Communication method, and device and storage medium

Abstract

The embodiments of the present disclosure relate to a communication method, and a device and a storage medium. The method comprises: receiving first information sent by a network device, wherein the first information is information corresponding to a first function, the first function is used for executing beam prediction, and the first function is an artificial intelligence (AI) model and / or an AI function; and on the basis of the first information, acquiring data required by the first function, wherein the first information comprises at least one of the following: a first identifier, the first identifier corresponding to the first function, and the first identifier comprising at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier and an additional condition identifier; and a data acquisition objective, the data acquisition objective comprising at least one of model training, model reasoning and performance monitoring, and the content of the first information corresponding to different data acquisition objectives being different. In this way, information corresponding to a first function can be flexibly configured, thereby improving the management efficiency of the first function.

Description

Communication method, device and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to the Chinese patent application filed with the China Patent Office on January 12, 2024, with application number 202410052316.9 and titled “Communication Method, Device and Storage Medium,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The present disclosure relates to the field of communication technology, and in particular to a communication method, device, and storage medium. Background Art

[0004] With the advancement of communication technology, prediction functions based on artificial intelligence (AI) have been introduced in wireless communication systems. The prediction functions can be AI functions and / or AI models. Through the prediction functions, prediction data in certain scenarios can be obtained, thereby improving network performance.

[0005] Summary of the Invention

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

[0007] According to a first aspect of an embodiment of the present disclosure, a communication method is proposed, which is performed by a terminal device. The method includes:

[0008] receiving first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence (AI) model and / or AI function;

[0009] acquiring data required by the first function according to the first information;

[0010] The first information includes at least one of the following:

[0011] a first identifier, the first identifier corresponding to the first function, the first identifier including at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier;

[0012] The purpose of data acquisition includes at least one of model training, model reasoning, and performance monitoring. Different data acquisition purposes correspond to different contents of the first information.

[0013] According to a second aspect of an embodiment of the present disclosure, a communication method is provided, which is performed by a network device. The method includes:

[0014] Sending first information to a terminal device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, the first function is an artificial intelligence (AI) model and / or an AI function, and the first information is used to instruct the terminal device to obtain data required for the first function according to the first information;

[0015] The first information includes at least one of the following:

[0016] a first identifier, the first identifier corresponding to the first function, the first identifier including at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier;

[0017] The purpose of data acquisition includes at least one of model training, model reasoning, and performance monitoring. Different data acquisition purposes correspond to different contents of the first information.

[0018] According to a third aspect of an embodiment of the present disclosure, a terminal device is provided, including:

[0019] A first transceiver module is configured to receive first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence (AI) model and / or AI function;

[0020] The first processing module is configured to obtain data required for the first function based on the first information; wherein, the first information includes at least one of the following: a first identifier, the first identifier corresponds to the first function, the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; a data acquisition purpose, the data acquisition purpose includes at least one of model training, model reasoning, and performance monitoring, and different data acquisition purposes correspond to different contents of the first information.

[0021] According to a fourth aspect of an embodiment of the present disclosure, a network device is provided, including:

[0022] The second transceiver module is configured to send first information to the terminal device; the first information is information corresponding to the first function, the first function is used to perform beam prediction, the first function is an artificial intelligence AI model and / or AI function, and the first information is used to instruct the terminal device to obtain data required for the first function according to the first information; wherein, the first information includes at least one of the following: a first identifier, the first identifier corresponds to the first function, the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; data acquisition purpose, the data acquisition purpose includes at least one of model training, model reasoning, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different.

[0023] According to a fifth aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the communication device can be used to execute an optional implementation of the first aspect or the second aspect.

[0024] According to a sixth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first aspect or the second aspect.

[0025] According to the seventh aspect of an embodiment of the present disclosure, a communication system is proposed, which may include: a terminal device and a network device; wherein the terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

[0026] The technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: receiving the first information sent by the network device; the first information is information corresponding to the first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and / or AI function; obtaining the data required for the first function according to the first information; wherein the first information includes at least one of the following: a first identifier, the first identifier corresponds to the first function, the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; a data acquisition purpose, the data acquisition purpose includes at least one of model training, model reasoning, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different. In this way, the information corresponding to the first function can be flexibly configured to improve the management efficiency of the first function.

[0027] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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.

[0029] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0030] FIG2 is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

[0031] FIG3 is a schematic diagram showing a first pattern according to an embodiment of the present disclosure.

[0032] FIG4 is a schematic diagram showing a second pattern according to an embodiment of the present disclosure.

[0033] FIG5 is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0034] FIG6 is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0035] FIG7 is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0036] FIG8 is a schematic structural diagram of a terminal device according to an embodiment of the present disclosure.

[0037] FIG9 is a schematic structural diagram of a network device according to an embodiment of the present disclosure.

[0038] FIG10 is a schematic structural diagram of a communication device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] The embodiments of the present disclosure provide a communication method, a device, and a storage medium.

[0040] In a first aspect, an embodiment of the present disclosure provides a communication method, which is executed by a terminal device. The method includes:

[0041] receiving first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence (AI) model and / or AI function;

[0042] Acquire data required for the first function according to the first information.

[0043] In the above embodiment, the information corresponding to the first function can be flexibly configured to improve the management efficiency of the first function.

[0044] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

[0045] a first identifier, the first identifier corresponding to the first function;

[0046] The data acquisition purpose is used to indicate the use of the data obtained through the first information.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the first information further includes at least one of the following:

[0048] a second identifier, where the second identifier corresponds to a configuration related to a measurement, where the measurement is used for data acquisition;

[0049] Beam information, where the beam information is used to indicate measured and / or predicted beam-related information;

[0050] An application example, where the application example is an example of applying the first function to perform beam prediction;

[0051] Reference signal resource information, where the reference signal resource information is used to determine a first beam and / or a second beam, where the first beam is a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

[0052] Coverage information, where the coverage information is used to indicate coverage-related information of the network device;

[0053] Terminal distribution information, the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage area of ​​the network device;

[0054] Measurement information, where the measurement information includes relevant information about the terminal device performing measurements based on the first information.

[0055] In the above embodiment, through any of the above items, the information required for the first function can be determined, thereby improving the flexibility of the terminal device in configuring the information related to the first function.

[0056] In conjunction with some embodiments of the first aspect, in some embodiments, the application example includes at least one of the following:

[0057] Spatial beam prediction example;

[0058] Time domain beam prediction example;

[0059] Spatial domain beam prediction example and time domain beam prediction example.

[0060] In the above embodiment, the first information can be controlled to be applied to spatial and / or temporal beam prediction instances through application instances to better adapt to different scenarios.

[0061] In conjunction with some embodiments of the first aspect, in some embodiments, the reference signal resource information includes at least one of the following:

[0062] a first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to the first beam;

[0063] a second reference signal resource set, where the reference signal resources of the second reference signal resource set correspond to the second beam;

[0064] A set relationship, the set relationship comprising a relationship between the first reference signal resource set and the second reference signal resource set;

[0065] time information corresponding to the first reference signal resource set;

[0066] time information corresponding to the second reference signal resource set;

[0067] A time pattern, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

[0068] In the above embodiment, the reference signal resource information related to the first function can be flexibly determined.

[0069] With reference to some embodiments of the first aspect, in some embodiments, the set relationship includes any one of the following:

[0070] The first reference signal resource set is a subset of the second reference signal resource set;

[0071] The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam;

[0072] The first reference signal resource set is the same as the second reference signal resource set.

[0073] In the above embodiment, the set relationship between the first reference signal resource set and the second reference signal resource set can be flexibly determined.

[0074] With reference to some embodiments of the first aspect, in some embodiments, the time pattern includes any one of the following:

[0075] a first pattern, where the first pattern is used to indicate N historical periods and M future periods, and measurement results of the N historical periods are used by the terminal device to obtain prediction results of the M future periods based on the first function;

[0076] The second pattern is used to indicate K historical periods, and the measurement results of the K historical periods are used by the terminal device to obtain L prediction results in the K+1th future period based on the first function.

[0077] In the above embodiment, the time relationship between the first reference signal resource set and the second reference signal resource set can be flexibly determined through the time pattern.

[0078] In conjunction with some embodiments of the first aspect, in some embodiments,

[0079] The coverage information includes the deployment type and / or inter-station distance of the network device; and / or,

[0080] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0081] In the above embodiment, coverage information and / or terminal distribution information may be flexibly indicated so that the terminal device can obtain data according to the information.

[0082] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement information includes at least one of the following:

[0083] Measurement quantities, the measurement quantities including physical layer reference signal received power L1-RSRP and / or physical layer signal to interference and noise ratio L1-SINR;

[0084] Event information, which is information related to the event that triggers the measurement report;

[0085] Reporting amount, where the reporting amount is used to indicate the information reported by the terminal device to the network device.

[0086] In the above embodiment, the measurement information can be flexibly indicated so that the terminal device can obtain data according to the measurement information.

[0087] In conjunction with some embodiments of the first aspect, in some embodiments, the reported amount includes a performance indicator of the first function, and the performance indicator includes at least one of the following:

[0088] a prediction accuracy rate, where the prediction accuracy rate is a probability that the predicted best reference signal resource includes an actual best reference signal resource, where the predicted best reference signal resource is the best reference signal resource predicted by the first function, the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, and the best reference signal resource is the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, where N is a positive integer;

[0089] The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0090] In the above embodiment, the reporting amount can be flexibly indicated so that the terminal device can obtain data according to the reporting amount.

[0091] In conjunction with some embodiments of the first aspect, in some embodiments, the first identifier includes at least one of the following:

[0092] Functional identification;

[0093] Model identification;

[0094] Dataset identifier;

[0095] Data acquisition configuration identifier;

[0096] Data acquisition identification;

[0097] Condition identification;

[0098] Additional condition identification.

[0099] In combination with some embodiments of the first aspect, in some embodiments, at least one of the first identifiers is the same, and the first information corresponds to the same first function.

[0100] In the above embodiment, the first identifier can be flexibly indicated so that the terminal device determines the first function corresponding to the first information according to the first identifier.

[0101] In conjunction with some embodiments of the first aspect, in some embodiments, the second identifier includes at least one of the following:

[0102] measurement identification;

[0103] Measurement object identification;

[0104] Report ID.

[0105] In the above embodiment, the second identifier may be flexibly indicated so that the terminal device determines measurement-related information according to the second identifier.

[0106] In conjunction with some embodiments of the first aspect, in some embodiments, the beam information includes at least one of the following:

[0107] A beam codebook identifier, where the beam codebook identifier is used to indicate codebook information used by the network device to send a beam;

[0108] Antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

[0109] Beam type, where the beam type is used to indicate the type of beam sent by the network device.

[0110] In the above embodiment, beam information can be flexibly indicated so that the terminal device can determine beam-related information.

[0111] In conjunction with some embodiments of the first aspect, in some embodiments, the data acquisition purpose includes at least one of the following:

[0112] Model training;

[0113] Model reasoning;

[0114] Performance monitoring.

[0115] In combination with some embodiments of the first aspect, in some embodiments, different data acquisition purposes correspond to different contents of the first information.

[0116] In the above embodiment, the data acquisition purpose can be flexibly indicated so that the terminal device can determine the data acquisition purpose and flexibly acquire corresponding data according to the data acquisition purpose.

[0117] In a second aspect, an embodiment of the present disclosure provides a communication method, which is performed by a network device. The method includes:

[0118] Sending first information to the terminal device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, the first function is an artificial intelligence AI model and / or AI function, and the first information is used to instruct the terminal device to obtain data required for the first function according to the first information.

[0119] In the above embodiment, the information corresponding to the first function can be flexibly configured to improve the management efficiency of the first function.

[0120] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following:

[0121] a first identifier, the first identifier corresponding to the first function;

[0122] The data acquisition purpose is used to indicate the use of the data obtained through the first information.

[0123] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following:

[0124] a second identifier, where the second identifier corresponds to a configuration related to a measurement, where the measurement is used for data acquisition;

[0125] Beam information, where the beam information is used to indicate measured and / or predicted beam-related information;

[0126] An application example, where the application example is an example of applying the first function to perform beam prediction;

[0127] Reference signal resource information, where the reference signal resource information is used to determine a first beam and / or a second beam, where the first beam is a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

[0128] Coverage information, where the coverage information is used to indicate coverage-related information of the network device;

[0129] Terminal distribution information, the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage area of ​​the network device;

[0130] Measurement information, where the measurement information includes relevant information about the terminal device performing measurements based on the first information.

[0131] In conjunction with some embodiments of the second aspect, in some embodiments, the application example includes at least one of the following:

[0132] Spatial beam prediction example;

[0133] Time domain beam prediction example;

[0134] Spatial domain beam prediction example and time domain beam prediction example.

[0135] With reference to some embodiments of the second aspect, in some embodiments, the reference signal resource information includes at least one of the following:

[0136] a first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to the first beam;

[0137] a second reference signal resource set, where the reference signal resources of the second reference signal resource set correspond to the second beam;

[0138] A set relationship, the set relationship comprising a relationship between the first reference signal resource set and the second reference signal resource set;

[0139] time information corresponding to the first reference signal resource set;

[0140] time information corresponding to the second reference signal resource set;

[0141] A time pattern, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

[0142] In conjunction with some embodiments of the second aspect, in some embodiments, the set relationship includes any one of the following:

[0143] The first reference signal resource set is a subset of the second reference signal resource set;

[0144] The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam;

[0145] The first reference signal resource set is the same as the second reference signal resource set.

[0146] In conjunction with some embodiments of the second aspect, in some embodiments, the time pattern includes any one of the following:

[0147] a first pattern, where the first pattern is used to indicate N historical periods and M future periods, and measurement results of the N historical periods are used by the terminal device to obtain prediction results of the M future periods based on the first function;

[0148] The second pattern is used to indicate K historical periods, and the measurement results of the K historical periods are used by the terminal device to obtain L prediction results in the K+1th future period based on the first function.

[0149] In conjunction with some embodiments of the second aspect, in some embodiments,

[0150] The coverage information includes the deployment type and / or inter-station distance of the network device; and / or,

[0151] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0152] In conjunction with some embodiments of the second aspect, in some embodiments, the measurement information includes at least one of the following:

[0153] Measurement quantities, the measurement quantities including physical layer reference signal received power L1-RSRP and / or physical layer signal to interference and noise ratio L1-SINR;

[0154] Event information, which is information related to the event that triggers the measurement report;

[0155] Reporting amount, where the reporting amount is used to indicate the information reported by the terminal device to the network device.

[0156] In conjunction with some embodiments of the second aspect, in some embodiments, the reported amount includes a performance indicator of the first function, and the performance indicator includes:

[0157] a prediction accuracy rate, where the prediction accuracy rate is a probability that the predicted best reference signal resource includes an actual best reference signal resource, where the predicted best reference signal resource is the best reference signal resource predicted by the first function, the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, and the best reference signal resource is the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, where N is a positive integer;

[0158] The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0159] In conjunction with some embodiments of the second aspect, in some embodiments, the first identifier includes at least one of the following:

[0160] Functional identification;

[0161] Model identification;

[0162] Dataset identifier;

[0163] Data acquisition configuration identifier;

[0164] Data acquisition identification;

[0165] Condition identification;

[0166] Additional condition identification.

[0167] In combination with some embodiments of the second aspect, in some embodiments, at least one of the first identifiers is the same, and the first information corresponds to the same first function.

[0168] In conjunction with some embodiments of the second aspect, in some embodiments, the second identifier includes at least one of the following:

[0169] measurement identification;

[0170] Measurement object identification;

[0171] Report ID.

[0172] In conjunction with some embodiments of the second aspect, in some embodiments, the beam information includes at least one of the following:

[0173] A beam codebook identifier, where the beam codebook identifier is used to indicate codebook information used by the network device to send a beam;

[0174] Antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

[0175] Beam type, where the beam type is used to indicate the type of beam sent by the network device.

[0176] In conjunction with some embodiments of the second aspect, in some embodiments, the data acquisition purpose includes at least one of the following:

[0177] Model training;

[0178] Model reasoning;

[0179] Performance monitoring.

[0180] In combination with some embodiments of the second aspect, in some embodiments, the content of the first information corresponding to different data acquisition purposes is different.

[0181] In a third aspect, an embodiment of the present disclosure proposes a terminal device, which may include a first transceiver module and a first processing module; wherein the terminal device can be used to execute the optional implementation method of the first aspect.

[0182] In a fourth aspect, an embodiment of the present disclosure proposes a network device, which may include a second transceiver module; wherein the network device can be used to execute the optional implementation method of the second aspect.

[0183] In a fifth aspect, an embodiment of the present disclosure proposes a communication device, which may include: one or more processors; wherein the communication device can be used to execute an optional implementation of the first aspect or the second aspect.

[0184] In a sixth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the optional implementation manner of the first aspect or the second aspect.

[0185] In a seventh aspect, an embodiment of the present disclosure proposes a program product, which, when executed by a communication device, enables the communication device to execute the method described in the optional implementation manner of the first aspect or the second aspect.

[0186] In an eighth 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 or second aspect.

[0187] In a ninth 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 or the second aspect.

[0188] In the tenth aspect, an embodiment of the present disclosure proposes a communication system, which may include: a terminal device and a network device; wherein, the terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

[0189] It is understandable that the above-mentioned terminal devices, network devices, communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems can all be 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.

[0190] The present disclosure provides a communication method, device, and storage medium. In some embodiments, the terms communication method and information processing method are interchangeable; the terms communication device and information processing device are interchangeable; and the terms information processing system and communication system are interchangeable.

[0191] 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.

[0192] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0193] 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.

[0194] 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.

[0195] In some embodiments, "plurality" may refer to two or more than two.

[0196] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0197] 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.

[0198] 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.

[0199] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] In some embodiments, devices and the like may be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as "device," "equipment," "device," "circuit," "network element," "node," "function," "unit," "section," "system," "network," "chip," "chip system," "entity," and "subject" may be used interchangeably.

[0204] In some embodiments, "network" can be interpreted as devices included in the network (eg, network equipment, access network equipment, core network equipment, etc.).

[0205] In some embodiments, the network device may include at least one of an access network device and a core network device.

[0206] In some embodiments, the terms "Access Network Device (AN Device)", "Radio Access Network Device (RAN Device)", "Base Station (BS)", "Radio Base Station (Radio Base Station)", "Fixed Station (Fixed Station)", "Node (Node)", "Access Point (Access Point)", "Transmission Point (TP)", "Reception Point (RP)", "Transmission and / or Reception Point (TRP))", "Panel (Panel)", "Antenna Panel (Antenna Panel)", "Antenna Array (Antenna Array)" "Cell (Cell)", "Macro Cell (Macro Cell)", "Small Cell (Small Cell)", "Femto Cell (Femto Cell)", "Pico Cell (Pico Cell)" "Sector (Sector)", "Cell Group (Cell Group)", "Serving Cell (Cell)", "Carrier (Carrier)", "Component Carrier (Component Carrier)", "Bandwidth Part (BWP)" and the like may be used interchangeably.

[0207] In some embodiments, the terms "terminal", "terminal device", "terminal side device", "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station (Subscriber Station), mobile unit (Mobile Unit), subscriber unit (Subscriber Unit), wireless unit (Wireless Unit), remote unit (Remote Unit), mobile device (Mobile Device), wireless device (Wireless Device), wireless communication device (Wireless Communication Device), remote device (Remote Device), mobile subscriber station (Mobile Subscriber Station), access terminal (Access Terminal), mobile terminal (Mobile Terminal), wireless terminal (Wireless Terminal), remote terminal (Remote Terminal), handset (Handset), user agent (User Agent), mobile client (Mobile Client), client (Client) and the like can be used interchangeably.

[0208] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal device. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal device is replaced by the communication between multiple terminal devices (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal device has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminal devices (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels or direct channels, and uplinks, downlinks, etc. can be replaced by side links or direct links.

[0209] In some embodiments, the terminal device may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal device.

[0210] 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.

[0211] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0212] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0213] 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.

[0214] FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , the communication system 100 may include a terminal device 101 and a network device 102 .

[0215] In some embodiments, the terminal device 101 may include at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a vehicle-mounted terminal, a tablet computer, a computer with wireless transceiver function, a road side unit (RSU), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, and a wireless terminal device in smart home, but is not limited thereto.

[0216] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0217] In some embodiments, the access network device may be a node or device that accesses the terminal device to the wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0218] 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.

[0219] 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 (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.

[0220] In some embodiments, the core network device may be a single device, or may be multiple devices or a group of devices. The core network may include at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0221] It can be understood that the communication system described in the embodiment of the present disclosure is to more clearly illustrate 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 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.

[0222] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are examples. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationship between the entities is an example. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0223] The embodiments of the present disclosure may 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 802.17 (WiMAX (registered trademark)), IEEE 802.18 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (WiMAX (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (WiMAX (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), 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).

[0224] In some embodiments of the present disclosure, the communication system may support beam-based transmission and reception. Beam-based transmission and reception can better align useful signals with corresponding terminal devices, prevent signal energy leakage from interfering with other terminal devices, improve the signal-to-interference-and-noise ratio, and enhance the coverage performance of the wireless communication system.

[0225] For example, in a communication system, such as NR communication, for the FR2 (frequency range 2) communication band, since the high-frequency channel attenuates relatively quickly, in order to ensure coverage, beam-based transmission and reception can be used.

[0226] In some embodiments, the network device may be configured with a reference signal resource set for beam measurement, and the terminal device may measure the reference signal resources in the reference signal resource set and report the IDs of X reference signal resources with relatively strong signal quality in the measurement results, as well as the physical layer reference signal received power (Layer 1-Reference Signal Receiving Power, L1-RSRP) and / or physical layer signal to interference plus noise ratio (Layer 1-Signal to Interference plus Noise Ratio, L1-SINR) of each reference signal resource in the X reference signal resources. The reference signal resource set configured by the network device includes X reference signal resources, each reference signal resource corresponds to a different transmit beam of the network device. For each reference signal resource, the terminal device needs to measure the reference signal resource through all receive beams, determine the beam measurement quality corresponding to each receive beam, and determine the strongest beam measurement quality from multiple beam measurement qualities. In the above measurement process, if the number of transmit beams of the network device is M and the number of receive beams of the terminal device is N, then the number of beam pairs that the terminal device needs to measure is M*N.

[0227] In some embodiments, the terms "beam", "beam pair", "beam width", "beam angular degree", "antenna", "antenna element", "antenna port", "antenna port group", "panel", "layer", "the number of layers", "rank", "resource", "resource set", "resource group", "Quasi-Colocation (QCL) Type D", "spatial setting", "spatial filter", "spatial relation info", "spatial RX parameters", "spatial Tx parameter", "Transmission Configuration Indication (TCI) state" and the like may be used interchangeably.

[0228] In some embodiments of the present disclosure, the above-mentioned communication system may support a first function, which may be used to perform beam prediction.

[0229] In some embodiments, the first function may be an AI function and / or an AI model.

[0230] In some embodiments, an AI function may correspond to one or more AI models. For example, an AI function may be a function or module implemented by multiple AI models. Different AI models may correspond to different conditions or additional conditions.

[0231] In other embodiments, one AI model may correspond to one or more AI functions.

[0232] In some other embodiments, AI functions and AI models may correspond one to one.

[0233] In some embodiments, the first function may be deployed on a terminal device and / or a network device.

[0234] In some embodiments, the name of the first function is not limited, for example, it can be "AI function", "AI model", "AI module", "prediction function", "prediction model", "prediction module", etc.

[0235] In some embodiments, a terminal device and / or network device may perform beam prediction based on the first function. For example, if a terminal device originally needs to measure a total of M*N beam pairs (where M is the number of base station transmit beams and N is the number of terminal receive beams), the first function may reduce the number of beam pairs measured by the terminal device, but the first function may still be used to predict and output beam information (e.g., beam quality, optimal beam, etc.) for the M*N beam pairs.

[0236] For example, for spatial beam prediction, the terminal device may measure only a portion of the multiple beam pairs. For example, the beam pairs measured by the terminal device may be 1 / 8, 1 / 4, etc. of the M*N beam pairs. The beam measurement quality of the measured partial beam pairs is used as the input value of the above-mentioned first function. The corresponding output value can be predicted through the above-mentioned first function. The output value can be the beam information of the M*N beam pairs, such as the beam quality of at least one beam pair, and / or the identifiers of the best K beam pairs. The output value may also be beam information related to the beams transmitted by M base stations, such as the beam quality of at least one base station transmit beam, and / or the identifiers of the best K base stations transmit beams. The beam measurement quality may include L1-RSRP and / or L1-SINR. The above-mentioned beam pair identifier may be a TxRx beam ID, and the Tx beam ID may be a reference signal resource identifier.

[0237] In some embodiments, the terminal device and / or network device may perform beam prediction based on the first function. For example, if the terminal device originally needs to measure a total of M base station transmit beams, the first function may reduce the number of base station transmit beams measured by the terminal device, but the first function may still be used to predict and output beam information (e.g., beam quality, optimal beam, etc.) of the transmit beams of the M base stations.

[0238] For example, for spatial beam prediction, the terminal device may only measure a portion of the multiple transmit beams of the base station. For example, the beam measured by the terminal device may be 1 / 8, 1 / 4, etc. of the M beams. The beam measurement quality of the measured partial beams is used as the input value of the above-mentioned first function. The corresponding output value can be predicted through the above-mentioned first function. The output value can be the beam information of the M transmit beams, such as the beam quality of at least one beam, and / or the best K beam identifiers. The beam measurement quality may include L1-RSRP and / or L1-SINR. The above-mentioned beam identifier may be a Tx beam ID, and the Tx beam ID may be a reference signal resource identifier.

[0239] For another example, for time domain beam prediction, the terminal device can measure the beam pair or the beam quality of the transmission beam at a historical time to obtain the historical beam measurement quality, and use the historical beam measurement quality as the input value of the above-mentioned first function. The corresponding output value is predicted through the above-mentioned first function, and the output value can be the beam pair at a future time or the beam information of the base station transmission beam (such as beam quality, optimal beam, etc.). The beam measurement quality may include L1-RSRP and / or L1-SINR. The best beam may include the best beam pair identifier, which may be a TxRx beam ID, and Tx may be a reference signal resource identifier; the best beam may include the best beam identifier, which may be a reference signal resource identifier. The above-mentioned transmission beam may be a transmission beam sent by the base station to the terminal device.

[0240] In some embodiments, the beam set of the terminal device may include a first beam set setB and a second beam set setA.

[0241] The first beam set setB includes one or more first beams, and the first beam may be a beam corresponding to an input value of the first function. For example, the terminal device measures the beam measurement quality that can be obtained by the first beam in the first beam set setB.

[0242] The second beam set setA includes one or more second beams, and the second beam may be a beam corresponding to the output value of the first function.

[0243] In some embodiments, for spatial beam prediction, the first function may predict the measurement result of the second beam in the second beam set set A based on the measurement result of the first beam in the first beam set set B. For example, the terminal device may measure the L1-RSRP and / or L1-SINR of the first beam in set B, input the measured L1-RSRP and / or L1-SINR into the first function, and the first function may predict the L1-RSRP and / or L1-SINR of the second beam in output set A.

[0244] For spatial beam prediction, the relationship between the first beam set setB and the second beam set setA may include at least one of the following:

[0245] setB may be a subset of setA; for example, setA includes 32 reference signal resources (each reference signal resource corresponds to a beam direction), and setB includes N reference signal resources, where N<32, for example, N=8;

[0246] setB is different from setA. The beam corresponding to setB is a wide beam, and the beam corresponding to setA is a narrow beam. Optionally, the beam coverage range of setB and setA can be the same or different. For example, setA includes 32 reference signal resources, each reference signal resource corresponds to a beam direction, and the coverage range of 32 reference signal resources is 120 degrees. setB includes N reference signal resources, for example, N=8, and the coverage range of N reference signal resources is also 120 degrees. That is to say, the beam directions of multiple reference signal resources in setB cover the beam directions of multiple reference signal resources in setA. It can also be understood that the relationship between 32 / N reference signal resources in setA and the same reference signal resource in setB is QCL Type D.

[0247] In some embodiments, for time-domain beam prediction, the first function may predict the measurement result of the second beam in the second beam set setA at a future time based on the measurement result of the first beam in the first beam set setB at a historical time. For example, the terminal device may measure the L1-RSRP and / or L1-SINR of the first beam in setB at a historical time, input the measured L1-RSRP and / or L1-SINR into the first function, and the first function may predict and output the L1-RSRP and / or L1-SINR of the second beam in setA at a future time.

[0248] For time-domain beam prediction, the relationship between the first beam set setB and the second beam set setA may include at least one of the following:

[0249] setB can be a subset of setA;

[0250] SetB is different from setA. The beam corresponding to setB is a wide beam, while the beam corresponding to setA is a narrow beam.

[0251] setB is the same as setA.

[0252] In some embodiments, the terminal device and / or network device may deploy one or more first functions. How to determine the information required by the first function becomes an urgent problem to be solved.

[0253] FIG2 is an interactive diagram of a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2 , the method may include:

[0254] Step S2101: The network device sends first information to the terminal device.

[0255] In some embodiments, the terminal device may receive the first information. For example, the terminal device may receive the first information sent by the network device.

[0256] In some embodiments, the first information may be information corresponding to the first function, for example, the first information may be used to indicate information required by the first function, the first function may be used to perform beam prediction, and the first function may be an AI model and / or AI function.

[0257] In some embodiments, the name of the first information is not limited, and may be, for example, "configuration information", "data configuration information", "function configuration information", "model configuration information", etc.

[0258] In some embodiments, the information required by the first function may include data required by the first function and / or information related to data acquisition, where the data is the data required by the first function. Optionally, the name of data acquisition is not limited, and may be, for example, "data collection," "data acquisition," "information acquisition," "information collection," or the like.

[0259] In some embodiments, the above-mentioned AI function may correspond to one or more AI models. For example, an AI function may be a function or module jointly implemented by multiple AI models.

[0260] In other embodiments, the aforementioned AI model may correspond to one or more AI functions.

[0261] In some other embodiments, the above-mentioned AI functions and AI models may correspond one to one.

[0262] In some embodiments, the information related to data acquisition may include the data itself, and may also include related information used to acquire the data, such as reference signal resource information used to acquire the data.

[0263] In some embodiments, the terminal device may determine information required for the first function based on the first information.

[0264] In some embodiments, the terminal device can obtain data based on the first information.

[0265] In some embodiments, the data may include at least one of the following:

[0266] Data used for model training;

[0267] Data used for model inference;

[0268] Data used for performance monitoring.

[0269] In some embodiments, terms such as "model training", "function training", and "AI model training" can be used interchangeably.

[0270] In some embodiments, terms such as "model reasoning", "functional reasoning", "AI model reasoning", "model application", "functional application", and "AI model application" can be used interchangeably.

[0271] In some embodiments, terms such as "performance monitoring", "performance evaluation", "performance acquisition", "performance detection", and "performance indicator acquisition" can be used interchangeably.

[0272] In some embodiments, terms such as "artificial intelligence (AI)", "machine learning (ML)", and "AI / ML" may be used interchangeably.

[0273] In some embodiments, the data used for model training may include data required for training the first function (AI function and / or AI model). For example, it may include data required for model input and data required for model labeling. Optionally, the data required for model labeling may be actual measurement data corresponding to the output of the first function.

[0274] For example, the data used for model inference may include data required for AI model input.

[0275] In one implementation, the first information may include data for model training. For example, the data for model training may be directly sent via the first information, and the terminal device may directly obtain the data for model training via the first information.

[0276] In another implementation, the first information may include reference signal resource configuration information required for obtaining data for model training. For example, the terminal device may perform measurements based on the first information and obtain data for model training. For example, the terminal device may obtain data for model training by measuring a reference signal corresponding to the reference signal resource configuration information indicated by the first information.

[0277] In this way, the terminal device can obtain data for model training and train the first function according to the data, thereby flexibly controlling the training of the first function.

[0278] In some embodiments, the data used for model inference may include data required for beam prediction or other model inference by the first function (AI function and / or AI model).

[0279] For example, the data used for model reasoning may include data required for AI model input. Optionally, the data used for model reasoning may be obtained by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information. For example, the terminal device may perform measurement based on the first information and obtain data for model reasoning. For example, the terminal device may obtain data for model reasoning by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information.

[0280] In this way, the terminal device can obtain data for model reasoning and perform beam prediction or other model reasoning through the first function, so that the execution of model reasoning on the first function can be flexibly controlled.

[0281] In some embodiments, the data used for performance monitoring may include: data used to monitor the performance of the AI ​​function of the first function, or data used to monitor the performance of at least one AI model of the first function.

[0282] For example, the data used for performance monitoring may include: data required for model input, data obtained by model output, and data required for model labels (e.g., actual measurement data corresponding to the model output). Optionally, the data used for performance monitoring can be obtained by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information, and can be obtained according to the model output. For example, the terminal device can perform measurements based on the first information and obtain data for performance monitoring. For example, the terminal device can obtain data for performance monitoring by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information, such as obtaining the data required for model input and the data required for the model label. For another example, the terminal device can obtain the data obtained by the model output through model reasoning.

[0283] In this way, the terminal device can obtain data for performance monitoring, compare the output of the first function with the label based on the data, and obtain the performance of the first function, thereby flexibly controlling the performance monitoring of the first function.

[0284] In some embodiments, the terminal device may obtain data required for the first function.

[0285] For example, the terminal device may obtain data required for the first function according to the first information.

[0286] In some embodiments, the terminal device may send the second information to the network device.

[0287] Optionally, the second information may be a response message to the first information, used by the network device to determine that the terminal device has received the first information.

[0288] Optionally, the second information may be a data acquisition result. For example, after the terminal device acquires the data required for the first function based on the first information, it may send a second message to the network device to notify the network device of the data acquisition result. The network device may determine whether to instruct the terminal device to stop data acquisition based on the data acquisition result. For example, if the data acquisition result is successful, the network device may instruct the terminal device to stop data acquisition via a third message. For another example, if the data acquisition result is a failure, the network device may not need to notify the terminal device to stop data acquisition.

[0289] In this way, data acquisition of terminal devices can be flexibly controlled.

[0290] In some embodiments, the first information may include at least one of the following:

[0291] a first identifier, the first identifier corresponding to a first function;

[0292] The data acquisition purpose is used to indicate the use of the data obtained through the first information.

[0293] In some other embodiments, the first information may include at least one of the following:

[0294] a first identifier, the first identifier corresponding to a first function;

[0295] a second identifier, the second identifier corresponding to a configuration related to a measurement, the measurement being used for data acquisition;

[0296] Beam information, where the beam information is used to indicate measured and / or predicted beam-related information;

[0297] An application example, where the application example is an example of applying the first function to perform beam prediction;

[0298] Reference signal resource information, where the reference signal resource information is used to determine a first beam and / or a second beam, where the first beam is a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

[0299] Coverage information, which is used to indicate coverage-related information of the network device;

[0300] Terminal distribution information, which is used to indicate the distribution of multiple terminal devices within the coverage area of ​​the network device;

[0301] a data acquisition purpose, where the data acquisition purpose is used to indicate a use of the data obtained through the first information;

[0302] Measurement information, where the measurement information includes relevant information about the terminal device performing measurements based on the first information.

[0303] In this way, the terminal device can determine the information required by the first function through at least one of the above items, so as to obtain the data required by the first function, thereby improving the management efficiency of the first function.

[0304] In some embodiments, the terminal device may have one or more first functions, and different first functions may correspond to the same or different first identifiers.

[0305] In some embodiments, the first identifier includes at least one of the following:

[0306] Function ID;

[0307] Model ID;

[0308] Dataset ID;

[0309] Data collection configuration ID;

[0310] Data collection ID;

[0311] Condition ID;

[0312] Additional condition ID.

[0313] In one implementation, the data acquisition configuration identifier may also be referred to as a data collection configuration identifier or a data acquisition configuration identifier.

[0314] In one implementation, the data acquisition identifier may also be referred to as a data collection identifier or a data acquisition identifier.

[0315] In one implementation, the aforementioned data is data used for model training and can be sent directly to the terminal device by the network device. For example, the network device can collect data used for model training and send it directly to the terminal device. The terminal device does not need to measure and obtain data used for model training based on reference signal resource configuration. In this case, the first identifier sent by the network device can include a dataset identifier. In this way, the terminal device can determine that the first function corresponding to the model trained based on the first dataset identifier #1 is function #1 or model #1.

[0316] In one implementation, the aforementioned data is data used for model training. This data may be obtained by measuring a reference signal corresponding to reference signal resource configuration information transmitted by a network device via a terminal. For example, the first identifier transmitted by the network device may include at least one identifier other than the dataset identifier in the first identifier, used to identify the first information corresponding to the first function. In this way, the terminal device can determine that the first function corresponding to the model trained based on the first identifier #1 is function #1 or model #1.

[0317] In another implementation, the above data is data used for model reasoning or performance monitoring, the first identifier includes a data set identifier, and the network device sends the reference signal resource configuration information to the terminal device. Through the above data set identifier, the terminal device can determine which first function (function #1 or model #1) the reference signal resource configuration information is for model reasoning or performance monitoring. Since during the model training process, the terminal device has determined that the first function corresponding to the model trained based on the first data set identifier #1 is function #1 or model #1, then when the network device indicates the first data set identifier #1 to the terminal device, the terminal device determines that the first function corresponding to the first data set identifier #1 is function #1 or model #1, then the terminal device can determine that the reference signal resource configuration information of the network device this time is for model reasoning or performance monitoring of function #1 or model #1.

[0318] In another implementation, the above data is data used for model reasoning or performance monitoring, the first identifier includes at least one identifier other than the data set identifier, and the network device sends the reference signal resource configuration information to the terminal device. Through the identifier in the above first identifier, the terminal device can determine which first function (for example, function #1 or model #1) the data obtained by measuring based on the reference signal corresponding to the reference signal resource configuration information is for model reasoning or performance monitoring. Since in the model training process, the terminal device has determined that the first function corresponding to the model trained based on the first identifier #1 is function #1 or model #1, then when the network device indicates the first identifier #1 to the terminal device, the terminal device determines that the first function corresponding to the first identifier #1 is function #1 or model #1, then the terminal device can determine that the reference signal resource configuration information of the network device this time is for model reasoning or performance monitoring of function #1 or model #1.

[0319] In some embodiments, at least one of the above-mentioned first identifiers is the same, and the first information corresponds to the same first function.

[0320] For example, after the terminal device performs model training based on the above-mentioned first identifier, it obtains the trained first function. Afterwards, if the network device configures some reference signal resource measurements and reports, and indicates the first identifier, and the indicated data acquisition purpose is performance monitoring and / or model reasoning, the terminal device determines that the data obtained based on the measurement and configuration of the reference signal resources can be used for performance monitoring and / or model reasoning of the first function obtained based on the previous data training corresponding to the first identifier.

[0321] Optionally, when the above-mentioned first identifiers are different, they may correspond to the same first function. The terminal can independently determine the correspondence between the first function and the first identifier. For example, the terminal device can train a first function based on multiple first identifiers, and then the terminal device can determine that the multiple first identifiers correspond to a first function. The first function is an AI function or an AI model.

[0322] Optionally, when the above-mentioned first identifiers are different, they may also correspond to the same AI function and the same AI model. The terminal can determine the correspondence between the AI ​​function or AI model and the first identifier. For example, the terminal device can train an AI function or AI model based on multiple first identifiers, and the terminal device can determine that the multiple first identifiers correspond to one AI function or AI model. For example, if the first identifiers are different and the terminal distribution information is different, the terminal device can train a model that can be applied to different terminal distributions based on the data corresponding to the different first identifiers.

[0323] Optionally, when the above-mentioned first identifiers are different, they may also correspond to the same AI function and different AI models. The terminal can determine the correspondence between the AI ​​function and the AI ​​model and the first identifier. For example, the terminal device can train multiple AI models of an AI function based on multiple first identifiers, and the terminal device can determine that the multiple first identifiers correspond to one AI function and multiple AI models, respectively. For example, if the first identifiers are different and the terminal distribution information is different, the terminal device trains different models suitable for different terminal distributions based on the data corresponding to different first identifiers, but corresponds to the same AI function. For example, the first information corresponding to the first AI function is the same except for the first identifier and the terminal distribution information. When the terminal distribution information is different, it corresponds to different AI models under the same AI function.

[0324] Optionally, when the above-mentioned first identifiers are different, they may also correspond to different AI functions. The terminal can determine the correspondence between the AI ​​function and the first identifier. For example, the terminal device can train multiple AI functions based on multiple first identifiers, and the terminal device can determine that the multiple first identifiers correspond to the multiple AI functions respectively. For example, the first identifiers are different and the application instances are different, such as one application instance is spatial beam prediction and the other application instance is time domain beam prediction. Then the terminal device trains different AI models based on the data corresponding to the two first information, corresponding to different AI functions.

[0325] In this way, the first function can be determined by the first identifier.

[0326] In some embodiments, the second identifier includes at least one of the following:

[0327] Measurement ID; the measurement ID may correspond to a measurement object and a report configuration;

[0328] Measurement object ID, which can be used to identify the measurement object;

[0329] Report ID, which can be used to determine the report configuration. Optionally, the report ID can also be called a report configuration identifier (ReportConfigId).

[0330] In this way, the measurement object and report configuration can be determined through the second identifier, so that the terminal device can perform measurement and obtain data.

[0331] In some embodiments, the beam information includes at least one of the following:

[0332] A beam codebook identifier, which may be used to indicate codebook information used by a network device to transmit a beam; optionally, the beam codebook identifier may also be referred to as a gNB beam codebook ID;

[0333] An antenna configuration identifier (ID) indicates the antenna configuration information of a network device. Optionally, this ID may also be referred to as a gNB antenna configuration ID. The antenna configuration information may include at least one of the following: the number of antenna panels, the number of antennas, the spacing between antennas, and the antenna array layout.

[0334] Beam type: The beam type (Beam type) can be used to indicate the type of beam sent by the network device.

[0335] In one implementation, the beam type may include at least one of a discrete Fourier transform (DFT) beam and a non-DFT beam.

[0336] In some embodiments, the application examples may include at least one of the following:

[0337] Spatial beam prediction example;

[0338] Time domain beam prediction example;

[0339] Spatial domain beam prediction example and time domain beam prediction example.

[0340] In some embodiments, the reference signal resource information may include at least one of the following:

[0341] a first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to a first beam, and the first beam is a beam corresponding to an input value of the first function;

[0342] a second reference signal resource set, where the reference signal resources of the second reference signal resource set correspond to a second beam, and the second beam is a beam corresponding to the output value of the first function;

[0343] A set relationship, the set relationship comprising a relationship between a first reference signal resource set and a second reference signal resource set;

[0344] Time information corresponding to the first reference signal resource set, where the time information may be a time quantity value, such as the number of historical measurement time instances;

[0345] time information corresponding to the second reference signal resource set, where the time information may be a time quantity value, such as a predicted number of future time instances;

[0346] A time pattern, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

[0347] In one implementation, when the application instance is a spatial beam prediction instance, the reference signal resource information includes at least one of the following: a first reference signal resource set, a second reference signal resource set, and a set relationship.

[0348] In another implementation, when the application example is a time-domain beam prediction example, the reference signal resource information includes at least one of the following: a first reference signal resource set, a second reference signal resource set, a set relationship, time information corresponding to the first reference signal resource set, time information corresponding to the second reference signal resource set, and a time pattern. For example, when the application example is a time-domain beam prediction example, the reference signal resource information may include time information corresponding to the first reference signal resource set, time information corresponding to the second reference signal resource set, and a time pattern.

[0349] In some embodiments, the above-mentioned first reference signal resource set may include the number of first reference signal resources in the first reference signal resource set, and may also include at least one of the resource identifier (resource ID), time-frequency resource, and reference signal (RS) sequence corresponding to each first reference signal resource.

[0350] In some embodiments, the second reference signal resource set may include the number of reference signal resources in the second reference signal resource set, and may also include at least one of a resource identifier, a time-frequency resource, and an RS sequence corresponding to each second reference signal resource.

[0351] In one implementation, the above-mentioned set relationship may include any one of the following:

[0352] The first reference signal resource set is a subset of the second reference signal resource set. For example, the second reference signal resource set may include M reference signal resources (each reference signal resource corresponds to a beam direction), for example, M=32, and the first reference signal resource set may include N reference signal resources, where N is less than M, for example, N=8. Wherein, M and N are both positive integers;

[0353] The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam; optionally, the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set may be the same or different;

[0354] The first reference signal resource set is the same as the second reference signal resource set.

[0355] Optionally, for spatial beam prediction, the above set relationship may include any one of the following:

[0356] The first reference signal resource set is a subset of the second reference signal resource set;

[0357] The first reference signal resource set is different from the second reference signal resource set. The beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam.

[0358] Optionally, for time-domain beam prediction, the above set relationship may include any one of the following:

[0359] The first reference signal resource set is a subset of the second reference signal resource set;

[0360] The first reference signal resource set is different from the second reference signal resource set. The beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam.

[0361] The first reference signal resource set is the same as the second reference signal resource set.

[0362] In one implementation, the time pattern may include any one of the following:

[0363] A first pattern (Pattern 1) is used to indicate N historical periods and M future periods, where measurement results of the N historical periods are used by the terminal device to obtain prediction results of the M future periods based on the first function;

[0364] The second pattern (Pattern2) is used to indicate K historical periods, and the measurement results of the K historical periods are used by the terminal device to obtain L prediction results in the K+1th future period based on the first function.

[0365] Figure 3 is a schematic diagram illustrating a first pattern according to an embodiment of the present disclosure. As shown in Figure 3 , based on the first pattern (Pattern 1), the measurement results of N historical cycles plus the prediction results of M future cycles form a repeating pattern. It should be noted that in Figure 3 , N = 3 and M = 2 are used as examples, and N and M can be any positive integer.

[0366] Optionally, in the first pattern, the above historical period and future period may be short periods, and a repeating pattern consisting of N historical periods + M future periods may be a long period.

[0367] In some embodiments, the terminal device may use measurement results of N historical periods as input values ​​of the first function, and obtain output values ​​predicted by the first function as prediction results of M future periods.

[0368] Optionally, the network device may not send reference signals for beam measurement within M future cycles. However, since the beam results within these M future cycles are predicted and cannot be used as input for the first function, after M cycles, the network device may continue to send reference signals for beam measurement in N historical cycles within the second long cycle, thereby performing a repeat pattern.

[0369] Figure 4 is a schematic diagram illustrating a second pattern according to an embodiment of the present disclosure. As shown in Figure 4 , based on the second pattern (Pattern 2), the measurement results of K historical cycles and the L prediction results contained in the K+1th future cycle are used as a repeating pattern. It should be noted that in Figure 4 , K=2 and L=3 are used as examples. K can be any positive integer, and L can also be any positive integer.

[0370] Optionally, in the second pattern, the above historical cycle and future cycle may be long cycles, and the K+1th future cycle may include L short cycles, and one short cycle corresponds to one prediction result.

[0371] In some embodiments, the terminal device may use the measurement results of K historical cycles starting from 1 as the input values ​​of the first function, and obtain the output values ​​predicted by the first function as the prediction results of L short cycles in the K+1th future cycle. Optionally, the terminal device may also use the measurement results of K+1 historical cycles starting from 2 as the input values ​​of the first function, and obtain the output values ​​predicted by the first function as the prediction results of L short cycles in the K+2th future cycle.

[0372] Optionally, the network device can send a parameter signal for beam measurement in each historical period (long period), and the terminal device can predict the prediction result of each short period. In this way, the repeat pattern of the second pattern can be simplified to a pattern such as a long period containing multiple short periods.

[0373] In some embodiments, the coverage information may include the deployment type and / or inter-station distance of the network device.

[0374] Optionally, the deployment type of the network device can be a deployment type of a base station. For example, the deployment type can be any one of the following: urban macro base station, urban micro base station, indoor base station, dense urban area, rural area, and hotspot.

[0375] Optionally, the inter-site distance may represent a distance between adjacent base stations, for example, the inter-site distance (ISD) may be 100 meters, 200 meters, 500 meters or 1000 meters.

[0376] In some embodiments, the terminal distribution information includes a ratio of indoor terminals (indoor UE) to outdoor terminals (outdoor UE).

[0377] Optionally, the terminal distribution information may also include at least one of the following information: the proportion of indoor terminals, the proportion of outdoor terminals, the number of terminals, etc.

[0378] Optionally, the indoor terminal may be a terminal device located indoors (eg, in a residential building, an office building, a shopping mall, etc.), and the outdoor terminal may be a terminal device located outdoors (eg, in a street, a park, etc.).

[0379] In some embodiments, the measurement information includes at least one of the following:

[0380] A measurement quantity, which may include a physical layer reference signal received power L1-RSRP and / or a physical layer signal to interference and noise ratio L1-SINR;

[0381] Event information: event information is information related to the event that triggers the measurement report;

[0382] Reporting amount: The reporting amount is used to indicate the information reported by the terminal device to the network device.

[0383] In some embodiments, the event information may include at least one of the following:

[0384] Event ID;

[0385] Event threshold, offset, and other information;

[0386] Event description, which can be used to indicate the triggering conditions for event reporting. For example, the event description can indicate that the event should be reported when the measured value (such as a performance indicator) is higher than a threshold; for another example, the event description can indicate that the event should be reported when the measured value is lower than a first threshold (such as a performance indicator).

[0387] In some embodiments, the reported amount may include at least one of the following:

[0388] Resource set ID;

[0389] Resource ID, for example, the reported quantity includes the model label: the strongest K resource IDs;

[0390] L1-RSRP, for example, the reported amount includes the model input: the resource identifier and the L1-RSRP corresponding to the resource identifier, or only the L1-RSRP corresponding to the resource identifier;

[0391] L1-SINR, for example, the reported quantity includes the model input: the resource identifier and the L1-SINR corresponding to the resource identifier, or only the L1-SINR corresponding to the resource identifier;

[0392] a performance metric, where the performance metric may be a performance metric related to the first function;

[0393] Event ID;

[0394] Determined functional operations, which may include activation, deactivation, fallback, etc., may also be referred to as model operations.

[0395] In some embodiments, the reported amount may include a performance indicator of the first function, and the performance indicator may include at least one of the following:

[0396] Prediction accuracy, where the prediction accuracy may be a probability that the predicted best reference signal resource includes an actual best reference signal resource, where the predicted best reference signal resource is the best reference signal resource predicted by the first function, the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, and the best reference signal resource is the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, where N is a positive integer;

[0397] Signal strength difference, which is the difference between the predicted signal strength and the actual signal strength.

[0398] In one implementation, accurate prediction may mean that the best N reference signal resource identifiers predicted by the first function include the actual best reference signal resource identifier, which may be a synchronization signal block (SSB) identifier, a channel state information reference signal (CSI-RS) identifier, or a sounding reference signal (SRS) identifier. Where N is a positive integer. For example, N may be 1 or greater.

[0399] Optionally, the probability may be a ratio between the number of times the predicted best reference signal resource includes the actual best reference signal resource and the total number of predictions. For example, if 100 predictions are made, and the predicted best reference signal resource includes the actual best reference signal resource 90 times and does not include the actual best reference signal resource 10 times, the probability is 90%, i.e., the prediction accuracy is 90%.

[0400] Optionally, the predicted signal strength and actual signal strength may be combined in various ways, for example:

[0401] In one implementation, the predicted signal strength is the predicted signal strength corresponding to the predicted best reference signal resource, and the actual signal strength is the actual signal strength corresponding to the actual best reference signal resource.

[0402] In one implementation, the predicted signal strength is the actual signal strength corresponding to the predicted best reference signal resource, and the actual signal strength is the actually measured signal strength corresponding to the actual best reference signal resource.

[0403] In another implementation, the predicted signal strength is the predicted signal strength corresponding to the predicted best reference signal resource, and the actual signal strength is the actual signal strength corresponding to the predicted best reference signal resource.

[0404] In another implementation, the predicted signal strength is the predicted signal strength corresponding to the actual best reference signal resource, and the actual signal strength is the actual signal strength corresponding to the actual best reference signal resource.

[0405] In one implementation, the signal strength difference may be a L1-RSRP difference, for example, a decibel (dB) difference of an average L1-RSRP.

[0406] For example, the signal strength difference may be a difference between the actual L1-RSRP corresponding to the predicted best reference signal resource identifier and the actual L1-RSRP corresponding to the actual best reference signal resource identifier.

[0407] For another example, the signal strength difference may be a difference between a predicted L1-RSRP corresponding to the predicted best reference signal resource identifier and an actual L1-RSRP corresponding to the predicted best reference signal resource identifier.

[0408] For another example, the signal strength difference may be a difference between an actual L1-RSRP corresponding to an actual best reference signal resource identifier and a predicted L1-RSRP corresponding to the actual best reference signal resource identifier.

[0409] For another example, the signal strength difference may be a difference between a predicted L1-RSRP corresponding to the predicted best reference signal resource identifier and an actual L1-RSRP corresponding to the actual best reference signal resource identifier.

[0410] In another implementation, the signal strength difference may be a L1-SINR difference, for example, a decibel (dB) difference of an average L1-SINR.

[0411] For example, the signal strength difference may be a difference between the actual L1-SINR corresponding to the predicted best reference signal resource identifier and the actual L1-SINR corresponding to the actual best reference signal resource identifier.

[0412] For another example, the signal strength difference may be a difference between a predicted L1-SINR corresponding to the predicted best reference signal resource identifier and an actual L1-SINR corresponding to the predicted best reference signal resource identifier.

[0413] For another example, the signal strength difference may be a difference between an actual L1-SINR corresponding to an actual best reference signal resource identifier and a predicted L1-SINR corresponding to the actual best reference signal resource identifier.

[0414] For another example, the signal strength difference may be a difference between a predicted L1-SINR corresponding to the predicted best reference signal resource identifier and an actual L1-SINR corresponding to the actual best reference signal resource identifier.

[0415] In this way, the performance index of the first function can be determined by the above-mentioned prediction accuracy and / or signal strength difference, so as to perform performance evaluation on the first function.

[0416] In some embodiments, the data acquisition purpose includes at least one of the following:

[0417] Model training;

[0418] Model reasoning;

[0419] Performance monitoring.

[0420] Optionally, the name of the "data acquisition purpose" is not limited, for example, it can be "data collection purpose", "information acquisition purpose", "information collection purpose", "data collection function", "information acquisition function", "information collection function", etc.

[0421] In some embodiments, the content of the first information corresponding to different data acquisition purposes is different or not completely the same.

[0422] For example, if the purpose of data acquisition is performance monitoring, the first information includes the above-mentioned event information, and the reported amount includes the above-mentioned performance indicators. Conversely, if the purpose of data acquisition is not performance monitoring, such as model training or model inference, the first information does not include the above-mentioned event information, and the reported amount may not include the above-mentioned performance indicators.

[0423] In this way, different first information can be determined according to different data acquisition purposes, thereby instructing the terminal device to acquire different data for the first function, thereby improving the flexibility of data acquisition.

[0424] FIG5 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0425] Step S5101: Obtain first information.

[0426] For optional implementations of step S5101, reference may be made to the optional implementations of step S2101 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 .

[0427] In some embodiments, the terminal device may receive the first information sent by the network device, but is not limited thereto. The terminal device may also receive the first information sent by other entities.

[0428] In some embodiments, the terminal device may obtain first information specified by the protocol.

[0429] In some embodiments, the terminal device may obtain the first information from an upper layer(s).

[0430] In some embodiments, the terminal device may perform processing to obtain the first information.

[0431] Step S5102: Acquire data required by the first function.

[0432] In some embodiments, the terminal device can obtain data required for the first function based on the first information.

[0433] For example, the terminal device may perform beam measurement according to the first information so as to obtain input data of the first function and obtain output data predicted by the first function.

[0434] Optionally, both step S5101 and step S5102 are optional steps. For example, the terminal device may only perform step S5101 or only perform step S5102.

[0435] In some embodiments, the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and / or AI function.

[0436] In some embodiments, the first information includes at least one of the following:

[0437] a first identifier, the first identifier corresponding to the first function;

[0438] The data acquisition purpose is used to indicate the use of the data obtained through the first information.

[0439] In some embodiments, the first information includes at least one of the following:

[0440] a first identifier, the first identifier corresponding to the first function;

[0441] a second identifier, where the second identifier corresponds to a configuration related to a measurement, where the measurement is used for data acquisition;

[0442] Beam information, where the beam information is used to indicate measured and / or predicted beam-related information;

[0443] An application example, where the application example is an example of applying the first function to perform beam prediction;

[0444] Reference signal resource information, where the reference signal resource information is used to determine a first beam and / or a second beam, where the first beam is a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

[0445] Coverage information, where the coverage information is used to indicate coverage-related information of the network device;

[0446] Terminal distribution information, the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage area of ​​the network device;

[0447] a data acquisition purpose, where the data acquisition purpose is used to indicate a use of the data obtained through the first information;

[0448] Measurement information, where the measurement information includes relevant information about the terminal device performing measurements based on the first information.

[0449] In some embodiments, the application example includes at least one of the following:

[0450] Spatial beam prediction example;

[0451] Time domain beam prediction example;

[0452] Spatial domain beam prediction example and time domain beam prediction example.

[0453] In some embodiments, the reference signal resource information includes at least one of the following:

[0454] a first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to the first beam;

[0455] a second reference signal resource set, where the reference signal resources of the second reference signal resource set correspond to the second beam;

[0456] A set relationship, the set relationship comprising a relationship between the first reference signal resource set and the second reference signal resource set;

[0457] time information corresponding to the first reference signal resource set;

[0458] time information corresponding to the second reference signal resource set;

[0459] A time pattern, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

[0460] In some embodiments, the set relationship includes any one of the following:

[0461] The first reference signal resource set is a subset of the second reference signal resource set;

[0462] The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam;

[0463] The first reference signal resource set is the same as the second reference signal resource set.

[0464] In some embodiments, the time pattern includes any one of the following:

[0465] a first pattern, where the first pattern is used to indicate N historical periods and M future periods, and measurement results of the N historical periods are used by the terminal device to obtain prediction results of the M future periods based on the first function;

[0466] The second pattern is used to indicate K historical periods, and the measurement results of the K historical periods are used by the terminal device to obtain L prediction results in the K+1th future period based on the first function.

[0467] In some embodiments,

[0468] The coverage information includes the deployment type and / or inter-station distance of the network device; and / or,

[0469] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0470] In some embodiments, the measurement information includes at least one of the following:

[0471] Measurement quantities, the measurement quantities including physical layer reference signal received power L1-RSRP and / or physical layer signal to interference and noise ratio L1-SINR;

[0472] Event information, which is information related to the event that triggers the measurement report;

[0473] Reporting amount, where the reporting amount is used to indicate the information reported by the terminal device to the network device.

[0474] In some embodiments, the reported amount includes a performance indicator of the first function, and the performance indicator includes at least one of the following:

[0475] a prediction accuracy rate, where the prediction accuracy rate is a probability that the predicted best reference signal resource includes an actual best reference signal resource, where the predicted best reference signal resource is the best reference signal resource predicted by the first function, the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, and the best reference signal resource is the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, where N is a positive integer;

[0476] The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0477] In some embodiments, the first identifier includes at least one of the following:

[0478] Functional identification;

[0479] Model identification;

[0480] Dataset identifier;

[0481] Data acquisition configuration identifier;

[0482] Data acquisition identification;

[0483] Condition identification;

[0484] Additional condition identification.

[0485] In some embodiments, at least one of the first identifiers is the same, and the first information corresponds to the same first function.

[0486] In some embodiments, the second identifier includes at least one of the following:

[0487] measurement identification;

[0488] Measurement object identification;

[0489] Report ID.

[0490] In some embodiments, the beam information includes at least one of the following:

[0491] A beam codebook identifier, where the beam codebook identifier is used to indicate codebook information used by the network device to send a beam;

[0492] Antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

[0493] Beam type, where the beam type is used to indicate the type of beam sent by the network device.

[0494] In some embodiments, the data acquisition purpose includes at least one of the following:

[0495] Model training;

[0496] Model reasoning;

[0497] Performance monitoring.

[0498] In some embodiments, the content of the first information corresponding to different data acquisition purposes is different.

[0499] FIG6 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG6 , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0500] Step S6101: Obtain first information.

[0501] For optional implementations of step S6101, reference may be made to the optional implementations of step S2101 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 .

[0502] In some embodiments, the terminal device may receive the first information sent by the network device, but is not limited thereto. The terminal device may also receive the first information sent by other entities.

[0503] Figure 7 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in Figure 7, the embodiment of the present disclosure relates to a communication method, which can be executed by a network device, and the method includes:

[0504] Step S7101: Send the first information.

[0505] For optional implementations of step S7101, reference may be made to the optional implementations of step S2101 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 .

[0506] In some embodiments, the network device may send the first information to the terminal device, but is not limited thereto. The network device may also send the first information to other entities.

[0507] In some embodiments, the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and / or AI function.

[0508] In some embodiments, the first information includes at least one of the following:

[0509] a first identifier, the first identifier corresponding to the first function;

[0510] a second identifier, where the second identifier corresponds to a configuration related to a measurement, where the measurement is used for data acquisition;

[0511] Beam information, where the beam information is used to indicate measured and / or predicted beam-related information;

[0512] An application example, where the application example is an example of applying the first function to perform beam prediction;

[0513] Reference signal resource information, where the reference signal resource information is used to determine a first beam and / or a second beam, where the first beam is a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

[0514] Coverage information, where the coverage information is used to indicate coverage-related information of the network device;

[0515] Terminal distribution information, the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage area of ​​the network device;

[0516] a data acquisition purpose, where the data acquisition purpose is used to indicate a use of the data obtained through the first information;

[0517] Measurement information, where the measurement information includes relevant information about the terminal device performing measurements based on the first information.

[0518] In some embodiments, the application example includes at least one of the following:

[0519] Spatial beam prediction example;

[0520] Time domain beam prediction example;

[0521] Spatial domain beam prediction example and time domain beam prediction example.

[0522] In some embodiments, the reference signal resource information includes at least one of the following:

[0523] a first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to the first beam;

[0524] a second reference signal resource set, where the reference signal resources of the second reference signal resource set correspond to the second beam;

[0525] A set relationship, the set relationship comprising a relationship between the first reference signal resource set and the second reference signal resource set;

[0526] time information corresponding to the first reference signal resource set;

[0527] time information corresponding to the second reference signal resource set;

[0528] A time pattern, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

[0529] In some embodiments, the set relationship includes any one of the following:

[0530] The first reference signal resource set is a subset of the second reference signal resource set;

[0531] The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam;

[0532] The first reference signal resource set is the same as the second reference signal resource set.

[0533] In some embodiments, the time pattern includes any one of the following:

[0534] a first pattern, where the first pattern is used to indicate N historical periods and M future periods, and measurement results of the N historical periods are used by the terminal device to obtain prediction results of the M future periods based on the first function;

[0535] The second pattern is used to indicate K historical periods, and the measurement results of the K historical periods are used by the terminal device to obtain L prediction results in the K+1th future period based on the first function.

[0536] In some embodiments,

[0537] The coverage information includes the deployment type and / or inter-station distance of the network device; and / or,

[0538] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0539] In some embodiments, the measurement information includes at least one of the following:

[0540] Measurement quantities, the measurement quantities including physical layer reference signal received power L1-RSRP and / or physical layer signal to interference and noise ratio L1-SINR;

[0541] Event information, which is information related to the event that triggers the measurement report;

[0542] Reporting amount, where the reporting amount is used to indicate the information reported by the terminal device to the network device.

[0543] In some embodiments, the reported amount includes a performance indicator of the first function, and the performance indicator includes at least one of the following:

[0544] a prediction accuracy rate, where the prediction accuracy rate is a probability that the predicted best reference signal resource includes an actual best reference signal resource, where the predicted best reference signal resource is the best reference signal resource predicted by the first function, the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, and the best reference signal resource is the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, where N is a positive integer;

[0545] The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0546] In some embodiments, the first identifier includes at least one of the following:

[0547] Functional identification;

[0548] Model identification;

[0549] Dataset identifier;

[0550] Data acquisition configuration identifier;

[0551] Data acquisition identification;

[0552] Condition identification;

[0553] Additional condition identification.

[0554] In some embodiments, at least one of the first identifiers is the same, and the first information corresponds to the same first function.

[0555] In some embodiments, the second identifier includes at least one of the following:

[0556] measurement identification;

[0557] Measurement object identification;

[0558] Report ID.

[0559] In some embodiments, the beam information includes at least one of the following:

[0560] A beam codebook identifier, where the beam codebook identifier is used to indicate codebook information used by the network device to send a beam;

[0561] Antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

[0562] Beam type, where the beam type is used to indicate the type of beam sent by the network device.

[0563] In some embodiments, the data acquisition purpose includes at least one of the following:

[0564] Model training;

[0565] Model reasoning;

[0566] Performance monitoring.

[0567] In some embodiments, the content of the first information corresponding to different data acquisition purposes is different.

[0568] In some embodiments of the present disclosure, a communication system is provided, which may include a terminal device and a network device, wherein the terminal device can execute the communication method executed by the terminal device in the aforementioned embodiment of the present disclosure; the network device can execute the communication method executed by the network device in the aforementioned embodiment of the present disclosure.

[0569] 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 device 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.

[0570] 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), and the functions of some or all of the above units or modules are realized 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.

[0571] 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, and the logical relationship of the above hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by a processor as 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 to implement 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 ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0572] FIG8 is a schematic diagram of the structure of a terminal device proposed in an embodiment of the present disclosure. As shown in FIG8 , the terminal device 101 may include: a first transceiver module 211 and / or a first processing module 212 .

[0573] In some embodiments, the first transceiver module 211 is configured to receive first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and / or AI function;

[0574] In some embodiments, the first processing module 212 is configured to obtain data required for the first function according to the first information.

[0575] In some embodiments, the first information includes at least one of the following: a first identifier, the first identifier corresponds to the first function, the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; a data acquisition purpose, the data acquisition purpose includes at least one of model training, model reasoning, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different.

[0576] In some embodiments, the specific implementation of the first information can refer to the description in the aforementioned embodiments of the present disclosure and will not be repeated here.

[0577] In some embodiments, the first transceiver module 211 may include a sending module and / or a receiving module. The sending module and the receiving module may be separate or integrated.

[0578] In some embodiments, the first processing module may be a single module or include multiple submodules. Optionally, the multiple submodules each execute all or part of the steps required by the first processing module. Optionally, the first processing module and the first processor may be interchangeable.

[0579] FIG9 is a schematic diagram of the structure of a network device proposed in an embodiment of the present disclosure. As shown in FIG9 , the network device 102 may include a second transceiver module 221 .

[0580] In some embodiments, the second transceiver module 221 is configured to send first information to the terminal device; the first information is information corresponding to the first function, the first function is used to perform beam prediction, the first function is an artificial intelligence AI model and / or AI function, and the first information is used to instruct the terminal device to obtain the data required for the first function according to the first information; wherein, the first information includes at least one of the following: a first identifier, the first identifier corresponds to the first function, the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; data acquisition purpose, the data acquisition purpose includes at least one of model training, model reasoning, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different.

[0581] In some embodiments, the specific implementation of the first information can refer to the description in the aforementioned embodiments of the present disclosure and will not be repeated here.

[0582] In some embodiments, the second transceiver module 221 may include a sending module and / or a receiving module. The sending module and the receiving module may be separate or integrated.

[0583] In some embodiments, the network device may further include a second processing module, which may be a single module or include multiple submodules. Optionally, the multiple submodules each execute all or part of the steps required by the second processing module. Optionally, the second processing module may be interchangeable with the second processor.

[0584] Figure 10 is a schematic diagram of the structure of a communication device 300 proposed in an embodiment of the present disclosure. Communication device 300 can be a network device (e.g., an access network device, a core network device, etc.), or a terminal device (e.g., a user device, etc.). It can also be a chip, chip system, or processor that supports a network device to implement any of the above methods, or a chip, chip system, or processor that supports a terminal device to implement any of the above methods. Communication device 300 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.

[0585] As shown in Figure 10, the communication device 300 includes one or more processors 301. The processor 301 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 300 can be used to perform any of the above methods. Optionally, one or more processors 301 are used to call instructions to enable the communication device 300 to perform any of the above methods.

[0586] In some embodiments, the communication device 300 may further include one or more transceivers 302. When the communication device 300 includes one or more transceivers 302, the transceiver 302 may perform the communication steps such as sending and / or receiving in the above method (e.g., S2101), and the processor 301 may perform other processing steps.

[0587] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0588] In some embodiments, the communication device 300 also includes one or more memories 303 for storing data. Alternatively, all or part of the memories 303 may be located outside the communication device 300. In alternative embodiments, the communication device 300 may include one or more interface circuits 304. Optionally, the interface circuits 304 are connected to the memories 303 and can be used to receive data from the memories 303 or other devices, or to send data to the memories 303 or other devices. For example, the interface circuits 304 can read data stored in the memories 303 and send the data to the processor 301.

[0589] The communication device 300 described in the above embodiment may be a network device or a terminal device, but the scope of the communication device 300 described in the present disclosure is not limited thereto, and the structure of the communication device 300 may not be limited by FIG10. 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 and 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.

[0590] The embodiment of the present disclosure further provides a chip, which includes one or more processors and can be used to execute any of the above methods.

[0591] In some embodiments, the chip further includes one or more interface circuits. Alternatively, the terms interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, the chip further includes one or more memories for storing data. Alternatively, all or part of the memories may be located off-chip.

[0592] Optionally, the interface circuit is connected to the memory, and the interface circuit can be used to receive data from the memory or other devices, and the interface circuit can be used to send data to the memory or other devices. For example, the interface circuit can read data stored in the memory and send the data to the processor.

[0593] In some embodiments, the interface circuit performs the communication steps of sending and / or receiving in the above method (e.g., S2101). For example, the interface circuit performing the communication steps of sending and / or receiving in the above method means that the interface circuit performs data exchange between the processor, chip, memory, or transceiver device. In some embodiments, the processor may perform other processing steps.

[0594] 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.

[0595] The embodiments of the present disclosure further provide a storage medium having instructions stored thereon, which, when executed on a communication device, causes the communication device 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 transient storage medium.

[0596] The embodiments of the present disclosure further provide a program product, which, when executed by a communication device, enables the communication device to perform any of the above methods. Optionally, the program product may be a computer program product.

[0597] The embodiments of the present disclosure further provide a computer program, which, when executed on a computer, enables the computer to execute any of the above methods.

Claims

1. A communication method, characterized in that, Executed by a terminal device, the method includes: Receiving first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence (AI) model and / or an AI function; Obtaining data required by the first function according to the first information; Wherein, the first information includes at least one of the following: A first identifier corresponding to the first function, the first identifier includes at least one of a function identifier, a model identifier, a dataset identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; A data acquisition purpose, the data acquisition purpose includes at least one of model training, model inference, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different.

2. The method according to claim 1, wherein The first information further includes at least one of the following: A second identifier corresponding to a measurement-related configuration, the measurement is used for data acquisition; Beam information, the beam information is used to indicate information related to the beam for measurement and / or prediction; An application instance, the application instance is an instance of applying the first function to perform beam prediction; Reference signal resource information, the reference signal resource information is used to determine a first beam and / or a second beam, the first beam is the beam corresponding to the input value of the first function, and the second beam is the beam corresponding to the output value of the first function; Coverage information, the coverage information is used to indicate information related to the coverage of the network device; Terminal distribution information, the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage of the network device; Measurement information, the measurement information includes information related to the measurement performed by the terminal device based on the first information.

3. The method according to claim 2, wherein The application instance includes at least one of the following: An airspace beam prediction instance; A time-domain beam prediction instance; An airspace beam prediction instance and a time-domain beam prediction instance.

4. The method according to claim 2, wherein The reference signal resource information includes at least one of the following: A first reference signal resource set, the reference signal resources in the first reference signal resource set correspond to the first beam; A second reference signal resource set, the reference signal resources in the second reference signal resource set correspond to the second beam; A set relationship, the set relationship includes the relationship between the first reference signal resource set and the second reference signal resource set; Time information corresponding to the first reference signal resource set; Time information corresponding to the second reference signal resource set; A time pattern, the time pattern is the pattern of the time corresponding to the first reference signal resource set and the time corresponding to the second reference signal resource set.

5. The method according to claim 4, characterized in that The set relationship includes any one of the following: The first reference signal resource set is a subset of the second reference signal resource set; The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam; The first reference signal resource set is the same as the second reference signal resource set.

6. The method according to claim 4, wherein The time pattern includes any one of the following: A first pattern, where the first pattern is used to indicate N historical periods and M future periods, and the measurement results of the N historical periods are used by the terminal device to obtain the prediction results of the M future periods based on the first function; A second pattern, where the second pattern is used to indicate K historical periods, and the measurement results of the K historical periods are used by the terminal device to obtain L prediction results within the (K + 1)-th future period based on the first function.

7. The method according to claim 2, wherein the coverage information includes the deployment type and / or the inter-site distance of the network device; and / or the terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

8. The method according to claim 2, wherein The measurement information includes at least one of the following: A measurement quantity, where the measurement quantity includes the physical layer reference signal received power L1-RSRP and / or the physical layer signal-to-interference-and-noise ratio L1-SINR; Event information, where the event information is information related to an event that triggers measurement reporting; A reporting quantity, where the reporting quantity is used to indicate the information reported by the terminal device to the network device.

9. The method according to claim 8, wherein The reporting quantity includes the performance metrics of the first function, and the performance metrics include at least one of the following: The prediction accuracy rate, where the prediction accuracy rate is the probability that the predicted best reference signal resource includes the actual best reference signal resource. The predicted best reference signal resource is the best reference signal resource predicted by the first function, the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, and the best reference signal resource is the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, where N is a positive integer; The signal strength difference, where the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

10. The method according to claim 2, wherein The second identifier includes at least one of the following: A measurement identifier; A measurement object identifier; A report identifier.

11. The method according to claim 2, wherein The beam information includes at least one of the following: A beam codebook identifier, where the beam codebook identifier is used to indicate the codebook information for the network device to send beams; An antenna configuration identifier, where the antenna configuration identifier is used to indicate the antenna configuration information of the network device; A beam type, where the beam type is used to indicate the type of the beam sent by the network device.

12. The method according to any one of claims 1 to 11, characterized in that, At least one of the first identifiers is the same, and the first information corresponds to the same first function.

13. A communication method, characterized in that, Executed by a network device, the method includes: Sending first information to the terminal device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, the first function is an artificial intelligence AI model and / or an AI function, and the first information is used to instruct the terminal device to obtain the data required by the first function according to the first information; Wherein, the first information includes at least one of the following: A first identifier, the first identifier corresponds to the first function, and the first identifier includes at least one of a function identifier, a model identifier, a dataset identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; The purpose of data acquisition, which includes at least one of model training, model inference, and performance monitoring, and the content of the first information corresponding to different purposes of data acquisition is different.

14. The method according to claim 13, characterized in that, The first information includes at least one of the following: A first identifier, which corresponds to the first function; A second identifier, which corresponds to the configuration related to measurement for data acquisition; Beam information, which is used to indicate information related to the beam for measurement and / or prediction; An application instance, which is an instance of performing beam prediction by applying the first function; Reference signal resource information, which is used to determine a first beam and / or a second beam, where the first beam is the beam corresponding to the input value of the first function, and the second beam is the beam corresponding to the output value of the first function; Coverage information, which is used to indicate information related to the coverage of the network device; Terminal distribution information, which is used to indicate the distribution of multiple terminal devices within the coverage range of the network device; The purpose of data acquisition, which is used to indicate the role of the data obtained through the first information; Measurement information, which includes information related to the measurement performed by the terminal device based on the first information.

15. The method according to claim 14, wherein The application instance includes at least one of the following: An airspace beam prediction instance; A time domain beam prediction instance; An airspace beam prediction instance and a time domain beam prediction instance.

16. The method according to claim 14, wherein The reference signal resource information includes at least one of the following: A first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the first beam; A second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the second beam; A set relationship, which includes the relationship between the first reference signal resource set and the second reference signal resource set; Time information corresponding to the first reference signal resource set; Time information corresponding to the second reference signal resource set; A time pattern, which is the pattern of the time corresponding to the first reference signal resource set and the time corresponding to the second reference signal resource set.

17. The method according to claim 16, characterized in that, The set relationship includes any one of the following: The first reference signal resource set is a subset of the second reference signal resource set; The first reference signal resource set is different from the second reference signal resource set, the beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam; The first reference signal resource set is the same as the second reference signal resource set.

18. The method according to claim 16, wherein The time pattern includes any one of the following: A first pattern, which is used to indicate N historical periods and M future periods, and the measurement results of the N historical periods are used for the terminal device to obtain the prediction results of the M future periods based on the first function; A second pattern, which is used to indicate K historical periods, and the measurement results of the K historical periods are used for the terminal device to obtain L prediction results within the (K + 1)-th future period based on the first function.

19. The method according to claim 14, wherein the coverage information includes the deployment type of the network device and / or the station spacing; and / or, the terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

20. The method according to claim 14, wherein The measurement information includes at least one of the following: a measurement quantity, the measurement quantity including a physical layer reference signal received power L1-RSRP and / or a physical layer signal-to-interference-plus-noise ratio L1-SINR; event information, the event information being information related to an event that triggers measurement reporting; a reporting quantity, the reporting quantity being used to indicate information reported by the terminal device to the network device.

21. The method according to claim 20, wherein The reporting quantity includes performance indicators of the first function, and the performance indicators include: a prediction accuracy rate, the prediction accuracy rate being the probability that the predicted best reference signal resource includes the actual best reference signal resource, the predicted best reference signal resource being the best reference signal resource predicted by the first function, the actual best reference signal resource being the best reference signal resource actually measured by the terminal device, the best reference signal resource being the top N reference signal resources with the largest L1-RSRP or L1-SINR in the second reference signal resource set, and N being a positive integer; a signal strength difference, the signal strength difference being the difference between the predicted signal strength and the actual signal strength.

22. The method according to claim 14, wherein The second identifier includes at least one of the following: a measurement identifier; a measurement object identifier; a report identifier.

23. The method according to claim 14, wherein The beam information includes at least one of the following: a beam codebook identifier, the beam codebook identifier being used to indicate the codebook information for the network device to send beams; an antenna configuration identifier, the antenna configuration identifier being used to indicate the antenna configuration information of the network device; a beam type, the beam type being used to indicate the type of the beam sent by the network device.

24. The method according to any one of claims 13 to 23, characterized in that At least one of the first identifiers is the same, and the first information corresponds to the same first function.

25. A terminal device, characterized in that, including: a first transceiver module, configured to receive first information sent by the network device; the first information being information corresponding to a first function, the first function being used to perform beam prediction, and the first function being an artificial intelligence AI model and / or an AI function; a first processing module, configured to obtain data required by the first function according to the first information; wherein, the first information includes at least one of the following: a first identifier, the first identifier corresponding to the first function, and the first identifier including at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; a data acquisition purpose, the data acquisition purpose including at least one of model training, model inference, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes being different.

26. A network device, characterized in that, including: a second transceiver module, configured to send first information to the terminal device The first information is information corresponding to a first function, and the first function is used to perform beam prediction. The first function is an artificial intelligence (AI) model and / or an AI function. The first information is used to instruct the terminal device to obtain data required by the first function according to the first information. Among them, the first information includes at least one of the following: a first identifier corresponding to the first function, and the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; a data acquisition purpose, and the data acquisition purpose includes at least one of model training, model inference, and performance monitoring. The content of the first information corresponding to different data acquisition purposes is different.

27. A communication device, characterized in that, Comprising: One or more processors; Among them, the communication device is used to execute the communication method according to any one of claims 1 to 12 or claims 13 to 24.

28. A storage medium storing instructions, characterized in that, When the instruction runs on the communication device, the communication device is caused to execute the communication method according to any one of claims 1 to 12 or claims 13 to 24.

29. A communication system, characterized in that, The communication system includes a terminal device and a network device. Among them, the terminal device is configured to implement the communication method according to any one of claims 1 to 12, and the network device is configured to implement the communication method according to any one of claims 13 to 24.

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