CN117596619B - Communication method, apparatus, and storage medium - Google Patents

Abstract

The disclosed embodiments relate to a communication method, device and storage medium. The method includes: receiving first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and/or 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, the first identifier includes at least one of a function identifier, a model identifier, a data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier; a data acquisition purpose, the data acquisition purpose includes at least one of model training, model reasoning, and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different. In this way, the information corresponding to the first function can be flexibly configured to improve the management efficiency of the first function.

Description

Communication method, device and storage medium

Technical Field

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

Background technique

With the advancement of communication technology, a prediction function based on artificial intelligence (AI) has been introduced into wireless communication systems. The prediction function may be an AI function and/or an AI model. Through the prediction function, prediction data in certain scenarios may be obtained, thereby improving network performance.

Summary of the invention

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

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

Receive first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and/or an AI function;

Acquire data required by the first function according to the first information;

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 data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier;

The purpose of data acquisition includes at least one of model training, model reasoning, and performance monitoring. The content of the first information corresponding to different data acquisition purposes is different.

According to a second aspect of an embodiment of the present disclosure, a communication method is proposed, which is performed 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 data required by the first function according to the first information;

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 data set identifier, a data acquisition configuration identifier, a data acquisition identifier, a condition identifier, and an additional condition identifier;

The purpose of data acquisition includes at least one of model training, model reasoning, and performance monitoring. The content of the first information corresponding to different data acquisition purposes is different.

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

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

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

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

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

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

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

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

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

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG. 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

FIG. 2 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure.

FIG. 3 is a schematic diagram of a first pattern according to an embodiment of the present disclosure.

FIG. 4 is a schematic diagram showing a second pattern according to an embodiment of the present disclosure.

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

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

FIG. 7 is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG8 is a schematic diagram of the structure of a terminal device according to an embodiment of the present disclosure.

FIG. 9 is a schematic diagram showing the structure of a network device according to an embodiment of the present disclosure.

FIG. 10 is a schematic diagram of the structure of a communication device according to an embodiment of the present disclosure.

Detailed ways

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

In a first aspect, an embodiment of the present disclosure provides a communication method, which is executed by a terminal device, and the method includes:

Receive first information sent by a network device; the first information is information corresponding to a first function, the first function is used to perform beam prediction, and the first function is an artificial intelligence AI model and/or an AI function;

Acquire data required by the first function according to the first information.

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

In conjunction with some embodiments of the first aspect, 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 data acquisition purpose is used to indicate the use of the data obtained through the first information.

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

A second identifier, the second identifier corresponds to a configuration related to a measurement, the measurement being used for data acquisition;

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

An application example, wherein the application example is an example of applying the first function to perform beam prediction;

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

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

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

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

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

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

Spatial beam prediction example;

Time domain beam prediction example;

Spatial domain beam prediction example and time domain beam prediction example.

In the above embodiment, the first information can be controlled to be applied to the spatial domain and/or time domain beam prediction instance through the application instance to better adapt to different scenarios.

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

a first reference signal resource set, wherein reference signal resources in the first reference signal resource set correspond to the first beam;

a second reference signal resource set, wherein the reference signal resources of the second reference signal resource set correspond to the second beam;

A set relationship, wherein the set relationship includes a 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, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

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

In conjunction with some embodiments of the first aspect, in some embodiments, 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.

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

In combination with some embodiments of the first aspect, in some embodiments, the time pattern includes any one of the following:

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

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+1th future period based on the first function.

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

In conjunction with some embodiments of the first aspect, in some embodiments,

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

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

In the above embodiment, coverage information and/or terminal distribution information may be flexibly indicated so that the terminal device acquires data according to the information.

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

A measurement quantity, wherein the measurement quantity includes a physical layer reference signal received power L1-RSRP and/or a physical layer signal to interference and noise ratio L1-SINR;

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

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

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

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

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

The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

In the above embodiment, the reporting amount can be flexibly indicated so that the terminal device obtains data according to the reporting amount.

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

Functional identification;

Model identification;

Dataset identifier;

Data acquisition configuration identifier;

Data acquisition identification;

Condition identification;

Additional condition identification.

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

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

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

Measurement identification;

Measurement object identification;

Report ID.

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

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

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

An antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

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

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

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

Model training;

Model reasoning;

Performance monitoring.

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

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

In a second aspect, an embodiment of the present disclosure provides a communication method, which is performed by a network device, and 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 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.

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

In conjunction with some embodiments of the second aspect, 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 data acquisition purpose is used to indicate the use of the data obtained through the first information.

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

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

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

An application example, wherein the application example is an example of applying the first function to perform beam prediction;

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

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

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

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

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

Spatial beam prediction example;

Time domain beam prediction example;

Spatial domain beam prediction example and time domain beam prediction example.

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

a first reference signal resource set, wherein the reference signal resources in the first reference signal resource set correspond to the first beam;

a second reference signal resource set, wherein the reference signal resources of the second reference signal resource set correspond to the second beam;

A set relationship, wherein the set relationship includes a 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, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

In conjunction with some embodiments of the second aspect, in some embodiments, 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.

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

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

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+1th future period based on the first function.

In conjunction with some embodiments of the second aspect, in some embodiments,

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

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

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

A measurement quantity, wherein the measurement quantity includes a physical layer reference signal received power L1-RSRP and/or a physical layer signal to interference and noise ratio L1-SINR;

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

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

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

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

The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

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

Functional identification;

Model identification;

Dataset identifier;

Data acquisition configuration identifier;

Data acquisition identification;

Condition identification;

Additional condition identification.

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.

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

Measurement identification;

Measurement object identification;

Report ID.

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

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

An antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

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

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

Model training;

Model reasoning;

Performance monitoring.

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

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

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

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

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

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

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

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

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

It is understandable that the above-mentioned terminal equipment, network equipment, communication equipment, communication system, storage medium, program product, computer program, chip or chip system can be used to execute the method proposed in the embodiment 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 repeated here.

In some embodiments, the communication method and information processing method can be used interchangeably; the communication device and information processing device, communication equipment and other terms can be used interchangeably; the information processing system and communication system and other terms can be used interchangeably.

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

In each embodiment of the present disclosure, unless otherwise specified or there is a logical conflict, the terms and/or descriptions between the embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form a new embodiment according to their internal logical relationships.

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.

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 "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun after the article may be understood as a singular expression or a plural expression.

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

In some embodiments, the terms “at least one,” “one or more,” “a plurality of,” “multiple,” etc. may be used interchangeably.

In some embodiments, "at least one of A and B", "A and/or B", "A in one case, B in another case", "in response to one case A, in response to another case B", etc., may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (A and B are both executed). When there are more branches such as A, B, C, etc., the above is also similar.

In some embodiments, the recording method of "A or B" may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). When there are more branches such as A, B, C, etc., the above is also similar.

The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute restrictions on the position, order, priority, quantity or content of the description objects. The statement of the description object refers to the description in the context of the claims or embodiments, and should not constitute redundant restrictions due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields", and the "first" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number, and can be one or more. Taking the "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes may be the same or different. For example, if the description object is "device", then the "first device" and the "second device" may be the same device or different devices, and their types may be the same or different. For another example, if the description object is "information", then the "first information" and the "second information" may be the same information or different information, and their contents may be the same or different.

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

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

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", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "no more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

In some embodiments, devices and the like can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and the like can be interchangeable.

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

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

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

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

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

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

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

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

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

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 the combination of any elements, any rows, or any columns may also be implemented as an independent embodiment.

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

In some embodiments, the terminal device 101 may include a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a vehicle-mounted terminal, a tablet computer (Pad), a computer with wireless transceiver function, a road side unit (RSU, Road Side Unit), a virtual reality (Virtual Reality, VR) terminal device, an augmented reality (Augmented Reality, AR) terminal device, a wireless terminal device in industrial control (Industrial Control), a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid (Smart Grid), a wireless terminal device in transportation safety (Transportation Safety), a wireless terminal device in a smart city (Smart City), and at least one of a wireless terminal device in a smart home (Smart Home), but is not limited thereto.

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

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

In some embodiments, the technical solution of the present disclosure may be applicable to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure may become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces may be implemented through software or programs.

In some embodiments, the access network device may be composed of a centralized unit (CU) and a distributed unit (DU), wherein the CU may also be referred to as a control unit (Control Unit). The CU-DU structure may be used to split the protocol layer of the access network device, with some functions of the protocol layer being centrally controlled by the CU, and the remaining part or all of the functions of the protocol layer being distributed in the DU, and the DU being centrally controlled by the CU, but not limited to this.

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

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

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

Embodiments of the present disclosure may be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, 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, and next-generation systems based on them. In addition, multiple systems can be combined (for example, a combination of LTE or LTE-A and 5G, etc.) for application.

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

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

In some embodiments, the network device may configure a reference signal resource set for beam measurement, and the terminal device may measure the reference signal resources in the reference signal resource set, and report the IDs of X reference signal resources with relatively strong signal quality in the measurement results, as well as the physical layer reference signal receiving power (Layer 1-Reference Signal Receiving Power, L1-RSRP) and/or physical layer signal to interference plus noise ratio (Layer 1- Signal to Interference plus Noise Ratio, L1- SINR) of each reference signal resource in the X reference signal resources. The reference signal resource set configured by the network device includes X reference signal resources, each of which 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.

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

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

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

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

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

In some other embodiments, the AI functions and AI models may correspond one to one.

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

In some embodiments, the name of the first function is not limited, and may be, for example, "AI function", "AI model", "AI module", "prediction function", "prediction model", "prediction module", etc.

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

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

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

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

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

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

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

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

In some embodiments, for spatial 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 the L1-RSRP and/or L1-SINR of the second beam in the output setA.

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:

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;

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 QCL Type D.

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.

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:

setB can be a subset of setA;

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

setB is the same as setA.

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.

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

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

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.

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

In some embodiments, the name of the first information is not limited, for example, it can be "configuration information", "data configuration information", "function configuration information", "model configuration information", etc.

In some embodiments, the information required by the first function may include data required by the first function and/or information related to data acquisition, where the data is 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.

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

In other embodiments, the above-mentioned one AI model may correspond to one or more AI functions.

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

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

In some embodiments, the terminal device can determine the information required for the first function based on the first information.

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

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

Data used for model training;

Data used for model inference;

Data used for performance monitoring.

In some embodiments, terms such as "model training", "function training", "AI model training", etc. can be used interchangeably.

In some embodiments, terms such as "model reasoning", "functional reasoning", "AI model reasoning", "model application", "functional application", "AI model application" etc. can be used interchangeably.

In some embodiments, terms such as "performance monitoring", "performance evaluation", "performance acquisition", "performance detection", and "performance indicator acquisition" may be used interchangeably.

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

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

For example, data used for model reasoning may include data required for AI model input.

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

In another implementation, the first information may include reference signal resource configuration information required for obtaining data for model training. For example, the terminal device may perform measurement according to the first information and obtain data for model training. For example, the terminal device may obtain data for model training by measuring the reference signal corresponding to the reference signal resource configuration information indicated by the first information.

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

In some embodiments, the data used for model reasoning may include data required for performing beam prediction or other model reasoning through the first function (AI function and/or AI model).

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

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

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

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

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

In some embodiments, the terminal device can obtain data required for the first function.

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

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

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

Optionally, the second information may be a data acquisition result. For example, after the terminal device acquires the data required for the first function according to the first information, the second information may be sent 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.

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

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

A first identifier, the first identifier corresponding to a first function;

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

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

A first identifier, the first identifier corresponding to a first function;

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

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

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

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

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

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

a data acquisition purpose, where the data acquisition purpose is used to indicate a function of the data obtained through the first information;

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

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

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.

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

Function ID

Model ID;

Dataset ID

Data collection configuration ID;

Data collection ID;

Condition ID

Additional condition ID.

In one implementation, the above-mentioned data acquisition configuration identifier may also be referred to as a data collection configuration identifier or a data acquisition configuration identifier.

In one implementation, the above-mentioned data acquisition identifier may also be referred to as a data collection identifier or a data acquisition identifier.

In one implementation, the above data is data for model training, and the data can be sent directly from the network device to the terminal device. For example, the network device can collect data for model training and send it directly to the terminal device. The terminal device does not need to measure and obtain 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 data set identifier. In this way, the terminal device can determine that the first function corresponding to the model trained based on the first data set identifier #1 is function #1 or model #1.

In one implementation, the above data is data used for model training, and the data can be obtained by measuring the reference signal corresponding to the reference signal resource configuration information sent by the terminal through the network device. For example, the first identifier sent by the network device may include at least one identifier other than the data set identifier in the first identifier, which is used to identify the first information corresponding to the first function. In this way, the terminal device can determine that the first function corresponding to the model trained based on the first identifier #1 is function #1 or model #1.

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

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

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

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

Optionally, when the above-mentioned first identifiers are different, they may also correspond to the same first function. The terminal may determine the correspondence between the first function and the first identifier by itself. For example, the terminal device may obtain a first function based on training of multiple first identifiers, and the terminal device may determine that the multiple first identifiers correspond to one first function. The first function is an AI function or an AI model.

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

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

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

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

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

Measurement ID: This measurement ID can correspond to a measurement object and a report configuration;

A measurement object ID, which can be used to identify the measurement object;

A report ID, which can be used to determine a report configuration. Optionally, the report ID can also be called a report configuration ID (ReportConfigId).

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

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

A beam codebook ID, which may be used to indicate codebook information used by a network device to send a beam; optionally, the beam codebook ID may also be referred to as a gNB beam codebook ID;

Antenna configuration identifier, which can be used to indicate antenna configuration information of a network device; optionally, the antenna configuration identifier can also be called a base station antenna configuration identifier (gNB antenna configuration ID). The antenna configuration information may include at least one of the number of antenna panels, the number of antennas, the spacing between antennas, and the antenna array arrangement.

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

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

In some embodiments, the above application examples may include at least one of the following:

Spatial beam prediction example;

Time domain beam prediction example;

Spatial domain beam prediction example and time domain beam prediction example.

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

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

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

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

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

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

A time pattern, where the time pattern (pattern) is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

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

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

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

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

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

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

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;

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

Optionally, for spatial beam prediction, the above set relationship may include any 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.

Optionally, for time domain beam prediction, the above set relationship may include 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.

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

A first pattern, the first pattern (Pattern1) is used to indicate N historical cycles and M future cycles, and the measurement results of the N historical cycles are used by the terminal device to obtain prediction results of the M future cycles based on the first function;

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

FIG3 is a schematic diagram of a first pattern according to an embodiment of the present disclosure. As shown in FIG3 , based on the first pattern (Pattern1), the measurement results of N historical cycles + the prediction results of M future cycles are a repeating pattern (repeat pattern). It should be noted that FIG3 takes N=3 and M=2 as examples, and N can be any positive integer, and M can also be any positive integer.

Optionally, in the first pattern, the above historical cycle and future cycle may be short cycles, and a repeating pattern consisting of N historical cycles + M future cycles may be a long cycle.

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

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

FIG4 is a schematic diagram of a second pattern according to an embodiment of the present disclosure. As shown in FIG4 , based on the second pattern (Pattern2), the measurement results of K historical cycles and the L prediction results contained in the K+1th future cycle are used as a repeat pattern. It should be noted that FIG4 takes K=2 and L=3 as examples, K can be any positive integer, and L can also be any positive integer.

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

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

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 may be simplified to a pattern of multiple short periods contained in a long period.

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

Optionally, the deployment type of the network device may be a deployment type of a base station, for example, the deployment type may be any one of the following: Urban macro base station, Urban micro base station, Indoor base station, Dense urban area, Rural area, Hotspot.

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

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

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

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

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

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

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

Reporting quantity: Reporting quantity is used to indicate the information reported by the terminal device to the network device.

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

Event ID

Event threshold, offset, and other information;

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

In some embodiments, the above-mentioned reported amount may include at least one of the following:

Resource set ID;

Resource ID, for example, the reported amount includes the model label: the strongest K resource IDs;

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

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

A performance metric, which may be a performance metric related to the first function;

Event ID

Determined functional operations, which may include operations such as activation, deactivation, and fallback, may also be referred to as model operations.

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

prediction accuracy, where the prediction accuracy may be a probability that the predicted best reference signal resource includes an actual best reference signal resource, where the predicted best reference signal resource is the best reference signal resource predicted by the first function, where the actual best reference signal resource is the best reference signal resource actually measured by the terminal device, where the best reference signal resource is the first 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;

Signal strength difference, the signal strength difference is the difference between the predicted signal strength and the actual signal strength.

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

Optionally, the above probability may be a ratio between the number of times the predicted best reference signal resource includes the actual best reference signal resource and the total number of predictions. For example, if 100 predictions are made, and 90 of the predicted best reference signal resources include the actual best reference signal resource, and 10 of the predicted best reference signal resources do not include the actual best reference signal resource, then the probability is 90%, that is, the prediction accuracy is 90%.

Optionally, the predicted signal strength and the actual signal strength may be combined in a variety of ways, for example:

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.

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

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

In another implementation manner, 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.

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

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

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

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

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

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

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

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

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

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

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

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

Model training;

Model reasoning;

Performance monitoring.

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

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

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

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

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

Step S5101: Obtain first information.

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

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

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

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

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

Step S5102: Acquire data required by the first function.

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

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

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.

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

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 data acquisition purpose is used to indicate the use of the data obtained through the first information.

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

a first identifier, the first identifier corresponding to the first function;

A second identifier, the second identifier corresponds to a configuration related to a measurement, the measurement being used for data acquisition;

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

An application example, wherein the application example is an example of applying the first function to perform beam prediction;

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

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

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

a purpose of data acquisition, where the purpose of data acquisition is used to indicate a function of the data obtained through the first information;

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

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

Spatial beam prediction example;

Time domain beam prediction example;

Spatial domain beam prediction example and time domain beam prediction example.

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

a first reference signal resource set, wherein the reference signal resources in the first reference signal resource set correspond to the first beam;

a second reference signal resource set, wherein the reference signal resources of the second reference signal resource set correspond to the second beam;

A set relationship, wherein the set relationship includes a 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, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

In some embodiments, 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.

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

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

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+1th future period based on the first function.

In some embodiments,

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

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

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

A measurement quantity, wherein the measurement quantity includes a physical layer reference signal received power L1-RSRP and/or a physical layer signal to interference and noise ratio L1-SINR;

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

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

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

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

The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

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

Functional identification;

Model identification;

Dataset identifier;

Data acquisition configuration identifier;

Data acquisition identification;

Condition identification;

Additional condition identification.

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

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

Measurement identification;

Measurement object identification;

Report ID.

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

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

An antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

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

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

Model training;

Model reasoning;

Performance monitoring.

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

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

Step S6101, obtain first information.

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

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

FIG7 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG7 , the present disclosure embodiment relates to a communication method, which can be executed by a network device, and the method includes:

Step S7101, sending the first information.

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

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

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

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

a first identifier, the first identifier corresponding to the first function;

A second identifier, the second identifier corresponds to a configuration related to a measurement, the measurement being used for data acquisition;

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

An application example, wherein the application example is an example of applying the first function to perform beam prediction;

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

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

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

a purpose of data acquisition, where the purpose of data acquisition is used to indicate a function of the data obtained through the first information;

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

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

Spatial beam prediction example;

Time domain beam prediction example;

Spatial domain beam prediction example and time domain beam prediction example.

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

a first reference signal resource set, wherein the reference signal resources in the first reference signal resource set correspond to the first beam;

a second reference signal resource set, wherein the reference signal resources of the second reference signal resource set correspond to the second beam;

A set relationship, wherein the set relationship includes a 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, where the time pattern is a pattern of time corresponding to the first reference signal resource set and time corresponding to the second reference signal resource set.

In some embodiments, 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.

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

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

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+1th future period based on the first function.

In some embodiments,

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

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

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

A measurement quantity, wherein the measurement quantity includes a physical layer reference signal received power L1-RSRP and/or a physical layer signal to interference and noise ratio L1-SINR;

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

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

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

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

The signal strength difference is the difference between the predicted signal strength and the actual signal strength.

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

Functional identification;

Model identification;

Dataset identifier;

Data acquisition configuration identifier;

Data acquisition identification;

Condition identification;

Additional condition identification.

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

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

Measurement identification;

Measurement object identification;

Report ID.

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

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

An antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of a network device;

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

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

Model training;

Model reasoning;

Performance monitoring.

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

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

The embodiments of the present disclosure also propose a device for implementing any of the above methods, for example, a device is proposed, the above device includes a unit or module for implementing each step performed by the terminal device in any of the above methods. For another example, another device is also proposed, including a unit or module for implementing each step performed by a network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

It should be understood that the division of the units or modules in the above devices is only a division of logical functions, and in actual implementation, they can be fully or partially integrated into one physical entity, or they can be 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 implement the functions of the units or modules of the above devices, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory inside the device or a memory outside the device. Alternatively, the units or modules in the device may be implemented in the form of hardware circuits, and the functions of some or all of the units or modules may be implemented by designing the hardware circuits. The hardware circuits may be understood as one or more processors; for example, in one implementation, the hardware circuits are application-specific integrated circuits (ASICs), and the functions of some or all of the above units or modules may be implemented by designing the logical relationship of the components in the circuits; for another example, in another implementation, the hardware circuits may be implemented by programmable logic devices (PLDs), and field programmable gate arrays (FPGAs) may be used as an example, which may include a large number of logic gate circuits, and the connection relationship between the logic gate circuits may be configured by configuring the configuration files, thereby implementing the functions of some or all of the above units or modules. All units or modules of the above devices may be implemented in the form of software called by the processor, or in the form of hardware circuits, or in the form of software called by the processor, and the remaining part may be implemented in the form of hardware circuits.

In the embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor may be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which may be understood as a microprocessor), or a digital signal processor (DSP); in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the above hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by a processor as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration may 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 may also be a hardware circuit designed for artificial intelligence, which may 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.

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

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

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

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

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

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

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

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

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

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

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

In some embodiments, the network device may further include a second processing module, which may be a module or may include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the second processing module. Optionally, the second processing module may be interchangeable with the second processor.

FIG10 is a schematic diagram of the structure of a communication device 300 proposed in an embodiment of the present disclosure. The communication device 300 may be a network device (e.g., an access network device, a core network device, etc.), or a terminal device (e.g., a user device, etc.), or a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal device to implement any of the above methods. The communication device 300 may be used to implement the method described in the above method embodiment, and the details may refer to the description in the above method embodiment.

As shown in FIG10 , the communication device 300 includes one or more processors 301. The processor 301 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process the communication protocol and the communication data, and the central processing unit may be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute a program, and process the data of the program. Optionally, the communication device 300 may be used to execute any of the above methods. Optionally, one or more processors 301 are used to call instructions so that the communication device 300 executes any of the above methods.

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

In some embodiments, the transceiver may include a receiver and/or a transmitter, and the receiver and the transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc. may be replaced with each other, the terms transmitter, transmission unit, transmitter, transmission circuit, etc. may be replaced with each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. may be replaced with each other.

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 optional embodiment, the communication device 300 may include one or more interface circuits 304. Optionally, the interface circuit 304 is connected to the memory 303, and the interface circuit 304 may be used to receive data from the memory 303 or other devices, and may be used to send data to the memory 303 or other devices. For example, the interface circuit 304 may read the data stored in the memory 303 and send the data to the processor 301.

The communication device 300 described in the above embodiment may be a network device or a terminal device, but the scope of the communication device 300 described in the present disclosure is not limited thereto, and the structure of the communication device 300 may not be limited by FIG. 10. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

The 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.

In some embodiments, the chip further includes one or more interface circuits. Optionally, the terms interface circuit, interface, transceiver pin, etc. can be interchangeable. In some embodiments, the chip further includes one or more memories for storing data. Optionally, all or part of the memories can be outside the chip.

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

In some embodiments, the interface circuit performs the communication steps such as sending and/or receiving in the above method (for example, S2101). The interface circuit performs the communication steps such as sending and/or receiving in the above method, for example, means that the interface circuit performs data interaction between the processor, chip, memory or transceiver device. In some embodiments, the processor can perform other processing steps.

The modules and/or devices described in the embodiments such as virtual devices, physical devices, chips, etc. can be combined or separated as needed. Optionally, some or all steps can also be performed by multiple modules and/or devices in collaboration, which is not limited here.

The embodiment of the present disclosure also proposes a storage medium, on which instructions are stored. When the instructions are executed on a communication device, the communication device executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited to this, and it can also be a storage medium readable by other devices. Optionally, the storage medium can be a non-transitory storage medium, but is not limited to this, and it can also be a temporary storage medium.

The embodiment of the present disclosure also provides a program product, and when the program product is executed by a communication device, the communication device executes any of the above methods. Optionally, the program product may be a computer program product.

The embodiment of the present disclosure also provides a computer program, which, when executed on a computer, enables the computer to execute any of the above methods.

Claims (27)

1. A method of communication, performed by a terminal device, the method comprising:

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 intelligent AI model and/or AI function;

acquiring data required by the first function according to the first information;

Wherein the first information includes at least one of:

The first identifier corresponds to the first function, and 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 purpose comprises at least one of model training, model reasoning and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different;

The first information further includes 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 a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

The reference signal resource information includes a time pattern including any one of:

A first pattern, wherein the first pattern is used for indicating N historical periods and M future periods, and the measurement results of the N historical periods are used for the terminal equipment to obtain prediction results of the M future periods based on the first function;

And the second pattern is used for indicating K historical periods, and the measurement results of the K historical periods are used for the terminal equipment to obtain L prediction results in the (K+1) th future period based on the first function.

2. The method of claim 1, wherein the first information further comprises at least one of:

the second identifier corresponds to measurement related configuration, and the measurement is used for data acquisition;

Beam information indicating measured and/or predicted beam-related information;

an application instance, which is an instance for performing beam prediction by applying the first function;

Coverage information, the coverage information being used to indicate coverage related information of a network device;

Terminal distribution information, wherein the terminal distribution information is used for indicating the distribution of a plurality of terminal devices in the coverage area of the network device;

Measurement information including information about which the terminal device performs measurement based on the first information.

3. The method of claim 2, wherein the application instance comprises at least one of:

An airspace beam prediction instance;

A time domain beam prediction instance;

a spatial beam prediction instance and a temporal beam prediction instance.

4. The method of claim 2, wherein the reference signal resource information further comprises at least one of:

a first set of reference signal resources, the reference signal resources in the first set of reference signal resources corresponding to the first beam;

A second set of reference signal resources, reference signal resources of the second set of reference signal resources corresponding to the second beam;

A set relationship comprising a relationship between the first set of reference signal resources and the second set of reference signal resources;

Time information corresponding to the first reference signal resource set;

and time information corresponding to the second reference signal resource set.

5. The method of claim 4, wherein the aggregate relationship comprises any one of:

the first set of reference signal resources is a subset of the second set of reference signal resources;

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 set of reference signal resources is the same as the second set of reference signal resources.

6. The method of claim 2, wherein the step of determining the position of the substrate comprises,

The coverage information comprises deployment type and/or station spacing of the network equipment; and/or

The terminal distribution information includes a ratio of an indoor terminal to an outdoor terminal.

7. The method of claim 2, wherein the measurement information comprises at least one of:

a measurement quantity comprising physical layer reference signal received power L1-RSRP and/or physical layer signal to interference plus noise ratio L1-SINR;

event information, wherein the event information is information related to an event which triggers measurement reporting;

Reporting amount, the reporting amount is used for indicating information reported by the terminal equipment to the network equipment.

8. The method of claim 7, wherein the reported quantity comprises a performance indicator of the first function, the performance indicator comprising at least one of:

The prediction accuracy is the probability that the predicted optimal reference signal resource comprises actual optimal reference signal resources, the predicted optimal reference signal resource is the optimal reference signal resource predicted by the first function, the actual optimal reference signal resource is the optimal reference signal resource actually measured by the terminal equipment, the optimal reference signal resource is the first 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;

And the signal intensity difference value is the difference value between the predicted signal intensity and the actual signal intensity.

9. The method of claim 2, wherein the second identification comprises at least one of:

measuring an identification;

Measuring an object identifier;

And reporting the identification.

10. The method of claim 2, wherein the beam information comprises at least one of:

a beam codebook identification, wherein the beam codebook identification is used for indicating codebook information used by a network device for transmitting a beam;

an antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of the network device;

and a beam type indicating the type of beam transmitted by the network device.

11. The method according to any one of claims 1 to 10, wherein at least one of the first identifications is identical and the first information corresponds to the same first function.

12. A method of communication, performed by a network device, the method comprising:

Sending first information to terminal equipment; the first information is information corresponding to a first function, the first function is used for executing beam prediction, the first function is an artificial intelligent AI model and/or an AI function, and the first information is used for indicating the terminal equipment to acquire data required by the first function according to the first information;

Wherein the first information includes at least one of:

The first identifier corresponds to the first function, and 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 purpose comprises at least one of model training, model reasoning and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different;

The first information further includes 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 a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

The reference signal resource information includes a time pattern including any one of:

A first pattern, wherein the first pattern is used for indicating N historical periods and M future periods, and the measurement results of the N historical periods are used for the terminal equipment to obtain prediction results of the M future periods based on the first function;

And the second pattern is used for indicating K historical periods, and the measurement results of the K historical periods are used for the terminal equipment to obtain L prediction results in the (K+1) th future period based on the first function.

13. The method of claim 12, wherein the first information comprises at least one of:

the second identifier corresponds to measurement related configuration, and the measurement is used for data acquisition;

Beam information indicating measured and/or predicted beam-related information;

an application instance, which is an instance for performing beam prediction by applying the first function;

Coverage information, the coverage information being used to indicate coverage related information of a network device;

Terminal distribution information, wherein the terminal distribution information is used for indicating the distribution of a plurality of terminal devices in the coverage area of the network device;

a data acquisition purpose for indicating an effect of the data obtained by the first information;

Measurement information including information about which the terminal device performs measurement based on the first information.

14. The method of claim 13, wherein the application instance comprises at least one of:

An airspace beam prediction instance;

A time domain beam prediction instance;

a spatial beam prediction instance and a temporal beam prediction instance.

15. The method of claim 13, wherein the reference signal resource information further comprises at least one of:

a first set of reference signal resources, the reference signal resources in the first set of reference signal resources corresponding to the first beam;

A second set of reference signal resources, reference signal resources of the second set of reference signal resources corresponding to the second beam;

A set relationship comprising a relationship between the first set of reference signal resources and the second set of reference signal resources;

Time information corresponding to the first reference signal resource set;

and time information corresponding to the second reference signal resource set.

16. The method of claim 15, wherein the aggregate relationship comprises any one of:

the first set of reference signal resources is a subset of the second set of reference signal resources;

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 set of reference signal resources is the same as the second set of reference signal resources.

17. The method of claim 13, wherein the step of determining the position of the probe is performed,

The coverage information comprises deployment type and/or station spacing of the network equipment; and/or the number of the groups of groups,

The terminal distribution information includes a ratio of an indoor terminal to an outdoor terminal.

18. The method of claim 13, wherein the measurement information comprises at least one of:

a measurement quantity comprising physical layer reference signal received power L1-RSRP and/or physical layer signal to interference plus noise ratio L1-SINR;

event information, wherein the event information is information related to an event which triggers measurement reporting;

Reporting amount, the reporting amount is used for indicating information reported by the terminal equipment to the network equipment.

19. The method of claim 18, wherein the reported quantity comprises a performance indicator of the first function, the performance indicator comprising:

The prediction accuracy is the probability that the predicted optimal reference signal resource comprises actual optimal reference signal resources, the predicted optimal reference signal resource is the optimal reference signal resource predicted by the first function, the actual optimal reference signal resource is the optimal reference signal resource actually measured by the terminal equipment, the optimal reference signal resource is the first 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;

And the signal intensity difference value is the difference value between the predicted signal intensity and the actual signal intensity.

20. The method of claim 13, wherein the second identification comprises at least one of:

measuring an identification;

Measuring an object identifier;

And reporting the identification.

21. The method of claim 13, wherein the beam information comprises at least one of:

a beam codebook identification, wherein the beam codebook identification is used for indicating codebook information used by a network device for transmitting a beam;

an antenna configuration identifier, where the antenna configuration identifier is used to indicate antenna configuration information of the network device;

and a beam type indicating the type of beam transmitted by the network device.

22. The method of any one of claims 12 to 21, wherein at least one of the first identifiers is the same and the first information corresponds to the same first function.

23. A terminal device, comprising:

The first transceiver module is configured to receive first information sent by the 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 intelligent AI model and/or AI function;

The first processing module is configured to acquire data required by the first function according to the first information; wherein the first information includes at least one of: the first identifier corresponds to the first function, and 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 purpose comprises at least one of model training, model reasoning and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different;

The first information further includes 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 a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

The reference signal resource information includes a time pattern including any one of:

A first pattern, wherein the first pattern is used for indicating N historical periods and M future periods, and the measurement results of the N historical periods are used for the terminal equipment to obtain prediction results of the M future periods based on the first function;

And the second pattern is used for indicating K historical periods, and the measurement results of the K historical periods are used for the terminal equipment to obtain L prediction results in the (K+1) th future period based on the first function.

24. A network device, comprising:

The second transceiver module is configured to send the first information to the terminal equipment; the first information is information corresponding to a first function, the first function is used for executing beam prediction, the first function is an artificial intelligent AI model and/or an AI function, and the first information is used for indicating the terminal equipment to acquire data required by the first function according to the first information; wherein the first information includes at least one of: the first identifier corresponds to the first function, and 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 purpose comprises at least one of model training, model reasoning and performance monitoring, and the content of the first information corresponding to different data acquisition purposes is different;

The first information further includes 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 a beam corresponding to an input value of the first function, and the second beam is a beam corresponding to an output value of the first function;

The reference signal resource information includes a time pattern including any one of:

A first pattern, wherein the first pattern is used for indicating N historical periods and M future periods, and the measurement results of the N historical periods are used for the terminal equipment to obtain prediction results of the M future periods based on the first function;

And the second pattern is used for indicating K historical periods, and the measurement results of the K historical periods are used for the terminal equipment to obtain L prediction results in the (K+1) th future period based on the first function.

25. A communication device, comprising:

One or more processors;

wherein the processor is configured to perform the communication method of any one of claims 1 to 11 or claims 12 to 22.

26. A storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method of any one of claims 1 to 11 or 12 to 22.

27. A communication system, characterized in that the communication system comprises a terminal device configured to implement the communication method of any of claims 1 to 11 and a network device configured to implement the communication method of any of claims 12 to 22.