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, wherein the first instruction is used for indicating related information of a first result, the first result is a location result determined by the second network device, AI models used for determining the location result are deployed in the first network device, and inputs of the AI models come from the first network device and at least one third network device. By means of embodiments of the present disclosure, the first network device sends the first instruction to the second network device, so that the second network device determines the related information of the first result, to achieve performance management of the AI models in a centralized deployment scenario of the AI models, 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 indicate relevant information of a first result, the first result being a positioning result determined by the second network device, an artificial intelligence (AI) model for determining the positioning result being deployed in the first network device, and input of the AI ​​model coming from the first 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, the first instruction being used to indicate relevant information of a first result, the first result being a positioning result determined by the second network device, an artificial intelligence (AI) model for determining the positioning result being deployed in the first network device, and input of the AI ​​model coming from the first 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 sent by a first network device, and / or receiving a third instruction sent by a second network device, wherein the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation, an artificial intelligence (AI) model for determining a positioning result is deployed in the first network device, and input of the AI ​​model comes from the first network device and at least one third network device.

[0010] According to a fourth 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, and / or the first network device sending a second instruction to a third network device; upon receiving the first instruction sent by the first network device, the second network device sends a third instruction to the third network device; the third network device receives the second instruction and / or the third instruction, wherein the first instruction is used to indicate relevant information of a first result, the first result being a positioning result determined by the second network device, an artificial intelligence (AI) model for determining the positioning result being deployed in the first network device, the input of the AI ​​model coming from the first network device and at least one third network device, the second instruction being used to instruct the third network device to perform a first operation, and the third instruction being used to instruct the third network device to perform a second operation.

[0011] According to a fifth aspect of an embodiment of the present disclosure, a first network device is proposed, including: a transceiver module, used to send a first instruction to a second network device, the first instruction being used to indicate relevant information of a first result, the first result being a positioning result determined by the second network device, an artificial intelligence (AI) model for determining the positioning result being deployed in the first network device, and the input of the AI ​​model coming from the first 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 first instruction sent by a first network device, the first instruction being used to indicate relevant information of a first result, the first result being a positioning result determined by the second network device, an artificial intelligence (AI) model for determining the positioning result being deployed in the first network device, and input of the AI ​​model coming from the first network device and at least one third network device.

[0013] According to the seventh aspect of an embodiment of the present disclosure, a third network device is proposed, including: a transceiver module, used to receive a second instruction sent by a first network device, and / or receive a third instruction sent by a second network device, the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation, an AI model for determining a positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0014] According to an eighth aspect of an embodiment of the present disclosure, a first network device is proposed, comprising: one or more processors; wherein the first network device is used to execute the first aspect and any one of the communication methods in the first aspect.

[0015] According to a ninth aspect of an embodiment of the present disclosure, a second network device is proposed, comprising: one or more processors; wherein the second network device is configured to execute the second aspect and any one of the communication methods in the second aspect.

[0016] According to a tenth aspect of an embodiment of the present disclosure, a third network device is proposed, comprising: one or more processors; wherein the third network device is configured to execute the third aspect and any one of the communication methods in the third aspect.

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

[0018] According to the twelfth aspect of the 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.

[0019] According to the thirteenth aspect of the embodiment of the present disclosure, a program product is proposed, including: a computer program, which, when executed by a communication device, enables the above-mentioned communication device to execute the method of the first aspect, the second aspect, or the third aspect.

[0020] Through the embodiments of the present disclosure, the first network device sends a first instruction to the second network device, so that the second network device determines relevant information about the first result, 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

[0021] 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.

[0022] FIG1A is a schematic diagram of the architecture of an AI function.

[0023] FIG1B is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure.

[0024] FIG1C is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure.

[0025] FIG1D is a schematic diagram of an AI-based positioning architecture according to an exemplary embodiment of the present disclosure.

[0026] FIG1E is a schematic diagram of a communication system architecture according to an embodiment of the present disclosure.

[0027] FIG2 is a schematic diagram showing an interaction of a communication method according to an embodiment of the present disclosure.

[0028] FIG3A is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0029] FIG3B is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0030] FIG4A is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0031] FIG4B is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0032] FIG4C is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0033] FIG5A is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0034] FIG5B is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0035] FIG6 is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0036] FIG7A is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0037] FIG7B is a flow chart 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 terminal, a network device, 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, where the first instruction is used to indicate relevant information of a first result, where the first result is a positioning result determined by the second network device, and an artificial intelligence AI model for determining the positioning result is deployed in the first network device, where the input of the AI ​​model comes from the first network device and at least one third network device.

[0045] In the above embodiment, a first instruction is sent to a second network device through a first network device, and the first instruction indicates relevant information of the first result, so that the second network device can clarify relevant information of the first result based on the first instruction, for example, whether the first result is obtained based on AI model positioning.

[0046] In combination with some embodiments of the first aspect, in some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein, the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0047] In the above embodiment, by defining the relevant information of the first result, when the first network device sends the first instruction to the second network device, the second network device can clearly understand the relevant information of the first result, and then the second network device can determine how the third network device receives the first result.

[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 the first operation.

[0049] In the above embodiment, through the second instruction, the third network device can perform corresponding operations in the scenario based on AI model positioning or not based on AI model positioning.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the first operation includes any one of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0051] In the above embodiment, by defining the content of the first operation, the third network device can clearly specify the specific operation that needs to be performed in the scenario based on AI model positioning or not based on AI model positioning.

[0052] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: the first network device receives auxiliary information, the auxiliary data is sent by the second network device and / or the third network device, and the auxiliary information is used to determine the first instruction.

[0053] In combination with some embodiments of the first aspect, in some embodiments, the auxiliary information includes at least one of the following: the coordinates of the terminal; the channel measurement value of the terminal under the second network device; the channel measurement value under the third network device; the receiving beam information of the second network device; the receiving beam information of the third network device.

[0054] 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 is used to indicate relevant information of a first result, the first result is a positioning result determined by the second network device, and an artificial intelligence AI model for determining the positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0055] In the above embodiment, a first instruction is sent to a second network device through a first network device, and the first instruction indicates relevant information of the first result, so that the second network device can clarify relevant information of the first result based on the first instruction, for example, whether the first result is obtained based on AI model positioning.

[0056] In combination with some embodiments of the second aspect, in some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein, the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0057] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: upon receiving the first instruction, sending a third instruction to the third network device, wherein the third instruction is used to instruct the third network device to perform the second operation.

[0058] In combination with some embodiments of the second aspect, in some embodiments, the second operation includes any one of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0059] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: sending auxiliary information to the first network device, where the auxiliary information is used to determine the first instruction.

[0060] In combination with some embodiments of the second aspect, in some embodiments, the auxiliary information includes at least one of the following: the coordinates of the terminal; the channel measurement value of the terminal under the second network device; the channel measurement value under the third network device; the receiving beam information of the second network device; the receiving beam information of the third network device.

[0061] In a third aspect, an embodiment of the present disclosure proposes a communication method, comprising: a third network device receiving a second instruction sent by a first network device, and / or receiving a third instruction sent by a second network device, wherein the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation, and an artificial intelligence AI model for determining a positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0062] In the above embodiment, the third network device can clearly specify the specific operations that need to be performed in the scenario based on AI model positioning or not based on AI model positioning through the received second instruction and / or third instruction.

[0063] In combination with some embodiments of the third aspect, in some embodiments, the first operation includes any one of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0064] In combination with some embodiments of the third aspect, in some embodiments, the second operation includes any one of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0065] In combination with some embodiments of the third aspect, in some embodiments, the method also includes: sending auxiliary information to the first network device, the auxiliary information is used to determine a first instruction, the first instruction is used to indicate relevant information of a first result, and the first result is the positioning result determined by the second network device.

[0066] In combination with some embodiments of the third aspect, in some embodiments, the auxiliary information includes at least one of the following: the coordinates of the terminal; the channel measurement value of the terminal under the second network device; the channel measurement value under the third network device; the receiving beam information of the second network device; the receiving beam information of the third network device.

[0067] In combination with some embodiments of the third aspect, in some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein, the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0068] In a fourth 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, and / or the first network device sends a second instruction to a third network device; when the second network device receives the first instruction sent by the first network device, it sends a third instruction to the third network device; the third network device receives the second instruction and / or the third instruction, wherein the first instruction is used to indicate relevant information of a first result, the first result is a positioning result determined by the second network device, an artificial intelligence AI model for determining the positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device, the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation.

[0069] In a fifth aspect, an embodiment of the present disclosure proposes a first network device, comprising: a transceiver module for sending a first instruction to a second network device, wherein the first instruction is used to indicate relevant information of a first result, the first result being a positioning result determined by the second network device, and an artificial intelligence AI model for determining the positioning result being deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0070] In a sixth 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 indicate relevant information of a first result, and the first result is a positioning result determined by the second network device. An artificial intelligence AI model for determining the positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0071] In the seventh aspect, an embodiment of the present disclosure proposes a third network device, including: a transceiver module, used to receive a second instruction sent by a first network device, and / or receive a third instruction sent by a second network device, the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation, and an artificial intelligence AI model for determining a positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0072] In an eighth aspect, a first network device is proposed, comprising: one or more processors; wherein the first network device is used to execute the first aspect and any one of the communication methods in the first aspect.

[0073] In a ninth aspect, a first network device is proposed, comprising: one or more processors; wherein the second network device is used to execute the second aspect and any one of the communication methods in the second aspect.

[0074] In the tenth aspect, a first network device is proposed, comprising: one or more processors; wherein the third network device is used to execute the third aspect and any one of the communication methods in the third aspect.

[0075] In the eleventh aspect, a communication system is proposed, 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.

[0076] In the twelfth aspect, 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.

[0077] In a thirteenth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first aspect, the second aspect, or the third aspect.

[0078] 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.

[0079] In the fifteenth aspect, an embodiment of the present disclosure proposes a program product, including: a computer program, which, when executed by a communication device, enables the above-mentioned communication device to execute the method of the first aspect, the second aspect, or the third aspect.

[0080] It is understandable that the first network device, second network device, third network device, communication system, storage medium, program product, computer program, chip, or chip system involved in each embodiment of the present disclosure 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.

[0081] The present disclosure provides communication methods, devices, equipment, and storage media. In some embodiments, the terms "communication method," "information processing method," and "communication method" are interchangeable; the terms "communication device," "information processing device," and "communication device" are interchangeable; and the terms "information processing system," "communication system," and "communication system" are interchangeable.

[0082] 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.

[0083] In each embodiment of the present disclosure, unless otherwise specified or provided for, the terms and / or descriptions between the embodiments are consistent and may be referenced by each other. The technical environments in different embodiments may be combined to form new embodiments based on their inherent logical relationships.

[0084] 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.

[0085] 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.

[0086] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0087] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0088] 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.

[0089] 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.

[0090] 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 example, if the description object is "information", then the "first information" and "the performance of each AI model" can be the same information or different information, and their contents can be the same or different.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0095] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0096] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or 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)", etc.

[0097] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0098] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0099] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0100] 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.

[0101] At present, the widespread application of 5G technology has brought about tremendous changes in all aspects of people's lives. According to the vision of the International Telecommunication Union (ITU), 5G will penetrate into all areas of future society and build a comprehensive information ecosystem centered on users. Among them, 5G user experience rates can reach 100 megabits per second (Mbit / s) to 1 gigabit (Gbit / s), which can support ultimate business experiences such as mobile virtual reality; 5G peak rates can reach 10Gbit / s to 20Gbit / s, and traffic density can reach 10 megabits per second per square meter (Mbit / s / m 2 ), which can support more than a thousand-fold growth in mobile business traffic in the future; the density of 5G connections can reach 1 million per square meter ( / m 2 ), effectively supporting massive numbers of IoT devices; 5G transmission latency can reach milliseconds, meeting the stringent requirements of the Internet of Vehicles and industrial control; and 5G can support mobile speeds of 500 kilometers per hour (km / h), ensuring a positive user experience in high-speed rail environments. It is conceivable that 5G, as a representative of new infrastructure, will reshape the future information society.

[0102] 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.

[0103] In wireless AI research, application cases of artificial intelligence include: AI-based CSI enhancement; AI-based beam management; and AI-based positioning.

[0104] 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.

[0105] AI-based positioning includes but is not limited to the following five deployment methods:

[0106] For example, AI / machine learning (ML) directly targets:

[0107] 1) UE-based positioning: the model is deployed on the UE side, and AI / ML directly locates;

[0108] 2) UE-assisted LMF-based positioning, the model is deployed on the LMF side, and AI / ML directly locates;

[0109] 3) Next generation radio access network (NG-RAN) node assisted positioning, the model is deployed on the LMF side, and AI / ML directly locates.

[0110] Another example is AI / ML-assisted positioning:

[0111] 4) UE-assisted LMF-based positioning, the model is deployed on the UE side, and AI / ML assists positioning;

[0112] 5) NG-RAN node-assisted positioning: the model is deployed on the gNB side, and AI / ML assists in positioning.

[0113] 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.

[0114] 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:

[0115] 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, PDP0~PDP17) 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.

[0116] 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 to TRP17) correspond to one ML model. The time domain carrier to interference ratio (CIR) (for example, CIR0 to CIR17) measured by each TRP is input into the same ML model, and the output unobserved direct path ToA is passed to the LMF. LMF performs positioning based on unobserved direct path ToA and traditional positioning methods. LMF outputs location information.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] FIG1E is a schematic diagram of a communication system architecture according to an embodiment of the present disclosure.

[0122] 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 devices in the communication system. Although this disclosure uses LMF and gNB as examples, it is not limited thereto.

[0123] In some embodiments, the network device may include at least one of an access network device and a core network device.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1E , or a portion thereof, but are not limited thereto. The entities shown in FIG1E are illustrative only. The communication system may include all or part of the entities shown in FIG1E , or may include other entities outside of FIG1E . The number and form of the entities are arbitrary. 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.

[0130] 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).

[0131] FIG2 is a schematic diagram of an interaction of a communication method according to an embodiment of the present disclosure. As shown in FIG2 , an embodiment of the present disclosure relates to a communication method for use in a communication system 100. For ease of understanding, in the following embodiments, each network device mentioned in the communication system 100 (including, for example, a first network device 101, a second network device 102, and a third network device 103) will be exemplified based on the following: the first network device 101 is an access network device, the second network device 102 is a core network device, and the third network device 103 is an access network device (including, for example, the third network device 103 is an access network device different from the first network device 101). In this case, the above method includes:

[0132] Step S2101 : The first network device 101 sends a first instruction to the second network device 102 .

[0133] In some embodiments, the second network device 102 receives a first instruction sent from the first network device 101 .

[0134] It should be noted that, to facilitate the understanding of the solution, the following embodiments will be described exemplarily through a positioning scenario based on an AI model. In this scenario, the first instruction may be, for example, a New Radio Positioning Protocol A (NR Positioning Protocol A, NRPPa) signaling, and / or Xn signaling. For other scenarios, the methods mentioned in the embodiments of the present disclosure may also be applicable. In the application of the embodiments of the present disclosure in other scenarios, the usefulness of some instructions may be adaptively adjusted or replaced.

[0135] For example, in a scenario where positioning is performed based on an AI model, the first network device 101 is an access network device that deploys an artificial intelligence (AI) model. The first network device 101 instructs the second network device whether to use the AI ​​model for positioning through a first instruction. The second network device 102 determines whether to perform positioning based on the AI ​​model based on the relevant information about the positioning result indicated by the first instruction sent by the first network device 101. The AI ​​model deployed by the first network device 101 is an AI model for determining the positioning result (including, for example, determining the positioning result of the terminal), and the second network device 102 is a core network device that has deployed a location management function (LMF) module.

[0136] It should be noted that the AI ​​model deployed for the first network device 101 can be used to perform corresponding calculations or reasoning (including, for example, taking the scenario based on AI model positioning as an example, the AI ​​model can calculate or reason about the positioning results of the terminal). The input of the AI ​​model can come from the first network device 101 and / or multiple third network devices 103.

[0137] The first network device 101 instructs the second network device 102 through the first instruction whether to perform positioning based on the AI ​​model, including the following two aspects: -A) positioning based on the AI ​​model; -B) not positioning based on the AI ​​model.

[0138] It is understandable that the indication of whether positioning is based on the AI ​​model can be reflected by different information contents in the first instruction. For example: if the first instruction sent indicates relevant information about the prediction results or reasoning results based on the AI ​​model, then it can be judged that positioning is based on the AI ​​model. If the first instruction sent indicates that there is no relevant information based on the prediction results or reasoning results of the AI ​​model (or the first instruction does not indicate relevant information based on the prediction results or reasoning results of the AI ​​model), then it can be judged that positioning is not based on the AI ​​model.

[0139] Based on this, the second network device 102 can determine whether positioning is based on the AI ​​model from the specific content of the first result in the relevant information about the first result indicated by the first instruction sent by the first network device 101, wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model (including, for example, the relevant information of the first result is carried in the first instruction).

[0140] Exemplarily, in a positioning scenario, the first result is, for example, a positioning result determined based on the output information of an AI model, or the first result is not a positioning result determined based on the output information of an AI model.

[0141] It is understandable that, when the first result is determined based on the output information of the AI ​​model, the first network device 101 sends a first instruction to the second network device 102, and the content indicated by the first instruction should be relevant information about positioning based on the AI ​​model. When the first result is not determined based on the output information of the AI ​​model, the first network device 101 sends a first instruction to the second network device 102, and the content indicated by the first instruction is not to perform positioning based on the AI ​​model.

[0142] Therefore, the first network device 101 can instruct the second network device 102 through the first instruction whether to perform positioning based on the AI ​​model.

[0143] In some embodiments, the relevant information about the first result is the relevant information about the first result being sent.

[0144] Optionally, the relevant information of the first result is the relevant information of the first result sent to the third network device 103 .

[0145] It is understandable that in the communication system 100, the first result needs to be sent to the third network device 103 so that the third network device 103 can perform subsequent corresponding operations. For example, when the first result is determined based on the output information of the AI ​​model, the first result is sent to the third network device 103, and the third network device 103 can subsequently provide the first network device 101 with relevant information for AI model input. When the first result is not determined based on the output information of the AI ​​model, the first result is sent to the third network device 103, and the third network device 103 may not subsequently provide the first network device 101 with relevant information for AI model input to save resource overhead. Therefore, it is necessary to clarify how to send the first result to the third network device 103.

[0146] In some embodiments, the manner in which the first result is sent to the third network device 103 is determined by using the relevant information of the first result.

[0147] In some embodiments, the relevant information of the first result may include any one of the following -a1) to -a2):

[0148] -a1) The first network device 101 sends the first result to the third network device 103 .

[0149] a2) The first network device 101 does not send the first result to the third network device 103 , and the first result is sent to the third network device 103 by the second network device 102 .

[0150] It is understandable that a1) can be used to send the first result directly from the first network device 101 to the third network device 103. By informing the second network device 102 of -a1), the second network device 102 does not need to send the first result to the third network device 103, thereby avoiding waste of resources.

[0151] Through -a2), the first result can be sent from the second network device 102 to the third network device 103. Notifying the second network device 102 of -a2) can make the second network device 102 clear that it needs to send the first result to the third network device 103.

[0152] In some embodiments, the first instruction may be determined through auxiliary information sent by the second network device 102 and / or the third network device 103 , wherein the auxiliary information may be used to determine the first instruction.

[0153] It can be understood that the auxiliary information sent by the second network device 102 can be measured by the second network device 102, or measured by other network devices (including, for example, other network devices other than the first network device 101, the second network device 102 and the third network device 103) and sent to the second network device 102. The auxiliary information sent by the third network device 103 can be measured by the third network device 103, or sent by other network devices (including, for example, other network devices other than the first network device 101, the second network device 102 and the third network device 103) to the third network device 103.

[0154] Optionally, the auxiliary information includes at least one of the following: the coordinates of the terminal, the channel measurement value of the terminal under the second network device 102, the channel measurement value under the third network device 103, the receiving beam information of the second network device 102, and the receiving beam information of the third network device 103.

[0155] It should be noted that, for the auxiliary information obtained by the second network device 102, compared with the auxiliary information obtained by the third network device 103, the categories of the two can be the same or different (including, for example: both can be channel measurement values, or one of them can be a channel measurement value and the other can be receiving beam information, etc.).

[0156] It should be noted that auxiliary information can also be used to calculate performance indicators, thereby monitoring whether the input of the AI ​​model is consistent with the distribution trend of the model input during training, or monitoring whether the output of the AI ​​model is consistent with the distribution trend of the model output during training, or monitoring the difference between the output of the AI ​​model and the ideal model output, etc.

[0157] For example, the first network device 101 determines performance indicators based on the auxiliary information 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.

[0158] 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.

[0159] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0160] 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.

[0161] 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.

[0162] Step S2102 : The second network device 102 determines relevant information of the first result.

[0163] In some embodiments, the second network device 102 determines relevant information about the first result based on the received first instruction.

[0164] Optionally, the second network device 102 determines relevant information of the first result to determine whether to send the first result to the third network device 103 .

[0165] It is understandable that the relevant information of the first result has been clearly explained in the above-mentioned step S2101 embodiment. For details, please refer to the relevant description and will not be repeated here.

[0166] Step S2103 : The first network device 101 sends a second instruction to the third network device 103 .

[0167] In some embodiments, the third network device 103 receives the second instruction sent from the first network device 101 .

[0168] In some embodiments, the second instruction is used to instruct the third network device 103 to perform the first operation.

[0169] It is understandable that, when it is clear that the first network device 101 has indicated through the first instruction whether to perform positioning based on the AI ​​model, based on the relevant instructions in step S2101, the third network device 103 needs to perform corresponding operations to correspond to whether positioning is based on the AI ​​model or not. The specific instructions for the third network device 103 to perform the corresponding operations can be provided by the first network device 101.

[0170] That is, the first network device 101 needs to instruct the third network device 103 to perform corresponding operations through the second instruction.

[0171] In some embodiments, the first operation includes any one of -b1) to -b4):

[0172] -b1) Sending a first measurement parameter to the first network device 101 .

[0173] -b2) Do not send the first measurement parameter to the first network device 101 .

[0174] -b3) receiving a first result sent by the first network device 101, where the first result is a positioning result determined by the first network device 101 based on the first measurement parameter.

[0175] -b4) receiving a second result determined by the second network device 102 , where the second result is determined by the second network device 102 based on the second measurement parameter sent by the third network device 103 .

[0176] It should be noted that in -b1) to -b4), the first measurement parameter and the second measurement parameter are measured by the third network device 103, and the first result is predicted by the AI ​​model based on the first measurement parameter, wherein the second measurement parameter is not equivalent to the first measurement parameter.

[0177] It is understandable that, when it is determined that positioning is performed based on the AI ​​model, the third network device 103 executes -b1) and / or -b3). When it is determined that positioning is not performed based on the AI ​​model, the third network device 103 executes -b2) and / or -b4).

[0178] In some embodiments, the first measurement parameter includes at least one of the following: time information, power information, and phase information of the measurement result.

[0179] In some embodiments, the first result is a result predicted by the AI ​​model based on the first measurement parameter.

[0180] Optionally, the first result includes at least one of the following: channel arrival time, arrival angle, departure angle, receiving power, line of sight (LOS) information, non-line of sight (NLOS) information, propagation path information, etc.

[0181] In some embodiments, the second result is a result calculated by the LMF based on the second measurement parameter.

[0182] Optionally, the second result includes at least one of the following: arrival time, arrival angle, departure angle, receiving power, LOS information, NLOS information, propagation path information, etc. of the channel.

[0183] Step S2104 : The second network device 102 sends a third instruction to the third network device 103 .

[0184] In some embodiments, the third network device 103 receives a third instruction from the second network device 102 .

[0185] In some embodiments, upon receiving the first instruction, the second network device 102 sends a third instruction to the third network device 103 .

[0186] It is understandable that in determining whether to perform positioning based on AI, a third network device is required to perform corresponding operations.

[0187] It is understandable that, when it is clear that the first network device 101 has indicated through the first instruction whether to perform positioning based on the AI ​​model, based on the relevant instructions in step S2101, the third network device 103 needs to perform corresponding operations to correspond to whether positioning is based on the AI ​​model or not. The specific instructions for the third network device 103 to perform the corresponding operations can be provided by the second network device 102.

[0188] That is, the second network device 102 is required to instruct the third network device 103 to perform corresponding operations through the third instruction.

[0189] In some embodiments, the second operation includes any one of -c1) to -c4):

[0190] - c1) Sending a first measurement parameter to the first network device 101 .

[0191] -c2) The first measurement parameter is not sent to the first network device 101 .

[0192] -c3) receiving a first result sent by the first network device 101, where the first result is a positioning result determined by the first network device 101 based on the first measurement parameter.

[0193] -b4) receiving a second result determined by the second network device 102 , where the second result is determined by the second network device 102 based on the second measurement parameter sent by the third network device 103 .

[0194] It should be noted that in -c1) to -c4), the first measurement parameter and the second measurement parameter are measured by the third network device 103, and the first result is predicted by the AI ​​model based on the first measurement parameter, wherein the second measurement parameter is not equal to the first measurement parameter.

[0195] It is understandable that, when it is determined that positioning is performed based on the AI ​​model, the third network device 103 executes -c1) and / or -c3). When it is determined that positioning is not performed based on the AI ​​model, the third network device 103 executes -c2) and / or -c4).

[0196] In some embodiments, the first measurement parameter includes at least one of the following: time information, power information, and phase information of the measurement result.

[0197] In some embodiments, the first result is a result predicted by the AI ​​model based on the first measurement parameter.

[0198] Optionally, the first result includes at least one of the following: arrival time, arrival angle, departure angle, receiving power, LOS information, NLOS information, propagation path information, etc. of the channel.

[0199] In some embodiments, the second result is a result calculated by the LMF based on the second measurement parameter.

[0200] Optionally, the second result includes at least one of the following: arrival time, arrival angle, departure angle, receiving power, LOS information, NLOS information, propagation path information, etc. of the channel.

[0201] It should be noted that the specific operation performed by third network device 103 can be performed as described in the relevant embodiment of step S2103, where first network device 101 directly notifies third network device 103. Alternatively, as described in the relevant embodiment of step S2104, second network device 102 notifies third network device 103. Either or both can be performed simultaneously. That is, either step S2103 or step S2104 can be performed simultaneously.

[0202] The communication method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2104. For example, step S2101 can be implemented as an independent embodiment, step S2103 can be implemented as an independent embodiment, step S2102 can be implemented as an independent embodiment, step S2104 can be implemented as an independent embodiment, step S2101 + step S2103 can be implemented as an independent embodiment, step S2101 + step S2104 can be implemented as an independent embodiment, step S2101 + step S2103 + step S2104 can be implemented as an independent embodiment, and step S2101 + step S2102 + step S2103 + step S2104 can be implemented as an independent embodiment, but the present invention is not limited thereto.

[0203] In some embodiments, steps S2101 and S2102 may be executed in an exchanged order or simultaneously, and steps S2103 and S2104 may be executed in an exchanged order or simultaneously.

[0204] In some embodiments, step S2101, step S2102, and step S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0205] In some embodiments, step S2102, step S2102, and step S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0206] In some embodiments, step S2101, step S2103, and step S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0207] In some embodiments, step S2102, step S2103, and step S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0208] 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:

[0209] Step S3101: Send a first instruction.

[0210] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0211] Step S3102: Send a second instruction.

[0212] The optional implementation of step S3102 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0213] The communication method involved in the embodiments of the present disclosure may include at least one of steps S3101 and S3102. For example, step S3101 may be implemented as an independent embodiment, step S3102 may be implemented as an independent embodiment, and step S3101 + step S3102 may be implemented as independent embodiments, but the present disclosure is not limited thereto.

[0214] In some embodiments, steps S3101 and S3102 may be executed in an interchanged order or simultaneously.

[0215] In some embodiments, step S3102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0216] 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, and the method includes:

[0217] Step S3201: Send a first instruction.

[0218] The optional implementation of step S3201 can refer to the optional implementation of steps S2101 to S2104 in Figure 2, steps S3101 to S3102 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0219] In some embodiments, the first instruction is used to indicate relevant information of the first result, where the first result is a positioning result determined by the second network device. An AI model for determining the positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0220] In some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0221] In some embodiments, the method further includes: the first network device sending a second instruction to the third network device, where the second instruction is used to instruct the third network device to perform the first operation.

[0222] In some embodiments, the first operation includes any one of the following: sending a first measurement parameter to a first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by a second network device, the second result being determined by the second network device based on a second measurement parameter sent by a third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0223] In some embodiments, the method further includes: the first network device receiving auxiliary information, the auxiliary data is sent by the second network device and / or the third network device, and the auxiliary information is used to determine the first instruction.

[0224] In some embodiments, the auxiliary information includes at least one of the following: coordinates of the terminal; channel measurement values ​​of the terminal under the second network device; channel measurement values ​​under the third network device; receiving beam information of the second network device; receiving beam information of the third network device.

[0225] FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0226] Step S4101, obtain the first instruction.

[0227] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0228] In some embodiments, the second network device 102 receives the first instruction sent by the first network device 101 , but is not limited thereto and may also receive the first instruction sent by other entities.

[0229] In some embodiments, the second network device 102 obtains a first instruction specified by the protocol.

[0230] In some embodiments, the second network device 102 obtains the first instruction from an upper layer(s).

[0231] In some embodiments, the second network device 102 performs processing to obtain the first instruction.

[0232] In some embodiments, step S4101 is omitted, and the second network device 102 autonomously implements the function indicated by the first instruction, or the above function is default or acquiescent.

[0233] Step S4102: Determine relevant information of the first result.

[0234] The optional implementation of step S4102 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0235] Step S4103: Send a third instruction.

[0236] The optional implementation of step S4103 can refer to the optional implementation of step S2104 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0237] The communication method involved in the embodiments of the present disclosure may include at least one of steps S4101 to S4103. For example, step S4101 may be implemented as an independent embodiment, step S4102 may be implemented as an independent embodiment, step S4103 may be implemented as an independent embodiment, step S4101 + step S4102 may be implemented as an independent embodiment, and step S4101 + step S4102 + step S4103 may be implemented as independent embodiments, but the present invention is not limited thereto.

[0238] In some embodiments, steps S4101 and S4103 may be executed in an interchanged order or simultaneously.

[0239] In some embodiments, step S4101 and step S4102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0240] In some embodiments, step S4101 and step S4103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0241] In some embodiments, step S4102 and step S4103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0242] 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:

[0243] Step S4201, obtain the first instruction.

[0244] The optional implementation of step S4201 can refer to the optional implementation of step S2101 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0245] Step S4202: Send a third instruction.

[0246] The optional implementation of step S4202 can refer to the optional implementation of step S2104 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0247] The communication method involved in the embodiments of the present disclosure may include at least one of steps S4201 and S4202. For example, step S4201 may be implemented as an independent embodiment, step S4202 may be implemented as an independent embodiment, and step S4201 + step S4202 may be implemented as independent embodiments, but the present invention is not limited thereto.

[0248] In some embodiments, steps S4201 and S4202 may be executed in an interchanged order or simultaneously.

[0249] In some embodiments, step S4202 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0250] FIG4C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4C , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0251] Step S4301: Get the first instruction.

[0252] The optional implementation of step S4301 can be found in steps S2101 to S2104 of Figure 2, steps S4101 to S4103 of Figure 4A, and the optional implementation of steps S4201 to S4202 of Figure 4B, as well as other related parts in the embodiments involved in Figures 2, 4A and 4B, which will not be repeated here.

[0253] In some embodiments, the first result is a positioning result determined by the second network device, an AI model for determining the positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0254] In some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0255] In some embodiments, the method further includes: upon receiving the first instruction, sending a third instruction to the third network device, where the third instruction is used to instruct the third network device to perform the second operation.

[0256] In some embodiments, the second operation includes any of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0257] In some embodiments, the method further includes: upon receiving the first instruction, sending a third instruction to the third network device, where the third instruction is used to instruct the third network device to perform the second operation.

[0258] In some embodiments, the second operation includes any of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0259] In some embodiments, auxiliary information is sent to the first network device, and the auxiliary information is used to determine the first instruction.

[0260] In some embodiments, the auxiliary information includes at least one of the following: coordinates of the terminal; channel measurement values ​​of the terminal under the second network device; channel measurement values ​​under the third network device; receiving beam information of the second network device; receiving beam information of the third network device.

[0261] 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:

[0262] Step S5101, obtain the second instruction.

[0263] In some embodiments, the third network device 103 receives the second instruction sent by the first network device 101 , but is not limited thereto and may also receive the second instruction sent by other entities.

[0264] In some embodiments, the third network device 103 obtains a second instruction specified by the protocol.

[0265] In some embodiments, the third network device 103 obtains the second instruction from an upper layer(s).

[0266] In some embodiments, the third network device 103 performs processing to obtain the second instruction.

[0267] In some embodiments, step S4101 is omitted, and the third network device 103 autonomously implements the function indicated by the second instruction, or the above function is default or acquiescent.

[0268] Step S5102: Obtain a third instruction.

[0269] In some embodiments, the third network device 103 receives the third instruction sent by the second network device 102, but is not limited thereto and may also receive the third instruction sent by other entities.

[0270] In some embodiments, the third network device 103 obtains a third instruction specified by the protocol.

[0271] In some embodiments, the third network device 103 obtains the third instruction from an upper layer(s).

[0272] In some embodiments, the third network device 103 performs processing to obtain a third instruction.

[0273] In some embodiments, step S5101 is omitted, and the third network device 103 autonomously implements the function indicated by the third instruction, or the above function is default or acquiescent.

[0274] 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:

[0275] Step S5201: Obtain a second instruction, and / or obtain a third instruction.

[0276] The optional implementation of step S5201 can refer to step S2103 and step S2104 in Figure 2, step S5101 in Figure 5A, the optional implementation of step S4102, and other related parts in the embodiments involved in Figures 2 and 5A, which will not be repeated here.

[0277] In some embodiments, the second instruction is used to instruct the third network device to perform the first operation, and the third instruction is used to instruct the third network device to perform the second operation. The first network device is deployed with an AI model for determining the positioning result, and the input of the AI ​​model comes from the first network device and at least one third network device.

[0278] In some embodiments, the first operation includes any one of the following: sending a first measurement parameter to a first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by a second network device, the second result being determined by the second network device based on a second measurement parameter sent by a third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0279] In some embodiments, the second operation includes any of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0280] In some embodiments, the method further includes: sending auxiliary information to the first network device, the auxiliary information is used to determine a first instruction, the first instruction is used to indicate relevant information of a first result, and the first result is a positioning result determined by the second network device.

[0281] In some embodiments, the auxiliary information includes at least one of the following: coordinates of the terminal; channel measurement values ​​of the terminal under the second network device; channel measurement values ​​under the third network device; receiving beam information of the second network device; receiving beam information of the third network device.

[0282] In some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0283] In some embodiments, the above method may also include the methods described in the above embodiments such as the communication system side, the terminal side, and the network device side, which will not be repeated here.

[0284] FIG6 is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0285] Step S6101: Send a first instruction.

[0286] The optional implementation of step S6101 can refer to the optional implementation of step S2101 and step S2102 in Figure 2, as well as other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0287] Step S6102: Send a second instruction.

[0288] The optional implementation of step S6101 can refer to the optional implementation of step S2103 in Figure 2, as well as other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0289] Step S6103: Send a third instruction.

[0290] The optional implementation of step S6103 can refer to the optional implementation of step S2104 in Figure 2, as well as other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0291] In some embodiments, the first network device sends a first instruction to the second network device, and / or the first network device sends a second instruction to the third network device; when the second network device receives the first instruction sent by the first network device, it sends a third instruction to the third network device; the third network device receives the second instruction and / or the third instruction, wherein the first instruction is used to indicate relevant information of the first result, the first result is the positioning result determined by the second network device, the first network device is deployed with an AI model for determining the positioning result, the input of the AI ​​model comes from the first network device and at least one third network device, the second instruction is used to instruct the third network device to perform the first operation, and the third instruction is used to instruct the third network device to perform the second operation.

[0292] In some embodiments, the present disclosure also proposes a communication method for determining how to perform performance management in a centralized deployment mode based on AI positioning.

[0293] For ease of understanding, the embodiments of the present disclosure will provide an exemplary illustration of the communication method based on Figures 7A and 7B. The communication system involved in the communication method includes a variety of different network devices, which are described as a first network device, a second network device, and a third network device for ease of description. It should be noted that in the embodiments related to Figures 7A and 7B, the first network device includes, for example, an access network device (inference gNB) that deploys an AI model for inference, the second network device includes, for example, a core network device with an LMF module, and the third network device includes, for example, an access network device that is different from the first network device (gNB that does not deploy an AI model, or a gNB that is not used for inference, etc.).

[0294] FIG7A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG7A , the embodiment of the present disclosure includes the following steps:

[0295] In step S7101, the third network device sends data for performance metric calculation to the first network device.

[0296] Optionally, the data sent by the third device for performance indicator calculation may be data measured by the third device, for example, including at least one of the following: the coordinates of the terminal; the measurement value of the terminal under the third network device; the channel information of the third network device; and the beam information received by the third network device.

[0297] The coordinates of the terminal may be relative coordinates of the terminal and the third network device.

[0298] 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.

[0299] Optionally, the data sent by the third network device for performance indicator calculation may also include input data for the AI ​​model or output data for the AI ​​model.

[0300] It is understood that the first network device calculates performance indicator information using the data used for performance indicator calculation. Performance monitoring indicator information may include, for example, at least one of the following: the distribution of model input information (e.g., mean, variance); the distribution of model output information; the mean, variance, or difference from a threshold of the model output information; and information about the terminal's environment, such as channel quality and the scenario (indoor, outdoor, factory, office). Performance monitoring indicators can be used to take corresponding actions on the AI ​​model, including activating the AI ​​model, deactivating the AI ​​model, switching the AI ​​model, and using non-AI operations. For example, if the AI ​​model matches, the AI ​​model is activated; if not, the AI ​​model is deactivated. This disclosure does not provide a complete list of examples, but is not intended to be limiting. For example, if the distribution trend of the AI ​​model input in the performance monitoring indicator matches the distribution trend of the model input during training, the AI ​​model can be activated. Conversely, if the distribution trend of the AI ​​model input in the performance monitoring indicator matches the distribution trend of the model input during training, the AI ​​model can be deactivated, switched, etc. This disclosure is provided for illustrative purposes only and is not intended to be limiting. For example, you can take corresponding measures on the AI ​​model by checking whether the output of the AI ​​model in the performance monitoring indicators is consistent with the distribution trend of the model output during training. Or you can take corresponding measures on the AI ​​model by checking whether the output of the AI ​​model in the performance monitoring indicators is consistent with the ideal model output. Based on the monitoring results, you can take corresponding measures on the AI ​​model.

[0301] In step S7102, the second network device sends data for performance indicator calculation to the first network device.

[0302] Optionally, the data sent by the second device for calculating the performance indicator may be data measured by the second device, for example, including at least one of the following: coordinates of the terminal; measurement values ​​of the terminal under the second network device; channel information of the second network device; and beam information received by the second network device.

[0303] The coordinates of the terminal may be relative coordinates of the terminal and the second network device.

[0304] The measurement value of the terminal under the second network device may also be referred to as a channel measurement value of the second network device for the terminal.

[0305] Optionally, the data sent by the second network device for performance indicator calculation may also include input data for the AI ​​model or output data for the AI ​​model.

[0306] It is understood that the first network device calculates the performance indicator using the data used for performance indicator calculation, thereby monitoring whether the AI ​​model input is consistent with the distribution trend of the model input during training, or whether the AI ​​model output is consistent with the distribution trend of the model output during training, or monitoring the difference between the AI ​​model output and the ideal model output, etc. Based on the monitoring results, corresponding measures can be taken for the AI ​​model, including starting the AI ​​model, shutting down the AI ​​model, switching the AI ​​model, using non-AI operations, etc.

[0307] It should be noted that step S7101 and step S7102 can be executed in a reversed order or simultaneously.

[0308] In step S7103, the first network device performs performance metric calculation.

[0309] In some embodiments, the first network device performs performance indicator calculation based on data used for performance indicator calculation.

[0310] Exemplarily, the first network device determines the first information based on data used for performance metric calculation. The first information may be, for example, a performance metric.

[0311] The first information includes, for example, at least one of the following: performance monitoring indicator information: such as the distribution of model input information (e.g., mean, variance), the distribution of model output information, the mean, variance, or difference from a certain threshold of the model output information; information about the terminal's environment: such as channel quality, the scenario (indoor, outdoor, factory, office); and recommended AI model operations. The AI ​​model operations may include, for example, activation of the AI ​​model / function, deactivation of the AI ​​model / function, switching of the AI ​​model / function, and fallback to a non-AI operating mode.

[0312] In step S7104, the first network device performs a life cycle management (LCM) decision.

[0313] In some embodiments, LCM decisions can be used to manage the lifecycle of AI models. Examples of AI model lifecycle management include activating, deactivating, and switching AI models. That is, LCM decisions can be used to activate, deactivate, and switch AI models.

[0314] It is understandable that during the application of AI models, lifecycle management of the AI ​​models is required, for example, through the results of LCM decisions.

[0315] In some embodiments, the first network device determines the operation of the AI ​​model based on the LCM decision (that is, to achieve management of the AI ​​model life cycle). Exemplarily, the first network device obtains an LCM decision result based on the LCM decision, and the LCM decision result includes the operation of the AI ​​model (for example, based on the LCM decision result, the corresponding AI model operation is performed on the AI ​​model to achieve management of the AI ​​model life cycle), wherein the operation of the AI ​​model may include at least one of the following: activation of the AI ​​model / function, deactivation of the AI ​​model / function, switching of the AI ​​model / function, and fallback to a non-AI operation mode.

[0316] In step S7105, the first network device sends a first instruction to the second network device. The first instruction may be an LCM command from the second network device for the first network device. The first network device may be, for example, a gNBx. That is, the first instruction may be an LCM command for gNBx (e.g., New Radio Positioning Protocol A (NR Positioning Protocol A, NRPPa) and / or Xn signaling).

[0317] In some embodiments, the second network device determines one of the following information (A1) to (A3) based on the first instruction. That is, the first instruction is used to indicate one of the following information (A1) to (A3):

[0318] (A1) The first network device sends the second result of the second network device to the third network device.

[0319] (A2) The first network device does not send the second result of the second network device to the third network device. The second result is sent by the second network device to the third network device.

[0320] (A3) The second result sent by the first network device or the second network device to the third network device is generated based on AI or is not generated based on AI.

[0321] In step S7106, the first network device sends a second instruction to the third network device. The second instruction may be an LCM command for the third network device, where the third network device may be, for example, gNBy. That is, the second instruction may be an LCM command for gNBy (LCM command for gNBy) (e.g., may include NRPPa and / or Xn signaling).

[0322] In some embodiments, the method further includes: the first network device sends a second instruction to the third network device, and the third network device determines to perform a corresponding first operation based on the second instruction, that is, the second instruction is used to indicate the first operation. In some embodiments, the first operation includes, for example, any one of the following (B1) to (B4):

[0323] (B1) Sending a first measurement parameter to a first network device.

[0324] (B2) The first measurement parameter is not sent to the first network device.

[0325] (B3) Receive the first result sent by the first network device.

[0326] (B4) The second network device calculates a second result based on the measurement information, and sends the second result to the third network device.

[0327] The first measurement parameter includes at least one of the following: time information, power information, and phase information of the measurement result.

[0328] The first result is a result determined by the first network device based on the first measurement parameter. The first result includes at least one of the following: arrival time, arrival angle, departure angle, received power, LOS information, NLOS information, and propagation path information of the base channel.

[0329] The second result is calculated by the second network device based on the measurement information. The measurement information may be the same as or different from the first measurement parameter, and the two are not completely identical.

[0330] The second result includes at least one of the following: arrival time, arrival angle, departure angle, receiving power, LOS information, NLOS information, and propagation path information of the base channel.

[0331] In some embodiments,

[0332] FIG7B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG7B , the embodiment of the present disclosure includes the following steps:

[0333] In step S7201, the third network device sends data for performance indicator calculation to the first network device.

[0334] For the relevant description of step S7201, please refer to the relevant embodiment description corresponding to step S7101, which will not be repeated here.

[0335] In step S7202, the second network device sends data for performance indicator calculation to the first network device.

[0336] For the relevant description of step S7202, please refer to the relevant embodiment description corresponding to step S7102, which will not be repeated here.

[0337] In step S7203, the first network device calculates the performance indicator.

[0338] For the relevant description of step S7203, please refer to the relevant embodiment description corresponding to step S7103, which will not be repeated here.

[0339] In step S7204, the first network device makes an LCM decision.

[0340] For the relevant description of step S7204, please refer to the relevant embodiment description corresponding to step S7104, which will not be repeated here.

[0341] In step S7205 , the first network device sends a first instruction to the second network device.

[0342] Optionally, the first instruction may be an LCM command (LCM command for AI operation) sent to the second network device to instruct an AI operation. The AI ​​operation may, for example, be to activate an AI model, deactivate an AI model, or switch an AI model. That is, the LCM decision may be used to activate, deactivate, or switch an AI model.

[0343] For the relevant description of step S7205, please refer to the relevant embodiment description corresponding to step S7105, which will not be repeated here.

[0344] In step S7206, the second network device sends a third instruction to the third network device. The third instruction may be an LCM instruction for the third network device (for example, may include NRPPa and / or Xn signaling).

[0345] In some embodiments, the second network device sends a third instruction to the third network device, and the third network device determines an operation indicated by the third instruction based on the third instruction, where the third instruction is used to indicate any one of the following operations (C1) to (C4):

[0346] (C1) Sending a first measurement parameter to a first network device.

[0347] (C2) The first measurement parameter is not sent to the first network device.

[0348] (C3) Receive the first result sent by the first network device.

[0349] (C4) The second network device calculates a second result based on the measurement information, and sends the second result to the third network device.

[0350] The first measurement parameter includes at least one of the following: time information, power information, and phase information of the measurement result.

[0351] The first result is a result determined by the first network device based on the first measurement parameter. The first result includes at least one of the following: arrival time, arrival angle, departure angle, received power, LOS information, NLOS information, and propagation path information of the base channel.

[0352] The second result is calculated by the second network device based on the measurement information. The measurement information may be the same as or different from the first measurement parameter, and the two are not completely identical.

[0353] The second result includes at least one of the following: arrival time, arrival angle, departure angle, receiving power, LOS information, NLOS information, and propagation path information of the base channel.

[0354] In some embodiments, the first network device further receives auxiliary data sent from the second network device and the third network device, where the auxiliary data is used to assist the first network device in determining the content of the first instruction.

[0355] Exemplarily, the auxiliary data includes coordinate information of the terminal, a channel measurement value of the third network device for the terminal, and receiving beam information of the third network device.

[0356] Optionally, the auxiliary data sent by the second network device and the third network device may be the same or different.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] Figure 8A is a structural diagram of the first network device proposed in an embodiment of the present disclosure. As shown in Figure 8A, the first network device 8100 may include: a transceiver module 8101, for sending a first instruction to the second network device, the first instruction is used to indicate relevant information of the first result, the first result is the positioning result determined by the second network device, and the first network device is deployed with an AI model for determining the positioning result, and the input of the AI ​​model comes from the first network device and at least one third network device. Optionally, the above-mentioned transceiver module 8101 is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2101, step S2103 but not limited to this) performed by the first network device 101 in any of the above methods, which will not be repeated here.

[0361] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0362] In some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0363] In some embodiments, the transceiver module 8101 is further configured to send a second instruction to the third network device, where the second instruction is configured to instruct the third network device to perform the first operation.

[0364] In some embodiments, the first operation includes any one of the following: sending a first measurement parameter to a first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by a second network device, the second result being determined by the second network device based on a second measurement parameter sent by a third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0365] In some embodiments, the transceiver module 8101 is further configured to receive auxiliary information, where the auxiliary data is sent by the second network device and / or the third network device, and the auxiliary information is used to determine the first instruction.

[0366] In some embodiments, the auxiliary information includes at least one of the following: coordinates of the terminal; channel measurement values ​​of the terminal under the second network device; channel measurement values ​​under the third network device; receiving beam information of the second network device; receiving beam information of the third network device.

[0367] Figure 8B is a structural diagram of the second network device proposed in an embodiment of the present disclosure. As shown in Figure 8B, the second network device 8200 may include: a transceiver module 8201, for receiving a first instruction sent by the first network device, the first instruction is used to indicate relevant information of the first result, the first result is a positioning result determined by the second network device, and an AI model for determining the positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device. Optionally, the above-mentioned transceiver module 8201 is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2101, step S2104 but not limited to this) performed by the second network device 102 in any of the above methods, and will not be repeated here.

[0368] In some embodiments, the second network device 8200 may further include a processing module 8202 configured to determine relevant information of the first result based on the first instruction.

[0369] In some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0370] In some embodiments, the transceiver module 8201 is further configured to send a third instruction to a third network device upon receiving the first instruction, where the third instruction is configured to instruct the third network device to perform the second operation.

[0371] In some embodiments, the second operation includes any of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0372] In some embodiments, the transceiver module 8201 is further configured to send auxiliary information to the first network device, where the auxiliary information is used to determine the first instruction.

[0373] In some embodiments, the auxiliary information includes at least one of the following: coordinates of the terminal; channel measurement values ​​of the terminal under the second network device; channel measurement values ​​under the third network device; receiving beam information of the second network device; receiving beam information of the third network device.

[0374] Figure 8C is a schematic diagram of the structure of the third network device proposed in an embodiment of the present disclosure. As shown in Figure 8C, the third network device 8300 may include: a transceiver module 8301, which is used to receive the second instruction sent by the first network device, and / or receive the third instruction sent by the second network device, the second instruction is used to instruct the third network device to perform the first operation, and the third instruction is used to instruct the third network device to perform the second operation. The first network device is deployed with an AI model for determining the positioning result, and the input of the AI ​​model comes from the first network device and at least one third network device. Optionally, the above-mentioned transceiver module 8301 is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2103, step S2104 but not limited to this) performed by the third network device 103 in any of the above methods, and will not be repeated here.

[0375] In some embodiments, the first operation includes any one of the following: sending a first measurement parameter to a first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by a second network device, the second result being determined by the second network device based on a second measurement parameter sent by a third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0376] In some embodiments, the second operation includes any of the following: sending a first measurement parameter to the first network device; not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, the first result being a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, the second result being determined by the second network device based on the second measurement parameter sent by the third network device; wherein the first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

[0377] In some embodiments, the transceiver module 8301 is also used to send auxiliary information to the first network device, the auxiliary information is used to determine the first instruction, the first instruction is used to indicate relevant information of the first result, and the first result is the positioning result determined by the second network device.

[0378] In some embodiments, the auxiliary information includes at least one of the following: coordinates of the terminal; channel measurement values ​​of the terminal under the second network device; channel measurement values ​​under the third network device; receiving beam information of the second network device; receiving beam information of the third network device.

[0379] In some embodiments, the relevant information of the first result includes any one of the following: the first network device sends the first result to the third network device; the first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; wherein the first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

[0380] 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.

[0381] 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.

[0382] 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 (for example, step S2101, step S2103, step S2104, but not limited thereto), and the processor 9101 performs at least one of the other steps (for example, step S2102, 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, interface, etc. may be interchangeable, the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc. may be interchangeable, and the terms receiver, receiving unit, receiver, receiving circuit, etc. may be interchangeable.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] The chip 9200 includes one or more processors 9201. The chip 9200 is configured to execute any of the above methods.

[0387] 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.

[0388] In some embodiments, the interface circuit 9202 performs at least one of the communication steps (e.g., but not limited to, step S2101) in the above method, such as sending and / or receiving. For example, the interface circuit 9202 performs the communication steps (e.g., sending and / or receiving) in the above method, which 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., but not limited to, step S2102).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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 indicate relevant information of a first result, where the first result is a positioning result determined by the second network device. An artificial intelligence (AI) model for determining the positioning result is deployed in the first network device, and input of the AI ​​model comes from the first network device and at least one third network device.

2. The method according to claim 1, characterized in that The relevant information of the first result includes any one of the following: The first network device sends the first result to the third network device; The first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; The first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

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 first operation.

4. The method according to claim 3, characterized in that The first operation includes any one of the following: Sending a first measurement parameter to the first network device; Not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, where the first result is a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, where the second result is determined by the second network device based on a second measurement parameter sent by the third network device; The first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

5. The method according to claim 1, wherein The method further comprises: The first network device receives auxiliary information, where the auxiliary data is sent by the second network device and / or the third network device, and the auxiliary information is used to determine the first instruction.

6. The method according to claim 5, characterized in that The auxiliary information includes at least one of the following: The coordinates of the terminal; a channel measurement value of the terminal under the second network device; The channel measurement value of the third network device; receiving beam information of the second network device; The third network device receives beam information.

7. 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 indicate relevant information of a first result, where the first result is a positioning result determined by the second network device, and an artificial intelligence (AI) model for determining the positioning result is deployed in the first network device, where input of the AI ​​model comes from the first network device and at least one third network device.

8. The method according to claim 7, characterized in that The relevant information of the first result includes any one of the following: The first network device sends the first result to the third network device; The first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; The first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

9. The method according to claim 7, characterized in that The method further comprises: When the first instruction is received, a third instruction is sent to the third network device, where the third instruction is used to instruct the third network device to perform a second operation.

10. The method according to claim 9, characterized in that The second operation includes any one of the following: Sending a first measurement parameter to the first network device; Not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, where the first result is a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, where the second result is determined by the second network device based on a second measurement parameter sent by the third network device; The first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

11. The method according to claim 7, characterized in that The method further comprises: Auxiliary information is sent to the first network device, where the auxiliary information is used to determine the first instruction.

12. The method according to claim 11, characterized in that The auxiliary information includes at least one of the following: The coordinates of the terminal; a channel measurement value of the terminal under the second network device; The channel measurement value of the third network device; receiving beam information of the second network device; The third network device receives beam information.

13. A communication method, characterized in that: The method comprises: The third network device receives a second instruction sent by the first network device, and / or receives a third instruction sent by the second network device, where the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation. An artificial intelligence (AI) model for determining a positioning result is deployed in the first network device, and input of the AI ​​model comes from the first network device and at least one third network device.

14. The method according to claim 13, characterized in that The first operation includes any one of the following: Sending a first measurement parameter to the first network device; Not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, where the first result is a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, where the second result is determined by the second network device based on a second measurement parameter sent by the third network device; The first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

15. The method according to claim 13, characterized in that The second operation includes any one of the following: Sending a first measurement parameter to the first network device; Not sending the first measurement parameter to the first network device; receiving a first result sent by the first network device, where the first result is a positioning result determined by the first network device based on the first measurement parameter; receiving a second result determined by the second network device, where the second result is determined by the second network device based on a second measurement parameter sent by the third network device; The first measurement parameter and the second measurement parameter are measured by the third network device, and the first result is predicted by the AI ​​model based on the first measurement parameter.

16. The method according to claim 13, characterized in that The method further comprises: Auxiliary information is sent to the first network device, where the auxiliary information is used to determine a first instruction, where the first instruction is used to indicate relevant information of a first result, where the first result is a positioning result determined by the second network device.

17. The method according to claim 16, characterized in that The auxiliary information includes at least one of the following: The coordinates of the terminal; a channel measurement value of the terminal under the second network device; The channel measurement value of the third network device; receiving beam information of the second network device; The third network device receives beam information.

18. The method according to claim 16, characterized in that The relevant information of the first result includes any one of the following: The first network device sends the first result to the third network device; The first network device does not send the first result to the third network device, and the first result is sent to the third network device by the second network device; The first result is determined based on the output information of the AI ​​model; or the first result is not determined based on the output information of the AI ​​model.

19. A communication method, characterized in that: The method comprises: The first network device sends a first instruction to the second network device, and / or the first network device sends a second instruction to the third network device; When the second network device receives the first instruction sent by the first network device, the second network device sends a third instruction to the third network device; The third network device receives the second instruction and / or the third instruction, Among them, the first instruction is used to indicate relevant information of the first result, the first result is the positioning result determined by the second network device, the first network device is deployed with an artificial intelligence AI model for determining the positioning result, the input of the AI ​​model comes from the first network device and at least one third network device, the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation.

20. A first network device, characterized in that: The first network device includes: A transceiver module is used to send a first instruction to a second network device, where the first instruction is used to indicate relevant information of a first result, where the first result is a positioning result determined by the second network device, and an artificial intelligence (AI) model for determining the positioning result is deployed in the first network device, where the input of the AI ​​model comes from the first network device and at least one third network device.

21. A second network device, characterized in that: The second network device includes: A transceiver module is used to receive a first instruction sent by a first network device, where the first instruction is used to indicate relevant information of a first result, where the first result is a positioning result determined by the second network device, and an artificial intelligence (AI) model for determining the positioning result is deployed in the first network device, where the input of the AI ​​model comes from the first network device and at least one third network device.

22. A third network device, characterized in that: The third network device includes: A transceiver module is used to receive a second instruction sent by a first network device and / or receive a third instruction sent by a second network device, wherein the second instruction is used to instruct the third network device to perform a first operation, and the third instruction is used to instruct the third network device to perform a second operation. An AI model for determining a positioning result is deployed in the first network device, and the input of the AI ​​model comes from the first network device and at least one third network device.

23. 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 6.

24. 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 7 to 12.

25. 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 13 to 18.

26. 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 6, the second network device is configured to implement the method according to any one of claims 7 to 12, and the third network device is configured to implement the method according to any one of claims 13 to 18.

27. 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 6, the method according to any one of claims 7 to 12, or the method according to any one of claims 13 to 18.

28. 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 6, or the method according to any one of claims 7 to 12, or the method according to any one of claims 13 to 18.

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