WO2025160849A1 - Communication method, device, communication system, and storage medium - Google Patents

Abstract

A communication method, a device, a communication system, and a storage medium. The method comprises: a terminal sending first data and second data, wherein the first data is data related to derivation of an artificial intelligence (AI) model, and the data related to the derivation of the AI model comprises data input by the AI model or data output from the AI model; and the second data comprises actual measurement data corresponding to the data output from the AI model, and the AI model is used for predicting beam information of a beam and/or a beam pair. Therefore, a network device can reliably monitor an AI model on the basis of first data and second data, such that the reliability of the currently used AI model can be effectively ensured, thereby ensuring the stability of communication.

Description

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

The present disclosure relates to the field of communication technologies, and in particular to communication methods, devices, communication systems, and storage media.

Background Art

The base station can configure a reference signal resource set for the terminal for beam measurement. After measuring the reference signal resources in the reference signal resource set, the terminal can report one or more strong reference signal resource identifiers and the corresponding Layer 1 Reference Signal Received Power (L1-RSRP) and/or Layer 1 Signal Interference Noise Ratio (L1-SINR). To reduce the measurement overhead of beams and/or beam pairs, artificial intelligence (AI) models can be used to predict beam information.

Summary of the Invention

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

According to a first aspect of an embodiment of the present disclosure, a communication method is provided, the method comprising:

The terminal sends first data and second data, where the first data is data related to the derivation of an artificial intelligence (AI) model, and the AI model derivation-related data includes data input to the AI model or data output by the AI model. The second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or beam pair.

According to a second aspect of an embodiment of the present disclosure, a communication method is provided, the method comprising:

The network device receives first data and second data, where the first data is data related to the derivation of an artificial intelligence (AI) model, and the AI model derivation-related data includes data input to the AI model or data output by the AI model; the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

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

A transceiver module, wherein the transceiver module is used to send first data and second data, the first data is data related to the derivation of an artificial intelligence (AI) model, the AI model derivation-related data includes data input to the AI model or data output by the AI model, the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

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

A transceiver module, wherein the transceiver module is used to receive first data and second data, the first data is data related to the derivation of an artificial intelligence (AI) model, the AI model derivation-related data includes data input to the AI model or data output by the AI model, and the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

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

one or more processors;

A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, causes the terminal to execute the communication method described in the first aspect.

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

one or more processors;

A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, causes the network device to execute the communication method described in the second aspect.

According to the seventh aspect of an embodiment of the present disclosure, a communication system is proposed, comprising a terminal and a network device, wherein the terminal is configured to implement the communication method described in the first aspect, and the network device is configured to implement the communication method described in the second aspect.

According to an eighth 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 communication method as described in the first aspect or the second aspect.

In the above embodiment, the terminal can send the first data and the second data so that the network device can reliably monitor the AI model based on the first data and the second data, which can effectively ensure the reliability of the currently used AI model and ensure the stability of communication.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

FIG2A is a schematic diagram of an exemplary interaction of a communication method provided according to an embodiment of the present disclosure.

FIG2B is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.

FIG3A is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG3B is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG3C is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG3D is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG3E is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG4A is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG4B is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG4C is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG4D is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG4E is a schematic diagram of an exemplary flow chart of a communication method provided according to an embodiment of the present disclosure.

FIG5 is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.

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

FIG7A is a schematic diagram of an exemplary structure of a terminal provided according to an embodiment of the present disclosure.

FIG7B is a schematic diagram of an exemplary structure of a network device provided according to an embodiment of the present disclosure.

FIG8A is a schematic diagram of an exemplary structure of a communication device provided according to an embodiment of the present disclosure.

FIG8B is a schematic diagram of an exemplary structure of a communication device provided according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

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

In a first aspect, an embodiment of the present disclosure provides a communication method, the method comprising:

The terminal sends first data and second data, where the first data is data related to the derivation of an artificial intelligence (AI) model, and the AI model derivation-related data includes data input to the AI model or data output by the AI model. The second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or beam pair.

In the above embodiment, the terminal can send the first data and the second data so that the network device can monitor the AI model more reliably based on the first data and the second data, which can effectively ensure the reliability of the currently used AI model and ensure the stability of communication.

In conjunction with some embodiments of the first aspect, in some embodiments, the AI model is used for spatial beam prediction, and the AI model is used to predict beam information of the second beam set based on actual measurement results of the first beam set; or,

The AI model is used for time-domain beam prediction, and the AI model is used to predict beam information corresponding to M prediction time instances of the second beam set based on actual measurement results of the first beam set corresponding to N historical measurement time instances;

The first beam set and the second beam set meet a preset condition, and N and M are both integers greater than or equal to 1.

In the above embodiment, the model for spatial domain beam prediction and the model for time domain beam prediction can be effectively monitored, which can ensure the reliability of these two types of AI models.

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

The first beam set is the same as the second beam set;

The first beam set is a subset of the second beam set;

The first beam set is a wide beam, and the second beam set is a narrow beam corresponding to the first beam set.

In the above embodiment, based on the AI model, the terminal may perform actual measurements on only part of the beams or beam pairs, or may measure the beams or beam pairs only at part of the time, which can effectively reduce the power consumption of the terminal.

In combination with some embodiments of the first aspect, in some embodiments, the AI model is deployed on the terminal, and the first data is data output by the AI model; or,

The AI model is deployed on a network device, and the first data is data input to the AI model.

In the above embodiment, when the AI model is deployed on different devices, the terminal can send different data, thereby effectively ensuring the reliability of the performance detection of the AI model.

In conjunction with some embodiments of the first aspect, in some embodiments, the terminal sending the first data and the second data includes:

The terminal sends a first report, where the first report includes the first data and the second data; or

The terminal sends a second report and a third report respectively, where the second report includes the first data, and the third report includes the second data.

In the above embodiment, the terminal may send the first data and the second data together, or may send the first data and the second data separately, so as to ensure the effectiveness of AI model monitoring in different situations.

In conjunction with some embodiments of the first aspect, in some embodiments, the terminal sends the first report, including at least one of the following:

Sending the first report based on Radio Resource Control (RRC) signaling or Medium Access Control Control Element (MAC CE);

The first report is sent based on uplink control information (UCI).

In the above embodiment, the terminal can send the first report in different ways, which can effectively improve the flexibility of data reporting and meet different reporting requirements.

In combination with some embodiments of the first aspect, in some embodiments, the first report includes at least one data sample, one data sample includes a first sample data and second sample data corresponding to the first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data.

In the above embodiment, by setting data samples and making a data sample include first sample data and second sample data that have an associated relationship, the network device can accurately match the model-derived related data with the actual measurement data one by one, thereby ensuring the accuracy of performance monitoring.

In conjunction with some embodiments of the first aspect, in some embodiments, the terminal sends the second report and the third report separately, including:

Sending, by the terminal, the second report based on the UCI;

The terminal sends the third report based on RRC signaling or MAC CE.

In the above embodiment, the terminal can send first data with higher latency requirements based on UCI, and send second data with lower latency requirements based on RRC signaling or MAC CE, which can effectively utilize resources while ensuring the reliability of AI model performance detection.

In combination with some embodiments of the first aspect, in some embodiments, the second report includes X first sample data, the third report includes Y second sample data, each second sample data corresponds to one first sample data, the first sample data is the sample corresponding to the first data, and the second sample data is the sample corresponding to the second data, where X and Y are both positive integers, and Y is less than or equal to X.

In the above embodiment, each second sample data sent by the terminal can correspond to a first sample data, so that the network device can accurately monitor the performance of the AI model based on these first sample data and second sample data.

In conjunction with some embodiments of the first aspect, in some embodiments, the terminal sending the second report based on the UCI includes:

The terminal sends the second report using at least one UCI, each UCI including one first sample data and/or first information, where the first information is used to indicate that one first sample data included in the UCI is the i-th first sample data among the X first sample data included in the second report.

In the above embodiment, by carrying the first information in the UCI to indicate the index of the corresponding first sample data, the network device can reliably determine the second sample data corresponding to each first sample data based on the index i, so that it can correctly match the model-derived related data and the actual measurement data one by one, thereby ensuring the accuracy of performance monitoring.

In conjunction with some embodiments of the first aspect, in some embodiments, the method includes:

The terminal determines that second information is received, and sets i corresponding to the next UCI sent by the terminal to 1, where the second information is used to indicate that the network device has received L first sample data and/or L second sample data; or

The terminal determines that i corresponding to the UCI currently being sent reaches L and/or the number of the second sample data that has been sent reaches L, and sets i corresponding to the next UCI to be sent by the terminal to 1.

In the above embodiment, by setting L, the terminal can reset i to 1 when the number of first sample data and/or second sample data sent reaches L, or the network device can reset i to 1 when it determines that the number of first sample data and/or second sample data received reaches L, which can effectively reduce the computing overhead of the device and reduce the power consumption of the device.

In combination with some embodiments of the first aspect, in some embodiments, the third report includes L second sample data, and the second sample data correspond to the first sample data in sequence according to a preset order; or,

The third report includes third information, where the third information is used to indicate the quantity of the second sample data in the third report, where the second sample data corresponds to the first sample data in sequence according to the preset order, or the third information includes L bits, where each bit is used to indicate whether the third report includes the second sample data corresponding to the bit.

In the above embodiments, the number of second sample data in the third report can be indicated by one or more of the above methods, and the network device can determine the second sample data corresponding to each first sample data based on the corresponding method, and then match the model-derived related data and the time measurement data one by one to ensure the accuracy of performance monitoring.

In combination with some embodiments of the first aspect, in some embodiments, the method includes: the terminal receives fourth information, and the fourth information is used to indicate the value of L; or, the terminal determines L based on the number of the second sample data in the third report; or, the terminal determines L preset by the protocol.

In the above embodiments, L can be determined by one or more of the above methods, which can effectively improve the flexibility of AI model detection.

In conjunction with some embodiments of the first aspect, in some embodiments, the AI model is used for spatial beam prediction,

The AI model is deployed on a network device, the first sample data includes actual measurement results for the first beam set, and the second sample data includes actual measurement results for the second beam set; or,

The AI model is deployed on the terminal, the first sample data includes predicted beam information for the second beam set, and the second sample data includes actual measurement results for the second beam set.

In conjunction with some embodiments of the first aspect, in some embodiments, the AI model is used for time domain beam prediction,

The AI model is deployed on a network device, the first sample data includes actual measurement results for a first beam set corresponding to N historical measurement time instances, and the second sample data includes actual measurement results for a second beam set corresponding to M predicted time instances; or

The AI model is deployed on the terminal, the first sample data includes predicted beam information corresponding to M predicted time instances, and the second sample data includes actual measurement results corresponding to the M predicted time instances.

In conjunction with some embodiments of the first aspect, in some embodiments, the method includes:

The terminal receives fifth information, where the fifth information is used to activate or deactivate the AI model.

In the above embodiment, the terminal can determine whether the corresponding AI model is activated or needs to be activated by receiving the fifth information sent by the network device, thereby ensuring that the currently used AI model is a model with better performance, further ensuring the reliability of communication.

In a second aspect, an embodiment of the present disclosure provides a communication method, the method comprising:

The network device receives first data and second data, where the first data is data related to the derivation of an artificial intelligence (AI) model, and the AI model derivation-related data includes data input to the AI model or data output by the AI model; the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

In conjunction with some embodiments of the second aspect, in some embodiments, the AI model is used for spatial beam prediction, and the AI model is used to predict beam information of the second beam set based on actual measurement results of the first beam set; or,

The AI model is used for time-domain beam prediction, and the AI model is used to predict beam information corresponding to M prediction time instances of the second beam set based on actual measurement results of the first beam set corresponding to N historical measurement time instances;

The first beam set and the second beam set meet a preset condition, and N and M are both integers greater than or equal to 1.

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

The first beam set is the same as the second beam set;

The first beam set is a subset of the second beam set;

The first beam set is a wide beam, and the second beam set is a narrow beam corresponding to the first beam set.

In conjunction with some embodiments of the second aspect, in some embodiments, the AI model is deployed on a terminal, and the first data is data output by the AI model; or,

The AI model is deployed on the network device, and the first data is data input to the AI model.

In conjunction with some embodiments of the second aspect, in some embodiments, the network device receiving the first data and the second data includes:

The network device receives a first report, where the first report includes the first data and the second data; or

The network device receives a second report and a third report respectively, where the second report includes the first data and the third report includes the second data.

In conjunction with some embodiments of the second aspect, in some embodiments, the network device receives a first report including at least one of the following:

The network device receives the first report based on radio resource control RRC signaling or MAC CE; or,

The network device receives the first report based on uplink control information UCI.

In combination with some embodiments of the second aspect, in some embodiments, the first report includes at least one data sample, one data sample includes a first sample data and second sample data corresponding to the first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data.

In conjunction with some embodiments of the second aspect, in some embodiments, the network device receives the second report and the third report respectively, including:

The network device receives the second report based on the UCI;

The network device receives the third report based on RRC signaling or MAC CE.

In combination with some embodiments of the second aspect, in some embodiments, the second report includes X first sample data, and the third report includes Y second sample data, each second sample data corresponds to one first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data, where X and Y are both positive integers, and Y is less than or equal to X.

In conjunction with some embodiments of the second aspect, in some embodiments, the network device receives the second report based on the UCI, including include:

The network device receives the second report sent using at least one UCI, each of the UCIs including one first sample data and/or first information, wherein the first information is used to indicate that one first sample data included in the UCI is the i-th first sample data among the X first sample data included in the second report.

In conjunction with some embodiments of the second aspect, in some embodiments, the method includes:

The network device determines that L first sample data and/or second sample data are received, and sends second information, where the second information is used to instruct the terminal to set i corresponding to the next UCI sent by the terminal to 1.

In combination with some embodiments of the second aspect, in some embodiments, the third report includes L second sample data, and the second sample data correspond to the first sample data in sequence according to a preset order; or,

The third report includes third information, where the third information is used to indicate the quantity of the second sample data in the third report, where the second sample data corresponds to the first sample data in sequence according to the preset order, or the third information includes L bits, where each bit is used to indicate whether the third report includes the second sample data corresponding to the bit.

In conjunction with some embodiments of the second aspect, in some embodiments, the method includes:

The network device sends fourth information, where the fourth information is used to indicate a value of L.

In conjunction with some embodiments of the second aspect, in some embodiments, the AI model is used for spatial beam prediction,

The AI model is deployed on a network device, the first sample data includes actual measurement results for the first beam set, and the second sample data includes actual measurement results for the second beam set; or,

The AI model is deployed on the terminal, the first sample data includes predicted beam information for the second beam set, and the second sample data includes actual measurement results for the second beam set.

In conjunction with some embodiments of the second aspect, in some embodiments, the AI model is used for time domain beam prediction,

The AI model is deployed on a network device, the first sample data includes actual measurement results for a first beam set corresponding to N historical measurement time instances, and the second sample data includes actual measurement results for a second beam set corresponding to M predicted time instances; or

The AI model is deployed on the terminal, the first sample data includes predicted beam information corresponding to M predicted time instances, and the second sample data includes actual measurement results corresponding to the M predicted time instances.

In conjunction with some embodiments of the second aspect, in some embodiments, the method includes:

The network device sends the fifth information, where the fifth information is used to activate or deactivate the AI model.

In a third aspect, an embodiment of the present disclosure provides a terminal, comprising:

A transceiver module, wherein the transceiver module is used to send first data and second data, the first data is data related to the derivation of an artificial intelligence (AI) model, the AI model derivation-related data includes data input to the AI model or data output by the AI model, the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

In a fourth aspect, an embodiment of the present disclosure provides a network device, comprising:

A transceiver module, wherein the transceiver module is used to receive first data and second data, the first data is data related to the derivation of an artificial intelligence (AI) model, the AI model derivation-related data includes data input to the AI model or data output by the AI model, and the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

In a fifth aspect, an embodiment of the present disclosure proposes a terminal comprising: one or more processors; a memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, enables the terminal to execute the communication method in the first aspect.

In the sixth aspect, an embodiment of the present disclosure proposes a network device, comprising: one or more processors; a memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, enables the network device to execute the communication method in the second aspect.

In the seventh aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal 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.

In an eighth aspect, an embodiment of the present disclosure proposes a storage medium, wherein the storage medium stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

In a ninth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

In a tenth aspect, the present disclosure provides a computer program that, when executed on a computer, causes the computer to execute the first The method described in the optional implementation of the first aspect and the second aspect.

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

In a twelfth aspect, an embodiment of the present disclosure provides a communication method, which is applied to a communication system, the communication system including a terminal and a network device, and the method includes:

The terminal determines that the terminal is in a near-field area, and determines a first codeword in a near-field codebook, where the near-field codebook is determined based on candidate first basis vectors, where the candidate first basis vectors are basis vectors used in the near-field area.

In conjunction with some embodiments of the twelfth aspect, in some embodiments, the method includes:

The terminal sends first information to the network device; wherein the first information is used to indicate the first codeword, or the first information is used to indicate the near-field codebook.

In conjunction with some embodiments of the twelfth aspect, in some embodiments, the method includes:

The terminal sends second information to the network device; wherein the second information is used to indicate a common phase coefficient corresponding to the first codeword.

In conjunction with some embodiments of the twelfth aspect, in some embodiments, the method includes:

The network device receives third information sent by the terminal; wherein the third information is used to instruct the terminal to switch between a far-field area and a near-field area.

It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

The present disclosure provides a communication method, terminal, communication system, and storage medium. In some embodiments, the terms "communication method" and "information processing method" and "model performance monitoring method" are interchangeable; the terms "communication device" and "information processing device" and "model performance monitoring device" are interchangeable; and the terms "information processing system" and "communication system" are interchangeable.

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

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

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

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

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

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

In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute any restrictions on the position, order, priority, quantity or content of the description objects. For the statement of the description objects, please refer to the description in the context of the claims or embodiments, and no unnecessary restrictions should be imposed 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 "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 "first level" and "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by ordinal numbers and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and "second device" can be the same device or different devices, and their types can be the same or different. For another example, if the description object is "information", then the "first information" and "second information" can be the same information or different information, and their contents can 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 "time/frequency" and "time/frequency domain" refer to the time domain and/or the frequency domain.

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 less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

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

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

In some embodiments, the terms “access network device (AN device)”, “radio access network device (RAN device)”, “base station (BS)”, “radio base station”, “fixed station”, “node”, “access point”, “transmission point (TP)”, “reception point (RP)”, “transmission/reception point (TRP)”, “panel”, “antenna panel”, “antenna array”, “cell”, “macro cell”, “small cell”, “femto cell”, “pico cell”, “sector”, “cell group”, “serving cell”, “carrier”, “component carrier”, “bandwidth part (BWP)” and the like may be used interchangeably.

In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

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

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

In some embodiments, obtaining 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 embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

FIG1 is a schematic diagram illustrating the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , communication system 100 includes a terminal 101 and a network device 102. In some embodiments, network device 102 may include at least one of an access network device and a core network device.

In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with a communication function, a smart car, a tablet computer (Pad), a computer with a wireless transceiver function, a virtual reality (VR), At least one of, but not limited to, wireless terminal devices in the fields of virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminal devices in industrial control, wireless terminal devices in self-driving, wireless terminal devices in remote medical surgery, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, and wireless terminal devices in smart homes.

In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved Node B (eNB), a next generation evolved Node B (ng-eNB), a next generation Node B (gNB), a node B (NB), a home node B (HNB), a home evolved node B (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 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), where the CU may also be called a control unit. The CU-DU structure may be used to split the protocol layers of the access network device, with some functions of the protocol layers centrally controlled by the CU, and the remaining functions of some or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

In some embodiments, a core network device may be a single device including a first network element, a second network element, etc., or may be a plurality of devices or a group of devices, each including all or part of the first network element, the second network element, etc. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

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

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

The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, the fourth generation mobile communication system (4G), the fifth 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 (FRA), and other technologies. The following systems may be used for communication: IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20 (Ultra-WideBand), Bluetooth, PLMN (Public Land Mobile Network), D2D (Device-to-Device), M2M (Machine-to-Machine), IoT (Internet of Things), V2X (Vehicle-to-Everything), other communication methods, and next-generation systems based on these systems. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

In some embodiments, for example, when the communication frequency band is in frequency range 2, since the high-frequency channel attenuates rapidly, in order to ensure the coverage range, beam-based transmission and reception are required.

In some embodiments, the base station configures a reference signal resource set for beam measurement. The terminal measures the reference signal resources in the reference signal resource set and then reports the X reference signal resource identifiers (IDs) and corresponding L1-RSRP and/or L1-SINR of the stronger ones. However, the problem is that the reference signal resource set configured by the base station contains X reference signals, each of which corresponds to a different transmit beam of the base station. For each reference signal, the terminal needs to use all receive beams to measure the reference signal. The reference signal is measured, and the beam measurement quality corresponding to all receive beams is obtained. The optimal beam measurement quality is determined. Therefore, the terminal needs to measure M*N beam pairs, where M is the number of transmit beams from the base station and N is the number of receive beams at the terminal. This results in excessive computational effort and high latency.

In this regard, some embodiments of the present disclosure propose using an AI model to obtain beam information.

For spatial prediction: the terminal measures the L1-RSRP of set B (which may also include the beam or beam pair ID), inputs it into the AI model, and predicts the L1-RSRP of set A.

Optionally, the relationship between set B and set A includes the following two types:

1. Set B is a subset of set A. For example, if set A contains 32 reference signals (each reference signal corresponds to a beam direction), then set B contains N of them, for example, N = 8. The above only considers the transmit beam.

If beam pairs are considered, the receiving beams of the terminal also need to be considered. For example, if there are 32 transmitting beams and the terminal has 4 receiving beams, then set A is 32*4 beam pairs; set B can be 32 beam pairs, or 16 beam pairs, and so on.

2. Set B is a wide beam, and set A is a narrow beam. For example, set A contains 32 reference signals (each corresponding to a beam direction, and the 32 reference signals cover 120 degrees). Set B contains another N reference signals, for example, N = 8, and these N reference signals also cover 120 degrees. That is, the beam direction of each reference signal in set B overlaps the beam directions of multiple reference signals in set A. This can be understood as the relationship between the 32/N reference signals in set A and the same reference signal in set B being QCL (quasi co-location) Type D.

In some of the following embodiments, set A may also be referred to as the second beam set, and set B may also be referred to as the first beam set.

In some embodiments, if there is no need to monitor the performance of the AI model, assuming that the AI model has been trained in advance, the base station only needs to periodically send the reference signals of set B (for example, the first period). The terminal then measures the L1-RSRP of the reference signals in set B and inputs it into the AI model. The L1-RSRP of all beams or beam pairs in set A or the strongest X reference signal IDs or beam pair IDs among the 32 reference signals in set A can be output.

In some embodiments, if AI model performance needs to be monitored, in addition to sending set B, the base station is required to periodically send reference signals from set A (e.g., the second period, which can be greater than the first period. Whether it is a multiple of the first period or how much greater is greater than the first period is not restricted here). The terminal then measures only the results from set B and inputs them into the AI model to derive predicted beam information, which is then reported to the base station. It also measures the L1-RSRP of all reference signals in set A and obtains beam information, which is reported to the base station as beam information obtained using traditional methods. If set B is a subset of set A, this is equivalent to the terminal only needing to measure all beams or beam pairs in set A.

In some embodiments, for time-domain prediction, the terminal measures the L1-RSRP at a historical time point, set B, and inputs this into an AI model to predict the L1-RSRP at a future time point, set A. In addition to the two aforementioned relationships between set B and set A, there is also a case where set B and set A are identical. Optionally, if the AI model is used, the reference signal for the future time point can be omitted. Instead, beam information is obtained based on the AI model output and reported to the base station.

In some embodiments, reference signals for future time periods also need to be transmitted. The terminal measures these reference signals and obtains beam information, which is then reported to the base station. Therefore, during model performance monitoring, as with spatial beam prediction, the base station periodically transmits the transmit beams in set B and set A, and the terminal measures all beams or beam pairs in set B and set A.

Based on the AI model, for example, the terminal originally needs to measure a total of M*N beam pairs (where M is the number of beams transmitted by the base station and N is the number of beams received by the terminal). However, thanks to the AI model, for spatial beam prediction, the terminal only needs to measure a portion of the M*N beam pairs, such as 1/8, 1/4, etc., and then input the measured beam measurement quality of these beam pairs into the AI model, and the model can output the beam information of the M*N beam pairs. For time domain beam prediction, the terminal can measure the beam quality of beam pairs at historical times to predict the beam information of beam pairs at future times. Of course, the input and output of the model do not consider the beam quality or beam ID of the beam pair, but only consider the beam quality or beam ID of the downlink transmit beam, that is, the AI model is based on the downlink beam, not the AI model based on the beam pair.

However, AI models have a lifecycle or a specific scope of application. For example, some models are suitable for suburban environments, some for urban areas, some for indoor environments, some for rush hour, and some for commuting during less crowded hours. Therefore, it is necessary to monitor the performance of AI models in real time. If the AI model performance is poor, it is necessary to update or switch the AI model.

In some embodiments, for the network side model, when the network side performs monitoring, the content reported by the terminal may include the following two parts of data: one part of the data is used as the input of the network side model, such as the RSRP of the beam (pair) in set B used for model input, or the RSRP of the beam (pair) and the ID of the corresponding beam (pair); the network side obtains the predicted best N beam (pair) IDs and/or RSRPs of set A based on the input of set B. The RSRP output here can be the RSRP corresponding to one or more beams in set A. The other part of the data includes the best N beam (pair) IDs and/or RSRPs in set A actually measured by the terminal. Here, the measurement The RSRP can be the RSRP corresponding to one or more beams in set A.

In some embodiments, for a terminal-side model, when the network monitors the model, the terminal reports the following two data components: One component is the output of the UE-side model, such as the RSRP and/or beam ID of a beam (pair) in set A output by the model; that is, the predicted beam information for set A output by the model. The other component includes the IDs and/or RSRP of the best N beams (pairs) in set A actually measured by the terminal, that is, the measured beam information for set A actually measured.

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

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

In some embodiments, the fourth information is used to indicate a value of L. Optionally, L is used to indicate a maximum number of first sample data sent by the terminal. Wherein, L can be a positive integer.

In some embodiments, step S2101 is optional, and the terminal may independently determine the value of L. Alternatively, the value of L may be a value pre-agreed upon in a protocol. Alternatively, the value of L may be determined based on the number of second sample data in the third report sent in step S2103.

In some embodiments, the terminal receives fourth information sent by the network device. Optionally, the terminal determines the value of L based on the fourth information.

In some embodiments, the terminal determines L according to the number of second sample data in the third report. Optionally, the terminal determines L preset by the protocol.

In some embodiments, the fourth information may be referred to as “quantity indication information”, “sample quantity indication”, etc., and the embodiments of the present disclosure do not limit its name.

Step S2102: The terminal sends a second report to the network device.

In some embodiments, the second report includes the first data. Optionally, the first data is data related to AI model derivation. Optionally, the AI model derivation-related data may include data input to the AI model or data output by the AI model.

In some embodiments, the AI model is used to predict beam information for beams and/or beam pairs.

For example, based on the AI model, the terminal can reduce the number of beams and/or beam pairs that need to be measured in the spatial domain, or the terminal can reduce the number of actual measurements of beams and/or beam pairs in the time domain.

In some embodiments, the AI model is used for spatial beam prediction, wherein the terminal can measure some beams in the spatial domain and predict beam information of other beams. Optionally, the AI model is used to predict beam information of a second beam set based on actual measurement results for the first beam set. Optionally, the AI model can predict beam information for the second beam set corresponding to a time instance, such as the actual measurement results for the first beam set corresponding to the time instance (measurement time instance), for example, predicting beam information for the second beam set corresponding to a prediction time instance corresponding to the measurement time instance, wherein the prediction time instance and the measurement time instance are the same time instance.

For example, the terminal can measure the beams and/or beam pairs in the first beam set at a certain time instance, obtain actual measurement results, and input the actual measurement results into the AI model to obtain beam information of the beams and/or beam pairs in the second beam set corresponding to the time instance.

In some embodiments, the AI model is used for time-domain beam prediction, where the terminal can measure the beam quality of beams and/or beam pairs at historical times to predict beam information of beams and/or beam pairs at future times. Optionally, the AI model is used to predict beam information corresponding to M predicted time instances for a second beam set based on actual measurement results for a first beam set corresponding to N historical measurement time instances. Where N and M are both integers greater than or equal to 1.

It can be understood that the values of N and M can be equal or different. For example, N can be greater than M, or N can be less than M. The embodiments of the present disclosure do not limit this. In addition, the values of N and M can be set based on the prediction effect of the AI model. For example, they can be set to N=3 and M=2. The embodiments of the present disclosure do not limit this.

For example, the terminal can measure the beams and/or beam pairs in the first beam set at N measurement time instances before the current moment and obtain N actual measurement results, and input these N actual measurement results into the AI model to obtain the beam information corresponding to the AI model for the beams and/or beam pairs in the second beam set at M predicted time instances.

In some embodiments, the first beam set and the second beam set satisfy a preset condition. Optionally, the preset condition includes at least one of the following: the first beam set and the second beam set are the same; the first beam set is a subset of the second beam set; or the first beam set is a wide beam and the second beam set is a narrow beam corresponding to the first beam set.

For example, when only transmit beams are considered, the second beam set (set A) can include 32 reference signals, where each reference signal corresponds to a beam direction, and the first beam set (set B) can include Q reference signals therein, for example, Q = 8. When considering beam pairs, for example, if the terminal corresponds to 4 receive beams, set A can include 32*4 beam pairs, and set B can include 32 beam pairs in set A, or 16 beam pairs, and so on.

Alternatively, the first beam set may be a wide beam, the second beam set may be a narrow beam corresponding to the first beam set, and the second beam set may be a narrow beam corresponding to the first beam set. A beam set can include 32 reference signals, each corresponding to a beam direction. The 32 reference signals cover a 120-degree direction. The first beam set can include Q reference signals, for example, Q = 8. These eight reference signals also cover a 120-degree direction. That is, the beam direction of each reference channel in the second beam set overlaps the beam directions of multiple reference signals in the first beam set. The 32/Qth reference signal in the second beam set is in a quasi-co-location (QCL) Type D relationship with the same reference signal in the second beam set.

In some embodiments, the AI model is deployed on a terminal, and the first data is data output by the AI model. Alternatively, the AI model is deployed on a network device, and the first data is data input to the AI model.

Optionally, when the AI model is deployed on a network device, the first data may include the RSRP of the beam (pair) in the first beam set used for model input, or the RSRP of the beam (pair) and the ID of the corresponding beam (pair). Optionally, when the AI model is deployed on a terminal, the first data includes the RSRP of the beam (pair) in the second beam set output by the model, and/or the ID of the beam (pair).

Among them, when the AI model is deployed on the terminal, the terminal can use the AI model to predict the beam information and obtain the data output by the AI model. When the AI model is deployed on the network device, the terminal needs to send the data input by the AI model to the network device, so that the network device can use the AI model to predict the beam information to obtain the data output by the AI model.

It is understandable that when the AI model is used for spatial beam prediction, the input data of the AI model may be the actual measurement results for the first beam set, such as the timing measurement results of the first beam set corresponding to a time instance, and the output data of the AI model may be the predicted beam information of the second beam set, such as the beam information of the second beam set corresponding to the time instance. When the AI model is used for time-domain beam prediction, the input data of the AI model may be the actual measurement results of the first beam set corresponding to N historical measurement time instances, and the output data of the AI model may be the predicted beam information of the second beam set corresponding to M predicted time instances.

In some embodiments, the terminal may send the second report based on the UCI. Alternatively, the terminal may send the second report based on the UCI via the PUCCH or the PUSCH.

In some embodiments, the terminal may use at least one UCI to send the second report. When multiple UCIs are used to send the second report, the information carried by each UCI may be part of the second report or the first data. For example, one UCI may include one first sample data.

In some embodiments, the second report includes X first sample data, where the first sample data is a sample corresponding to the first data. Alternatively, the first data may be composed of X first sample data. Alternatively, the first sample data is a subset of the first data.

In some embodiments, an AI model is used for spatial beam prediction and is deployed on a network device. The first sample data includes actual measurement results for a first beam set, such as actual measurement results for the first beam set corresponding to a time instance. The first sample data is input data to the AI model.

In some embodiments, an AI model is used for spatial beam prediction and is deployed on a terminal. The first sample data includes predicted beam information for a second beam set, such as beam information corresponding to the second beam set at a time instance. The first sample information is data output by the AI model.

In some embodiments, an AI model is used for time-domain beam prediction and is deployed on a network device, and the first sample data includes actual measurement results for a first beam set corresponding to N historical measurement time instances. The first sample data is input data to the AI model.

In some embodiments, the AI model is used for time-domain beam prediction and is deployed on a terminal, and the first sample data includes predicted beam information corresponding to M prediction time instances. The first sample data is data output by the AI model.

For example, the second report or the first data may include X groups of actual measurement results for the first beam set corresponding to historical measurement time instances, where one group of actual measurement results may include actual measurement results for the first beam set corresponding to N historical measurement time instances, and the first sample data may be actual measurement results for the first beam set corresponding to one group of these historical measurement time instances, where N is a positive integer.

Alternatively, the second report or the first data may include actual measurement results for the first beam set corresponding to X time instances, and the first sample data may be the actual measurement result for the first beam set corresponding to one of the time instances, where X is a positive integer.

In some embodiments, the terminal sends the second report using at least one UCI, where each UCI includes one first sample data and/or first information. Optionally, the terminal sends the second report using X UCIs.

In some embodiments, the first information is used to indicate that the first sample data included in the UCI is the i-th first sample data among X first sample data, where i is a positive integer. Optionally, the first information is used to indicate the index i corresponding to the first sample data included in the UCI, where i is 0 or a positive integer.

In some embodiments, i can be used to indicate that the corresponding first sample data is the i-th first sample data among X first sample data, or to indicate the index of the corresponding first sample data. For example, the network device can determine whether the corresponding second sample data is received based on i.

That is, when i represents the number corresponding to the first sample data, it can start from 1 and take values upwards, and when i represents the index corresponding to the first sample data, it can start from 0 and take values upwards.

In some embodiments, the network device receives a second report. Optionally, the network device receives a second report sent using at least one UCI. Optionally, the network device receives a second report sent using X UCIs, each UCI including a first sample data and/or first information.

In some embodiments, the terminal determines that the number i corresponding to the first sample data in the currently transmitted UCI reaches L, and sets i corresponding to the first sample data in the next UCI transmitted by the terminal to 1.

In some embodiments, the terminal determines that the index i corresponding to the first sample data in the currently transmitted UCI reaches L-1, and sets the index i corresponding to the first sample data in the next UCI transmitted by the terminal to 0.

In some embodiments, the network device may determine, based on the first information, whether each UCI sent by the terminal and the corresponding first sample data and/or second sample data are received.

In some embodiments, the second report may also be referred to as “model derivation data”, “model derivation report”, etc. The present disclosure does not limit the name of the second report.

Step S2103: The terminal sends a third report to the network device.

In some embodiments, the third report includes second data. Optionally, the second data includes actual measurement data corresponding to the data output by the AI model. The data output by the AI model may be the first data, or data obtained by the network device based on the first data. For example, the first data is the data input to the AI model, and the network device may obtain the corresponding data output by the AI model based on the first data.

Optionally, when the AI model is deployed on a network device, the second data may include the IDs and/or RSRPs of the best N beams (pairs) in the second beam set actually measured by the terminal, where the RSRP measured here may be the RSRP corresponding to one or more beams in set A. Optionally, when the AI model is deployed on a terminal, the second data may include the IDs and/or RSRPs of the best N beams (pairs) in the second beam set actually measured by the terminal, where the RSRP measured here may be the RSRP corresponding to one or more beams in set A.

For example, if the AI model is deployed on a network device, the first data includes the data input to the AI model, such as the actual measurement results of the first beam set corresponding to one or more time instances (such as the measurement time instance). The data output by the AI model can include the beam information of the second beam set corresponding to these one or more time instances (such as the predicted time instance corresponding to the measurement time instance). Accordingly, the second data can include the actual measurement results of the second beam set corresponding to these one or more time instances. In the case of spatial beam prediction, the predicted time instance and the measured time instance are the same time instance.

In some embodiments, the terminal may send the third report based on RRC or MAC CE. Alternatively, the terminal may send the third report via PUSCH based on RRC or MAC CE.

In some embodiments, the third report includes Y second sample data, where the second sample data is a sample corresponding to the second data. Optionally, Y is less than or equal to X.

In some embodiments, the second data may be composed of Y second sample data. Optionally, the second sample data is a subset of the second data.

In some embodiments, the AI model is used for spatial beam prediction and is deployed on a network device, and the second sample data includes actual measurement results for the second beam set, such as actual measurement results for the second beam set corresponding to a time instance.

In some embodiments, the AI model is used for spatial beam prediction and is deployed on the terminal, and the second sample data includes actual measurement results for the second beam set, such as actual measurement results for the second beam set corresponding to a time instance.

In some embodiments, the AI model is used for time-domain beam prediction and is deployed on a network device, and the second sample data includes actual measurement results for the second beam set corresponding to M prediction time instances.

In some embodiments, the AI model is used for time-domain beam prediction and is deployed on the terminal, and the second sample data includes actual measurement results corresponding to M prediction time instances.

In some embodiments, the third report includes L second sample data. Optionally, the L second sample data correspond to the first sample data in the second report in sequence according to a preset order.

Optionally, the preset order can be, for example, the order of time from near to far or from far to near when the terminal sends the first sample data. For example, when using multiple UCIs to send the second report, the first sample data corresponds one-to-one with the second sample data in the third report according to the size of the corresponding i, such as the index corresponding to the first sample data or the corresponding number.

For example, when the first sample data most recently sent by the terminal is the predicted beam information for the second beam set corresponding to the time instance P or the actual measurement result for the first beam set, and the first second sample data in the third report can correspond to the first sample data, that is, the second sample data can be the actual measurement result for the second beam set corresponding to the time instance P, and the second first second sample data in the third report can correspond to the sample data sent by the terminal before sending the above-mentioned first sample data, such as the time instance P-1.

In some embodiments, the third report includes third information. Optionally, the third information is used to indicate the number of second sample data in the third report, where the second sample data corresponds to the first sample data in the second report in a predetermined order. Optionally, the third information includes L bits, each bit being used to indicate whether the third report includes the second sample data corresponding to the bit.

For example, when the third information indicates that the number of second sample data in the third report is J, these J second sample data may correspond to the J first sample data most recently sent by the terminal, for example, the J first sample data with the largest corresponding i value, that is, the first sample data sent earliest by the terminal has the lowest priority.

Alternatively, if the value of L is 4, when the third information is 1001, it may indicate that the third report only includes the second sample data corresponding to L-1 or the Lth first sample data and the second sample data corresponding to L-4 or the L-3th first sample data. For example, the L-1 or Lth first sample data may be the first sample data most recently sent by the terminal to the network device, and the L-4 or L-3th first sample data may be the first sample data earliest sent by the terminal to the network device.

In some embodiments, the third information may be the least significant bit in the third report. For example, if the third report includes at most K second sample data, the first bit of the third report may be the least significant bit in the third report. bits can be used to indicate the actual number of second sample data in the third report. Alternatively, the first L bits in the third report can be used to indicate whether the third report includes the second sample data corresponding to each of the L first sample data in the second report.

In some embodiments, the terminal determines that the number of second sample data that has been sent reaches L, and sets the number i corresponding to the next UCI to be sent to 1 or the index i to 0. Optionally, after sending the third report, the terminal sets the number i corresponding to the next UCI to be sent to 1 or the index i to 0.

In some embodiments, the network device receives a third report. Optionally, the network device determines the number of second sample data based on the third information. Optionally, the network device determines, based on the third information, whether the third report includes the second sample data corresponding to each first sample data. Optionally, the network device determines the first sample data corresponding to each of the L second sample data. Optionally, the network device determines, based on the third information, the first sample data corresponding to each of the at least one second sample data.

In some embodiments, after receiving the third report, the network device executes at least one of step S2104 and step S2105.

In some embodiments, the third aspect may also be referred to as “actual measurement data”, “real data information”, etc., and the present disclosure does not limit its name.

In some embodiments, the third information may also be referred to as “quantity indication information”, “actual measurement data indication information”, etc., and the embodiments of the present disclosure do not limit the names thereof.

Step S2104: The network device sends second information to the terminal.

In some embodiments, the second information is used to indicate that the network device has received L first sample data and/or second sample data. Optionally, the second information is used to instruct the terminal to set the number i corresponding to the next sent UCI to 1 or the index i to 0.

For example, the network device may send the second information to the terminal after receiving L first sample data.

For example, the network device may send the second information to the terminal after receiving L second sample data.

For example, the network device may send the second information to the terminal after receiving L first sample data and second sample data corresponding to the L first sample data.

In some embodiments, the second information may be sent by the network device after receiving the third report. Optionally, the second information may be HARQ ACK information for PUSCH.

In some embodiments, the terminal receives the second information. Optionally, the terminal determines that the second information has been received, and sets the number i corresponding to the next UCI to be sent to 1 or the index i to 0.

In some embodiments, step S2104 is optional. For example, the terminal may independently determine whether to set the number i corresponding to the next transmitted UCI to be 1 or the index i to be 0.

In some embodiments, the second information may also be called "data response information", "data confirmation information", etc., and the present disclosure does not limit its name.

Step S2105: The network device sends fifth information to the terminal.

In some embodiments, the fifth information is used to indicate activation or deactivation of an AI model. Optionally, the fifth information may include one bit, the value of which may be used to instruct the terminal to activate or deactivate the AI model. Optionally, the fifth information is used to instruct the network device to deactivate a previously used AI model. Optionally, the fifth information is used to indicate the currently activated AI model of the network device.

For example, the fifth information may include one bit. When the AI model is deployed on a terminal and the value of the bit is 1, it can be used to instruct the terminal to activate the AI model or maintain the activation state of the AI model. When the value of the bit is 0, it can be used to instruct the terminal to deactivate the AI model or not activate the AI model. Alternatively, when the AI model is deployed on a network device and the value of the bit is 0, the bit can be used to instruct the network device to deactivate the currently used AI model. When the value of the bit is 1, it can be used to instruct the network device to activate and use the AI model.

In some embodiments, the first data and the second data are used to monitor the performance of the AI model. Optionally, the network device sends fifth information to the terminal based on the first data and the second data.

In some embodiments, the network device determines a performance metric corresponding to the AI model based on the first data and the second data, and sends corresponding fifth information based on the performance metric.

For example, if the AI model is the beam prediction model currently used by the terminal, and the first data is the data output by the AI model, the network device can compare the received first data (for example, the first sample data in the second report) with the second data (for example, the corresponding second sample data in the third report) to determine whether the beam information output by the AI model is accurate, and then determine whether to keep the AI model activated or deactivate the AI model, and send the corresponding fifth information.

Alternatively, if the AI model is a candidate beam prediction model for the network device and the first data is data input to the AI model, the network device can first input the first data into the AI model and obtain the beam information output by the AI model, and compare it with the received second data to determine whether the beam information output by the AI model is better than the currently used AI model, and then determine whether to activate the candidate AI model, and deactivate the currently used AI model, and send the corresponding fifth information to enable the terminal to know the AI model currently used by the network device.

In some embodiments, the fifth information may also be referred to as "model indication information", "deactivation indication information", etc., which is not limited in the embodiments of the present disclosure.

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

In some embodiments, terms such as "uplink", "uplink", "physical uplink" can be interchangeable with each other, and terms such as "downlink", "downlink", "physical downlink" can be interchangeable with each other, and terms such as "side", "sidelink", "side communication", "sidelink communication", "direct connection", "direct link", "direct communication", "direct link communication" can be interchangeable with each other.

In some embodiments, the terms "downlink control information (DCI)", "downlink (DL) assignment (assignment)", "DL DCI", "uplink (UL) grant (grant)", "UL DCI" and so on can be used interchangeably.

In some embodiments, the terms "physical downlink shared channel (PDSCH)", "DL data", etc. can be used interchangeably, and the terms "physical uplink shared channel (PUSCH)", "UL data", etc. can be used interchangeably.

In some embodiments, terms such as "synchronization signal (SS)", "synchronization signal block (SSB)", "reference signal (RS)", "pilot", and "pilot signal" can be used interchangeably.

In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

In some embodiments, the terms "precoding", "precoder", "weight", "precoding weight", "quasi-co-location (QCL)", "transmission configuration indication (TCI) state", "spatial relation", "spatial domain filter", "transmission power", "phase rotation", "antenna port", "antenna port group", "layer", "the number of layers", "rank", "resource", "resource set", "resource group", "beam", "beam width", "beam angular degree", "antenna", "antenna element", "panel" and the like are interchangeable.

In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and/or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

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

In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values (for example, comparison with a predetermined value), but is not limited thereto.

In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and/or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the recipient to respond to the content sent.

The communication method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2105. For example, step S2102 may be implemented as an independent embodiment, step S2103 may be implemented as an independent embodiment, step S2104 may be implemented as an independent embodiment, step S2105 may be implemented as an independent embodiment, step S2102 + step S2103 may be implemented as an independent embodiment, and step S2101 + step S2102 + step S2103 may be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, step S2102 and step S2103 may be executed simultaneously, and step S2103 and step S2104 may be executed in an exchanged order or simultaneously.

In some embodiments, step S2101 and steps S2103 to S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

In some embodiments, steps S2101 to S2102 and steps S2104 and S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

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

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

Step S2201: The terminal sends a first report to the network device.

In some embodiments, the first report includes first data and second data.

Among them, for the optional implementation methods of the first data and the second data, please refer to the corresponding optional implementation methods of step S2102 and step S2103 in Figure 2A, as well as the related parts in Figure 2A, which are not repeated here.

In some embodiments, the terminal sends the first report based on RRC signaling or MAC CE. Optionally, the terminal determines that the AI model is an inactive model and sends the first report based on RRC signaling. Optionally, the terminal sends the first report via PUSCH based on RRC signaling or MAC CE.

In some embodiments, the terminal sends the first report based on the UCI. Optionally, the terminal determines that the AI model is an activated model and sends the first report based on the UCI. Optionally, the terminal sends the first report based on the UCI via the PUCCH or PUSCH.

In some embodiments, the first report includes at least one data sample, where each data sample includes a first sample data and a second sample data corresponding to the first sample data. Optionally, the terminal may send the first report using multiple UCIs, each UCI including a data sample.

In some embodiments, the AI model is used for spatial beam prediction and deployed on a network device. A data sample may include an actual measurement result for a first beam set corresponding to a time instance, i.e., a first sample data, and an actual measurement result for a second beam set corresponding to the time instance, i.e., a second sample data corresponding to the first sample data.

In some embodiments, the AI model is used for spatial beam prediction and deployed on the terminal. A data sample may include beam information corresponding to the second beam set at a time instance, that is, a first sample data, and the actual measurement result of the second beam set corresponding to the time instance, that is, the second sample data corresponding to the first sample data.

In some embodiments, the AI model is used for time-domain beam prediction and deployed on a network device. A data sample may include actual measurement results for a first beam set corresponding to N historical measurement time instances, i.e., a first sample data, and actual measurement results for a second beam set corresponding to M prediction time instances, i.e., second sample data corresponding to the first sample data.

In some embodiments, the AI model is used for time domain beam prediction and is deployed on the terminal. A data sample may include predicted beam information corresponding to M predicted time instances, that is, a first sample data, and actual measurement results corresponding to the M predicted time instances, that is, second sample data corresponding to the first sample data.

For optional implementations of the first sample data and the second sample data, reference may be made to step S2102 and step S2103 in FIG. 2A and related parts in FIG. 2A , which will not be described in detail here.

In some embodiments, a data sample may also be referred to as a "sample data pair", "a set of sample data", etc., and the embodiments of the present disclosure do not limit the names.

In some embodiments, the first data and the second data are used to monitor the performance of the AI model.

In some embodiments, the network device receives the first report. Optionally, the network device monitors the performance of the AI model based on the first data and the second data. Optionally, after receiving the first report, the network device executes step S2202.

Step S2202: The network device sends fifth information to the terminal.

The optional implementation of step S2202 can refer to the optional implementation of step S2105 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

The communication method involved in the embodiment of the present disclosure may include at least one of steps S2201 and S2202. For example, step S2201 may be implemented as an independent embodiment, and step S2202 may be implemented as an independent embodiment.

In some embodiments, step S2202 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

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

FIG3A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a communication method (terminal side), the method comprising:

Step S3101, obtain the fourth information.

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

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

In some embodiments, the terminal obtains fourth information specified by the protocol.

In some embodiments, the terminal obtains the fourth information from an upper layer(s).

In some embodiments, the terminal performs processing to obtain the fourth information.

In some embodiments, step S3101 is omitted, and the terminal autonomously implements the function indicated by the fourth information, or the above function is default or by default.

Step S3102, sending a second report.

The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

In some embodiments, the terminal sends the second report to the network device, but is not limited thereto, and the second report may also be sent to other entities.

Step S3103: Send a third report.

The optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

In some embodiments, the terminal sends the third report to the network device, but is not limited thereto, and the third report may also be sent to other entities.

Optionally, the second report and the third report are used by network devices to monitor the AI model.

Step S3104, obtaining the second information.

The optional implementation of step S3104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

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

In some embodiments, the terminal obtains second information specified by the protocol.

In some embodiments, the terminal obtains the second information from an upper layer(s).

In some embodiments, the terminal performs processing to obtain the second information.

In some embodiments, step S3101 is omitted, and the terminal autonomously implements the function indicated by the second information, or the above function is default or by default.

Step S3105, obtain the fifth information.

The optional implementation of step S3101 can refer to the optional implementation of step S2105 in Figure 2A, step S2202 in Figure 2B, and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

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

In some embodiments, the terminal obtains fifth information specified by the protocol.

In some embodiments, the terminal obtains the fifth information from an upper layer(s).

In some embodiments, the terminal performs processing to obtain the fifth information.

In some embodiments, step S3101 is omitted, and the terminal autonomously implements the function indicated by the fifth information, or the above function is default or by default.

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

In some embodiments, step S3102 and step S3103 may be executed simultaneously, and step S3103 and step S3104 may be executed in an exchanged order or simultaneously.

In some embodiments, step S3101 and steps S3103 to S3105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

In some embodiments, steps S3101 to S3102 and steps S3104 and S3105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

FIG3B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a communication method (terminal side), the method comprising:

Step S3201, sending a second report.

The optional implementation of step S3201 can be found in step S2102 of FIG. 2A , the optional implementation of step S3102 , and other related parts in the embodiments involved in FIG. 2A , FIG. 2B , and FIG. 3A , which will not be described in detail here.

Step S3202, sending the third report.

The optional implementation of step S3202 can be found in step S2103 of FIG. 2A , the optional implementation of step S3103 , and other related parts in the embodiments involved in FIG. 2A , FIG. 2B , and FIG. 3A , which will not be described in detail here.

Step S3203, obtain the fifth information.

The optional implementation of step S3203 can be found in step S2105 of Figure 2A, step S2202 of Figure 2B, the optional implementation of step S3105 of Figure 3A, and other related parts in the embodiments involved in Figures 2A, 2B, and 3A, which will not be repeated here.

The communication method involved in the embodiments of the present disclosure may include at least one of steps S3201 to S3203. For example, step S3201 may be implemented as an independent embodiment, step S3202 may be implemented as an independent embodiment, step S3201 + step S3203 may be implemented as an independent embodiment, and step S3202 + step S3203 may be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, step S3201 and step S3202 may be performed simultaneously.

In some embodiments, step S3203 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a communication method (terminal side), the method comprising:

Step S3301, sending the first report.

The optional implementation of step S3301 can refer to the optional implementation of step S2201 in Figure 2B and other related parts in the embodiments involved in Figures 2A, 2B, 3A, and 3B, which will not be repeated here.

Step S3302, obtain the fifth information.

The optional implementation of step S3302 can be found in the optional implementation of step S2105 in Figure 2A, step S2202 in Figure 2B, step S3105 in Figure 3A, step S3203 in Figure 3B, and other related parts in the embodiments involved in Figures 2A, 2B, 3A, and 3B, which will not be repeated here.

The communication method involved in the embodiment of the present disclosure may include at least one of steps S3301 and S3302. For example, step S3301 may be implemented as an independent embodiment, and step S3302 may be implemented as an independent embodiment.

In some embodiments, step S3302 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

FIG3D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a communication method (terminal side), the method comprising:

Step S3401, sending the first report.

The optional implementation of step S3301 can be found in step S2201 of Figure 2B, the optional implementation of step S3301 of Figure 3C, and other related parts in the embodiments involved in Figures 2A, 2B, 3A, 3B, and 3C, which will not be repeated here.

FIG3E is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3E , the embodiment of the present disclosure relates to a communication method (terminal side), the method comprising:

Step S3501: Send first data and second data.

For the optional implementation of step S3501, please refer to steps S2101 to S2104 of Figure 2A, step S2101 of Figure 2B, steps S3101 to S3104 of Figure 3A, steps S3201 to S3202 of Figure 3B, step S3301 of Figure 3C, and the optional implementation of step S3401 of Figure 3D, as well as other related parts in the embodiments involved in Figures 2A, 2B, 3A, 3B, 3C, and 3D, which will not be repeated here.

In some embodiments, the first data is data related to the derivation of an artificial intelligence (AI) model, and the data related to the derivation of the AI model includes data input to the AI model or data output by the AI model. The second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

In some embodiments, the AI model is used for spatial beam prediction, and the AI model is used to predict beam information of the second beam set based on actual measurement results of the first beam set; or,

The AI model is used for time-domain beam prediction. The AI model is used to predict the beam information corresponding to M prediction time instances of the second beam set based on the actual measurement results of the first beam set corresponding to N historical measurement time instances;

The first beam set and the second beam set meet a preset condition, and N and M are both integers greater than or equal to 1.

In some embodiments, the preset condition includes at least one of the following:

The first beam set is the same as the second beam set;

The first beam set is a subset of the second beam set;

The first beam set is a wide beam, and the second beam set is a narrow beam corresponding to the first beam set.

In some embodiments, the AI model is deployed on a terminal, and the first data is data output by the AI model; or,

The AI model is deployed on a network device, and the first data is the data input to the AI model.

In some embodiments, the terminal sends the first data and the second data, including:

The terminal sends a first report, where the first report includes the first data and the second data; or,

The terminal sends a second report and a third report respectively, where the second report includes the first data and the third report includes the second data.

In some embodiments, the terminal sends a first report including at least one of the following:

Sending a first report based on radio resource control RRC signaling or MAC CE;

A first report is sent based on uplink control information UCI.

In some embodiments, the first report includes at least one data sample, and a data sample includes a first sample data and a second sample data corresponding to the first sample data. The first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data.

In some embodiments, the terminal sends the second report and the third report respectively, including:

The terminal sends a second report based on the UCI;

The terminal sends the third report based on RRC signaling or MAC CE.

In some embodiments, the second report includes X first sample data, and the third report includes Y second sample data, each second sample data corresponds to one first sample data, the first sample data is the sample corresponding to the first data, and the second sample data is the sample corresponding to the second data, where X and Y are both positive integers, and Y is less than or equal to X.

In some embodiments, the terminal sends a second report based on the UCI, including:

The terminal sends a second report using at least one UCI, each UCI including a first sample data and/or first information, and the first information is used to indicate that the first sample data included in the UCI is the i-th first sample data among the X first sample data included in the second report.

In some embodiments, the method comprises:

The terminal determines that the second information is received, and sets i corresponding to the next UCI sent by the terminal to 1, where the second information is used to indicate that the network device has received L first sample data and/or second sample data; or

The terminal determines that i corresponding to the currently sent UCI reaches L and/or the number of second sample data that has been sent reaches L, and sets i corresponding to the next UCI sent by the terminal to 1.

In some embodiments, the third report includes L second sample data, and the second sample data correspond to the first sample data in sequence according to a preset order; or,

The third report includes third information, and the third information is used to indicate the quantity of second sample data in the third report, and the second sample data corresponds to the first sample data in sequence according to a preset order, or the third information includes L bits, and each bit is used to indicate whether the third report includes the second sample data corresponding to the bit.

In some embodiments, the method comprises:

The terminal receives fourth information, where the fourth information is used to indicate a value of L; or,

The terminal determines L according to the number of second sample data in the third report; or,

The terminal determines the L preset by the protocol.

In some embodiments, the AI model is used for spatial beam prediction.

The AI model is deployed on a network device, the first sample data includes actual measurement results for a first beam set, and the second sample data includes actual measurement results for a second beam set; or

The AI model is deployed on the terminal. The first sample data includes predicted beam information for the second beam set. The second sample data includes Actual measurement results for the second set of beams.

In some embodiments, the AI model is used for time-domain beam prediction.

The AI model is deployed on a network device, and the first sample data includes actual measurement results of a first beam set corresponding to N historical measurement time instances, and the second sample data includes actual measurement results of a second beam set corresponding to M predicted time instances; or, the AI model is deployed on a terminal, and the first sample data includes predicted beam information corresponding to M predicted time instances, and the second sample data includes actual measurement results corresponding to M predicted time instances.

In some embodiments, the method includes: the terminal receives fifth information, and the fifth information is used to activate or deactivate the AI model.

FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

Step S4101, sending the fourth information.

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

In some embodiments, the network device sends the fourth information to the terminal, but is not limited thereto, and the fourth information may also be sent to other entities.

Step S4102, obtain the second report.

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

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

Step S4103, obtain the third report.

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

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

Step S4104, sending the second information.

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

In some embodiments, the network device sends the second information to the terminal, but is not limited thereto, and the second information may also be sent to other entities.

Step S4105, sending the fifth information.

The optional implementation of step S4105 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

In some embodiments, the network device sends the fifth information to the terminal, but is not limited thereto, and the fifth information may also be sent to other entities.

The communication method involved in the embodiments of the present disclosure may include at least one of steps S4101 to S4105. For example, step S4102 may be implemented as an independent embodiment, step S4103 may be implemented as an independent embodiment, step S4104 may be implemented as an independent embodiment, step S4105 may be implemented as an independent embodiment, step S4102 + step S4103 may be implemented as an independent embodiment, and step S4101 + step S4102 + step S4103 may be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, step S4102 and step S4103 may be executed simultaneously, and step S4103 and step S4104 may be executed in an exchanged order or simultaneously.

In some embodiments, step S4101 and steps S4103 to S4105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

In some embodiments, steps S4101 to S4102 and steps S4104 and S4105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

Step S4201, obtain the second report.

The optional implementation of step S4201 can be found in step S2102 of FIG. 2A , the optional implementation of step S4102 , and other related parts in the embodiments involved in FIG. 2A , FIG. 2B , and FIG. 4A , which will not be described in detail here.

Step S4202, obtain the third report.

The optional implementation of step S4202 can refer to step S2103 of FIG. 2A , the optional implementation of step S4103 , and FIG. 2A , Other related parts of the embodiments involved in FIG. 2B and FIG. 3A will not be described in detail here.

Step S4203, sending the fifth information.

The optional implementation of step S4203 can be found in step S2105 of Figure 2A, step S2202 of Figure 2B, the optional implementation of step S4105 of Figure 4A, and other related parts in the embodiments involved in Figures 2A, 2B, and 4A, which will not be repeated here.

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

In some embodiments, step S4201 and step S4202 may be performed simultaneously.

In some embodiments, step S4203 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

FIG4C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4C , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

Step S4301, obtain the first report.

The optional implementation of step S4301 can refer to the optional implementation of step S2201 in Figure 2B and other related parts in the embodiments involved in Figures 2A, 2B, 4A, and 4B, which will not be repeated here.

Step S4302, sending the fifth information.

The optional implementation of step S4302 can be found in the optional implementation of step S2105 in Figure 2A, step S2202 in Figure 2B, step S4105 in Figure 4A, step S4203 in Figure 4B, and other related parts in the embodiments involved in Figures 2A, 2B, 4A, and 4B, which will not be repeated here.

The communication method involved in the embodiment of the present disclosure may include at least one of steps S4301 and S4302. For example, step S4301 may be implemented as an independent embodiment, and step S4302 may be implemented as an independent embodiment.

In some embodiments, step S4302 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

FIG4D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4D , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

Step S4401, obtain the first report.

The optional implementation of step S3301 can be found in step S2201 of Figure 2B, the optional implementation of step S3301 of Figure 3C, and other related parts in the embodiments involved in Figures 2A, 2B, 3A, 3B, and 3C, which will not be repeated here.

FIG4E is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4E , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

Step S4501, obtaining first data and second data.

For the optional implementation of step S4501, please refer to steps S2101 to S2104 of Figure 2A, step S2101 of Figure 2B, steps S4101 to S4104 of Figure 4A, steps S4201 to S4202 of Figure 4B, step S4301 of Figure 4C, and the optional implementation of step S4401 of Figure 4D, as well as other related parts in the embodiments involved in Figures 2A, 2B, 4A, 4B, 4C, and 4D, which will not be repeated here.

In some embodiments, the first data is data related to the derivation of an artificial intelligence (AI) model, and the data related to the derivation of the AI model includes data input to the AI model or data output by the AI model. The second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair.

In some embodiments, the AI model is used for spatial beam prediction, and the AI model is used to predict beam information of the second beam set based on actual measurement results of the first beam set; or,

The AI model is used for time-domain beam prediction. The AI model is used to predict the beam information corresponding to M prediction time instances of the second beam set based on the actual measurement results of the first beam set corresponding to N historical measurement time instances;

The first beam set and the second beam set meet a preset condition, and N and M are both integers greater than or equal to 1.

In some embodiments, the preset condition includes at least one of the following:

The first beam set is identical to the second beam set;

The first beam set is a subset of the second beam set;

The first beam set is a wide beam, and the second beam set is a narrow beam corresponding to the first beam set.

In some embodiments, the AI model is deployed on a terminal, and the first data is data output by the AI model; or,

The AI model is deployed on a network device, and the first data is the data input to the AI model.

In some embodiments, a network device receives first data and second data, including:

The network device receives a first report, where the first report includes first data and second data; or,

The network device receives a second report and a third report respectively, where the second report includes the first data and the third report includes the second data.

In some embodiments, the network device receives a first report including at least one of:

The network device receives the first report based on radio resource control RRC signaling or MAC CE; or,

The network device receives a first report based on uplink control information UCI.

In some embodiments, the first report includes at least one data sample, and a data sample includes a first sample data and a second sample data corresponding to the first sample data. The first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data.

In some embodiments, the network device receives the second report and the third report respectively, including:

The network device receives a second report based on the UCI;

The network device receives the third report based on RRC signaling or MAC CE.

In some embodiments, the second report includes X first sample data, and the third report includes Y second sample data, where each second sample data corresponds to one first sample data, the first sample data is the sample corresponding to the first data, and the second sample data is the sample corresponding to the second data, where X and Y are both positive integers, and Y is less than or equal to X.

In some embodiments, the network device receives a second report based on the UCI, including:

The network device receives a second report sent using at least one UCI, each UCI including a first sample data and/or first information, and the first information is used to indicate that the first sample data included in the UCI is the i-th first sample data among the X first sample data included in the second report.

In some embodiments, the method comprises:

The network device determines that L first sample data and/or second sample data are received, and sends second information, where the second information is used to instruct the terminal to set i corresponding to the next UCI sent by the terminal to 1.

In some embodiments, the third report includes L second sample data, and the second sample data correspond to the first sample data in sequence according to a preset order; or,

The third report includes third information, and the third information is used to indicate the quantity of second sample data in the third report, and the second sample data corresponds to the first sample data in sequence according to a preset order, or the third information includes L bits, and each bit is used to indicate whether the third report includes the second sample data corresponding to the bit.

In some embodiments, the method includes: the network device sends fourth information, where the fourth information is used to indicate a value of L.

In some embodiments, the AI model is used for spatial beam prediction.

The AI model is deployed on a network device, the first sample data includes actual measurement results for a first beam set, and the second sample data includes actual measurement results for a second beam set; or, the AI model is deployed on a terminal, the first sample data includes predicted beam information for a second beam set, and the second sample data includes actual measurement results for the second beam set.

In some embodiments, the AI model is used for time-domain beam prediction.

The AI model is deployed on a network device, and the first sample data includes actual measurement results of a first beam set corresponding to N historical measurement time instances, and the second sample data includes actual measurement results of a second beam set corresponding to M predicted time instances; or, the AI model is deployed on a terminal, and the first sample data includes predicted beam information corresponding to M predicted time instances, and the second sample data includes actual measurement results corresponding to M predicted time instances.

In some embodiments, the method includes: the network device sends fifth information, and the fifth information is used to activate or deactivate the AI model.

FIG5 is an interactive diagram 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, and the method includes:

Step S5101: The terminal sends first data and second data to a network device.

For the optional implementation of step S4501, please refer to steps S2101 to S2104 in Figure 2A, step S2101 in Figure 2B, steps S3101 to S3104 in Figure 3A, steps S3201 to S3202 in Figure 3B, step S3301 in Figure 3C, step S3401 in Figure 3D, step S3501 in Figure 3E, steps S4101 to S4104 in Figure 4A, steps S4201 to S4202 in Figure 4B, step S4301 in Figure 4C, step S4401 in Figure 4D, and step S4501 in Figure 4E, as well as other related parts in the embodiments involved in Figures 2A, 2B, 3A, 3B, 3C, 3D, 3E, 4A, 4B, 4C, 4D, and 4E, which will not be repeated here.

In some embodiments, the above method may include the method described in the above terminal side, network device side, etc. embodiments, which will not be repeated here.

FIG6 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the present disclosure embodiment relates to a communication method, which includes:

In step S6101, the terminal sends AI model derivation related data and actual measurement data.

In some embodiments, the AI model derivation-related data includes AI model input data or AI model output data. The actual measurement data includes actual measurement results corresponding to the data output by the AI model.

Optionally, if the model is on the terminal side, the data related to the AI model deduction is the data output by the AI model, that is, the output of the AI model on the terminal side, that is, the prediction information of set A

Optionally, if the model is on the terminal side, the data related to the AI model deduction is the data input to the AI model (measurement information of set B), that is, the terminal side sends the input data for the AI model to the network, and the network obtains the output of the AI model based on the AI model, that is, the prediction information.

In some embodiments, the terminal may send AI model derivation related data and actual measurement data in either of the following two ways.

Method 1: The terminal can send the AI model-derived data and actual measurement data in one report.

For example, for an inactive model, since it has not yet been used for prediction, the data related to AI model derivation does not need to be sent as promptly. Instead, it can be sent along with the actual measurement data used for performance monitoring. Multiple samples can be sent together, with each sample including a set of model derivation-related data and a set of actual measurement data. For example, this can be sent via PUSCH based on RRC signaling or MAC CE.

For example, the report format is:

Sample #1: The first set of model-derived data and the first set of actual measured data;

Sample #2: The second set of model-derived data and the second set of actual measured data;

Sample#2: The second set of model-derived data and the second set of actual measured data.

It should be noted that for spatial beam prediction, a set of data (for example, a sample) corresponds to a measurement time instance.

In some embodiments, if the model derivation related data is the model input data, then a set of data includes the measurement results of set B and the measurement results of set A for a measurement time instance.

In some embodiments, if the model-derived related data is model output data, then a set of data includes the prediction results of set A for a measurement time instance, and the measurement results of set A.

In some embodiments, if it is time domain beam prediction, if the prediction results of M predicted future time instances are obtained based on the measurement results of N historical measurement time instances.

In some embodiments, if the model derivation related data is the model input data, then a set of data includes set B measurement results of N history measurement time instances and set A measurement results of M predicted future time instances.

In some embodiments, if the model-derived related data is model output data, then a set of data includes the prediction results of set A of M predicted future time instances and the measurement results of set A of M predicted future time instances.

In some embodiments, if the model is active and the latency requirements for model derivation-related data are very high, it can be sent based on beam measurement reports fed back by traditional Channel State Information (CSI). For example, each transmission includes only one sample of data, and each sample includes a set of model derivation-related data and actual measurement results. Optionally, due to the high latency requirements, this can be sent based on the UCI through the Physical Uplink Control Channel (PUCCH) or the Physical Uplink Shared Channel (PUSCH).

Method 2: The terminal sends the AI model-derived data and actual measurement data in different reports.

For example, for an active model, data related to model derivation has high latency requirements and can be sent based on UCI. However, actual measurement results are used for performance monitoring and have less stringent latency requirements, so they can be sent based on RRC signaling or MAC CE. These two data can be sent separately. When sending them separately, how can we indicate the one-to-one correspondence between actual measurement results and model derivation data?

Specifically, in some embodiments, for data related to model derivation, when each model derivation related data is sent, a total of L samples uploaded are attached, which also means that the current one is the Lth one. If the network device only receives L-4 and L-1, it means that the middle L-3 and L-2 were not successfully received by the network side.

In some embodiments, the maximum value of L is configured by the network or specified by the protocol. After the actual measurement results corresponding to L samples are sent or successfully received by the network, for example, when the network sends a hybrid automatic repeat-request (HARQ) acknowledgment (ACK) message for the PUSCH, the value of L is reset to 1.

In some embodiments, the maximum value of L may also correspond to the number of actual measurement results included in a report.

In some embodiments, after uploading L model-derived data, the terminal uploads a report containing actual measurement data corresponding to multiple model-derived data, and the lowest bit in this report will indicate how many samples correspond to the actual measurement data reported. Subsequently, the actual measurement data corresponding to each sample is given in order from the most recent to the oldest sample (or vice versa).

In some embodiments, the least significant bit indicates how many samples of actual measurement data are reported. This may include at least one of the following three indication methods:

1. Only the number of samples is indicated, and these samples correspond to L samples and go forward in sequence, that is, the actual measurement data corresponding to the first sample has the lowest sending priority.

2. There are L bits, one bit for each sample. "1" indicates that the sample has actual measurement data sent, and "0" indicates that it does not.

3. If this bit is not set, all L messages will be sent by default.

In some embodiments, after receiving the one-to-one correspondence between model derivation related data and actual measurement data, the network device obtains a performance metric, determines whether the model (or functionality) is active or deactive, and then sends an indication message to the terminal.

In the disclosed embodiment, the terminal sends AI model derivation-related data and actual measurement data in two ways: joint sending and separate sending. It is ensured that when sending separately, the base station can correctly match the model derivation-related data and the time measurement data one by one, thereby ensuring the accuracy of AI communication.

In the embodiments of the present disclosure, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations of other embodiments.

The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, a terminal, a network device, etc.) in any of the above methods.

It should be understood that the division of the various units or modules in the above device is only a division of logical functions. 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 various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device 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 above-mentioned hardware circuits may be understood as one or more processors. For example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above-mentioned units or modules may be implemented by designing the logical relationship between the components in the circuit. For another example, in another implementation, the above-mentioned hardware circuit may be implemented by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it may include a large number of logic gate circuits, and the connection relationship between the logic gate circuits may be configured through a configuration file, thereby implementing the functions of some or all of the above-mentioned units or modules. All units or modules of the above-mentioned devices may be implemented entirely by the processor calling software, or entirely by hardware circuits, or partially by the processor calling software, and the remaining part by hardware circuits.

In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of a hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as ASIC, such as the Neural Network Processing Unit (NPU), the Tensor Processing Unit (TPU), the Deep Learning Processing Unit (DPU), etc.

Figure 7A is a structural diagram of the terminal proposed in an embodiment of the present disclosure. As shown in Figure 7A, the terminal 7100 may include: at least one of a transceiver module 7101, a processing module 7102, etc. In some embodiments, the transceiver module 7101 is used to send first data and second data, the first data being data related to the derivation of an artificial intelligence AI model, the AI model derivation-related data including data input to the AI model or data output by the AI model, the second data including actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of beams and/or beam pairs. Optionally, the transceiver module 7101 is used to execute at least one of the communication steps such as sending and/or receiving executed by the terminal in any of the above methods, which will not be repeated here. Optionally, the processing module 7102 is used to execute at least one of the other steps executed by the terminal in any of the above methods, which will not be repeated here.

FIG7B is a schematic diagram of the structure of the network device proposed in the embodiment of the present disclosure. As shown in FIG7B , the network device 7200 may include at least one of a transceiver module 7201 and a processing module 7202. In some embodiments, the transceiver module 7201 is used to receive the first One data and second data, the first data is data related to the derivation of an artificial intelligence AI model, the AI model derivation related data includes data input to the AI model or data output by the AI model, the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict the beam information of the beam and/or beam pair. Optionally, the above-mentioned transceiver module 7201 is used to execute at least one of the communication steps such as sending and/or receiving performed by the network device in any of the above methods, which will not be repeated here. Optionally, the above-mentioned processing module 7202 is used to execute at least one of the other steps performed by the network device in any of the above methods, which will not be repeated here.

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

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

Figure 8A is a schematic diagram of the structure of a communication device 8100 proposed in an embodiment of the present disclosure. Communication device 8100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 8100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

As shown in Figure 8A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. Optionally, the communication device 8100 is used to perform any of the above methods. Optionally, one or more processors 8101 are used to call instructions to enable the communication device 8100 to perform any of the above methods.

In some embodiments, the communication device 8100 further includes one or more transceivers 8102. When the communication device 8100 includes one or more transceivers 8102, the transceiver 8102 performs at least one of the communication steps, such as sending and/or receiving, in the above-described method, and the processor 8101 performs at least one of the other steps. In an optional embodiment, the transceiver may include a receiver and/or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

In some embodiments, the communication device 8100 further includes one or more memories 8103 for storing data. Alternatively, all or part of the memories 8103 may be located outside the communication device 8100. In alternative embodiments, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuits 8104 are connected to the memories 8103 and may be configured to receive data from the memories 8103 or other devices, or to send data to the memories 8103 or other devices. For example, the interface circuits 8104 may read data stored in the memories 8103 and send the data to the processor 8101.

The communication device 8100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 8100 described in the present disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG. 8A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

FIG8B is a schematic diagram of the structure of a chip 8200 according to an embodiment of the present disclosure. If the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 8200 shown in FIG8B , but the present disclosure is not limited thereto.

The chip 8200 includes one or more processors 8201. The chip 8200 is configured to execute any of the above methods.

In some embodiments, chip 8200 further includes one or more interface circuits 8202. Terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 8200 further includes one or more memories 8203 for storing data. Alternatively, all or part of memory 8203 may be located external to chip 8200. Optionally, interface circuit 8202 is connected to memory 8203 and may be used to receive data from memory 8203 or other devices, or may be used to send data to memory 8203 or other devices. For example, interface circuit 8202 may read data stored in memory 8203 and send the data to processor 8201.

In some embodiments, the interface circuit 8202 performs at least one of the communication steps, such as sending and/or receiving, in the above-described method. For example, the interface circuit 8202 performing the communication steps, such as sending and/or receiving, in the above-described method means that the interface circuit 8202 performs data exchange between the processor 8201, the chip 8200, the memory 8203, or the transceiver device. In some embodiments, the processor 8201 performs at least one of the other steps.

The modules and/or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and/or devices, which is not limited here.

The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 8100, causes the communication device 8100 to execute any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto, and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a temporary storage medium.

The present disclosure also provides a program product, which, when executed by the communication device 8100, enables the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims (38)

A communication method, characterized in that the method comprises: The terminal sends first data and second data, where the first data is data related to the derivation of an artificial intelligence (AI) model, and the AI model derivation-related data includes data input to the AI model or data output by the AI model. The second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or beam pair. The method according to claim 1, characterized in that The AI model is used for spatial beam prediction, and the AI model is used to predict beam information of the second beam set based on actual measurement results of the first beam set; or, The AI model is used for time-domain beam prediction, and the AI model is used to predict beam information corresponding to M prediction time instances of the second beam set based on actual measurement results of the first beam set corresponding to N historical measurement time instances; The first beam set and the second beam set meet a preset condition, and N and M are both integers greater than or equal to 1. The method according to claim 2, wherein the preset condition includes at least one of the following: The first beam set is the same as the second beam set; The first beam set is a subset of the second beam set; The first beam set is a wide beam, and the second beam set is a narrow beam corresponding to the first beam set. The method according to any one of claims 1 to 3, characterized in that The AI model is deployed on the terminal, and the first data is data output by the AI model; or The AI model is deployed on a network device, and the first data is data input to the AI model. The method according to any one of claims 1 to 4, wherein the terminal sending the first data and the second data comprises: The terminal sends a first report, where the first report includes the first data and the second data; or The terminal sends a second report and a third report respectively, where the second report includes the first data, and the third report includes the second data. The method according to claim 5, wherein the terminal sends the first report, including at least one of the following: Sending the first report based on radio resource control RRC signaling or media access control element MAC CE; The first report is sent based on uplink control information UCI. The method according to claim 5 or 6 is characterized in that the first report includes at least one data sample, and one data sample includes a first sample data and second sample data corresponding to the first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data. The method according to claim 5, wherein the terminal sends the second report and the third report separately, comprising: Sending, by the terminal, the second report based on the UCI; The terminal sends the third report based on RRC signaling or MAC CE. The method according to claim 5 or 8, characterized in that the second report includes X first sample data, the third report includes Y second sample data, each second sample data corresponds to one first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data, wherein X and Y are both positive integers, and Y is less than or equal to X. The method according to claim 9, wherein the terminal sending the second report based on the UCI comprises: The terminal sends the second report using at least one UCI, each UCI including one first sample data and/or first information, where the first information is used to indicate that one first sample data included in the UCI is the i-th first sample data among the X first sample data included in the second report. The method according to claim 10, characterized in that the method comprises: The terminal determines that the second information is received, sets i corresponding to the next UCI sent by the terminal to 1, and the second The information is used to indicate that the network device has received L first sample data and/or L second sample data; or, The terminal determines that i corresponding to the UCI currently being sent reaches L and/or the number of the second sample data that has been sent reaches L, and sets i corresponding to the next UCI to be sent by the terminal to 1. The method according to any one of claims 9 to 11, characterized in that The third report includes L pieces of the second sample data, and the second sample data correspond to the first sample data in sequence according to a preset order; or The third report includes third information, where the third information is used to indicate the quantity of the second sample data in the third report, where the second sample data corresponds to the first sample data in sequence according to the preset order, or the third information includes L bits, where each bit is used to indicate whether the third report includes the second sample data corresponding to the bit. The method according to claim 11 or 12, characterized in that the method comprises: The terminal receives fourth information, where the fourth information is used to indicate a value of L; or, The terminal determines L according to the number of the second sample data in the third report; or The terminal determines L preset by the protocol. The method according to any one of claims 6 to 13, wherein the AI model is used for spatial beam prediction, The AI model is deployed on a network device, the first sample data includes actual measurement results for the first beam set, and the second sample data includes actual measurement results for the second beam set; or, The AI model is deployed on the terminal, the first sample data includes predicted beam information for the second beam set, and the second sample data includes actual measurement results for the second beam set. The method according to any one of claims 6 to 13, wherein the AI model is used for time domain beam prediction, The AI model is deployed on a network device, the first sample data includes actual measurement results for a first beam set corresponding to N historical measurement time instances, and the second sample data includes actual measurement results for a second beam set corresponding to M predicted time instances; or The AI model is deployed on the terminal, the first sample data includes predicted beam information corresponding to M predicted time instances, and the second sample data includes actual measurement results corresponding to the M predicted time instances. The method according to any one of claims 1 to 15, characterized in that the method comprises: The terminal receives fifth information, where the fifth information is used to activate or deactivate the AI model. A communication method, characterized in that the method comprises: The network device receives first data and second data, where the first data is data related to the derivation of an artificial intelligence (AI) model, and the AI model derivation-related data includes data input to the AI model or data output by the AI model; the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair. The method according to claim 17, characterized in that The AI model is used for spatial beam prediction, and the AI model is used to predict beam information of the second beam set based on actual measurement results of the first beam set; or, The AI model is used for time-domain beam prediction, and the AI model is used to predict beam information corresponding to M prediction time instances of the second beam set based on actual measurement results of the first beam set corresponding to N historical measurement time instances; The first beam set and the second beam set meet a preset condition, and N and M are both integers greater than or equal to 1. The method according to claim 18, wherein the preset condition includes at least one of the following: The first beam set is the same as the second beam set; The first beam set is a subset of the second beam set; The first beam set is a wide beam, and the second beam set is a narrow beam corresponding to the first beam set. The method according to any one of claims 17 to 19, characterized in that The AI model is deployed on a terminal, and the first data is data output by the AI model; or The AI model is deployed on the network device, and the first data is data input to the AI model. The method according to any one of claims 17 to 20, wherein the network device receives the first data and the second data, comprising: The network device receives a first report, where the first report includes the first data and the second data; or The network device receives a second report and a third report respectively, where the second report includes the first data and the third report includes the second data. The method according to claim 21, wherein the network device receives a first report comprising at least one of the following: The network device receives the first report based on radio resource control RRC signaling or media access control element MAC CE; or The network device receives the first report based on uplink control information UCI. The method according to claim 21 or 22 is characterized in that the first report includes at least one data sample, and one data sample includes a first sample data and a second sample data corresponding to the first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data. The method according to claim 21, wherein the network device receives the second report and the third report respectively, comprising: The network device receives the second report based on the UCI; The network device receives the third report based on RRC signaling or MAC CE. The method according to claim 21 or 24, characterized in that the second report includes X first sample data, the third report includes Y second sample data, each second sample data corresponds to one first sample data, the first sample data is a sample corresponding to the first data, and the second sample data is a sample corresponding to the second data, wherein X and Y are both positive integers, and Y is less than or equal to X. The method according to claim 25, wherein the network device receives the second report based on the UCI, comprising: The network device receives the second report sent using at least one UCI, each of the UCIs including one first sample data and/or first information, wherein the first information is used to indicate that one first sample data included in the UCI is the i-th first sample data among the X first sample data included in the second report. The method according to claim 26, characterized in that the method comprises: The network device determines that L first sample data and/or second sample data are received, and sends second information, where the second information is used to instruct the terminal to set i corresponding to the next UCI sent by the terminal to 1. The method according to any one of claims 25 to 27, characterized in that The third report includes L pieces of the second sample data, and the second sample data correspond to the first sample data in sequence according to a preset order; or The third report includes third information, where the third information is used to indicate the quantity of the second sample data in the third report, where the second sample data corresponds to the first sample data in sequence according to the preset order, or the third information includes L bits, where each bit is used to indicate whether the third report includes the second sample data corresponding to the bit. The method according to claim 27 or 28, characterized in that the method comprises: The network device sends fourth information, where the fourth information is used to indicate a value of L. The method according to any one of claims 23 to 29, wherein the AI model is used for spatial beam prediction, The AI model is deployed on a network device, the first sample data includes actual measurement results for the first beam set, and the second sample data includes actual measurement results for the second beam set; or, The AI model is deployed on the terminal, the first sample data includes predicted beam information for the second beam set, and the second sample data includes actual measurement results for the second beam set. The method according to any one of claims 23 to 29, wherein the AI model is used for time domain beam prediction, The AI model is deployed on a network device, the first sample data includes actual measurement results for a first beam set corresponding to N historical measurement time instances, and the second sample data includes actual measurement results for a second beam set corresponding to M predicted time instances; or The AI model is deployed on the terminal, the first sample data includes predicted beam information corresponding to M predicted time instances, and the second sample data includes actual measurement results corresponding to the M predicted time instances. The method according to any one of claims 17 to 31, characterized in that the method comprises: The network device sends the fifth information, where the fifth information is used to activate or deactivate the AI model. A terminal, characterized in that the terminal comprises: A transceiver module, wherein the transceiver module is used to send first data and second data, the first data is data related to the derivation of an artificial intelligence (AI) model, the AI model derivation-related data includes data input to the AI model or data output by the AI model, the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair. A network device, characterized in that the network device comprises: A transceiver module, wherein the transceiver module is used to receive first data and second data, the first data is data related to the derivation of an artificial intelligence (AI) model, the AI model derivation-related data includes data input to the AI model or data output by the AI model, and the second data includes actual measurement data corresponding to the data output by the AI model, and the AI model is used to predict beam information of a beam and/or a beam pair. A terminal, comprising: one or more processors; A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, enable the terminal to execute the communication method according to any one of claims 1 to 16. A network device, comprising: one or more processors; A memory coupled to the one or more processors, the memory comprising executable instructions, which, when executed by the one or more processors, cause the network device to perform the communication method described in claims 17-32. A communication system, characterized in that it includes a terminal and a network device, wherein the terminal is configured to implement the communication method according to any one of claims 1 to 16, and the network device is configured to implement the communication method according to any one of claims 17 to 32. A storage medium storing instructions, characterized in that when the instructions are executed on a communication device, the communication device executes the communication method according to any one of claims 1 to 16 or claims 17 to 32.