Communication method and device, and storage medium

Abstract

The embodiment of the invention relates to a communication method, equipment and a storage medium. The method comprises the following steps: receiving first information sent by network equipment; 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; acquiring data required by the first function according to the first information; wherein the first information comprises at least one of the following items: a first identifier, the first identifier corresponds to a first function, and the first identifier comprises 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; the data acquisition purposes comprise at least one of model training, model reasoning and performance monitoring, and the contents of the first information corresponding to different data acquisition purposes are different. Therefore, the information corresponding to the first function can be flexibly configured, and the management efficiency of the first function is improved.

Description

[0001] This disclosure is a divisional application based on a Chinese patent application submitted to the Chinese Patent Office on January 12, 2024, with the application number 202410052316.9 and the title "Communication Method, Device, and Storage Medium". Technical Field

[0002] This disclosure relates to the field of communication technologies, and in particular, to a communication method, device, and storage medium. Background Art

[0003] With the progress of communication technologies, a prediction function based on Artificial Intelligence (AI) has been introduced in wireless communication systems. This prediction function can be an AI function and / or an AI model, and through this prediction function, prediction data in certain scenarios can be obtained, thereby improving the performance of the network. Summary of the Invention

[0004] Embodiments of this disclosure propose a communication method, device, and storage medium.

[0005] According to a first aspect of the embodiments of this disclosure, a communication method is proposed, which is executed by a terminal device. The method includes:

[0006] Receiving first information sent by a network device; the first information is information corresponding to a first function for performing beam prediction, and the first function is an Artificial Intelligence (AI) model and / or an AI function;

[0007] Obtaining data required by the first function according to the first information;

[0008] Wherein, the first information includes at least one of the following:

[0009] A first identifier corresponding to the first function, the first identifier including 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;

[0010] 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 is different.

[0011] According to a second aspect of the embodiments of this disclosure, a communication method is proposed, which is executed by a network device. The method includes:

[0012] Send the first information to the terminal device; the first information is the 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 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;

[0013] Wherein, the first information includes at least one of the following:

[0014] 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 dataset identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier;

[0015] 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 is different.

[0016] According to the third aspect of the embodiments of the present disclosure, a terminal device is provided, including:

[0017] A first transceiver module, configured to receive the first information sent by the network device; the first information is the 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 an AI function;

[0018] A first processing module, configured 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 corresponding to the first function, the first identifier including 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 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 is different.

[0019] According to the fourth aspect of the embodiments of the present disclosure, a network device is provided, including:

[0020] A second transceiver module, configured to send 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 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 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 inference, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different.

[0021] According to a fifth aspect of the embodiments of the present disclosure, a communication device is provided, including: one or more processors; wherein, the communication device can be used to execute the optional implementation manners of the first aspect or the second aspect.

[0022] According to a sixth aspect of the embodiments of the present disclosure, a storage medium is provided, and the storage medium stores instructions, which when run on a communication device, cause the communication device to execute the method described in the optional implementation manners of the first aspect or the second aspect.

[0023] According to a seventh aspect of the embodiments of the present disclosure, a communication system is provided, 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.

[0024] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: 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, the first identifier corresponds 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, 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. In this way, the information corresponding to the first function can be flexibly configured, and the management efficiency of the first function can be improved.

[0025] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following introduces the drawings required for describing the embodiments. The following drawings are only some embodiments of the present disclosure and do not specifically limit the protection scope of the present disclosure.

[0027] Figure 1 It is a schematic diagram of the architecture of a communication system shown according to an embodiment of the present disclosure.

[0028] Figure 2 It is an interaction schematic diagram of a communication method shown according to an embodiment of the present disclosure.

[0029] Figure 3 It is a schematic diagram of a first pattern shown according to an embodiment of the present disclosure.

[0030] Figure 4 It is a schematic diagram of a second pattern shown according to an embodiment of the present disclosure.

[0031] Figure 5 It is a schematic flowchart of a communication method shown according to an embodiment of the present disclosure.

[0032] Figure 6 It is a schematic flowchart of a communication method shown according to an embodiment of the present disclosure.

[0033] Figure 7 It is a schematic flowchart of a communication method shown according to an embodiment of the present disclosure.

[0034] Figure 8 It is a schematic diagram of the structure of a terminal device shown according to an embodiment of the present disclosure.

[0035] Figure 9 It is a schematic diagram of the structure of a network device shown according to an embodiment of the present disclosure.

[0036] Figure 10 It is a schematic diagram of the structure of a communication device shown according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Embodiments of the present disclosure propose a communication method, device, and storage medium.

[0038] In a first aspect, embodiments of the present disclosure propose a communication method executed by a terminal device, the method including:

[0039] 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;

[0040] Obtain the data required for the first function according to the first information.

[0041] In the above embodiments, the information corresponding to the first function can be flexibly configured, improving the management efficiency of the first function.

[0042] Combined with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

[0043] A first identifier, where the first identifier corresponds to the first function;

[0044] A data acquisition purpose, where the data acquisition purpose is used to indicate the role of the data obtained through the first information.

[0045] Combined with some embodiments of the first aspect, in some embodiments, the first information further includes at least one of the following:

[0046] A second identifier, where the second identifier corresponds to a measurement-related configuration, and the measurement is used for data acquisition;

[0047] Beam information, where the beam information is used to indicate information related to the beam for measurement and / or prediction;

[0048] An application instance, where the application instance is an instance of performing beam prediction by applying the first function;

[0049] Reference signal resource information, where 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;

[0050] Coverage information, where the coverage information is used to indicate information related to the coverage of the network device;

[0051] Terminal distribution information, where the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage range of the network device;

[0052] Measurement information, where the measurement information includes information related to the measurement performed by the terminal device based on the first information.

[0053] In the above embodiments, through any one of the above, the information required for the first function can be determined, improving the flexibility of the terminal device in configuring information related to the first function.

[0054] Combined with some embodiments of the first aspect, in some embodiments, the application instance includes at least one of the following:

[0055] An airspace beam prediction instance;

[0056] A time-domain beam prediction instance;

[0057] Spatial domain beam prediction examples and time domain beam prediction examples.

[0058] In the above embodiments, through application examples, the first information can be controlled to be applied to spatial domain and / or time domain beam prediction examples to better adapt to different scenarios.

[0059] Combined with some embodiments of the first aspect, in some embodiments, the reference signal resource information includes at least one of the following:

[0060] A first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the first beam;

[0061] A second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the second beam;

[0062] A set relationship, where the set relationship includes the relationship between the first reference signal resource set and the second reference signal resource set;

[0063] Time information corresponding to the first reference signal resource set;

[0064] Time information corresponding to the second reference signal resource set;

[0065] A time pattern, where 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.

[0066] In the above embodiments, the reference signal resource information related to the first function can be flexibly determined.

[0067] Combined with some embodiments of the first aspect, in some embodiments, the set relationship includes any one of the following:

[0068] The first reference signal resource set is a subset of the second reference signal resource set;

[0069] 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;

[0070] The first reference signal resource set is the same as the second reference signal resource set.

[0071] In the above embodiments, the set relationship between the first reference signal resource set and the second reference signal resource set can be flexibly determined.

[0072] Combined with some embodiments of the first aspect, in some embodiments, the time pattern includes any one of the following:

[0073] 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;

[0074] 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.

[0075] In the above embodiments, 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.

[0076] Combined with some embodiments of the first aspect, in some embodiments,

[0077] The coverage information includes the deployment type and / or the station spacing of the network device; and / or,

[0078] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0079] In the above embodiments, the coverage information and / or the terminal distribution information can be flexibly indicated so that the terminal device can obtain data according to this information.

[0080] Combined with some embodiments of the first aspect, in some embodiments, the measurement information includes at least one of the following:

[0081] Measurement quantities, where the measurement quantities include the physical layer reference signal received power L1-RSRP and / or the physical layer signal-to-interference-plus-noise ratio L1-SINR;

[0082] Event information, where the event information is information related to the event that triggers the measurement report;

[0083] Reporting quantities, where the reporting quantities are used to indicate the information reported by the terminal device to the network device.

[0084] In the above embodiments, the measurement information can be flexibly indicated so that the terminal device can obtain data according to this measurement information.

[0085] Combined with some embodiments of the first aspect, in some embodiments, the reporting quantities include the performance indicators of the first function, and the performance indicators include at least one of the following:

[0086] 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, and the actual best reference signal resource is the best reference signal resource actually measured by the terminal device. 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, and N is a positive integer;

[0087] Signal strength difference, where the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0088] In the above embodiments, the reporting quantity can be flexibly indicated so that the terminal device can obtain data according to the reporting quantity.

[0089] Combined with some embodiments of the first aspect, in some embodiments, the first identifier includes at least one of the following:

[0090] Function identifier;

[0091] Model identifier;

[0092] Dataset identifier;

[0093] Data acquisition configuration identifier;

[0094] Data acquisition identifier;

[0095] Condition identifier;

[0096] Additional condition identifier.

[0097] Combined 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.

[0098] In the above embodiments, the first identifier can be flexibly indicated so that the terminal device can determine the first function corresponding to the first information according to the first identifier.

[0099] Combined with some embodiments of the first aspect, in some embodiments, the second identifier includes at least one of the following:

[0100] Measurement identifier;

[0101] Measurement object identifier;

[0102] Report identifier.

[0103] In the above embodiments, the second identifier can be flexibly indicated so that the terminal device can determine the measurement-related information according to the second identifier.

[0104] In some embodiments in combination with some embodiments of the first aspect, the beam information includes at least one of the following:

[0105] A beam codebook identifier, which is used to indicate the codebook information used by the network device to send beams;

[0106] An antenna configuration identifier, which is used to indicate the antenna configuration information of the network device;

[0107] A beam type, which is used to indicate the type of beam sent by the network device.

[0108] In the above embodiments, the beam information can be flexibly indicated so that the terminal device can determine the beam-related information.

[0109] In some embodiments in combination with some embodiments of the first aspect, the data acquisition purposes include at least one of the following:

[0110] Model training;

[0111] Model inference;

[0112] Performance monitoring.

[0113] In some embodiments in combination with some embodiments of the first aspect, the content of the first information corresponding to different data acquisition purposes is different.

[0114] In the above embodiments, the data acquisition purpose can be flexibly indicated so that the terminal device can determine the data acquisition purpose and flexibly acquire the corresponding data according to the data acquisition purpose.

[0115] In a second aspect, embodiments of the present disclosure propose a communication method, which is executed by a network device. The method includes:

[0116] 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 acquire the data required by the first function according to the first information.

[0117] In the above embodiments, the information corresponding to the first function can be flexibly configured to improve the management efficiency of the first function.

[0118] In some embodiments in combination with some embodiments of the second aspect, the first information includes at least one of the following:

[0119] A first identifier, which corresponds to the first function;

[0120] The purpose of data acquisition, which is used to indicate the function of the data obtained through the first information.

[0121] In combination with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following:

[0122] A second identifier, where the second identifier corresponds to a configuration related to measurement, and the measurement is for data acquisition;

[0123] Beam information, which is used to indicate information related to the beam for measurement and / or prediction;

[0124] An application instance, which is an instance of performing beam prediction by applying the first function;

[0125] Reference signal resource information, which 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;

[0126] Coverage information, which is used to indicate information related to the coverage of a network device;

[0127] Terminal distribution information, which is used to indicate the distribution of multiple terminal devices within the coverage of the network device;

[0128] Measurement information, which includes information related to the measurement performed by the terminal device based on the first information.

[0129] In combination with some embodiments of the second aspect, in some embodiments, the application instance includes at least one of the following:

[0130] An airspace beam prediction instance;

[0131] A time-domain beam prediction instance;

[0132] An airspace beam prediction instance and a time-domain beam prediction instance.

[0133] In combination with some embodiments of the second aspect, in some embodiments, the reference signal resource information includes at least one of the following:

[0134] A first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the first beam;

[0135] A second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the second beam;

[0136] A set relationship, which includes the relationship between the first reference signal resource set and the second reference signal resource set;

[0137] Time information corresponding to the first reference signal resource set;

[0138] Time information corresponding to the second reference signal resource set;

[0139] A time pattern, where the time pattern is a pattern of the time corresponding to the first reference signal resource set and the time corresponding to the second reference signal resource set.

[0140] Combined with some embodiments of the second aspect, in some embodiments, the set relationship includes any one of the following:

[0141] The first reference signal resource set is a subset of the second reference signal resource set;

[0142] 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;

[0143] The first reference signal resource set is the same as the second reference signal resource set.

[0144] Combined with some embodiments of the second aspect, in some embodiments, the time pattern includes any one of the following:

[0145] 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 by the terminal device to obtain the prediction results of the M future periods based on the first function;

[0146] A second pattern, which 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.

[0147] Combined with some embodiments of the second aspect, in some embodiments,

[0148] The coverage information includes the deployment type of the network device and / or the site spacing; and / or,

[0149] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0150] Combined with some embodiments of the second aspect, in some embodiments, the measurement information includes at least one of the following:

[0151] 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-plus-noise ratio L1-SINR;

[0152] Event information, where the event information is information related to an event that triggers measurement reporting;

[0153] Reporting quantity, where the reporting quantity is used to indicate information reported by the terminal device to the network device.

[0154] In combination with some embodiments of the second aspect, in some embodiments, the reporting quantity includes performance indicators of the first function, and the performance indicators include:

[0155] 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, and the actual best reference signal resource is the best reference signal resource actually measured by the terminal device. 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, and N is a positive integer;

[0156] Signal strength difference, where the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0157] In combination with some embodiments of the second aspect, in some embodiments, the first identifier includes at least one of the following:

[0158] Function identifier;

[0159] Model identifier;

[0160] Dataset identifier;

[0161] Data acquisition configuration identifier;

[0162] Data acquisition identifier;

[0163] Condition identifier;

[0164] Additional condition identifier.

[0165] 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.

[0166] In combination with some embodiments of the second aspect, in some embodiments, the second identifier includes at least one of the following:

[0167] Measurement identifier;

[0168] Measurement object identifier;

[0169] Report identifier.

[0170] In combination with some embodiments of the second aspect, in some embodiments, the beam information includes at least one of the following:

[0171] A beam codebook identifier, which is used to indicate the codebook information for the network device to send beams;

[0172] An antenna configuration identifier, which is used to indicate the antenna configuration information of the network device;

[0173] A beam type, which is used to indicate the type of the beam sent by the network device.

[0174] Combined with some embodiments of the second aspect, in some embodiments, the purposes of data acquisition include at least one of the following:

[0175] Model training;

[0176] Model inference;

[0177] Performance monitoring.

[0178] Combined with some embodiments of the second aspect, in some embodiments, the content of the first information corresponding to different purposes of data acquisition is different.

[0179] In a third aspect, an embodiment of the present disclosure provides a terminal device, which may include a first transceiver module and a first processing module; wherein, the terminal device may be used to execute the optional implementation manners of the first aspect.

[0180] In a fourth aspect, an embodiment of the present disclosure provides a network device, which may include a second transceiver module; wherein, the network device may be used to execute the optional implementation manners of the second aspect.

[0181] In a fifth aspect, an embodiment of the present disclosure provides a communication device, which may include: one or more processors; wherein, the communication device may be used to execute the optional implementation manners of the first aspect or the second aspect.

[0182] In a sixth aspect, an embodiment of the present disclosure provides a storage medium, which stores instructions that, when running on a communication device, cause the communication device to execute the method described in the optional implementation manners of the first aspect or the second aspect.

[0183] In a seventh aspect, an embodiment of the present disclosure provides a program product, which, when executed by a communication device, causes the communication device to execute the method described in the optional implementation manners of the first aspect or the second aspect.

[0184] In an eighth aspect, an embodiment of the present disclosure provides a computer program, which, when running on a computer, causes the computer to execute the method described in the optional implementation manners of the first aspect or the second aspect.

[0185] In a ninth aspect, an embodiment of the present disclosure provides a chip or a chip system. The chip or the chip system includes a processing circuit configured to execute the method described in the optional implementation manners of the first aspect or the second aspect.

[0186] In a tenth aspect, an embodiment of the present disclosure provides 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 manners of the first aspect, and the network device is configured to execute the method described in the optional implementation manners of the second aspect.

[0187] It can be understood that the above-mentioned terminal device, network device, communication device, communication system, storage medium, program product, computer program, chip or chip system can all be used to execute the method proposed by the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0188] An embodiment of the present disclosure provides a communication method, device and storage medium. In some embodiments, terms such as communication method and information processing method can be replaced with each other; terms such as communication device and information processing device, communication equipment can be replaced with each other; terms such as information processing system and communication system can be replaced with each other.

[0189] The embodiments of the present disclosure are not exhaustive, but only schematic illustrations of some embodiments, and do not constitute specific limitations on the protection scope of the present disclosure. Without contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily. For example, the solution after removing some steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily. In addition, the optional implementation manners in an embodiment can be combined arbitrarily; furthermore, the embodiments can be combined arbitrarily. For example, some or all of the steps of different embodiments can be combined arbitrarily, and an embodiment can be combined arbitrarily with the optional implementation manners of other embodiments.

[0190] In each embodiment of the present disclosure, if there is no special description and logical conflict, the terms and / or descriptions between the embodiments are consistent and can be cited from each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0191] 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.

[0192] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or may also mean "one or more", "at least one", etc. For example, in the case of using articles such as "a", "an", "the" in English translation, the noun after the article can be understood as a singular expression or a plural expression.

[0193] In some embodiments, "a plurality of" may refer to two or more.

[0194] In some embodiments, terms such as "at least one of (at least one item, at least one)", "one or more (one or more items)", "a plurality of", "multiple", etc. may be used interchangeably.

[0195] In some embodiments, notations such as "at least one of A and B", "A and / or B", "in one case A, in another case B", "in response to one case A, in response to another case B", etc. may, depending on the circumstances, include the following technical solutions: In some embodiments, A (performing A independently of B); in some embodiments, B (performing B independently of A); in some embodiments, selectively performing from A and B (A and B are selectively performed); in some embodiments, A and B (both A and B are performed). The same is true when there are more branches such as A, B, C, etc.

[0196] In some embodiments, notations such as "A or B" may, depending on the circumstances, include the following technical solutions: In some embodiments, A (performing A independently of B); in some embodiments, B (performing B independently of A); in some embodiments, selectively performing from A and B (A and B are selectively performed). The same is true when there are more branches such as A, B, C, etc.

[0197] The prefix words such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different described objects, and do not limit the position, order, priority, quantity, content, etc. of the described objects. The description of the described objects refers to the description in the context of the claims or embodiments, and should not constitute redundant limitations due to the use of prefix words. For example, if the described object is "field", the ordinal numbers before "field" in "first field" and "second field" do not limit the position or order between the "fields", and "first" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of "first field" and "second field". Another example, if the described object is "level", the ordinal numbers before "level" in "first level" and "second level" do not limit the priority between the "levels". Another example, the quantity of the described object is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the quantity of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different. For example, if the described object is "device", "first device" and "second device" can be the same device or different devices, and their types can be the same or different; another example, if the described object is "information", "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0198] In some embodiments, "including A", "containing A", "used to indicate A", "carrying A" can be interpreted as directly carrying A or indirectly indicating A.

[0199] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "when...", "when...", "if...", "if..." can be substituted for each other.

[0200] 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 lower than", "above", etc. can be substituted for 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", "below", etc. can be substituted for each other.

[0201] In some embodiments, a device, etc. can be interpreted as physical or virtual, and its name is not limited to the names described in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc. can be used interchangeably.

[0202] In some embodiments, a "network" can be interpreted as the devices included in the network (e.g., network devices, access network devices, core network devices, etc.).

[0203] In some embodiments, a network device may include at least one of an access network device and a core network device.

[0204] In some embodiments, terms such as "Access Network Device (AN Device)", "Radio Access Network Device (RAN Device)", "Base Station (BS)", "Radio Base Station", "Fixed Station", "Node", "Access Point", "Transmission Point (TP)", "Reception Point (RP)", "Transmission / Reception Point (TRP)", "Panel", "Antenna Panel", "Antenna Array", "Cell", "Macro Cell", "Small Cell", "Femto Cell", "Pico Cell", "Sector", "Cell Group", "serving cell", "Carrier", "Component Carrier", "Bandwidth Part (BWP)", etc. can be used interchangeably.

[0205] In some embodiments, terms such as "Terminal", "Terminal Device", "Terminal-side Device", "User Equipment (UE)", "User Terminal", "Mobile Station (MS)", "Mobile Terminal (MT)", Subscriber Station, Mobile Unit, Subscriber Unit, Wireless Unit, Remote Unit, Mobile Device, Wireless Device, Wireless Communication Device, Remote Device, Mobile Subscriber Station, Access Terminal, Mobile Terminal, Wireless Terminal, Remote Terminal, Handset, User Agent, Mobile Client, Client, etc. may be used interchangeably.

[0206] In some embodiments, an access network device, a core network device, or a network device may be replaced by a terminal device. For example, for a structure in which communication between an access network device, a core network device, or a network device and a terminal device is replaced with communication between multiple terminal devices (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.), the embodiments of the present disclosure may also be applied. In this case, it may also be configured such that the terminal device has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" may also be replaced with terms corresponding to communication between terminal devices (e.g., "side"). For example, an uplink channel, a downlink channel, etc. may be replaced with a side channel or a direct connection channel, and an uplink, a downlink, etc. may be replaced with a side link or a direct connection link.

[0207] In some embodiments, a terminal device may be replaced by an access network device, a core network device, or a network device. In this case, it may also be configured such that the access network device, the core network device, or the network device has all or part of the functions of the terminal device.

[0208] In some embodiments, the names of information and the like 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", "chip", "codebook", "codeword", "codepoint", "bit", "data", "program", "chip", etc. may be used interchangeably.

[0209] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where it is located.

[0210] In some embodiments, data, information, etc. may be obtained after obtaining the consent of the user.

[0211] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure may be implemented as an independent embodiment, and any combination of any element, any row, and any column may also be implemented as an independent embodiment.

[0212] Figure 1 It is a schematic diagram of the architecture of a communication system shown according to the embodiments of the present disclosure. As Figure 1 shown, the communication system 100 may include a terminal device 101 and a network device 102.

[0213] 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 vehicle with communication function, a smart vehicle, an in-vehicle terminal, a tablet computer (Pad), a computer with wireless transceiver function, a roadside 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.

[0214] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0215] In some embodiments, the access network device may be a node or device that connects the terminal device to a wireless network. The access network device may include at least one of an evolved NodeB (eNB) in a 5G communication system, a next generation eNB (ng-eNB), a next generation NodeB (gNB), a NodeB (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 radio access network (Open RAN), a cloud radio access network (Cloud RAN), a base station in other communication systems, and an access node in a Wi-Fi system, but is not limited thereto.

[0216] In some embodiments, the technical solutions of the present disclosure can be applied to the Open RAN architecture. At this time, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can become the internal interfaces of Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0217] In some embodiments, the access network device can be composed of a Central Unit (CU) and a Distributed Unit (DU). Among them, the CU can also be referred to as the Control Unit. Adopting the CU-DU structure can split the protocol layer of the access network device. The functions of some protocol layers are centrally controlled by the CU, and the functions of the remaining part or all protocol layers are distributed in the DU. The CU centrally controls the DU, but it is not limited to this.

[0218] In some embodiments, the core network device can be a single device, or multiple devices or a group of devices. The core network can include at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0219] It can be understood that the communication system described in the embodiments of the present disclosure is for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions proposed in the embodiments of the present disclosure. Those of ordinary skill in the art know that with the evolution of the system architecture and the emergence of new service scenarios, the technical solutions proposed in the embodiments of the present disclosure are equally applicable to similar technical problems.

[0220] The following embodiments of the present disclosure can be applied to Figure 1 the communication system 100 shown, or a part of the main body, but it is not limited to this. Figure 1 Each main body shown is an example. The communication system can include Figure 1 all or part of the main bodies in Figure 1 or can include other main bodies outside

[0221] Embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, the 4th generation mobile communication system (4G), the 5th generation mobile communication system (5G), 5G New Radio (NR), Future Radio Access (FRA), New Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), systems using other communication methods, next-generation systems extended based on them, etc. In addition, multiple systems can be combined (for example, a combination of LTE or LTE-A and 5G, etc.) and applied.

[0222] In some embodiments of the present disclosure, the above communication system can support beam-based transmission and reception. Beam-based transmission and reception can better align the useful signal with the corresponding terminal device, and also avoid interference to other terminal devices caused by signal energy leakage, which can improve the signal-to-interference-plus-noise ratio and enhance the coverage performance of the wireless communication system.

[0223] Exemplarily, in a communication system, such as in NR communication, for the FR2 (frequency range 2) communication band, due to the relatively fast attenuation of high-frequency channels, beam-based transmission and reception can be used to ensure the coverage range.

[0224] In some embodiments, the network device may configure a set of reference signal resources for beam measurement. The terminal device may measure the reference signal resources in the set of reference signal resources and report the IDs of the X reference signal resources with relatively strong signal quality in the measurement results, as well as the physical layer reference signal receiving power (Layer 1-Reference Signal Receiving Power, L1-RSRP) and / or the physical layer signal to interference plus noise ratio (Layer 1-Signal to Interference plus Noise Ratio, L1-SINR) of each of the X reference signal resources. The set of reference signal resources configured by the network device includes X reference signal resources, and each reference signal resource corresponds to a different transmission beam of the network device. For each reference signal resource, the terminal device needs to measure the reference signal resource through all receiving beams, determine the beam measurement quality corresponding to each receiving beam, and determine the strongest beam measurement quality from multiple beam measurement qualities. In the above measurement process, if the number of transmission beams of the network device is M and the number of receiving beams of the terminal device is N, the number of beam pairs that the terminal device needs to measure is M*N.

[0225] In some embodiments, terms such as "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" can be used interchangeably.

[0226] In some embodiments of the present disclosure, the above communication system may support a first function, which can be used to perform beam prediction.

[0227] In some embodiments, the first function may be an AI function and / or an AI model.

[0228] In some embodiments, one AI function may correspond to one or more AI models. For example, one AI function may be a function or module jointly implemented by multiple AI models. Different AI models may correspond to different conditions or additional conditions.

[0229] In some other embodiments, one AI model may correspond to one or more AI functions.

[0230] In some other embodiments, the AI function and the AI model may correspond one-to-one.

[0231] In some embodiments, the first function may be deployed on the terminal device and / or the network device.

[0232] In some embodiments, the name of the first function is not limited. For example, it may be an "AI function", "AI model", "AI module", "prediction function", "prediction model", "prediction module", etc.

[0233] In some embodiments, the terminal device and / or the network device may perform beam prediction based on the above first function. For example, the total number of beam pairs that the terminal device originally needs to measure is M*N (where M is the number of base station transmission beams and N is the number of terminal reception beams). Based on this first function, the number of beam pairs measured by the terminal device can be reduced, but the beam information (such as beam quality, best beam, etc.) of M*N beam pairs can still be predicted and output based on this first function.

[0234] For example, for spatial domain beam prediction, the terminal device may only measure a part of the beam pairs among 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 part of the beam pairs is used as the input value of the above first function. Through the above first function, the corresponding output value can be predicted. The output value may be the beam information of M*N beam pairs, such as the beam quality of at least one beam pair, and / or the identification of the best K beam pairs. The output value may also be the beam information related to the M base station transmission beams, such as the beam quality of at least one base station transmission beam, and / or the identification of the best K base station transmission beams. Among them, the beam measurement quality may include L1-RSRP and / or L1-SINR. The above beam pair identification may be TxRx beam ID, and the Tx beam ID may be a reference signal resource identifier.

[0235] In some embodiments, the terminal device and / or the network device may perform beam prediction based on the above first function. For example, the original number of base station transmission beams that the terminal device needs to measure is M. Based on this first function, the number of base station transmission beams measured by the terminal device can be reduced, but the beam information (such as beam quality, best beam, etc.) of M base station transmission beams can still be predicted and output based on this first function.

[0236] For example, for spatial domain beam prediction, the terminal device may measure only a part of the multiple transmission beams of the base station. For example, the beams 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 first function. Through the above first function, the corresponding output value can be predicted. The output value may be the beam information of the M transmission beams, such as the beam quality of at least one beam, and / or the best K beam identifiers. Among them, the beam measurement quality may include L1-RSRP and / or L1-SINR. The above beam identifier may be a Tx beam ID, and the Tx beam ID may be a reference signal resource identifier.

[0237] For another example, for time domain beam prediction, the terminal device may measure the beam quality of beam pairs or transmission beams in the historical time to obtain the historical beam measurement quality. The historical beam measurement quality is used as the input value of the above first function. Through the above first function, the corresponding output value can be predicted. The output value may be the beam information (such as beam quality, best beam, etc.) of beam pairs or base station transmission beams in the future time. Among them, the beam measurement quality may include L1-RSRP and / or L1-SINR. The best beam may include the best beam pair identifier, and the beam pair identifier may be a TxRx beam ID, where Tx may be a reference signal resource identifier; the best beam may include the best beam identifier, and the beam identifier may be a reference signal resource identifier. The above transmission beam may be the transmission beam sent by the base station to the terminal device.

[0238] In some embodiments, the beam set of the terminal device may include a first beam set setB and a second beam set setA. Among them:

[0239] The first beam set setB includes one or more first beams, and the first beam may be the beam corresponding to the input value of the first function. For example, the terminal device may measure the beam measurement quality that can be obtained by measuring the first beam in the first beam set setB.

[0240] The second beam set setA includes one or more second beams, and the second beam may be the beam corresponding to the output value of the first function.

[0241] In some embodiments, for spatial domain beam prediction, the first function may predict the measurement result of the second beam in the second beam set setA based on the measurement result of the first beam in the first beam set setB. For example, the terminal device may measure the L1-RSRP and / or L1-SINR of the first beam in setB, 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.

[0242] 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:

[0243] 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, N<32, for example, N=8;

[0244] 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 the 32 reference signal resources is 120 degrees. setB includes N reference signal resources, for example, N=8, and the coverage range of the 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 the 32 / N reference signal resources in setA and the same reference signal resource in setB is QCLType D.

[0245] 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.

[0246] 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:

[0247] setB can be a subset of setA;

[0248] SetB is different from setA. The beam corresponding to setB is a wide beam, while the beam corresponding to setA is a narrow beam.

[0249] setB is the same as setA.

[0250] In some embodiments, the terminal device and / or the 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.

[0251] Figure 2 It is an interaction schematic diagram of a communication method shown according to an embodiment of the present disclosure. This method can be executed by the above communication system. As Figure 2 shown, this method may include:

[0252] Step S2101, the network device sends the first information to the terminal device.

[0253] 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.

[0254] In some embodiments, the first information may be information corresponding to a first function. For example, the first information may be used to indicate the 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 an AI function.

[0255] In some embodiments, the name of the first information is not limited. For example, it may be "configuration information", "data configuration information", "function configuration information", "model configuration information", etc.

[0256] In some embodiments, the information required by the above first function may include the data required by the first function and / or information related to data acquisition. The data is the data required by the first function. Optionally, the name of data acquisition is not limited. For example, it may be "data collection", "data acquisition", "information acquisition", "information collection", "information acquisition", etc.

[0257] In some embodiments, the above one AI function may correspond to one or more AI models. For example, one AI function may be a function or module jointly implemented by multiple AI models.

[0258] In other embodiments, the above one AI model may correspond to one or more AI functions.

[0259] In still other embodiments, the above AI functions and AI models may correspond one-to-one.

[0260] In some embodiments, the information related to data acquisition may include the data itself or may also include the relevant information for obtaining the data, such as the reference signal resource information for obtaining the data, etc.

[0261] In some embodiments, the terminal device may determine the information required by the first function according to the first information.

[0262] In some embodiments, the terminal device may obtain data according to the first information.

[0263] In some embodiments, the above data may include at least one of the following:

[0264] Data for model training;

[0265] Data for model inference;

[0266] Data for performance monitoring.

[0267] In some embodiments, terms such as "model training", "function training", "AI model training", etc. may be interchangeable.

[0268] In some embodiments, terms such as "model inference", "function inference", "AI model inference", "model application", "function application", "AI model application", etc. may be interchangeable.

[0269] In some embodiments, terms such as "performance monitoring", "performance evaluation", "performance acquisition", "performance detection", "performance metric acquisition", etc. may be interchangeable.

[0270] In some embodiments, terms such as "Artificial Intelligence (AI)", "Machine Learning (ML)", "AI / ML", etc. may be interchangeable.

[0271] In some embodiments, the data for model training may include the data required for training a first function (AI function and / or AI model). For example, it includes the data required for model input and the data required for model labels. Optionally, the data required for model labels may be the actual measurement data corresponding to the output of the first function.

[0272] Exemplarily, the data for model inference may include the data required for AI model input.

[0273] In one implementation, the first information may include the data for model training. Exemplarily, the data for model training may be directly sent through the first information, and the terminal device may directly obtain the data for model training through the first information.

[0274] In another implementation, the first information may include the reference signal resource configuration information required to obtain the data for model training. Exemplarily, the terminal device may perform measurements according to the first information and obtain the data for model training. For example, the terminal device may measure the reference signal corresponding to the reference signal resource configuration information indicated by the first information to obtain the data for model training.

[0275] In this way, the terminal device can obtain the data for model training and train the first function based on this data, so as to flexibly control the training of the first function.

[0276] In some embodiments, the data for model inference may include the data required for beam prediction or other model inferences through the first function (AI function and / or AI model).

[0277] Exemplarily, the data for model inference may include the data required for the input of the AI model. Optionally, the data for model inference may be obtained by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information. Exemplarily, the terminal device may perform measurements according to the first information and obtain the data for model inference. For example, the terminal device may measure the reference signal corresponding to the reference signal resource configuration information indicated by the first information to obtain the data for model inference.

[0278] In this way, the terminal device can obtain the data for model inference and perform beam prediction or other model inferences through the first function, so as to flexibly control the execution of model inference on the first function.

[0279] In some embodiments, the data for performance monitoring may include: the data for monitoring the performance of the AI function of the first function, or the data for monitoring the performance of at least one AI model of the first function.

[0280] Exemplarily, the data for performance monitoring may include: the data required for model input, the data obtained from model output, and the data required for model label (e.g., the actual measurement data corresponding to the model output). Optionally, the data for performance monitoring may be obtained by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information, and may also be obtained according to the model output. Exemplarily, the terminal device may perform measurements according to the first information and obtain the data for performance monitoring. For example, the terminal device may measure the reference signal corresponding to the reference signal resource configuration information indicated by the first information to obtain the data for performance monitoring, such as obtaining the data required for model input and the data required for model label. Another example is that the terminal device may obtain the data obtained from model output through model inference.

[0281] In this way, the terminal device can obtain the data for performance monitoring, compare the output and label of the first function according to this data, obtain the performance of the first function, so as to flexibly control the performance monitoring of the first function.

[0282] In some embodiments, the terminal device can obtain the data required by the first function.

[0283] For example, the terminal device may obtain the data required for the first function according to the first information.

[0284] In some embodiments, the terminal device may send the second information to the network device.

[0285] Optionally, the second information may be a response message for the first information, used by the network device to determine that the terminal device has received the first information.

[0286] Optionally, the second information may be a data acquisition result. For example, after the terminal device obtains the data required for the first function according to the first information, it may send the second information 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 according to 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 through 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.

[0287] In this way, the data acquisition of the terminal device can be flexibly controlled.

[0288] In some embodiments, the above first information may include at least one of the following:

[0289] A first identifier corresponding to the first function;

[0290] A data acquisition purpose for indicating the role of the data obtained through the first information.

[0291] In some other embodiments, the above first information may include at least one of the following:

[0292] A first identifier corresponding to the first function;

[0293] A second identifier corresponding to the measurement-related configuration for data acquisition;

[0294] Beam information for indicating beam-related information for measurement and / or prediction;

[0295] An application instance for performing beam prediction by applying the first function;

[0296] Reference signal resource information for determining 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;

[0297] Coverage information for indicating network device coverage-related information;

[0298] Terminal distribution information, which is used to indicate the distribution of multiple terminal devices within the coverage of the network device;

[0299] Data acquisition purpose, which is used to indicate the function of the data obtained through the first information;

[0300] Measurement information, which includes information related to the measurement performed by the terminal device based on the first information.

[0301] In this way, the terminal device can determine the information required for the first function through the above at least one item, so as to obtain the data required for the first function, thereby improving the management efficiency of the first function.

[0302] 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.

[0303] In some embodiments, the above first identifier includes at least one of the following:

[0304] Function ID;

[0305] Model ID;

[0306] Dataset ID;

[0307] Data collection configuration ID;

[0308] Data collection ID;

[0309] Condition ID;

[0310] Additional condition ID.

[0311] In one implementation, the above data collection configuration ID may also be referred to as a data collection configuration identifier or a data acquisition configuration identifier.

[0312] In one implementation, the above data collection ID may also be referred to as a data collection identifier or a data acquisition identifier.

[0313] In one implementation, the above data is data for model training. This data can be directly sent by the network device to the terminal device. For example, the network device can collect data for model training and directly send it to the terminal device. The terminal device does not need to measure and obtain the data for model training based on the reference signal resource configuration. In this case, the first identifier sent by the network device may 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.

[0314] In one implementation, the above data is data for model training. This data can be obtained by the terminal through measurement of the reference signal corresponding to the reference signal resource configuration information sent by the network device. For example, the first identifier sent by the network device may include at least one identifier other than the dataset identifier in the first identifier, for identifying 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.

[0315] In another implementation, the above data is data for model inference or performance monitoring. The first identifier includes a dataset identifier, and the network device sends the reference signal resource configuration information to the terminal device. Through the above dataset identifier, the terminal device can determine for which first function (function #1 or model #1) the reference signal resource configuration information is for model inference or performance monitoring. Since during the model training process, the terminal device has already determined that the first function corresponding to the model trained based on the first dataset identifier #1 is function #1 or model #1, then when the network device indicates the first dataset identifier #1 to the terminal device, the terminal device determines that the first function corresponding to the first dataset identifier #1 is function #1 or model #1. Then the terminal device can determine that the network device's current reference signal resource configuration information is for model inference or performance monitoring of function #1 or model #1.

[0316] In another implementation, the above data is data for model inference or performance monitoring. The first identifier includes at least one identifier other than the dataset identifier, and the network device sends the reference signal resource configuration information to the terminal device. Through the identifiers in the above first identifier, the terminal device can determine which first function (such as 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 inference or performance monitoring. Since during the model training process, the terminal device has already 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, so the terminal device can determine that the reference signal resource configuration information of the network device this time is for model inference or performance monitoring of function #1 or model #1.

[0317] In some embodiments, at least one of the above first identifiers is the same, and the first information corresponds to the same first function.

[0318] For example, after the terminal device performs model training based on the above first identifier and obtains the trained first function, if the network device then configures the measurement and reporting of some reference signal resources, indicates the first identifier, and the indicated data acquisition purpose is performance monitoring and / or model inference, 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 inference of the first function obtained by training with the previous data corresponding to the first identifier.

[0319] Optionally, when the above first identifiers are different, they can also correspond to the same first function. The terminal can determine the corresponding relationship between the first function and the first identifier by itself. 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 one first function. Among them, the first function is an AI function or an AI model.

[0320] Optionally, when the above first identifiers are different, they can also correspond to the same AI function and the same AI model. The terminal can determine the corresponding relationship between the AI function or AI model and the first identifier. For example, the terminal device can train an AI function or an AI model based on multiple first identifiers, and then 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, then the terminal device trains a model based on the data corresponding to different first identifiers that can be applicable to different terminal distributions at the same time.

[0321] Optionally, when the above 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 AI models and the first identifiers. For example, the terminal device can train multiple AI models for one AI function based on multiple first identifiers, and then 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, then the terminal device trains different models applicable to different terminal distributions based on the data corresponding to different first identifiers, but they correspond to the same AI function. For example, for the corresponding first AI function, except for the first identifier and the terminal distribution information in the first information corresponding to the first AI function, other information is the same. When the terminal distribution information is different, different AI models under the same AI function are corresponding.

[0322] Optionally, when the above 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 identifiers. For example, the terminal device can train multiple AI functions based on multiple first identifiers, and then the terminal device can determine that the multiple first identifiers correspond to multiple AI functions respectively. For example, if the first identifiers are different and the application instances are different, such as one application instance being airspace beam prediction and the other application instance being time-domain beam prediction. Then the terminal device trains different AI models based on the data corresponding to these two first pieces of information, corresponding to different AI functions.

[0323] In this way, the first function can be determined through the first identifier.

[0324] In some embodiments, the above second identifier includes at least one of the following:

[0325] Measurement ID; this Measurement ID can correspond to a measurement object and a reporting configuration;

[0326] Measurement object ID, which can be used to determine the measurement object;

[0327] Report ID, which can be used to determine the reporting configuration. Optionally, this Report ID can also be referred to as ReportConfigId.

[0328] In this way, the measurement object and the reporting configuration can be determined through the second identifier, so that the terminal device can perform measurements to obtain data.

[0329] In some embodiments, the above beam information includes at least one of the following:

[0330] Beam codebook identifier, which can be used to indicate the codebook information of the beam used by the network device for transmission; optionally, the beam codebook identifier can also be referred to as the gNB beam codebook ID;

[0331] Antenna configuration identifier, which can be used to indicate the antenna configuration information of the network device; optionally, the antenna configuration identifier can also be referred to as the gNB antenna configuration ID. The antenna configuration information can include at least one of the number of antenna panels, the number of antennas, the spacing between antennas, and the antenna array arrangement.

[0332] Beam type, the Beam type can be used to indicate the type of the beam transmitted by the network device.

[0333] In one implementation, the beam type can include at least one of Discrete Fourier Transform (DFT) beam and non-DFT beam.

[0334] In some embodiments, the above application examples can include at least one of the following:

[0335] Spatial domain beam prediction example;

[0336] Time domain beam prediction example;

[0337] Spatial domain beam prediction example and time domain beam prediction example.

[0338] In some embodiments, the above reference signal resource information can include at least one of the following:

[0339] The first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the first beam, and the first beam is the beam corresponding to the input value of the first function;

[0340] The second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the second beam, and the second beam is the beam corresponding to the output value of the first function;

[0341] Set relationship, which includes the relationship between the first reference signal resource set and the second reference signal resource set;

[0342] The time information corresponding to the first reference signal resource set, and the time information can be a time quantity value, such as the number of historical measurement time instances;

[0343] The time information corresponding to the second reference signal resource set, which can be a time quantity value, for example, the number of future time instances to be predicted;

[0344] 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.

[0345] In one implementation, when the application instance is an airspace beam prediction instance, the above reference signal resource information includes at least one of the following: the first reference signal resource set, the second reference signal resource set, and the set relationship.

[0346] In another implementation, when the application instance is a time domain beam prediction instance, the above reference signal resource information includes at least one of the following: the first reference signal resource set, the second reference signal resource set, the set relationship, the time information corresponding to the first reference signal resource set, the time information corresponding to the second reference signal resource set, and the time pattern. For example, when the application instance is a time domain beam prediction instance, the above reference signal resource information may include the time information corresponding to the first reference signal resource set, the time information corresponding to the second reference signal resource set, and the time pattern.

[0347] In some embodiments, the above 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.

[0348] In some embodiments, the above 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 the resource identifier, time-frequency resource, and RS sequence corresponding to each second reference signal resource.

[0349] In one implementation, the above set relationship may include any one of the following:

[0350] The first reference signal resource set is a subset of the second reference signal resource set. Exemplarily, the second reference signal 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. Here, both M and N are positive integers;

[0351] 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;

[0352] The first reference signal resource set is the same as the second reference signal resource set.

[0353] Optionally, for spatial domain beam prediction, the above set relationship may include any one of the following:

[0354] The first reference signal resource set is a subset of the second reference signal resource set;

[0355] 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.

[0356] Optionally, for time domain beam prediction, the above set relationship may include any one of the following:

[0357] The first reference signal resource set is a subset of the second reference signal resource set;

[0358] 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.

[0359] The first reference signal resource set is the same as the second reference signal resource set.

[0360] In one implementation, the above time pattern may include any one of the following:

[0361] The first pattern, which is used to indicate N historical periods and M future periods. 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;

[0362] The second pattern, which is used to indicate K historical periods. 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.

[0363] Figure 3 is a schematic diagram of a first pattern shown according to an embodiment of the present disclosure. As Figure 3As shown, based on this first pattern (Pattern1), the measurement results of N historical periods + the prediction results of M future periods are used as a repeat pattern. It should be noted that Figure 3 uses N = 3 and M = 2 as examples. N can be any positive integer, and M can also be any positive integer.

[0364] Optionally, in the first pattern, the above-mentioned historical periods and future periods can be short periods, and the repeat pattern composed of N historical periods + M future periods can be a long period.

[0365] In some embodiments, the terminal device can use the measurement results of N historical periods as the input value of the first function, and obtain the output value predicted by the first function as the prediction results of M future periods.

[0366] Optionally, within M future periods, the network device may not send reference signals for beam measurement. However, since the beam results within these M future periods are predicted and cannot be used as the input of the first function, after M periods, the network device can continue to send reference signals for beam measurement during the N historical periods within the second long period, and then perform a repeat pattern.

[0367] Figure 4 is a schematic diagram of a second pattern shown according to an embodiment of the present disclosure. As Figure 4 shown, based on this second pattern (Pattern2), the measurement results of K historical periods and the L prediction results included in the (K + 1)-th future period are used as a repeat pattern. It should be noted that Figure 4 uses K = 2 and L = 3 as examples. K can be any positive integer, and L can also be any positive integer.

[0368] Optionally, in the second pattern, the above-mentioned historical periods and future periods can be long periods, and the (K + 1)-th future period may include L short periods, and one short period corresponds to one prediction result.

[0369] In some embodiments, the terminal device can use the measurement results of K historical periods starting from 1 as the input value of the first function, and obtain the output value predicted by the first function as the prediction results of the L short periods within the (K + 1)-th future period. Optionally, the terminal device can also use the measurement results of (K + 1) historical periods starting from 2 as the input value of the first function, and obtain the output value predicted by the first function as the prediction results of the L short periods within the (K + 2)-th future period.

[0370] Optionally, the network device may send a parameter signal for beam measurement in each historical period (long period), and the terminal device may predict the prediction result of each short period. In this way, the repeat pattern of the second pattern can be simplified to a pattern where a long period contains multiple short periods.

[0371] In some embodiments, the above coverage information may include the deployment type of the network device and / or the site spacing.

[0372] Optionally, the deployment type of the network device may be the deployment type of the base station. For example, the deployment type may be any one of the following: Urban macro base station, Urban micro basestation, Indoor base station, Dense urban area, Rural area, Hotspot.

[0373] Optionally, the above site spacing may represent the distance between adjacent base stations. For example, the Inter-Site Distance (ISD) may be 100 meters, 200 meters, 500 meters, or 1000 meters.

[0374] In some embodiments, the above terminal distribution information includes the ratio of indoor terminals (indoor UEs) to outdoor terminals (outdoor UEs).

[0375] Optionally, the terminal distribution information may also include at least one of the information such as the proportion of indoor terminals, the proportion of outdoor terminals, and the number of terminals.

[0376] Optionally, the indoor terminal may be a terminal device located indoors (such as in a residential building, an office building, a shopping mall, etc.), and the outdoor terminal may be a terminal device located outdoors (such as in a street, a park, etc.).

[0377] In some embodiments, the above measurement information includes at least one of the following:

[0378] Measurement quantity, which may include the physical layer reference signal received power L1-RSRP and / or the physical layer signal-to-interference-plus-noise ratio L1-SINR;

[0379] Event information, which is information related to the event that triggers measurement reporting;

[0380] Reporting quantity, which is used to indicate the information reported by the terminal device to the network device.

[0381] In some embodiments, the above event information may include at least one of the following:

[0382] Event ID;

[0383] Information such as the threshold and offset of the event;

[0384] Event description, which can be used to indicate the trigger condition for event reporting. For example, the event description can indicate that the event is reported when the measured value (e.g., performance metric) is higher than the threshold; or, for another example, the event description can indicate that the event is reported when the measured value (e.g., performance metric) is lower than the first threshold.

[0385] In some embodiments, the above reported quantity may include at least one of the following:

[0386] Resource set ID;

[0387] Resource ID. For example, the reported quantity includes a model label: the top K resource IDs with the strongest performance;

[0388] L1-RSRP. For example, the reported quantity includes a model input: the resource ID and the L1-RSRP corresponding to the resource ID, or only includes the L1-RSRP corresponding to the resource ID;

[0389] L1-SINR. For example, the reported quantity includes a model input: the resource ID and the L1-SINR corresponding to the resource ID, or only includes the L1-SINR corresponding to the resource ID;

[0390] Performance metric, which can be a performance metric related to the first function;

[0391] Event ID;

[0392] Determined functional operations, which may include operations such as activation, deactivation, fallback, etc., and these functional operations can also be referred to as model operations.

[0393] In some embodiments, the above reported quantity may include the performance metric of the first function, and the performance metric may include at least one of the following:

[0394] Prediction accuracy, which can be 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, and the actual best reference signal resource is the best reference signal resource actually measured by the terminal device. 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;

[0395] Signal strength difference, which is the difference between the predicted signal strength and the actual signal strength.

[0396] In one implementation, prediction accuracy can mean that the identifiers of the top N best reference signal resources predicted by the first function include the identifier of the actual best reference signal resource. The reference signal identifier can be a synchronization signal block (SSB) identifier, a Channel State Information Reference Signal (CSI-RS) identifier, or a Sounding Reference Signal (SRS) identifier. Here, N is a positive integer. For example, N can be 1 or greater than 1.

[0397] Optionally, the above probability can be the 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 there are 100 predictions, and in 90 of them the predicted best reference signal resource includes the actual best reference signal resource, and in 10 of them the predicted best reference signal resource does not include the actual best reference signal resource, then the probability is 90%, that is, the prediction accuracy is 90%.

[0398] Optionally, there can be multiple combination ways for the above predicted signal strength and actual signal strength. By way of example:

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] In one implementation, the above signal strength difference can be the difference between L1 - RSRP. For example, the decibel (dB) difference of the average L1 - RSRP.

[0404] For example, the signal strength difference can be the 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.

[0405] For another example, the signal strength difference can be the difference between the predicted L1 - RSRP corresponding to the predicted best reference signal resource identifier and the actual L1 - RSRP corresponding to the predicted best reference signal resource identifier.

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

[0407] For yet another example, the signal strength difference can be the difference between the predicted 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.

[0408] In another implementation, the above signal strength difference can be the difference between L1 - SINR. For example, the decibel (dB) difference of the average L1 - SINR.

[0409] For example, the signal strength difference can be the 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.

[0410] For another example, the signal strength difference can be the difference between the predicted L1 - SINR corresponding to the predicted best reference signal resource identifier and the actual L1 - SINR corresponding to the predicted best reference signal resource identifier.

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

[0412] For yet another example, the signal strength difference can be the difference between the predicted 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.

[0413] In this way, the performance index of the first function can be determined through the above-mentioned prediction accuracy rate and / or signal strength difference, so as to evaluate the performance of the first function.

[0414] In some embodiments, the purposes of data acquisition include at least one of the following:

[0415] Model training;

[0416] Model inference;

[0417] Performance monitoring.

[0418] Optionally, the name of the "purpose of data acquisition" is not limited. For example, it can be "purpose of data collection", "purpose of information acquisition", "purpose of information collection", "function of data collection", "function of information acquisition", "function of information collection", etc.

[0419] In some embodiments, the content of the first information corresponding to different purposes of data acquisition is different or not completely the same.

[0420] Exemplarily, if the purpose of data acquisition is performance monitoring, the first information includes the above-mentioned event information, and the reported quantity includes the above-mentioned performance index. On the contrary, 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 quantity may not include the above-mentioned performance index.

[0421] In this way, different first information can be determined according to different purposes of data acquisition, so as to instruct the terminal device to acquire different data for the first function, improving the flexibility of data acquisition.

[0422] Figure 5 It is a schematic flowchart of a communication method shown according to an embodiment of the present disclosure. As Figure 5 shown, the embodiment of the present disclosure relates to a communication method, and this method can be executed by a terminal device. This method may include:

[0423] Step S5101, acquire the first information.

[0424] For the optional implementation manner of this step S5101, reference can be made to Figure 2 the optional implementation manner of step S2101 in Figure 2 and other related parts in the embodiments involved.

[0425] In some embodiments, the terminal device may receive the first information sent by the network device, but not limited thereto. The terminal device may also receive the first information sent by other entities.

[0426] In some embodiments, the terminal device may acquire the first information stipulated by the protocol.

[0427] In some embodiments, the terminal device may obtain first information from the upper layer(s).

[0428] In some embodiments, the terminal device may perform processing to obtain first information.

[0429] Step S5102: Obtain data required for the first function.

[0430] In some embodiments, the terminal device may obtain data required for the first function according to the first information.

[0431] Exemplarily, the terminal device may perform beam measurement according to the first information, so as to obtain input data for the first function and obtain output data after prediction by the first function.

[0432] Optionally, both step S5101 and step S5102 are optional steps. For example, the terminal device may only execute step S5101 or only execute step S5102.

[0433] In some embodiments, 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 an AI function.

[0434] In some embodiments, the first information includes at least one of the following:

[0435] A first identifier corresponding to the first function;

[0436] A data acquisition purpose for indicating the role of the data obtained through the first information.

[0437] In some embodiments, the first information includes at least one of the following:

[0438] A first identifier corresponding to the first function;

[0439] A second identifier corresponding to the configuration related to measurement, and the measurement is used for data acquisition;

[0440] Beam information for indicating information related to the beam for measurement and / or prediction;

[0441] An application instance, which is an instance of applying the first function to perform beam prediction;

[0442] Reference signal resource information for determining 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;

[0443] Coverage information, where the coverage information is used to indicate information related to the coverage of a network device;

[0444] Terminal distribution information, where the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage of the network device;

[0445] Data acquisition purpose, where the data acquisition purpose is used to indicate the role of the data obtained through the first information;

[0446] Measurement information, where the measurement information includes information related to the measurement performed by the terminal device based on the first information.

[0447] In some embodiments, the application instance includes at least one of the following:

[0448] Spatial domain beam prediction instance;

[0449] Temporal domain beam prediction instance;

[0450] Spatial domain beam prediction instance and temporal domain beam prediction instance.

[0451] In some embodiments, the reference signal resource information includes at least one of the following:

[0452] The first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the first beam;

[0453] The second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the second beam;

[0454] Set relationship, where the set relationship includes the relationship between the first reference signal resource set and the second reference signal resource set;

[0455] Time information corresponding to the first reference signal resource set;

[0456] Time information corresponding to the second reference signal resource set;

[0457] Time pattern, where 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.

[0458] In some embodiments, the set relationship includes any one of the following:

[0459] The first reference signal resource set is a subset of the second reference signal resource set;

[0460] 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;

[0461] The first reference signal resource set is the same as the second reference signal resource set.

[0462] In some embodiments, the time pattern includes any one of the following:

[0463] A first pattern for indicating 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;

[0464] A second pattern for indicating 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.

[0465] In some embodiments,

[0466] The coverage information includes the deployment type of the network device and / or the site spacing; and / or,

[0467] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0468] In some embodiments, the measurement information includes at least one of the following:

[0469] A measurement quantity, which includes the physical layer reference signal received power L1-RSRP and / or the physical layer signal-to-interference-plus-noise ratio L1-SINR;

[0470] Event information, which is information related to the event that triggers measurement reporting;

[0471] A reporting quantity, which is used to indicate the information reported by the terminal device to the network device.

[0472] In some embodiments, the reporting quantity includes the performance indicators of the first function, and the performance indicators include at least one of the following:

[0473] 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, and the actual best reference signal resource is the best reference signal resource actually measured by the terminal device. 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, and N is a positive integer;

[0474] Signal strength difference, where the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0475] In some embodiments, the first identifier includes at least one of the following:

[0476] Function identifier;

[0477] Model identifier;

[0478] Dataset identifier;

[0479] Data acquisition configuration identifier;

[0480] Data acquisition identifier;

[0481] Condition identifier;

[0482] Additional condition identifier.

[0483] In some embodiments, at least one of the first identifiers is the same, and the first information corresponds to the same first function.

[0484] In some embodiments, the second identifier includes at least one of the following:

[0485] Measurement identifier;

[0486] Measurement object identifier;

[0487] Report identifier.

[0488] In some embodiments, the beam information includes at least one of the following:

[0489] Beam codebook identifier, which is used to indicate the codebook information used by the network device to send beams;

[0490] Antenna configuration identifier, which is used to indicate the antenna configuration information of the network device;

[0491] Beam type, which is used to indicate the type of the beam sent by the network device.

[0492] In some embodiments, the data acquisition purpose includes at least one of the following:

[0493] Model training;

[0494] Model inference;

[0495] Performance monitoring.

[0496] In some embodiments, the content of the first information corresponding to different data acquisition purposes is different.

[0497] Figure 6 It is a schematic flowchart of a communication method shown according to an embodiment of the present disclosure. As Figure 6 shown, the embodiment of the present disclosure relates to a communication method, and this method can be executed by a terminal device. This method may include:

[0498] Step S6101, obtain the first information.

[0499] For the optional implementation of this step S6101, reference can be made to Figure 2 the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in

[0500] In some embodiments, the terminal device may receive the first information sent by the network device, but not limited thereto, and the terminal device may also receive the first information sent by other entities.

[0501] Figure 7 It is a schematic flowchart of a communication method shown according to an embodiment of the present disclosure. As Figure 7 shown, the embodiment of the present disclosure relates to a communication method, and this method can be executed by the network device. The above method includes:

[0502] Step S7101, send the first information.

[0503] For the optional implementation of this step S7101, reference can be made to Figure 2 the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in

[0504] In some embodiments, the network device may send the first information to the terminal device, but not limited thereto, and the network device may also send the first information to other entities.

[0505] In some embodiments, the first information is the information corresponding to the first function, and the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and / or an AI function.

[0506] In some embodiments, the first information includes at least one of the following:

[0507] A first identifier, and the first identifier corresponds to the first function;

[0508] A second identifier, where the second identifier corresponds to a measurement-related configuration, and the measurement is used for data acquisition;

[0509] Beam information, where the beam information is used to indicate information related to the beam for measurement and / or prediction;

[0510] An application instance, where the application instance is an instance of performing beam prediction by applying the first function;

[0511] Reference signal resource information, where the reference signal resource information is used to determine a first beam and / or a second beam, the first beam being the beam corresponding to the input value of the first function, and the second beam being the beam corresponding to the output value of the first function;

[0512] Coverage information, where the coverage information is used to indicate information related to the coverage of a network device;

[0513] Terminal distribution information, where the terminal distribution information is used to indicate the distribution of multiple terminal devices within the coverage of the network device;

[0514] Data acquisition purpose, where the data acquisition purpose is used to indicate the role of the data obtained through the first information;

[0515] Measurement information, where the measurement information includes information related to the measurement performed by the terminal device based on the first information.

[0516] In some embodiments, the application instance includes at least one of the following:

[0517] An airspace beam prediction instance;

[0518] A time-domain beam prediction instance;

[0519] An airspace beam prediction instance and a time-domain beam prediction instance.

[0520] In some embodiments, the reference signal resource information includes at least one of the following:

[0521] A first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the first beam;

[0522] A second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the second beam;

[0523] A set relationship, where the set relationship includes the relationship between the first reference signal resource set and the second reference signal resource set;

[0524] Time information corresponding to the first reference signal resource set;

[0525] Time information corresponding to the second reference signal resource set;

[0526] A time pattern, where the time pattern is a pattern of the time corresponding to the first reference signal resource set and the time corresponding to the second reference signal resource set.

[0527] In some embodiments, the set relationship includes any one of the following:

[0528] The first reference signal resource set is a subset of the second reference signal resource set;

[0529] 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;

[0530] The first reference signal resource set is the same as the second reference signal resource set.

[0531] In some embodiments, the time pattern includes any one of the following:

[0532] 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 by the terminal device to obtain the prediction results of the M future periods based on the first function;

[0533] A second pattern, which 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.

[0534] In some embodiments,

[0535] The coverage information includes the deployment type and / or the site spacing of the network device; and / or,

[0536] The terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

[0537] In some embodiments, the measurement information includes at least one of the following:

[0538] 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-plus-noise ratio L1-SINR;

[0539] Event information, where the event information is information related to an event that triggers measurement reporting;

[0540] A reporting quantity, where the reporting quantity is used to indicate the information reported by the terminal device to the network device.

[0541] In some embodiments, the reported quantity includes the performance metrics of the first function, and the performance metrics include at least one of the following:

[0542] 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, and the actual best reference signal resource is the best reference signal resource actually measured by the terminal device. 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, and N is a positive integer;

[0543] Signal strength difference, where the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

[0544] In some embodiments, the first identifier includes at least one of the following:

[0545] Function identifier;

[0546] Model identifier;

[0547] Dataset identifier;

[0548] Data acquisition configuration identifier;

[0549] Data acquisition identifier;

[0550] Condition identifier;

[0551] Additional condition identifier.

[0552] In some embodiments, at least one of the first identifiers is the same, and the first information corresponds to the same first function.

[0553] In some embodiments, the second identifier includes at least one of the following:

[0554] Measurement identifier;

[0555] Measurement object identifier;

[0556] Report identifier.

[0557] In some embodiments, the beam information includes at least one of the following:

[0558] Beam codebook identifier, which is used to indicate the codebook information for the network device to send beams;

[0559] Antenna configuration identifier, which is used to indicate the antenna configuration information of the network device;

[0560] Beam type, which is used to indicate the type of the beam sent by the network device.

[0561] In some embodiments, the purposes of data acquisition include at least one of the following:

[0562] Model training;

[0563] Model inference;

[0564] Performance monitoring.

[0565] In some embodiments, the content of the first information corresponding to different purposes of data acquisition is different.

[0566] In some embodiments of the present disclosure, a communication system is provided. The communication system may include a terminal device and a network device. Among them, the terminal device may execute the communication method performed by the terminal device in the foregoing embodiments of the present disclosure; the network device may execute the communication method performed by the network device in the foregoing embodiments of the present disclosure.

[0567] The embodiments of the present disclosure also propose an apparatus for implementing any of the above methods. For example, an apparatus is proposed. The above apparatus includes units or modules for implementing the steps performed by the terminal device in any of the above methods. Again, another apparatus is proposed, including units or modules for implementing the steps performed by the network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0568] It should be understood that the division of each unit or module in the above device is only a division of logical functions. In actual implementation, it can be fully or partially integrated into a physical entity, or physically separated. In addition, the units or modules in the device can 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 instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or the functions of each unit or module of the above device. 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 inside or 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 implemented through the design of the hardware circuits. The above hardware circuits can be understood as one or more processors. For example, in one implementation, the above hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units or modules are implemented through the design of the logical relationship of the components in the circuit. Again, in another implementation, the above hardware circuit can be implemented 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 through a configuration file, so as to implement the functions of some or all of the above units or modules. All units or modules of the above device can be all implemented in the form of a processor calling software, or all implemented in the form of hardware circuits, or some implemented in the form of a processor calling software, and the remaining part implemented in the form of hardware circuits.

[0569] In the embodiments of the present disclosure, a processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and running 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), etc.; in another implementation, the processor can achieve certain functions through the logical relationship of hardware circuits, and the logical relationship of the above hardware circuits is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an Application-Specific Integrated Circuit (ASIC) or a Programmable Logic Device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), a Deep learning Processing Unit (DPU), etc.

[0570] Figure 8 is a schematic structural diagram of a terminal device proposed in the embodiments of the present disclosure. As Figure 8 shown, the terminal device 101 may include: a first transceiver module 211 and / or a first processing module 212.

[0571] 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, and the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and / or an AI function;

[0572] In some embodiments, the first processing module 212 is configured to obtain data required by the first function according to the first information.

[0573] In some embodiments, the first information includes at least one of the following: 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 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 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.

[0574] In some embodiments, the specific implementation of the first information may refer to the description in the foregoing embodiments of the present disclosure, and will not be elaborated herein.

[0575] In some embodiments, the first transceiver module 211 may include a sending module and / or a receiving module, and the sending module and the receiving module may be separate or integrated together.

[0576] In some embodiments, the foregoing first processing module may be a single module or may include multiple sub-modules. Optionally, the foregoing multiple sub-modules respectively execute all or part of the steps required to be executed by the first processing module. Optionally, the first processing module may be replaced with the first processor.

[0577] Figure 9 is a schematic structural diagram of a network device proposed in an embodiment of the present disclosure. As Figure 9 shown, the network device 102 may include a second transceiver module 221.

[0578] In some embodiments, the second transceiver module 221 is configured to send first information to a terminal device; the first information is information corresponding to a first function, the first function being used to perform beam prediction, the first function being an artificial intelligence AI model and / or an AI function, and the first information being used to instruct the terminal device to acquire 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, the first identifier including 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 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.

[0579] In some embodiments, the specific implementation of the first information may refer to the description in the foregoing embodiments of the present disclosure, and will not be elaborated herein.

[0580] In some embodiments, the second transceiver module 221 may include a transmitting module and / or a receiving module. The transmitting module and the receiving module may be separate or integrated together.

[0581] In some embodiments, the network device may further include a second processing module, which may be a single module or may include multiple sub-modules. Optionally, the above-mentioned multiple sub-modules respectively execute all or part of the steps that the second processing module needs to execute. Optionally, the second processing module may be interchangeable with the second processor.

[0582] Figure 10 It is a schematic structural diagram of a communication device 300 proposed by an embodiment of the present disclosure. The communication device 300 may be a network device (such as an access network device, a core network device, etc.), or a terminal device (such as a user equipment, etc.), or a chip, a chip system, or a processor, etc. that supports the network device to implement any of the above methods, and may also be a chip, a chip system, or a processor, etc. that supports the terminal device to implement any of the above methods. The communication device 300 can be used to implement the methods described in the above method embodiments, and specific reference can be made to the descriptions in the above method embodiments.

[0583] As Figure 10 shown, the communication device 300 includes one or more processors 301. The processor 301 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control a 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 execute any of the above methods. Optionally, one or more processors 301 are used to call instructions to cause the communication device 300 to execute any of the above methods.

[0584] 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 transceivers 302 may perform communication steps such as transmitting and / or receiving in the above methods (such as S2101), and the processor 301 may perform other processing steps.

[0585] In some embodiments, the transceiver may include a receiver and / or a transmitter. The receiver and the transmitter may be separate or integrated together. Optionally, terms such as transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc. may be interchangeable, terms such as transmitter, transmitter unit, transmitter, transmitter circuit, etc. may be interchangeable, and terms such as receiver, receiver unit, receiver, receiver circuit, etc. may be interchangeable.

[0586] In some embodiments, the communication device 300 further includes one or more memories 303 for storing data. Optionally, all or part of the memories 303 may also be outside the communication device 300. In an alternative embodiment, the communication device 300 may include one or more interface circuits 304. Optionally, the interface circuit 304 is connected to the memory 303. The interface circuit 304 can be used to receive data from the memory 303 or other devices and can be used to send data to the memory 303 or other devices. For example, the interface circuit 304 can read the data stored in the memory 303 and send the data to the processor 301.

[0587] The communication device 300 described in the above embodiments may be a network device or a terminal device, but the scope of the communication device 300 described in this disclosure is not limited thereto, and the structure of the communication device 300 may not be subject to Figure 10 restrictions. 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 chip, or chip system or subsystem; (2) a collection of one or more ICs. Optionally, the above IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, a smart terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0588] An embodiment of the present disclosure also provides a chip, which includes one or more processors, and the chip can be used to execute any of the above methods.

[0589] In some embodiments, the chip further includes one or more interface circuits. Optionally, terms such as interface circuit, interface, and transceiver pin can be replaced with each other. In some embodiments, the chip further includes one or more memories for storing data. Optionally, all or part of the memories may be outside the chip.

[0590] Optionally, the interface circuit is connected to the memory. 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 the data stored in the memory and send the data to the processor.

[0591] In some embodiments, the interface circuit executes communication steps such as sending and / or receiving in the above method (such as S2101). The interface circuit executing the communication steps such as sending and / or receiving in the above method generally means that the interface circuit performs data interaction between the processor, the chip, the memory, or the transceiver device. In some embodiments, the processor may execute other processing steps.

[0592] In various embodiments such as virtual devices, physical devices, chips, etc., the various modules and / or components described can be combined or separated arbitrarily according to circumstances. Optionally, some or all of the steps can also be executed collaboratively by multiple modules and / or components, which is not limited herein.

[0593] Embodiments of the present disclosure also propose a storage medium, on which instructions are stored. When the above instructions run on a communication device, the communication device is caused to execute any of the above methods. Optionally, the above storage medium is an electronic storage medium. Optionally, the above storage medium is a computer-readable storage medium, but is not limited thereto, and it can also be other device-readable storage mediums. Optionally, the above storage medium can be a non-transitory storage medium, but is not limited thereto, and it can also be a transitory storage medium.

[0594] Embodiments of the present disclosure also propose a program product. When the above program product is executed by a communication device, the communication device is caused to execute any of the above methods. Optionally, the above program product can be a computer program product.

[0595] Embodiments of the present disclosure also propose a computer program. When it runs on a computer, the computer is caused 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 for performing 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 including 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 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 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 for data acquisition; Beam information for indicating information related to the beam for measurement and / or prediction; An application instance, which is an instance of applying the first function to perform beam prediction; Reference signal resource information for determining 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 for indicating information related to the coverage of the network device; Terminal distribution information for indicating the distribution of multiple terminal devices within the coverage of the network device; Measurement information, which 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, 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.

5. The method according to claim 4, wherein 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, characterized in that The time pattern includes any one of the following: The 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 by the terminal device to obtain the prediction results of the M future periods based on the first function; The second pattern, which 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 of the network device and / or the station spacing; and / or the terminal distribution information includes the ratio of indoor terminals to outdoor terminals.

8. The method according to claim 2, characterized in that The measurement information includes at least one of the following: Measurement quantity, where the measurement quantity includes the physical layer reference signal received power L1-RSRP and / or the physical layer signal-to-interference-plus-noise ratio L1-SINR; Event information, where the event information is information related to the event that triggers measurement reporting; 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 indicators of the first function, and the performance indicators include at least one of the following: 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, and N is a positive integer; 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, characterized in that The second identifier includes at least one of the following: Measurement identifier; Measurement object identifier; Report identifier.

11. The method according to claim 2, wherein The beam information includes at least one of the following: Beam codebook identifier, which is used to indicate the codebook information used by the network device to send beams; Antenna configuration identifier, which is used to indicate the antenna configuration information of the network device; Beam type, which 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 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 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 corresponding 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. The content of the first information corresponding to different data acquisition purposes is different.

14. The method according to claim 13, wherein 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. 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, wherein 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. 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, which is used to indicate K historical periods. 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.

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

21. The method according to claim 20, wherein The reporting quantity includes the performance indicators of the first function, and the performance indicators include: prediction accuracy rate, 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, 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, and N is a positive integer; signal strength difference, the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

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

23. The method according to claim 14, characterized in that, The beam information includes at least one of the following: beam codebook identifier, the beam codebook identifier is used to indicate the codebook information used by the network device to send beams; antenna configuration identifier, the antenna configuration identifier is used to indicate the antenna configuration information of the network device; beam type, the beam type is 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 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 an AI function; a first processing module, configured 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; 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.

26. A network device, characterized in that, including: a second transceiver module, configured to send the 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 dataset 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, the storage medium stores 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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