3204 can refer to the optional implementation of step S2204 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0315] In some embodiments, the first network device determines the first AI operation according to the first information.

[0316] Step S3205: Send the first instruction.

[0317] The optional implementation of step S3205 can refer to the optional implementation of step S2205 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0318] In some embodiments, the first network device sends a first instruction to the second network device.

[0319] Step S3206: Send the second instruction.

[0320] The optional implementation of step S3206 can refer to the optional implementation of step S2206 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0321] In some embodiments, the first network device sends a second instruction to the third network device.

[0322] The communication method involved in the embodiment of the present disclosure may include at least one of steps S3201 to S3206. For example, step S3203 may be implemented as an independent embodiment, step S3204 may be implemented as an independent embodiment, and step S3205 may be implemented as an independent embodiment, but are not limited thereto.

[0323] In some embodiments, steps S3201, S3202, S3203, and S3206 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0324] FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4 , the embodiment of the present disclosure relates to a communication method, which includes:

[0325] Step S4101: Acquire auxiliary data sent by the first network device.

[0326] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0327] In some embodiments, the second network device receives the auxiliary data sent by the first network device.

[0328] Step S4102: Acquire auxiliary data sent by the third network device.

[0329] The optional implementation of step S4102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0330] In some embodiments, the second network device receives the auxiliary data sent by the third network device.

[0331] Step S4103: determine the first information.

[0332] The optional implementation of step S4103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0333] In some embodiments, the first network device determines the first information.

[0334] Step S4104, sending the first information.

[0335] The optional implementation of step S4104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0336] In some embodiments, the second network device sends the first information to the first network device.

[0337] Step S4105: Get the first instruction.

[0338] The optional implementation of step S4105 can refer to the optional implementation of step S2106 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0339] In some embodiments, the second network device receives the first instruction sent by the first network device.

[0340] Step S4106: Send a third instruction.

[0341] The optional implementation of step S4106 can refer to the optional implementation of step S2108 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0342] In some embodiments, the second network device sends a third instruction to the third network device.

[0343] The communication method involved in the embodiment of the present disclosure may include at least one of steps S4101 to S4106. For example, step S4103 may be implemented as an independent embodiment, step S4104 may be implemented as an independent embodiment, and step S4105 may be implemented as an independent embodiment, but are not limited thereto.

[0344] In some embodiments, steps S4101 and S4106 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0345] 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, and the method includes:

[0346] Step S4201: Send auxiliary data.

[0347] The optional implementation of step S4201 can refer to the optional implementation of step S2201 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0348] In some embodiments, the second network device sends the assistance data to the first network device.

[0349] Step S4202: Get the first instruction.

[0350] The optional implementation of step S4202 can refer to the optional implementation of step S2205 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0351] In some embodiments, the second network device receives the first instruction sent by the first network device.

[0352] Step S4203: Send the third instruction.

[0353] The optional implementation of step S4203 can refer to the optional implementation of step S2207 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0354] In some embodiments, the second network device sends a third instruction to the third network device.

[0355] The communication method involved in the embodiment of the present disclosure may include at least one of steps S4201 to S4203. For example, step S4201 may be implemented as an independent embodiment, step S4202 may be implemented as an independent embodiment, and step S4203 may be implemented as an independent embodiment, but are not limited thereto.

[0356] In some embodiments, steps S4201 and S4203 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0357] FIG5A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5A , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0358] Step S5101: Send auxiliary data.

[0359] The optional implementation of step S5101 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0360] In some embodiments, the third network device sends the assistance data to the second network device.

[0361] Step S5102: Get the second instruction.

[0362] The optional implementation of step S5102 can refer to the optional implementation of step S2107 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0363] In some embodiments, the third network device receives the second instruction sent by the first network device.

[0364] Step S5103: Obtain a third instruction.

[0365] The optional implementation of step S5103 can refer to the optional implementation of step S2108 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0366] In some embodiments, the third network device receives a third instruction sent by the second network device.

[0367] The communication method involved in the embodiment of the present disclosure may include at least one of steps S5101 to S5103. For example, step S5101 may be implemented as an independent embodiment, step S5102 may be implemented as an independent embodiment, and step S5103 may be implemented as an independent embodiment, but is not limited thereto.

[0368] In some embodiments, steps S5101, S5102, and S5103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0369] FIG5B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5B , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0370] Step S5201, sending auxiliary data.

[0371] The optional implementation of step S5201 can refer to the optional implementation of step S2202 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0372] In some embodiments, the third network device sends the assistance data to the first network device.

[0373] Step S5202: Get the first instruction.

[0374] The optional implementation of step S5202 can refer to the optional implementation of step S2206 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0375] In some embodiments, the third network device receives the first instruction sent by the first network device.

[0376] Step S5203: Get the second instruction.

[0377] The optional implementation of step S5203 can refer to the optional implementation of step S2207 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0378] In some embodiments, the third network device receives the second instruction sent by the second network device.

[0379] The communication method involved in the embodiment of the present disclosure may include at least one of steps S5201 to S5203. For example, step S5201 may be implemented as an independent embodiment, step S5202 may be implemented as an independent embodiment, and step S5203 may be implemented as an independent embodiment, but is not limited thereto.

[0380] In some embodiments, steps S5201, S5202, and S5203 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0381] Figure 6 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 6, the embodiment of the present disclosure relates to a communication method, which includes:

[0382] Step S6101: The first network device sends a first instruction to the second network device.

[0383] Optional implementations of step S6101 may refer to step S2106 of FIG. 2A , step S2205 of FIG. 2B , and other related parts of the embodiments involved in FIG. 2A and FIG. 2B , which will not be described in detail here.

[0384] In some embodiments, the above method may include the method of the above-mentioned embodiments on the communication system side, network device side, etc., which will not be repeated here.

[0385] FIG7A is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG7A , the embodiment of the present disclosure relates to an information method, and the method includes:

[0386] In step S7101, gNBy sends data for performance indicator calculation to inference gNBx.

[0387] In some embodiments, the inference gNBx may be a gNB base station deployed with an AI model, and gNBy may be a gNB base station not deployed with an AI model. gNBy sends data for performance metric calculation to the inference gNBx, which may also be referred to as auxiliary data.

[0388] In some embodiments, the assistance data may include at least one of the following: the coordinates of the terminal, the measurement value of the terminal under gNBy, and the channel information of the terminal under gNBy.

[0389] In step S7102, the LMF sends data for performance indicator calculation to the inference gNBx.

[0390] In some embodiments, the assistance data may include at least one of the following: coordinates of the terminal, measurement values ​​of the terminal under LMF, and channel information of the terminal under LMF.

[0391] Step S7103: The inferred gNBx calculates performance indicators.

[0392] In some embodiments, the inference gNBx determines first information content based on the received gNBy and the assistance data sent by the LMF. The first information may be, for example, a performance metric.

[0393] In some embodiments, the first information may include at least one of the following:

[0394] Performance monitoring indicator information: such as the distribution of model input information (e.g., mean, variance), the distribution of model output information, the mean and variance of model output information, or the difference from a certain threshold;

[0395] The terminal's environment information: such as channel quality and the scenario (indoor, outdoor, factory, office);

[0396] Recommended first action.

[0397] Step S7104: Inferring gNBx sends first information to LMF.

[0398] In some embodiments, the first information may be carried in Xn signaling and / or NRPPa (NR Positioning Protocol).

[0399] Step S7105: LMF makes LCM decision.

[0400] In some embodiments, the first information is used to assist the LMF in determining the first AI operation indicated by the first instruction.

[0401] In some embodiments, the LMF determines a first AI operation based on the first information.

[0402] In some embodiments, the result of the LCM decision includes a first AI operation.

[0403] In some embodiments, the first AI operation includes at least one of the following: activation of an AI model / function, deactivation of an AI model / function, switching of an AI model / function, and falling back to a non-AI operation mode.

[0404] Step S7106: LMF sends a first instruction to the inference gNBx.

[0405] In some embodiments, the first instruction may be an LCM instruction for gNBx.

[0406] In some embodiments, the first instruction includes a first AI operation.

[0407] In some embodiments, the first instruction may be carried in Xn signaling and / or NRPPa signaling.

[0408] Step S7107: LMF sends a second instruction to gNBy.

[0409] In some embodiments, the second instruction may be an LCM instruction for gNBy.

[0410] In some embodiments, the second instruction is used to instruct gNBy to perform a corresponding second AI operation, where the second AI operation may include at least one of the following: sending the first measurement parameter to gNBx, not sending the measurement parameter to gNBx, sending the second measurement parameter to the LMF, and not sending the measurement parameter to the LMF.

[0411] In some embodiments, the second instruction may instruct gNBy whether to send the measurement parameters to gNBx, and whether to send the measurement parameters to the LMF.

[0412] In some embodiments, the first measurement parameter includes any one of measured time information, power information, phase information, angle information, and transmission path information; the second measurement parameter includes any one of measured time information, power information, phase information, angle information, and transmission path information.

[0413] In some embodiments, when the second instruction indicates that measurement parameters are to be sent to gNBx, gNBy sends the measurement parameters to gNBx to obtain the result of the second AI operation. When the second signaling indicates that measurement parameters are not to be sent to gNBx but to the LMF, gNBy does not send the measurement parameters to gNBx, but instead calculates intermediate positioning parameters (e.g., ToA) based on the measurement data and then sends the measurement parameters to the LMF.

[0414] In some embodiments, the second instruction is carried in Xn signaling and / or NRPPa signaling.

[0415] It can be understood that the present disclosure does not limit the order of execution of step S7106 and step S7107, that is, step S7106 can be executed first and then step S7107, or step S7107 can be executed first and then step S7106, or step S7106 and step S7107 can be executed simultaneously.

[0416] FIG7B is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG7B , the embodiment of the present disclosure relates to an information method, and the method includes:

[0417] In step S7201, gNBy sends data for performance indicator calculation to the inference gNBx.

[0418] In some embodiments, the inference gNBx may be a gNB base station deployed with an AI model, and gNBy may be a gNB base station not deployed with an AI model. gNBy sends data for performance metric calculation to the inference gNBx, which may also be referred to as auxiliary data.

[0419] In some embodiments, the assistance data may include at least one of the following: the coordinates of the terminal, the measurement value of the terminal under gNBy, and the channel information of the terminal under gNBy.

[0420] In step S7202, the LMF sends data for performance indicator calculation to the inference gNBx.

[0421] In some embodiments, the assistance data may include at least one of the following: coordinates of the terminal, measurement values ​​of the terminal under LMF, and channel information of the terminal under LMF.

[0422] Step S7203: The inferred gNBx performs performance indicator calculations.

[0423] In some embodiments, the inference gNBx determines first information content based on the assistance data received from gNBy and sent by the LMF. The first information may be, for example, a performance metric.

[0424] In some embodiments, the first information may include at least one of the following:

[0425] Performance monitoring indicator information: such as the distribution of model input information (e.g., mean, variance), the distribution of model output information, the mean and variance of model output information, or the difference from a certain threshold;

[0426] The terminal's environment information: such as channel quality and the scenario (indoor, outdoor, factory, office);

[0427] Recommended first action.

[0428] Step S7204: Inferring gNBx sends first information to LMF.

[0429] In some embodiments, the first information may be carried in Xn signaling.

[0430] Step S7205: LMF makes LCM decision.

[0431] In some embodiments, the first information is used to assist the LMF in determining the first AI operation indicated by the first instruction.

[0432] In some embodiments, the LMF determines a first AI operation based on the first information.

[0433] In some embodiments, the first AI operation includes at least one of the following: activation of an AI model / function, deactivation of an AI model / function, switching of an AI model / function, and falling back to a non-AI operation mode.

[0434] Step S7206: LMF sends a first instruction to the inference gNBx.

[0435] In some embodiments, the first instruction may be an LCM instruction for gNBx.

[0436] In some embodiments, the first instruction includes a first AI operation.

[0437] In some embodiments, the first instruction may be carried in Xn signaling and / or NRPPa signaling.

[0438] Step S7207: Inferring gNBx sends a third instruction to gNBy.

[0439] In some embodiments, in response to receiving the first instruction sent by the LMF, the inference gNBx sends a third instruction to gNBy. The third instruction is used to instruct gNBy to perform a corresponding second AI operation, where the second AI operation may include at least one of the following: sending the first measurement parameter to gNBx, not sending the measurement parameter to gNBx, sending the second measurement parameter to the LMF, and not sending the measurement parameter to the LMF.

[0440] In some embodiments, the third instruction may be carried in Xn signaling and / or NRPPa signaling.

[0441] FIG7C is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG7C , the embodiment of the present disclosure relates to an information method, and the method includes:

[0442] Step S7301: gNBy sends data for performance indicator calculation to LMF.

[0443] In some embodiments, gNBy is a gNB base station that does not deploy an AI model. gNBy sends data for performance metric calculation to the inference LMF. This data may also be referred to as auxiliary data.

[0444] In some embodiments, the assistance data may include at least one of the following: the coordinates of the terminal, the measurement value of the terminal under gNBy, and the channel information of the terminal under gNBy (for example, receiving beam information).

[0445] Step S7302: The inference gNBx sends data for performance indicator calculation to the LMF.

[0446] In some embodiments, the inference gNBx can be a gNB base station deployed with an AI model.

[0447] In some embodiments, the assistance data may include at least one of the following: the coordinates of the terminal, the measurement value of the terminal under gNBx, and the channel information of the terminal under gNBx (for example, receiving beam information).

[0448] In some embodiments, the assistance data packets provided by gNBx and gNBy may be different.

[0449] In step S7303, LMF calculates performance indicators.

[0450] In some embodiments, the LMF determines the first information content based on the received assistance data sent by gNBy and gNBx. The first information may be, for example, a performance metric.

[0451] In some embodiments, the first information may include at least one of the following:

[0452] Performance monitoring indicator information: such as the distribution of model input information (e.g., mean, variance), the distribution of model output information, the mean and variance of model output information, or the difference from a certain threshold;

[0453] The terminal's environment information: such as channel quality and the scenario (indoor, outdoor, factory, office);

[0454] Recommended first action.

[0455] Step S7304: LMF makes LCM decision.

[0456] In some embodiments, the auxiliary data is used to assist the LMF in determining the first AI operation indicated by the first instruction.

[0457] In some embodiments, the LMF determines a first AI operation based on the assistance data.

[0458] In some embodiments, the first AI operation includes at least one of the following: activation of an AI model / function, deactivation of an AI model / function, switching of an AI model / function, and falling back to a non-AI operation mode.

[0459] Step S7305: LMF sends a first instruction to the inference gNBx.

[0460] In some embodiments, the first instruction may be an LCM instruction for gNBx.

[0461] In some embodiments, the first instruction includes a first AI operation.

[0462] In some embodiments, the first instruction may be carried in Xn signaling and / or NRPPa signaling.

[0463] Step S7306: LMF sends a second instruction to gNBy.

[0464] In some embodiments, the second instruction may be an LCM instruction for gNBy.

[0465] In some embodiments, the second instruction is used to instruct gNBy to perform a corresponding second AI operation, where the second AI operation may include at least one of the following: sending the first measurement parameter to gNBx, not sending the measurement parameter to gNBx, sending the second measurement parameter to the LMF, and not sending the measurement parameter to the LMF.

[0466] In some embodiments, the second instruction may instruct gNBy whether to send the measurement parameters to gNBx, and whether to send the measurement parameters to the LMF.

[0467] In some embodiments, the first measurement parameter includes any one of measured time information, power information, phase information, angle information, and transmission path information; the second measurement parameter includes any one of measured time information, power information, phase information, angle information, and transmission path information.

[0468] In some embodiments, when the second instruction indicates that measurement parameters are to be sent to gNBx, gNBy sends the measurement parameters to gNBx to obtain the result of the second AI operation. When the second signaling indicates that measurement parameters are not to be sent to gNBx but to the LMF, gNBy does not send the measurement parameters to gNBx, but instead calculates intermediate positioning parameters (e.g., ToA) based on the measurement data and then sends the measurement parameters to the LMF.

[0469] In some embodiments, the second instruction is carried in Xn signaling and / or NRPPa signaling.

[0470] FIG7D is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG7D , the embodiment of the present disclosure relates to an information method, and the method includes:

[0471] Step S7401: gNBy sends data for performance indicator calculation to LMF.

[0472] In some embodiments, gNBy is a gNB base station that does not deploy an AI model. gNBy sends data for performance metric calculation to the inference LMF. This data may also be referred to as auxiliary data.

[0473] In some embodiments, the assistance data may include at least one of the following: the coordinates of the terminal, the measurement value of the terminal under gNBy, and the channel information of the terminal under gNBy (for example, receiving beam information).

[0474] Step S7402: The inference gNBx sends data for performance indicator calculation to the LMF.

[0475] In some embodiments, the inference gNBx can be a gNB base station deployed with an AI model.

[0476] In some embodiments, the assistance data may include at least one of the following: the coordinates of the terminal, the measurement value of the terminal under gNBx, and the channel information of the terminal under gNBx (for example, receiving beam information).

[0477] In some embodiments, the assistance data packets provided by gNBx and gNBy may be different.

[0478] In step S7403, LMF calculates performance indicators.

[0479] In some embodiments, the LMF determines the first information content based on the received assistance data sent by gNBy and gNBx. The first information may be, for example, a performance metric.

[0480] In some embodiments, the first information may include at least one of the following:

[0481] Performance monitoring indicator information: such as the distribution of model input information (e.g., mean, variance), the distribution of model output information, the mean and variance of model output information, or the difference from a certain threshold;

[0482] The terminal's environment information: such as channel quality and the scenario (indoor, outdoor, factory, office);

[0483] Recommended first action.

[0484] Step S7404: LMF makes LCM decision.

[0485] In some embodiments, the auxiliary data is used to assist the LMF in determining the first AI operation indicated by the first instruction.

[0486] In some embodiments, the LMF determines a first AI operation based on the assistance data.

[0487] In some embodiments, the first AI operation includes at least one of the following: activation of an AI model / function, deactivation of an AI model / function, switching of an AI model / function, and falling back to a non-AI operation mode.

[0488] Step S7405: LMF sends a first instruction to the inference gNBx.

[0489] In some embodiments, the first instruction may be an LCM instruction for gNBx.

[0490] In some embodiments, the first instruction includes a first AI operation.

[0491] In some embodiments, the first instruction may be carried in Xn signaling and / or NRPPa signaling.

[0492] Step S7406: Inferring gNBx sends a third instruction to gNBy.

[0493] In some embodiments, gNBx, in response to receiving the first instruction sent by the LMF, sends a third instruction to gNBy. The third instruction is used to instruct gNBy to perform a corresponding second AI operation, where the second AI operation may include at least one of the following: sending the first measurement parameter to gNBx, not sending the measurement parameter to gNBx, sending the second measurement parameter to the LMF, and not sending the measurement parameter to the LMF.

[0494] In some embodiments, the third instruction may be carried in Xn signaling and / or NRPPa signaling.

[0495] 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.

[0496] 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, etc.) in any of the above methods.

[0497] 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.

[0498] 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.

[0499] Figure 8A is a schematic diagram of the structure of a first network device according to an embodiment of the present disclosure. As shown in Figure 8A, first network device 8100 may include a transceiver module 8101. In some embodiments, transceiver module 8101 is configured to send a first instruction to a second network device. Optionally, the transceiver module is configured to execute at least one of the steps (e.g., steps S2101, S2106, and S2107, but not limited thereto) performed by the first network device in any of the above methods, which will not be further described herein.

[0500] Figure 8B is a schematic diagram of the structure of a second network device according to an embodiment of the present disclosure. As shown in Figure 8B, second network device 8200 may include a transceiver module 8201. In some embodiments, transceiver module 8201 is configured to receive a first instruction sent by a first network device. Optionally, the transceiver module is configured to execute at least one of the steps (e.g., but not limited to, step S2104) performed by the second network device in any of the above methods, and will not be further described herein.

[0501] Figure 8C is a schematic diagram of the structure of a third network device proposed in an embodiment of the present disclosure. As shown in Figure 8C, third network device 8300 may include a transceiver module 8301. In some embodiments, transceiver module 8101 is configured to receive a second instruction. Optionally, the transceiver module is configured to execute at least one of the steps (e.g., steps S2102 and S2108, but not limited thereto) performed by the third network device in any of the above methods, which will not be further described here.

[0502] 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.

[0503] Figure 9A is a schematic diagram of the structure of a communication device 9100 proposed in an embodiment of the present disclosure. Communication device 9100 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 9100 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.

[0504] As shown in Figure 9A, the communication device 9100 includes one or more processors 9101. The processor 9101 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 9100 is used to perform any of the above methods. Optionally, one or more processors 9101 are used to call instructions to enable the communication device 9100 to perform any of the above methods.

[0505] In some embodiments, the communication device 9100 further includes one or more transceivers 9102. When the communication device 9100 includes one or more transceivers 9102, the transceiver 9102 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., step S2101, step S2102, step S2104, step S2106, step S2107, step S2108, but not limited thereto), and the processor 9101 performs at least one of the other steps (e.g., step S2103, step S2105, but not limited thereto). 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 interchangeable, the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be interchangeable, and the terms receiver, receiving unit, receiver, and receiving circuit may be interchangeable.

[0506] In some embodiments, the communication device 9100 further includes one or more memories 9103 for storing data. Alternatively, all or part of the memories 9103 may be located outside the communication device 9100. In alternative embodiments, the communication device 9100 may include one or more interface circuits 9104. Optionally, the interface circuits 9104 are connected to the memories 9103 and may be configured to receive data from the memories 9103 or other devices, or to send data to the memories 9103 or other devices. For example, the interface circuits 9104 may read data stored in the memories 9103 and send the data to the processor 9101.

[0507] The communication device 9100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 9100 described in the present disclosure is not limited thereto, and the structure of the communication device 9100 may not be limited by FIG. 9A. 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.

[0508] 9B is a schematic diagram of the structure of a chip 9200 according to an embodiment of the present disclosure. If the communication device 9100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 9200 shown in FIG9B , but the present disclosure is not limited thereto.

[0509] The chip 9200 includes one or more processors 9201. The chip 9200 is configured to execute any of the above methods.

[0510] In some embodiments, chip 9200 further includes one or more interface circuits 9202. Terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 9200 further includes one or more memories 9203 for storing data. Alternatively, all or part of memory 9203 may be located external to chip 9200. Optionally, interface circuit 9202 is connected to memory 9203 and may be used to receive data from memory 9203 or other devices, or may be used to send data to memory 9203 or other devices. For example, interface circuit 9202 may read data stored in memory 9203 and send the data to processor 9201.

[0511] In some embodiments, the interface circuit 9202 performs at least one of the communication steps (e.g., steps S2101, S2102, S2104, S2106, S2107, and S2108) of the aforementioned method. For example, the interface circuit 9202 performing the communication steps (e.g., steps S2101, S2102, S2104, S2106, S2107, and S2108) of the aforementioned method means that the interface circuit 9202 performs data exchange between the processor 9201, chip 9200, memory 9203, or a transceiver device. In some embodiments, the processor 9201 performs at least one of the other steps (e.g., steps S2103 and S2105, but not limited thereto).

[0512] 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.

[0513] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 9100, causes the communication device 9100 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.

[0514] The present disclosure also provides a program product, which, when executed by the communication device 9100, enables the communication device 9100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0515] 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 first network device sends a first instruction to the second network device, where the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, where input of the AI ​​model comes from the second network device and at least one third network device.

2. The method according to claim 1, characterized in that The first AI operation includes at least one of the following: Activation operation of the AI ​​model or AI function; Deactivation of the AI ​​model or AI function; Switching operation of the AI ​​model or AI function; Fall back to non-AI mode operation.

3. The method according to claim 1, characterized in that The method further comprises: The first network device sends a second instruction to the third network device, where the second instruction is used to instruct the third network device to perform a second AI operation.

4. The method according to claim 3, characterized in that The second AI operation includes at least one of the following: The third network device sends a first measurement parameter to the second network device; The third network device sends a second measurement parameter to the first network device.

5. The method according to claim 1 or 2, characterized in that The method further comprises: The first network device receives first information sent by the second network device, where the first information is used to determine the first AI operation.

6. The method according to claim 5, characterized in that The first information includes at least one of the following: Performance monitoring indicator information of the AI ​​model; Environmental information of the terminal; Recommended first AI action.

7. The method according to claim 5, characterized in that The method further comprises: The first network device sends auxiliary data to the second network device, where the auxiliary data is used to determine the first information.

8. The method according to claim 1 or 2, characterized in that The method further comprises: The first network device receives auxiliary data sent by the second network device and / or the third network device, where the auxiliary data is used to determine the first AI operation.

9. The method according to claim 7 or 8, characterized in that The auxiliary data includes at least one of the following: The coordinates of the terminal; The terminal's measurement values ​​under the network equipment; Channel information of network devices; The network device includes at least one of the first network device, the second network device and the third network device.

10. A communication method, characterized in that: The method comprises: The second network device receives a first instruction sent by the first network device, where the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, where input of the AI ​​model comes from the second network device and at least one third network device.

11. The method according to claim 10, characterized in that The first AI operation includes at least one of the following: Activation operation of the AI ​​model or AI function; Deactivation of the AI ​​model or AI function; Switching operation of the AI ​​model or AI function; Fall back to non-AI mode operation.

12. The method according to claim 10, characterized in that The method further comprises: The second network device sends a third instruction to the third network device, where the third instruction is used to instruct the third network device to perform a second AI operation.

13. The method according to claim 12, characterized in that The second AI operation includes at least one of the following: The third network device sends a first measurement parameter to the second network device; The third network device sends a second measurement parameter to the first network device.

14. The method according to claim 10 or 11, characterized in that The method further comprises: The second network device sends first information to the second network device, where the first information is used to determine the first AI operation.

15. The method according to claim 14, characterized in that The first information includes at least one of the following: Performance monitoring indicator information of the AI ​​model; Environmental information of the terminal; Recommended first AI action.

16. The method according to claim 14, characterized in that The method further comprises: The second network device receives auxiliary data sent by the first network device and / or the third network device, where the auxiliary data is used to determine the first information.

17. The method according to claim 10 or 11, characterized in that The method further comprises: The second network device sends auxiliary data to the first network device, where the auxiliary data is used to determine the first AI operation.

18. The method according to claim 16 or 17, characterized in that The auxiliary data includes at least one of the following: The coordinates of the terminal; The terminal's measurement values ​​under the network equipment; Channel information of network devices; The network device includes at least one of the first network device, the second network device and the third network device.

19. A communication method, characterized in that: The method comprises: The third network device receives a second instruction, where the second instruction is used to instruct the third network device to perform a second AI operation. The second instruction is determined based on the first AI operation indicated by the first instruction. The first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, where input of the AI ​​model comes from the second network device and the third network device.

20. The method according to claim 19, characterized in that The first AI operation includes at least one of the following: Activation operation of the AI ​​model or AI function; Deactivation of the AI ​​model or AI function; Switching operation of the AI ​​model or AI function; Fall back to non-AI mode operation.

21. The method according to claim 19, wherein The second instruction is sent by the second network device or the first network device.

22. The method according to claim 21, characterized in that The second AI operation includes at least one of the following: The third network device sends a first measurement parameter to the second network device; The third network device sends a second measurement parameter to the first network device.

23. The method according to claim 19, wherein The method further comprises: The third network device sends auxiliary data to the second network device, where the auxiliary data is used to determine the first information.

24. The method according to claim 23, wherein The first information includes at least one of the following: Performance monitoring indicator information of the AI ​​model; Environmental information of the terminal; Recommended first AI action.

25. The method according to claim 19, wherein The method further comprises: The third network device sends auxiliary data to the first network device, where the auxiliary data is used to determine the first AI operation.

26. The method according to any one of claims 23 to 25, characterized in that The auxiliary data includes at least one of the following: The coordinates of the terminal; a measurement value of the terminal under the third network device; Channel information of the third network device.

27. A first network device, characterized in that: include: A transceiver module is used to send a first instruction to a second network device, where the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, where the input of the AI ​​model comes from the second network device and at least one third network device.

28. A second network device, characterized in that: include: A transceiver module is used to receive a first instruction sent by a first network device, where the first instruction is used to instruct the second network device to perform a first AI operation on an artificial intelligence (AI) model, where the input of the AI ​​model comes from the second network device and at least one third network device.

29. A third network device, characterized in that: include: The transceiver module is configured to receive a second instruction, wherein the second instruction is configured to instruct the third network device to perform a second AI operation. The second instruction is determined based on the first AI operation indicated by the first instruction, where the first instruction is used to instruct the second network device to perform the first AI operation on the artificial intelligence AI model, and the input of the AI ​​model comes from the second network device and the third network device.

30. A first network device, characterized in that: include: one or more processors; The first network device is configured to execute the method according to any one of claims 1 to 9.

31. A second network device, characterized in that: include: one or more processors; The second network device is configured to execute the method according to any one of claims 10 to 18.

32. A third network device, characterized in that: include: one or more processors; The third network device is configured to execute the method according to any one of claims 19 to 26.

33. A communication system, characterized in that: The method comprises a first network device, a second network device and a third network device, wherein the first network device is configured to implement the method according to any one of claims 1 to 9, the second network device is configured to implement the method according to any one of claims 10 to 18, and the third network device is configured to implement the method according to any one of claims 19 to 26.

34. 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 method according to any one of claims 1 to 9, the method according to any one of claims 10 to 18, or the method according to any one of claims 19 to 26.

35. A program product, characterized in that include: A computer program, which, when executed by a communication device, causes the communication device to perform the method according to any one of claims 1 to 9, the method according to any one of claims 10 to 18, or the method according to any one of claims 19 to 26.

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