Communication method, communication device, storage medium and program product

Abstract

The present disclosure belongs to the technical field of communications, and relates to a communication method, a communication device, a storage medium and a program product. The method comprises: determining a first channel state information (CSI) report and a second CSI report, wherein at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model; and abandoning a third CSI report, wherein the third CSI report is a CSI report with the lower priority between the first CSI report and the second CSI report. The priority of a CSI report corresponding to model inference, model performance monitoring, or model training can be determined, thereby facilitating the processing of the report on the basis of the priority.

Description

Communication methods, communication equipment, storage media and software products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to communication methods, communication devices, storage media, and program products. Background Technology

[0002] In the field of communications, artificial intelligence (AI) / machine learning (ML) technologies can be used for beam prediction and channel state information (CSI) prediction. This facilitates beam management based on the prediction results and optimizes channel state, thereby improving communication efficiency and signal quality. Summary of the Invention

[0003] This disclosure provides a communication method, communication device, storage medium, and program product that can be used in the field of communication technology to determine the priority of CSI reporting.

[0004] According to a first aspect of the present disclosure, a communication method is proposed, executed by a terminal, comprising: determining a first channel state information (CSI) report and a second CSI report, wherein at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model; and discarding a third CSI report, wherein the third CSI report is a CSI report with lower priority than the first CSI report and the second CSI report.

[0005] According to a second aspect of the present disclosure, a communication method is proposed, executed by a network device, comprising: receiving a fourth CSI report sent by a terminal or receiving a fourth CSI report and a third CSI report sent by a terminal, wherein the fourth CSI report is a higher priority CSI report among the first CSI report and the second CSI report, the third CSI report is a higher priority CSI report among the first CSI report and the second CSI report, the third CSI report is an unupdated CSI report, and at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model.

[0006] According to a third aspect of the present disclosure, a communication device is provided that can implement the communication methods described in the first and second aspects of the present disclosure.

[0007] According to a fourth aspect of the present disclosure, a computer storage medium is provided, wherein the computer storage medium stores computer-executable instructions; after being executed by a processor, the computer-executable instructions are able to implement the communication method described in any one of the first and second aspects of the present disclosure.

[0008] According to a fifth aspect of the present disclosure, a program product is provided, including at least one of a program and instructions, wherein when the program and instructions are executed by a communication device, they implement the communication method described in any one of the first and second aspects of the present disclosure.

[0009] According to the communication method proposed in the embodiments of this disclosure, the priority of CSI reports corresponding to model inference, model performance monitoring, or model training can be determined, which facilitates the processing of reports according to priority. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0011] Figure 1 is a schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0012] Figure 2 is a schematic diagram of a communication method provided according to an embodiment of the present disclosure;

[0013] Figure 3 is a schematic diagram of another communication method provided according to an embodiment of the present disclosure;

[0014] Figure 4 is a schematic diagram of another communication method provided according to an embodiment of the present disclosure;

[0015] Figure 5A is a schematic diagram of the structure of a terminal provided according to an embodiment of the present disclosure;

[0016] Figure 5B is a schematic diagram of the structure of a network device provided according to an embodiment of the present disclosure;

[0017] Figure 6A is a schematic diagram of the structure of a communication device according to an embodiment of the present disclosure;

[0018] Figure 6B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0019] This disclosure provides a communication method, communication device, storage medium, and program product.

[0020] In a first aspect, embodiments of this disclosure provide a communication method executed by a terminal, comprising: determining a first channel state information (CSI) report and a second CSI report, wherein at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model; and discarding a third CSI report, wherein the third CSI report is a CSI report with lower priority than the first CSI report and the second CSI report.

[0021] In the above embodiments, the terminal can determine the priority of the CSI report corresponding to model inference, model performance monitoring, or model training, which facilitates the reporting of CSI according to the priority.

[0022] In conjunction with some embodiments of the first aspect, in some embodiments discarding a third CSI report includes: determining that the first CSI report and the second CSI report meet preset conditions, and then discarding the third CSI report.

[0023] In the above embodiments, if the first CSI report and the second CSI report meet preset conditions, the lower-priority CSI report in the first CSI report and the second CSI report can be discarded, thereby realizing the reporting of CSI information according to priority.

[0024] In conjunction with some embodiments of the first aspect, in some embodiments, the preset conditions include at least one of the following: the first CSI report and the second CSI report need to be updated or calculated on the same symbol; the first CSI report and the second CSI report need to be sent to the network device on the same uplink resource.

[0025] In the above embodiments, when the first CSI report and the second CSI report need to use the same symbol or the same uplink resources, the report to be reported can be selected according to the priority of the first CSI report and the second CSI report.

[0026] In conjunction with some embodiments of the first aspect, in some embodiments, abandoning the third CSI report includes at least one of the following: not updating or calculating the third CSI report; not sending the third CSI report to the network device.

[0027] In the above embodiments, reports with lower priority can be discarded, which can prevent reports with lower priority from occupying computing resources or transmission resources, and avoid problems such as affecting urgent services.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the type of the first CSI report or the type of the second CSI report includes at least one of the following: a CSI report corresponding to AI / ML function or AI / ML model derivation; a CSI report corresponding to AI / ML function or AI / ML model performance monitoring; and a CSI report corresponding to AI / ML function or AI / ML model training data collection.

[0029] In the above embodiments, the type of the first CSI report and the type of the second CSI report can be determined, which facilitates the determination of the priority of CSI reports based on the type of the first CSI report and the type of the second CSI report.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the priority of the first CSI report and / or the second CSI report is determined based on at least one of the following: a first parameter; a second parameter; a third parameter; a serving cell index; a maximum number of serving cells; and a report configuration identifier for a maximum number of CSI reports.

[0031] In the above embodiments, the priority of the first CSI report and / or the second CSI report can be determined, which makes it easier to discard reports with lower priority and avoids reports with lower priority occupying computing resources or transmission resources, thus avoiding problems such as affecting urgent services.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: the value of the first parameter is determined based on the transmission characteristics corresponding to the first CSI report or the second CSI report, the transmission characteristics including at least one of the following: CSI reports transmitted aperiodically on the Physical Uplink Shared Channel (PUSCH); CSI reports transmitted semi-persistently on the PUSCH; CSI reports transmitted semi-persistently on the Physical Uplink Control Channel (PUCCH); and CSI reports transmitted periodically on the PUCCH.

[0033] In the above embodiments, a first parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the first parameter.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: the value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, the content including at least one of the following: including Layer 1 Reference Signal Received Power L1-RSRP or Layer 1 Signal-to-Interference-plus-Noise Ratio L1-SINR; including beam accuracy indication information; not including L1-RSRP or L1-SINR; not including beam accuracy indication information.

[0035] In the above embodiments, a second parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the second parameter.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: determining the second parameter based on the content contained in the L1-RSRP or L1-SINR included in the first CSI report or the second CSI report, including: determining the second parameter based on the features of the L1-RSRP or L1-SINR included in the first CSI report or the second CSI report, wherein the features of the L1-RSRP or L1-SINR include at least one of the following: the L1-RSRP or L1-SINR corresponds to the CSI report of AI / ML function or AI / ML model derivation; the L1-RSRP or L1-SINR corresponds to the CSI report of AI / ML function or AI / ML model performance monitoring; the L1-RSRP or L1-SINR corresponds to the CSI report related to the collection of AI / ML function or AI / ML model training data.

[0037] In the above embodiments, a second parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the second parameter.

[0038] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: beam accuracy indication information corresponding to a CSI report for AI / ML function or AI / ML model performance monitoring, wherein the beam accuracy indication information includes at least one of the following: the predicted N best beams include the measured best beam; the predicted best beam is among the measured best N beams; the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam; the probability that the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam is within a threshold; the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the predicted best beam; the probability that the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam is within a threshold.

[0039] In the above embodiments, beam accuracy indication information can be determined.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, the value of the third parameter is determined based on whether the first CSI report or the second CSI report is related to the AI / ML function or the AI / ML model.

[0041] In the above embodiments, the value of the third parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the value of the third parameter.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model inference is greater than the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model inference is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model inference is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; wherein, the parameter is a first parameter, a second parameter, or a third parameter. In the above embodiments, it can be determined that the first parameter, the second parameter, or the third parameter is determined based on the type of the first CSI report and the type of the second CSI report.

[0043] In conjunction with some embodiments of the first aspect, in some embodiments, the CSI report corresponding to the AI / ML function or AI / ML model derivation includes the following features: the terminal receives CSI report configuration information corresponding to the CSI report derived by the AI / ML function or AI / ML model; based on the CSI report configuration information, a first reference signal resource set and a second reference signal resource set are determined; the CSI report is the measurement result of the terminal based on the first reference signal resource set and the prediction result corresponding to the second reference signal resource set obtained by the AI / ML function or AI / ML model; wherein the first reference signal resource set and the second reference signal resource set contain at least one different reference signal resource, or the time domain positions corresponding to the first reference signal resource set and the second reference signal resource set are different.

[0044] In the above embodiments, the characteristics of the CSI report corresponding to the AI / ML function or AI / ML model derivation can be determined.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the CSI report corresponding to the performance monitoring of AI / ML functions or AI / ML models includes at least one of the following features: the terminal receives CSI report configuration information of the CSI report corresponding to the performance monitoring of AI / ML functions or AI / ML models, and the CSI configuration information includes a CSI report configuration identifier corresponding to the performance monitoring of AI / ML functions or AI / ML model derivation and / or a CSI configuration information indicating the reporting beam accuracy indication.

[0046] In the above embodiments, the characteristics of the CSI report corresponding to AI / ML function or AI / ML model performance monitoring can be determined.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the CSI report corresponding to the AI / ML function or AI / ML model training data collection includes the following features: the terminal receives CSI report configuration information of the CSI report corresponding to the AI / ML function or AI / ML model training data collection, the CSI report configuration information includes an association identifier, the association identifier is used to reflect the beam characteristics corresponding to the reference signal resource or reference signal resource set indicated in the CSI report configuration information.

[0048] In the above embodiments, the characteristics of the CSI report corresponding to the AI / ML function or AI / ML model training data collection can be determined.

[0049] Secondly, embodiments of this disclosure provide a communication method executed by a network device, comprising: receiving a fourth CSI report sent by a terminal or receiving a fourth CSI report and a third CSI report sent by a terminal, wherein the fourth CSI report is a higher priority CSI report among the first CSI report and the second CSI report, the third CSI report is a lower priority CSI report among the first CSI report and the second CSI report, the third CSI report is an unupdated CSI report, and at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model.

[0050] In the above embodiments, CSI reports can be received according to CSI report priority.

[0051] In conjunction with some embodiments of the second aspect, in some embodiments, the type of the first CSI report or the type of the second CSI report includes at least one of the following: a CSI report corresponding to AI / ML function or AI / ML model derivation; a CSI report corresponding to AI / ML function or AI / ML model performance monitoring; and a CSI report corresponding to AI / ML function or AI / ML model training data collection.

[0052] In the above embodiments, the type of the first CSI report or the type of the second CSI report can be determined.

[0053] In conjunction with some embodiments of the second aspect, in some embodiments, the priority of the first CSI report and / or the second CSI report is determined based on at least one of the following: a first parameter; a second parameter; a third parameter; a serving cell index; a maximum number of serving cells; a report configuration identifier; and a maximum number of CSI reports.

[0054] In the above embodiments, the priority of the first CSI report and / or the second CSI report can be determined.

[0055] In conjunction with some embodiments of the second aspect, in some embodiments, the value of the first parameter is determined based on the transmission characteristics corresponding to the first CSI report or the second CSI report, and the transmission characteristics include at least one of the following: CSI reports transmitted aperiodically on the Physical Uplink Shared Channel (PUSCH); CSI reports transmitted semi-persistently on the PUSCH; CSI reports transmitted semi-persistently on the Physical Uplink Control Channel (PUCCH); and CSI reports transmitted periodically on the PUCCH.

[0056] In the above embodiments, the value of the first parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the first parameter.

[0057] In conjunction with some embodiments of the second aspect, in some embodiments, the value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, the content including at least one of the following: including Layer 1 Reference Signal Received Power (L1-RSRP) or Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR); including beam accuracy indication information; not including L1-RSRP or L1-SINR; not including beam accuracy indication information.

[0058] In the above embodiments, the value of the second parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the second parameter.

[0059] In conjunction with some embodiments of the second aspect, in some embodiments, the value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, including: the second parameter is determined based on the features of the L1-RSRP or L1-SINR contained in the first CSI report or the second CSI report, wherein the features of the L1-RSRP or L1-SINR include at least one of the following: the L1-RSRP or L1-SINR corresponds to the CSI report of AI / ML function or AI / ML model derivation; the L1-RSRP or L1-SINR corresponds to the CSI report of AI / ML function or AI / ML model performance monitoring; the L1-RSRP or L1-SINR corresponds to the CSI report related to the collection of AI / ML function or AI / ML model training data.

[0060] In the above embodiments, the value of the second parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the second parameter.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the beam accuracy indication information corresponds to the CSI report of AI / ML function or AI / ML model performance monitoring. The beam accuracy indication information includes at least one of the following: the predicted N best beams include the measured best beam; the predicted best beam is among the measured best N beams; the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam; the probability that the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam is within a threshold; the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the predicted best beam; the probability that the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam is within a threshold.

[0062] In the above embodiments, beam accuracy indication information can be determined.

[0063] In conjunction with some embodiments of the second aspect, in some embodiments, the value of the third parameter is determined based on whether the first CSI report or the second CSI report is related to the AI / ML function or the AI / ML model.

[0064] In the above embodiments, the value of the third parameter can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the third parameter.

[0065] In conjunction with some embodiments of the second aspect, in some embodiments, the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is greater than the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; wherein, the parameter is a first parameter, a second parameter, or a third parameter.

[0066] In the above embodiments, parameters can be determined to facilitate the determination of the priority of the first CSI report and / or the second CSI report based on the parameters.

[0067] In conjunction with some embodiments of the second aspect, in some embodiments, the CSI report corresponding to the AI / ML function or AI / ML model derivation includes the following features: the network device sends CSI report configuration information corresponding to the CSI report derived by the AI / ML function or AI / ML model; the CSI report configuration information is used by the terminal to determine the first reference signal resource set and the second reference signal resource set; the CSI report is the measurement result of the terminal based on the first reference signal resource set and the prediction result corresponding to the second reference signal resource set obtained by the AI / ML function or AI / ML model; wherein the first reference signal resource set and the second reference signal resource set contain at least one different reference signal resource, or the time domain positions corresponding to the first reference signal resource set and the second reference signal resource set are different.

[0068] In the above embodiments, the characteristics of the CSI report corresponding to the AI / ML function or AI / ML model derivation can be determined.

[0069] In conjunction with some embodiments of the second aspect, in some embodiments, the CSI report corresponding to the performance monitoring of AI / ML functions or AI / ML models includes at least one of the following features: the network device sends CSI report configuration information for the CSI report corresponding to the performance monitoring of AI / ML functions or AI / ML models, and the CSI configuration information includes a CSI report configuration identifier corresponding to the performance monitoring of AI / ML functions or AI / ML model derivation and / or a CSI configuration information indicating the reporting beam accuracy indication.

[0070] In the above embodiments, the characteristics of the CSI report corresponding to AI / ML function or AI / ML model performance monitoring can be determined.

[0071] In conjunction with some embodiments of the second aspect, in some embodiments, the CSI report corresponding to the collection of AI / ML function or AI / ML model training data includes the following features: the network device sends CSI report configuration information for the CSI report corresponding to the collection of AI / ML function or AI / ML model training data, the CSI report configuration information includes an association identifier, the association identifier is used to reflect the beam characteristics corresponding to the reference signal resource or reference signal resource set indicated in the CSI report configuration information.

[0072] In the above embodiments, the characteristics of the CSI report corresponding to the AI / ML function or AI / ML model training data collection can be determined.

[0073] Thirdly, embodiments of this disclosure provide a communication device for performing the methods described in any one of the first and second aspects of embodiments of this disclosure.

[0074] Fourthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method described in any one of the first or second aspects of embodiments of this disclosure.

[0075] Fifthly, embodiments of this disclosure provide a program product, including at least one of a program and instructions, wherein when the program or instructions are executed by a communication device, they implement the steps of the method described in any one of the first and second aspects of embodiments of this disclosure.

[0076] It is understood that the aforementioned communication equipment, storage medium, and program product are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0077] This disclosure provides a communication method, a communication device, a communication system, a storage medium, and a program product. In some embodiments, terms such as communication method and information processing method can be used interchangeably, as can terms such as network device, information processing apparatus, and communication apparatus, and terms such as information processing system and communication system.

[0078] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular 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 particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments. In all embodiments of this disclosure, unless otherwise specified or logically conflicting, the terminology and / or descriptions between the embodiments are consistent and can be mutually referenced. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0079] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0080] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0081] In the embodiments disclosed herein, "multiple" refers to two or more.

[0082] In some embodiments, the terms "at least one of A or B, at least one of A and B", "one or more", "a plurality of", "multiple" and the like can be used interchangeably.

[0083] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of whether there is a branch B); in some embodiments, B (execute B regardless of whether there is a branch A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, both A and B are executed. The same applies when there are more branches such as A, B, C, etc.

[0084] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execute A regardless of whether a branch B exists); in some embodiments, B (execute B regardless of whether a branch A exists); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, and C.

[0085] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0086] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0087] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0088] In some embodiments, terms such as “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “when…”, “if…”, etc. can be used interchangeably. These descriptions all refer to the device making a corresponding action under certain objective circumstances. They do not necessarily limit the time, nor do they require the device to make a judgment action when implementing it, nor do they mean that there must be other limitations.

[0089] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “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”.

[0090] In some embodiments, devices, etc., may be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as “device,” “equipment,” “circuit,” “network element,” “network function,” “network device,” “function,” “node,” “unit,” “section,” “system,” “network,” “chip,” “chip system,” “entity,” and “subject” are interchangeable.

[0091] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0092] 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," and "bandwidth part (BWP)" can be used interchangeably.

[0093] 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", and "client" can be used interchangeably.

[0094] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0095] 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, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0096] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0097] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0098] In some embodiments, "acquire," "get," "obtain," "receive," "transmit," "bidirectional transmission," and "send and / or receive" can be used interchangeably and can be interpreted as receiving from other entities, acquiring from protocols, acquiring from higher layers, obtaining through self-processing, or autonomous implementation. Protocols include, for example, at least one of the 3GPP protocol, Wi-Fi protocol, and audio and / or video protocols.

[0099] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0100] This disclosure provides a communication method, communication device, communication system, storage medium, and program product that can determine the priority of CSI reports corresponding to model inference, model performance monitoring, or model training, and facilitate the processing of reports according to priority.

[0101] The method proposed in this disclosure is applicable to various communication systems, including but not limited to 4G, 5G, 5G-advance and subsequent communication technologies (such as 6G).

[0102] The application fields of this disclosure are not limited to the field of AI, but can also include the fields of artificial intelligence, machine learning, etc.

[0103] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 may include a terminal 101 and a network device 102.

[0104] In some embodiments, the method disclosed herein can be executed by a terminal in a communication system. Optionally, after the terminal determines the priority of multiple CSI reports, it can report CSI reports to a network device according to the priority of the CSI reports, at which time the network device can receive the CSI reports.

[0105] In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0106] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system, but is not limited thereto.

[0107] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of the following: Evolved Packet Core (EPC), 5G Core Network (5GCN), and Next Generation Core (NGC).

[0108] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0109] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0110] The method disclosed herein can be applied to a communication system, which may include a terminal 101 and a network device 102. That is, the terminal can determine the priority of multiple reports. Optionally, after determining the priority of multiple reports, the terminal can report the reports to the network device according to the priority of the reports.

[0111] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0112] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0113] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), 6th generation mobile communication system (6G), 6G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future Generation Radio Access (FX), Global System for Mobile Communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), and IEEE 802.16 (WiMAX, a registered trademark), IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0114] For multiple CSI reports, the terminal needs to determine the priority of each CSI report. When CSI reports of different priorities need to be updated or calculated at the same time, if the terminal determines that the CSI processing unit (CPU) required for updating or calculating multiple CSI reports exceeds the maximum CPU supported by the terminal, the terminal can only update / calculate the higher-priority CSI report and may not update / calculate the lower-priority CSI report. Furthermore, when the reporting resources corresponding to multiple CSI reports, such as physical uplink control channel (PUCCH) / physical uplink shared channel (PUSCH) resources, overlap, and the resource cannot support multiple CSI reports, the terminal also needs to determine the priority of reporting based on priority, and may not report the lower-priority CSI report.

[0115] In the field of communications, artificial intelligence (AI) / machine learning (ML) technologies can be used for beam prediction and channel state information (CSI) prediction to obtain corresponding beam reports or CSI reports. Determining the priority of the beam reports and CSI reports corresponding to the functional / model inference, functional / model performance monitoring, or functional / model training is a problem that needs to be solved.

[0116] Therefore, in order to solve the above-mentioned technical problems, this disclosure proposes a communication method that determines the priority of different CSI reports in AI / ML-based CSI reports, and unifies the priority of different CSI reports between the base station and the terminal when multiple CSI reports need to be updated or reported at the same time.

[0117] The following is a schematic diagram of a communication method provided in this disclosure. Embodiments of this disclosure relate to a communication method that can be executed by a terminal in a communication system, such as terminal 101 in the communication system 100 shown in FIG. 1. The communication system includes terminal 101 and network device 102. The communication method may include the following specific methods:

[0118] Figure 2 is one of the interactive schematic diagrams of the communication method provided in this embodiment of the present disclosure. As shown in Figure 2, the method includes the following steps:

[0119] Step 2101: The terminal determines the first Channel State Information (CSI) report and the second CSI report.

[0120] In some embodiments, at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model, wherein the AI / ML function can be an AI function or a machine learning (ML) function, and the AI / ML model can be an AI or an ML model. That is, the first CSI report can be a channel state information report obtained by using an AI / ML function or using an AI / ML model for prediction.

[0121] Optionally, the first CSI report and the second CSI report can be reports corresponding to AI / ML functions / model inference, or reports corresponding to AI / ML functions / model performance monitoring, or reports corresponding to AI / ML functions / model training, etc.

[0122] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0123] In some embodiments, the terms "codebook," "codeword," and "precoding matrix" can be used interchangeably. For example, a codebook can be a collection of one or more codewords / precoding matrices.

[0124] In some embodiments, the terms "precoding", "precoder", "weight", "precoding weight", "quasi-co-location (QCL)", "transmission configuration indication (TCI) status", "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", and "panel" can be used interchangeably.

[0125] In some embodiments, optionally, the terminal may determine a first CSI report and / or a second CSI report by receiving at least one reference signal and using AI / ML functions or AI / ML models based on the reference signal, wherein the reference signal may be a downlink reference signal sent to the terminal by the network device.

[0126] In some embodiments, the terms "uplink", "uplink", and "physical uplink" can be used interchangeably, as can the terms "downlink", "downlink", and "physical downlink", as well as the terms "sidelink", "sidelink", "sidelink communication", "sidelink communication", "direct connection", "direct link", "direct communication", and "direct link communication".

[0127] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0128] In some embodiments, the type of the first CSI report or the type of the second CSI report includes at least one of the following: a CSI report corresponding to AI / ML function or AI / ML model derivation; a CSI report corresponding to AI / ML function or AI / ML model performance monitoring; or a CSI report corresponding to AI / ML function or AI / ML model training data collection.

[0129] Step 2102: The terminal abandons the third CSI report.

[0130] In some embodiments, the third CSI report is a lower-priority CSI report among the first and second CSI reports.

[0131] In some embodiments, optionally, discarding a third CSI report includes: determining that the first CSI report and the second CSI report meet preset conditions, and then discarding the third CSI report. Discarding the third CSI report includes at least one of the following: not updating or calculating the third CSI report; or not sending the third CSI report to the network device.

[0132] In some embodiments, optionally, the number of CSI reports can be multiple. That is, when multiple CSI reports meet the preset conditions, the CSI report with lower priority can be identified and discarded. Optionally, the number of third CSI reports can be one or more. That is, the CSI report with the lowest priority can be discarded, or multiple CSI reports with lower priority can be discarded. For example, the number of third CSI reports can be determined according to a ratio, or the number of third CSI reports can be determined according to a preset value, etc. This disclosure does not limit this.

[0133] In other words, when preset conditions are met, lower-priority reports among multiple CSI reports can be discarded, while higher-priority reports can be processed. This processing could include, for example, updating, calculating, or reporting the report to network devices. The preset conditions include at least one of the following: the first CSI report and the second CSI report need to be updated or calculated on the same symbol; the first CSI report and the second CSI report need to be sent to network devices on the same uplink resources.

[0134] The preset conditions can be used to indicate whether there are resource conflicts between multiple CSI reports. For example, when multiple CSI reports need to be updated or calculated on the same symbol, the number of CSI processing units required may exceed the maximum number of CSI processing units supported by the terminal, causing the terminal to be unable to update or calculate multiple CSI reports at the same time. Therefore, based on the priority of multiple CSI reports, the CSI report to be processed first can be determined, that is, the report that needs to be abandoned can be determined. For the abandoned report, no update or calculation is required.

[0135] For example, when multiple CSI reports need to be sent to network devices on the same uplink resources, the uplink resources required by the multiple CSI reports overlap, which may result in the uplink resources being unable to support multiple CSI reports. In this case, the priority of the multiple CSI reports can be determined, and the CSI reports with higher priority are reported first, while the reports with lower priority are abandoned.

[0136] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0137] In some embodiments, optionally, the priority of a first CSI report and a second CSI report can be determined, thereby determining a third CSI report based on the priority of the first and second CSI reports. At least one of the first and second CSI reports is related to an AI / ML function or AI / ML model. For example, the first CSI report can be a legacy CSI report, i.e., a CSI report unrelated to an AI / ML function or AI / ML model, and the second CSI report can be a CSI report related to an AI / ML function or AI / ML model. Alternatively, both the first and second CSI reports can be CSI reports related to an AI / ML function or AI / ML model.

[0138] In some embodiments, the reporting volume of a first CSI report and / or the reporting volume of a second CSI report may be determined, wherein the reporting volume of the first CSI report may include at least one of a first reporting volume and a second reporting volume; and the reporting volume of the second CSI report may include at least one of a first reporting volume and a second reporting volume.

[0139] The first reported quantity may include at least one of the following: CSI-RS Resource Indicator-Reference Signal Receiving Power (cri-RSRP), Synchronization Signal Block-Index-Reference Signal Receiving Power (ssb-Index-RSRP), CSI-RS Resource Indicator-Signal to Interference plus Noise Ratio (cri-SINR), and Synchronization Signal Block-Index-Signal to Interference plus Noise Ratio (ssb-Index-SINR), or the first reported quantity may be empty (none).

[0140] In some embodiments, the terms "synchronization signal (SS)," "synchronization signal block (SSB)," "reference signal (RS)," "pilot," and "pilot signal" can be used interchangeably.

[0141] The second reported quantity may include at least one of the following: Reference Resource Indicator-Rank Indication-Precoding Matrix Indicator-Channel Quality Indicator (CSI-RI-PMI-CQI), cri-RI-i1, cri-RI-i1-CQI, cri-RI-CQI, Reference Resource Indicator-Rank Indication-Layer Indicator-Precoding Matrix Indicator-Channel Quality Indicator (CSI-RI-PMI-CQI, cri-RI-LI-PMI-CQI), and subband CSI.

[0142] When the number of ports is greater than 4, the codebook type is one of the following: type I-multipanel, type II, type II-portselection, type II-r16, type II-portselection-r16, type II-portselection-r17, TDCP, CJT, type II-Doppler-r18, type II-Doppler-PortSelection-r18, CJT calibration.

[0143] Optionally, when the first CSI report is a traditional CSI report or a CSI report related to AI / ML functions or AI / ML models, and the second CSI report is a CSI report related to AI / ML functions or AI / ML models, the priority of the first CSI report and / or the second CSI report can be determined based on at least one of the following: first parameter; second parameter; third parameter; serving cell index; maximum number of serving cells; report configuration ID; maximum number of CSI reports.

[0144] In some embodiments, the value of the first parameter is determined based on the transmission characteristics corresponding to the first CSI report or the second CSI report, and the transmission characteristics include at least one of the following: CSI reports transmitted aperiodically on the Physical Uplink Shared Channel (PUSCH); CSI reports transmitted semi-persistently on the PUSCH; CSI reports transmitted semi-persistently on the Physical Uplink Control Channel (PUCCH); and CSI reports transmitted periodically on the PUCCH.

[0145] For example, when the first CSI report or the second CSI report is a non-periodic CSI report transmitted on the Physical Uplink Shared Channel (PUSCH), the first parameter is valued as a; when the first CSI report or the second CSI report is a semi-persistent CSI report transmitted on the PUSCH, the value is b; when the first CSI report or the second CSI report is a semi-persistent CSI report transmitted on the Physical Uplink Control Channel (PUCCH), the value is c; and when the first CSI report or the second CSI report is a periodic CSI report transmitted on the PUCCH, the value is d. Here, a, b, c, and d are different values ​​or value ranges, and the first parameter values ​​corresponding to different transmission characteristics satisfy a preset size relationship, which can be used to determine the priority of CSI reports.

[0146] Specifically, when the CSI report is a non-periodic CSI report transmitted on the Physical Uplink Shared Channel (PUSCH), the value of the first parameter can be 0; when the CSI report is a semi-persistent CSI report transmitted on the PUSCH, the value of the first parameter can be 1; when the CSI report is a semi-persistent CSI report transmitted on the Physical Uplink Control Channel (PUCCH), the value of the first parameter can be 2; and when the CSI report is a periodic CSI report transmitted on the PUCCH, the value of the first parameter is 3.

[0147] In some embodiments, the value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, which includes at least one of the following: containing Layer 1 Reference Signal Received Power (L1-RSRP) or Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR); containing beam accuracy indication information; not containing L1-RSRP or L1-SINR; or not containing beam accuracy indication information.

[0148] In some embodiments, the value of the second parameter is 0 when the CSI report includes Layer 1 Reference Signal Received Power (L1-RSRP) or Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR), and the value of the second parameter is 1 when the CSI report does not include L1-RSRP or L1-SINR.

[0149] In some embodiments, when the CSI report includes Layer 1 Reference Signal Received Power (L1-RSRP) or Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR), the value of the second parameter is L#0; when the CSI report includes beam accuracy indication information, the value of the second parameter is L#1; when the CSI report does not include Layer 1 Reference Signal Received Power (L1-RSRP) or Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR), the value of the second parameter is L#2; when the CSI report does not include beam accuracy indication information, the value of the second parameter is L#3; and when the CSI report does not include both Layer 1 Reference Signal Received Power (L1-RSRP) and Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR) and beam accuracy indication information, the value of the second parameter is L#4. Optionally, L#0 is less than or equal to L#1, or L#0 is greater than or equal to L#1; optionally, L#1 is less than or equal to L#3, and L#0 is less than or equal to L#4. Optionally, L#2 equals L#3 and L#4.

[0150] In some embodiments, the beam accuracy indication information corresponds to the CSI report for AI / ML function or AI / ML model performance monitoring. The beam accuracy indication information includes at least one of the following: the predicted N best beams include the measured best beam; the predicted best beam is among the measured best N beams; the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam; the probability that the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam is within a threshold; the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the predicted best beam; the probability that the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam is within a threshold.

[0151] In the above embodiments, the L1-RSRP measurement can be obtained by actual beam measurement, meaning the above measurement and the actual measurement can be used interchangeably. Predicting the optimal beam can be achieved by using AI / ML functions or AI / ML models to predict the predicted L1-RSRP of each beam, with the beam having the largest predicted L1-RSRP being the predicted optimal beam. Alternatively, predicting the optimal beam can be achieved by using AI / ML functions or AI / ML models to predict the probability that each beam is the optimal beam, with the beam having the highest probability being the predicted optimal beam.In other words, beam accuracy indication information can be data related to model performance monitoring, specifically, it can be any of the following: the predicted N best beams contain the actual best (strongest measured L1-RSRP) beam; or, it can be the probability that the predicted N best beams contain the actual best (strongest measured L1-RSRP) beam. Optionally, it can be the result determined by a single derivation of prediction data and corresponding performance monitoring measurement data: yes or no; or it can be the probability evaluated by multiple derivations of prediction data and corresponding performance monitoring measurement data, such as Y evaluations, where the result of X evaluations is "yes," for example, the predicted N best beams contain... If the actual optimal beam (strongest L1-RSRP measured) is found X times, then the beam accuracy is X / Y. Alternatively, the beam accuracy can also be the value of X mentioned above. The predicted optimal beam is among the N actual optimal beams, or it can be the probability that the predicted optimal beam is among the N actual optimal beams. Optionally, it can be a single, definitive result: yes or no. Or it can be the probability of evaluation based on multiple derived prediction data and corresponding performance monitoring measurement data. For example, Y evaluations, where X results are "yes". For instance, if the predicted optimal beam is among the N actual optimal beams X times, then the beam accuracy is X / Y. Alternatively, the beam accuracy can also be... The value of X is as described above; the difference between the predicted actual L1-RSRP and the actual L1-RSRP of the best beam; the probability that the difference between the predicted actual L1-RSRP and the actual L1-RSRP of the best beam is within a threshold. Optionally, this can be a single, determined result: yes or no, or it can be the ratio of the number of times the difference between the predicted actual L1-RSRP and the actual L1-RSRP of the best beam is within a threshold to the number of multiple evaluations. For example, if there are Y evaluations, and the result of X is "yes", then the beam accuracy is X / Y. Alternatively, the beam accuracy can also be the value of X mentioned above. The value of ; the difference between the predicted L1-RSRP of the predicted optimal beam and the actual L1-RSRP of the predicted optimal beam; the probability that the difference between the predicted L1-RSRP of the predicted optimal beam and the actual L1-RSRP of the predicted optimal beam is within a threshold, optionally, can be a single determined result: yes or no, or can be the ratio of the number of times the difference between the predicted L1-RSRP of the predicted optimal beam and the actual L1-RSRP of the predicted optimal beam is within a threshold to the number of multiple evaluations, where the result of X times is "yes", then the beam accuracy is X / Y, or the beam accuracy can also be the value of X mentioned above.

[0152] In some embodiments, the second parameter is determined based on the content of the L1-RSRP or L1-SINR contained in the first CSI report or the second CSI report, including: the second parameter is determined based on the characteristics of the L1-RSRP or L1-SINR contained in the first CSI report or the second CSI report, wherein the characteristics of the L1-RSRP or L1-SINR include at least one of the following: the L1-RSRP or L1-SINR corresponds to the CSI report of AI / ML function or AI / ML model derivation; the L1-RSRP or L1-SINR corresponds to the CSI report of AI / ML function or AI / ML model performance monitoring; the L1-RSRP or L1-SINR corresponds to the CSI report related to the collection of AI / ML function or AI / ML model training data.

[0153] In some embodiments, priority can be determined based on the following rules: the smaller the parameter value, the higher the priority of the corresponding CSI report. That is, the parameters corresponding to the CSI report can be determined according to the transmission characteristics of the CSI report, the content contained in the CSI report, the type of the CSI report, and whether the CSI report is related to AI / ML functions or the AI / ML model, and the priority of different CSI reports can be determined according to the parameters of the CSI report.

[0154] Optionally, the priority of CSI reports can be determined according to the type of CSI report. For example, the parameter size relationship of CSI reports for AI / ML functions or AI / ML model derivation, CSI reports for AI / ML function or AI / ML model performance monitoring, and CSI reports for AI / ML function or AI / ML model training data collection can be determined, and the priority order of CSI reports can be determined according to the parameter size relationship.

[0155] Specifically, the priority of the first CSI report and the second CSI report is determined based on the above parameters as shown in the following formula: Pri iCSI (y,k,c,s)=2·N cells ·M s ·y+N cells ·M s ·k+M s ·c+s (Formula 1)

[0156] Where c is the serving cell index, N cells The maximum number of serving cells configured for high-level parameters; s is the report configuration identifier, M s The maximum number of CSI reports configured for high-level personnel, where y is the first parameter mentioned above and k is the second parameter mentioned above, and the values ​​of y and k are shown in the table below:

[0157] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, as can terms such as "physical uplink shared channel (PUSCH)" and "UL data".

[0158] Optionally, when the first CSI report and the second CSI report are determined to have the same priority according to the above formula, the CSI report related to the AI / ML function or AI / ML model can be determined to have a higher priority. If only the second CSI report is related to the AI / ML function or AI / ML model, the second CSI report can be determined to have a higher priority.

[0159] In some embodiments, where the priority of the first CSI report is equal to the priority of the second CSI report, the priority of the first CSI report and the priority of the second CSI report may be determined based on a third parameter.

[0160] In some embodiments, the priority of the first CSI report and the second CSI report is determined according to the following formula: Pri iCSI (y,k,c,s)=2·N cells ·M s ·y+N cells ·M s ·k+M s • c + s + third parameter (Formula 2)

[0161] The values ​​of the other parameters, except for the third parameter, are the same as those described in the above embodiments.

[0162] Optionally, even if the priority of the first CSI report differs from that of the second CSI report based on other parameters, the priority of the first CSI report and the second CSI report can be determined using Formula 1 or Formula 2 above, without limitation.

[0163] In some embodiments, the value of the third parameter is determined based on whether the first CSI report or the second CSI report is related to the AI / ML function or the AI / ML model.

[0164] For example, when the first or second CSI report is related to AI / ML functions or AI / ML models, the value of the third parameter can be r. When the first or second CSI report is not related to AI / ML functions or AI / ML models, the value of the third parameter is s. The values ​​of r and s are different. For example, r can be less than s. This is used to reflect the difference in CSI report priority. That is, the order of CSI report priority can be determined according to the value of the third parameter.

[0165] In some embodiments, optionally, the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is greater than the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; the parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring is less than the parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; wherein, the parameter is a first parameter, a second parameter, or a third parameter.

[0166] In some embodiments, optionally, the first parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is greater than the first parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the first parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the first parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the first parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the first parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; and the first parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring is less than the first parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection.

[0167] In some embodiments, optionally, the second parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is greater than the second parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the second parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model performance monitoring is less than the second parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the second parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the second parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; and the second parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring is less than the second parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection.

[0168] In some embodiments, optionally, the third parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is greater than the third parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the third parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model performance monitoring is less than the third parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring; the third parameter corresponding to the CSI report derived from the AI / ML function or AI / ML model is less than the third parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection; and the third parameter corresponding to the CSI report for AI / ML function or AI / ML model performance monitoring is less than the third parameter corresponding to the CSI report for AI / ML function or AI / ML model training data collection.

[0169] In some embodiments, the value of the third parameter is determined based on whether the first or second CSI report is related to an AI / ML function or AI / ML model. Optionally, the value of the third parameter related to an AI / ML function or AI / ML model differs from the value of the third parameter not related to an AI / ML function or AI / ML model. In other words, the third parameter can distinguish whether a CSI report is related to an AI / ML function or AI / ML model through different values.

[0170] In some embodiments, the value of the third parameter is determined based on whether the first or second CSI report is related to an AI / ML function or AI / ML model. Optionally, the value of the third parameter corresponding to a CSI report related to an AI / ML function or AI / ML model is less than the value of the third parameter corresponding to a CSI report not related to an AI / ML function or AI / ML model. In some embodiments, both the first and second CSI reports may be CSI reports related to an AI / ML function or AI / ML model, and the priority of the first and / or second CSI reports can be determined according to at least one of the following: first parameter; second parameter; third parameter; serving cell index; maximum number of serving cells; report configuration ID; maximum number of CSI reports. For example, it can be determined according to the formula Pri iCSI (y,k,c,s)=2·N cells ·M s ·y+N cells ·M s ·k+M sThe priority of the first and second CSI reports is determined by the third parameter c+s+. Specifically, the priority of the first or second CSI report differs from at least two of the following: when the first or second CSI report is a CSI report derived from AI / ML functions or AI / ML model derivation; when the first or second CSI report is a CSI report for AI / ML function or AI / ML model performance monitoring; and when the first or second CSI report is a CSI report for AI / ML function or AI / ML model training data collection. The second parameter is k.

[0171] Optionally, the third parameter can distinguish the type of the first or second CSI report. Types include CSI reports corresponding to function or model derivation, CSI reports corresponding to function or model performance monitoring, and CSI reports related to function or model training data collection. In this case, the priority of the first and second CSI reports can be determined based on their type. Specifically, different types of CSI reports can correspond to different third parameter values, and the priority value of the CSI report can be determined based on the third parameter value; the lower the priority value, the higher the priority of the CSI report.

[0172] In some embodiments, a first parameter, a second parameter, or a third parameter can be determined based on the type of CSI report, and the priority of the CSI report can be determined based on the first parameter, the second parameter, or the third parameter. Optionally, the parameter value corresponding to the CSI report related to function or model derivation is the smallest, followed by the CSI report related to function or model performance monitoring, and the parameter value corresponding to the CSI report related to function or model training data collection is the largest; that is, the CSI report related to function or model derivation has the highest priority, followed by the CSI report related to function or model performance monitoring, and the CSI report related to function or model training data collection has the lowest priority.

[0173] In some embodiments, a first parameter, a second parameter, or a third parameter can be determined based on the type of CSI report, and the priority of the CSI report can be determined based on the first parameter, the second parameter, or the third parameter. Optionally, the parameter value corresponding to the CSI report for function or model performance monitoring is the lowest, followed by the CSI report for function or model derivation, and the parameter value corresponding to the CSI report related to function or model training data collection is the highest; that is, the CSI report for function or model performance monitoring has the highest priority, followed by the CSI report for function or model derivation, and the CSI report related to function or model training data collection has the lowest priority.

[0174] In some embodiments, a first parameter, a second parameter, or a third parameter can be determined based on the type of CSI report, and the priority of the CSI report can be determined based on the first parameter, the second parameter, or the third parameter. Optionally, the parameter value corresponding to the CSI report corresponding to the function or model derivation is the smallest, followed by the CSI report corresponding to the function or model performance monitoring; that is, the CSI report corresponding to the function or model derivation has the highest priority, followed by the CSI report corresponding to the function or model performance monitoring.

[0175] In some embodiments, a first parameter, a second parameter, or a third parameter can be determined based on the type of CSI report, and the priority of the CSI report can be determined based on the first parameter, the second parameter, or the third parameter. Optionally, the parameter value corresponding to the CSI report corresponding to the function or model performance monitoring is the smallest, followed by the CSI report corresponding to the function or model derivation; that is, the CSI report corresponding to the function or model performance monitoring has the highest priority, followed by the CSI report corresponding to the function or model derivation.

[0176] Specifically, the CSI report corresponding to the function or model derivation is the CSI report related to AI / ML function or AI / ML model, the CSI report corresponding to function or model performance monitoring, and the CSI report related to function or model training data collection. It can be a CSI report related to AI / ML function or AI / ML model, or it can be a traditional CSI. Therefore, the CSI report corresponding to the function or model derivation has the highest priority.

[0177] In some embodiments, the CSI report corresponding to the AI / ML function or AI / ML model derivation includes the following features: the terminal receives CSI report configuration information corresponding to the CSI report derived by the AI / ML function or AI / ML model; based on the CSI report configuration information, it determines a first reference signal resource set and a second reference signal resource set; the CSI report is the measurement result of the terminal based on the first reference signal resource set and the prediction result corresponding to the second reference signal resource set obtained by the AI / ML function or AI / ML model; wherein the first reference signal resource set and the second reference signal resource set contain at least one different reference signal resource, or the time domain positions corresponding to the first reference signal resource set and the second reference signal resource set are different.

[0178] In some embodiments, optionally, the network device may send CSI report configuration information to the terminal. This CSI report configuration information is used by the terminal to determine a first reference signal resource set and a second reference signal resource set. For example, the network device may send CSI report configuration information corresponding to a CSI report derived from an AI / ML function or AI / ML model to the terminal; or, the network device may send CSI report configuration information for a CSI report corresponding to performance monitoring of an AI / ML function or AI / ML model to the terminal; or, the network device may send CSI report configuration information for a CSI report corresponding to the collection of training data for an AI / ML function or AI / ML model to the terminal.

[0179] In some embodiments, terminal 101 receives CSI report configuration information sent by network device 102, but is not limited thereto; terminal 101 may also receive CSI report configuration information sent by other entities.

[0180] In some embodiments, terminal 101 obtains CSI report configuration information as specified by the protocol.

[0181] In some embodiments, terminal 101 obtains CSI report configuration information from upper layer(s).

[0182] In some embodiments, terminal 101 processes information to obtain CSI report configuration information.

[0183] In the above embodiments, optionally, the terminal can perform spatial beam prediction based on CSI report configuration information to obtain a first reference signal resource set and a second reference signal resource set. The first reference signal resource set can be set B, and the second reference signal resource set can be set A. That is, the terminal can receive the CSI report configuration information (CSI reportconfig) corresponding to the CSI report derived by the AI / ML function or AI / ML model, and measure the reference signal resources in set B according to the CSI report configuration information to obtain the measurement results. It does not need to measure the reference signal resources in set A; instead, it obtains the prediction results for set A based on the measurement results of set B and the predictions of the AI / ML function or AI / ML model. Here, set B is a subset of set A; or set B and set A are different, but the number of reference signal resources contained in set B is less than the number of reference signal resources corresponding to set A.

[0184] In some embodiments, optionally, the terminal can perform time-domain beamforming based on CSI report configuration information to obtain a first reference signal resource set and a second reference signal resource set. That is, the terminal can receive CSI report configuration information (CSI reportconfig) corresponding to the CSI report derived by the AI / ML function or AI / ML model, and measure the reference signal resources of set B at the historical measurement time according to the CSI report configuration information to obtain the measurement result. It does not need to measure set A at the future prediction time; instead, it obtains the prediction result of set A at the future prediction time based on the measurement result of set B and the AI / ML function or AI / ML model. Here, the time corresponding to set B is different from the time corresponding to set A.

[0185] B corresponds to the historical measurement time, and set A corresponds to the future prediction time. Optionally, the reference signal resources included in set B can be the same as set A.

[0186] A is the same; or set B is a subset of set A; or set B is different from set A, but set B contains fewer reference signal resources than set A.

[0187] In some embodiments, the terminal can also perform CSI prediction based on CSI report configuration information to determine the first reference signal resource set and the second reference signal resource set. For example, it can obtain the prediction result corresponding to the future prediction time based on the measurement result corresponding to the reference signal resource at the historical measurement time.

[0188] In some embodiments, beam prediction can yield a beam prediction report, which may include a Reference Signal Identification (RSID) and / or L1-RSRP; CSI prediction can yield a CSI prediction report, which may include CQI, PMI, RI, LI, etc.

[0189] In some embodiments, the CSI report corresponding to AI / ML function or AI / ML model performance monitoring includes at least one of the following features: the terminal receives CSI report configuration information for the CSI report corresponding to AI / ML function or AI / ML model performance monitoring, the CSI configuration information including a CSI report configuration identifier (CSI reportconfigID) corresponding to the AI / ML function or AI / ML model derivation for performance monitoring and / or a CSI configuration information indicating the reporting beam accuracy indicator. Optionally, for performance monitoring, the terminal needs to compare the predicted information of the beam predicted by the AI / ML function or AI / ML model with the actual measured information of the beam used for performance monitoring to obtain the beam accuracy indicator.

[0190] In the above embodiments, the CSI report configuration identifier can be used to uniquely identify the associated derivation-corresponding CSI report, so as to determine the priority of the CSI report corresponding to performance monitoring based on the characteristics of the derivation-corresponding CSI report, and / or the CSI configuration information indicates the reporting beam accuracy indicator, which is used to further determine the characteristics and priority of the CSI report corresponding to performance monitoring.

[0191] In some embodiments, the CSI report corresponding to the AI / ML function or AI / ML model training data collection includes the following features: the terminal receives CSI report configuration information of the CSI report corresponding to the AI / ML function or AI / ML model training data collection, and the CSI report configuration information includes an associated ID, which is used to reflect the beam characteristics corresponding to the reference signal resource or reference signal resource set indicated in the CSI report configuration information.

[0192] In the above embodiments, the association identifier reflects the beam characteristics corresponding to the reference signal resource or reference signal resource set indicated in the CSI report configuration information through a specific encoding method or numerical range, such as beamwidth, beam direction, beam gain, etc. These beam characteristics are used to help determine the priority of the CSI report.

[0193] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0194] Step 2103: The terminal sends a fourth CSI report to the network device, or sends a fourth CSI report and a third CSI report to the network device.

[0195] In some embodiments, after determining the priorities of the first CSI report and the second CSI report, the terminal can send the CSI reports in sequence according to the priorities of the first CSI report and the second CSI report, that is, it can send the fourth CSI report with higher priority first and then send the third CSI report with lower priority; or, the terminal can give up sending the CSI report with lower priority, that is, give up sending the third CSI report. In this case, the terminal can send only the fourth CSI report to the network device.

[0196] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0197] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0198] Figure 3 is a schematic diagram of one of the communication methods provided in this disclosure. As shown in Figure 3, the method includes the following steps:

[0199] Step 3101: Determine the first Channel State Information (CSI) report and the second CSI report.

[0200] In some embodiments, refer to the steps (such as step 2101 in Figure 2) and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, which will not be repeated here.

[0201] Step 3102: Discard the third CSI report.

[0202] In some embodiments, refer to the steps (such as step 2102 in Figure 2) and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, which will not be repeated here.

[0203] Step 3103: Send the fourth CSI report, or send both the fourth and third CSI reports.

[0204] In some embodiments, refer to the steps (such as step 2103 in Figure 2) and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, which will not be repeated here.

[0205] Figure 4 is a schematic diagram of one of the communication methods provided in this disclosure. As shown in Figure 3, the method includes the following steps:

[0206] Step 4101: Receive the fourth CSI report or receive both the fourth and third CSI reports.

[0207] In some embodiments, refer to the steps (such as step 2103 in Figure 2 and step 3103 in Figure 3) and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, which will not be repeated here.

[0208] The following is an exemplary description of the above method.

[0209] The method illustrated in this disclosure relates to an AI-based method for prioritizing CSI reports, the full details of which are as follows.

[0210] 1. The terminal determines the first type of CSI report and the second type of CSI report. When the first condition is met, the terminal discards the first type of CSI report or the second type of CSI report with lower priority than the first type of CSI report and the second type of CSI report. The first type of CSI report and / or the second type of CSI report are CSI reports related to AI / ML functions / models.

[0211] 2. Based on 1, the first condition includes at least one of the following:

[0212] 1) Both Type 1 and Type 2 CSI reports need to be updated / calculated on the same symbols;

[0213] 2) Both Type 1 and Type 2 CSI reports need to be sent to the network side on the same PUCCH / PUSCH resource, i.e., sent simultaneously.

[0214] 3. Based on 1 or 2, the terminal discards the lower-priority Type I CSI report or Type II CSI report among the Type I and Type II CSI reports, including at least one of the following:

[0215] 1) The terminal does not update / calculate low-priority Type 1 or Type 2 CSI reports;

[0216] 2) The terminal does not report low-priority Type 1 or Type 2 CSI reports, i.e., it does not send them to the network side.

[0217] 4. Based on any one of 1-3, Type I CSI reports and Type II CSI reports include at least one of the following:

[0218] 1) Combination 1: The first type of CSI report is the legacy CSI report, and the second type of CSI report is the AI-related CSI report.

[0219] At this time, the parameters reported by the first type of CSI report and the second type of CSI report may include at least one of the reporting quantity 1 and the reporting quantity 2:

[0220] Among them, Report Quantity 1 includes at least one of the following: cri-RSRP, ssb-Index-RSRP, cri-SINR, ssb-Index-SINR, or Report Quantity 1 can be empty (none).

[0221] Among them, reported quantity 2 may include at least one of the following: cri-RI-PMI-CQI, cri-RI-i1, cri-RI-i1-CQI, cri-RI-CQI, cri-RI-LI-PMI-CQI, subband CSI.

[0222] Optionally, when the number of ports is greater than 4, the codebook type is one of the following: type I-multipanel, type II, type II-portselection, type II-r16, type II-portselection-r16, type II-portselection-r17, TDCP, CJT, type II-Doppler-r18, type II-Doppler-PortSelection-r18, CJT calibration.

[0223] In some embodiments, determining the priority of combination 1 specifically includes: according to the formula Pri iCSI (y,k,c,s)=2·N cells ·M s ·y+N cells ·M s ·k+M s • If the priority is determined based on the formula, that is, when several parameter values ​​are the same, the second type of CSI report has a higher priority (or a parameter corresponding to whether it is an AI-related CSI report is introduced, for example, if it is an AI-related CSI report, the parameter value is 0, otherwise it is 1, so the priority value of AI-related reports is low, and therefore the priority is high).

[0224] 2) Combination 2: The first type of CSI report is an AI-related CSI report, and the second type of CSI report is also an AI-related CSI report.

[0225] At this point, we can first base our formula Pri iCSl (y,k,c,s)=2·N cells ·M s ·y+N cells ·M s ·k+M s • C+S determines the priority. If the determined priorities are the same, the AI-related type of the first type CSI report and the second type CSI report can be determined, and the priority can be determined according to the AI-related type.

[0226] Specifically, if the first type is a CSI report corresponding to the AI ​​function / model derivation, and the second type is a CSI report corresponding to the performance monitoring of the same AI function / model, or a CSI report related to the collection of AI function / model training data, then new parameters are introduced for each type of CSI report, and different parameter values ​​are used to determine the priority value of different CSI reports, with lower priority values ​​having higher priority.

[0227] Optionally, the CSI report with the smallest parameter value is the one corresponding to function / model derivation, followed by the CSI report corresponding to function / model performance monitoring, and then the CSI report corresponding to function / model training.

[0228] CSI reports corresponding to AI / ML function / model derivation are always AI / ML function / model related CSI reports. CSI reports corresponding to AI / ML function / model performance monitoring or training data collection can be categorized as AI / ML function / model related CSI reports (i.e., corresponding to the parameters of the AI ​​function / model) or as legacy CSI reports, i.e., non-AI function / model related CSI reports.

[0229] The characteristic of the CSI report corresponding to AI / ML functions / model inference is that the terminal can determine two sets of reference signal resources based on its corresponding CSI reportconfig configuration information, as follows:

[0230] 1) For BM case 1 spatial beam prediction: There is a first set of reference signal resources, set B, and a second set of reference signal resources, set A, where set B is a subset of set A; or set B and set A are different, but set B contains fewer reference signal resources than set A. The terminal needs to measure the reference signal resources in set B to obtain the measurement results, but does not need to measure the reference signal resources in set A. Instead, it obtains the prediction results for set A based on the measurement results of set B and the AI ​​function / model prediction.

[0231] 2) For BM case 2 time-domain beamforming: Similar to BM case 1, it also involves set B and set A. However, the time corresponding to set B is different from that corresponding to set A. Set B corresponds to the historical measurement time, while set A corresponds to the future prediction time. Additionally, there is a possibility that set B contains the same reference signal resources as set A. The terminal only needs to measure the reference signal resources of set B at the historical measurement time to obtain the measurement result, without needing to measure set A at the future prediction time. Instead, it obtains the prediction result of set A at the future prediction time based on the measurement result of set B and the AI ​​function / model.

[0232] 3) For CSI prediction, different times are determined based on the measurement results corresponding to the reference signal resources at historical measurement times to obtain the prediction results corresponding to the future prediction time. Unlike beam prediction, which includes RS ID and / or L1-RSRP, CSI prediction reports include CQI, PMI, RI, LI, etc.

[0233] The characteristic of the CSI report corresponding to AI / ML function / model performance monitoring is that the terminal can obtain a CSI reportconfigID corresponding to the AI / ML function / model based on its corresponding CSI reportconfig configuration information.

[0234] The characteristic of the CSI report corresponding to AI / ML function / model training data collection is that the terminal can obtain an associated ID based on its corresponding CSI reportconfig configuration information, which is used to reflect the corresponding beam characteristics.

[0235] In the embodiments disclosed herein, 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 in other embodiments.

[0236] This disclosure also proposes an apparatus (also referred to as a communication device, etc.) for implementing any of the above methods. For example, an apparatus is proposed, which includes units or modules for implementing the steps performed by the terminal in any of the above methods.

[0237] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0238] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute 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 relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0239] Figure 5A is a schematic diagram of the structure of a terminal according to an embodiment of this disclosure. The terminal 5100 is used to execute any of the above methods. In some embodiments, as shown in Figure 5A, the terminal 5100 may include a processing module 5101.

[0240] In some embodiments, the processing module is configured to determine a first Channel State Information (CSI) report and a second CSI report, at least one of which is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model; and to discard a third CSI report, which is a lower priority CSI report among the first and second CSI reports. Optionally, the processing module is configured to perform at least one of the communication steps (e.g., steps 2101, 2102, 3101, 3102, but not limited thereto) performed by the terminal 5100 in any of the above methods, which will not be elaborated here.

[0241] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module.

[0242] In some embodiments, the terminal further includes a sending module for sending a fourth CSI report to the network device, or sending a fourth CSI report and a third CSI report to the network device.

[0243] In some embodiments, the transceiver module is also used to receive CSI report configuration information.

[0244] In some embodiments, the processing module can be replaced by the processor, and the transceiver module can be replaced by the transceiver.

[0245] Figure 5B is a schematic diagram of the structure of a network device according to an embodiment of this disclosure. The network device 5200 is used to perform any of the above methods. In some embodiments, as shown in Figure 5B, the terminal 5200 may include a transceiver module 5201.

[0246] In some embodiments, the transceiver module is used to receive a fourth CSI report sent by the terminal, or to receive both a fourth CSI report and a third CSI report sent by the terminal. The fourth CSI report is a higher-priority CSI report among the first and second CSI reports, and the third CSI report is a lower-priority CSI report among the first and second CSI reports. The third CSI report is an outdated CSI report, and at least one of the first and second CSI reports is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model. Optionally, the transceiver module is used to perform at least one of the communication steps (e.g., steps 2103, 3103, 4101, etc., but not limited thereto) performed by the network device 5200 in any of the above methods, which will not be elaborated here.

[0247] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0248] In some embodiments, the processing module can be replaced by the processor, and the transceiver module can be replaced by the transceiver.

[0249] In some embodiments, the transceiver module is also used to send CSI report configuration information.

[0250] Figure 6A is a schematic diagram of the structure of the communication device 6100 proposed in an embodiment of this disclosure. The communication device 6100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 6100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0251] As shown in Figure 6A, the communication device 6100 is used to execute any of the above methods. In some embodiments, the communication device 6100 includes one or more processors 6101. The processor 6101 may be a general-purpose processor or a special-purpose processor, such as a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 6100 is used to execute any of the above methods. Optionally, one or more processors 6101 are used to invoke instructions to cause the communication device 6100 to execute any of the above methods.

[0252] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps (e.g., steps 2103, 3103, and 6101) in the above method, and the processor 6101 performs at least one of other steps (e.g., steps 2101, 2102, 3101, and 3102, but not limited thereto). In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface can be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit can be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit can be used interchangeably.

[0253] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data and / or instructions. Optionally, one or more processors 6101 are used to invoke instructions stored in the memory 6103 to cause the communication device 6100 to perform any of the above methods. Optionally, all or part of the memory 6103 may also be located outside the communication device 6100. In an optional embodiment, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6102 and can be used to receive data and / or instructions from the memory 6102 or other devices, and can be used to send data and / or instructions to the memory 6102 or other devices. For example, the interface circuit 6104 can read data and / or instructions stored in the memory 6102 and send the data and / or instructions to the processor 6101.

[0254] The communication device 6100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 6100 described in this disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited by FIG. 6A. The communication device may be a standalone device or a part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data, programs and / or instructions; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0255] Figure 6B is a schematic diagram of the structure of chip 6200 according to an embodiment of this disclosure. For cases where the communication device 6100 can be a chip or a chip system, please refer to the schematic diagram of chip 6200 shown in Figure 6B, but it is not limited thereto.

[0256] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0257] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data and / or instructions. Optionally, all or part of the memories 6203 may be located outside of chip 6200. Optionally, interface circuit 6202 is connected to memory 6203, and interface circuit 6202 can be used to receive data and / or instructions from memory 6203 or other devices, and interface circuit 6202 can be used to send data and / or instructions to memory 6203 or other devices. For example, interface circuit 6202 can read data and / or instructions stored in memory 6203 and send the data and / or instructions to processor 6201.

[0258] In some embodiments, the interface circuit 6202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method. For example, the interface circuit 6202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 6202 performs data and / or instruction interaction between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs at least one of other steps (e.g., steps 2101, 2102, 3101, 3102, but not limited thereto).

[0259] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0260] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform 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 not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0261] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by a communication device, cause the communication device to perform any of the above methods. Optionally, the program product is a computer program product. Optionally, the program product is stored on the storage medium.

[0262] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method, characterized in that, The method is executed by a terminal, and the method includes: Determine a first Channel State Information (CSI) report and a second CSI report, wherein at least one of the first CSI report and the second CSI report is related to an artificial intelligence (AI) / machine learning (ML) function or an AI / ML model; Discard the third CSI report, which is the CSI report with lower priority between the first and second CSI reports.

2. The method according to claim 1, characterized in that, The abandonment of the third CSI report includes: If the first CSI report and the second CSI report are determined to meet the preset conditions, the third CSI report is discarded.

3. The method according to claim 2, characterized in that, The preset conditions include at least one of the following: The first CSI report and the second CSI report need to be updated or calculated on the same symbols; The first CSI report and the second CSI report need to be sent to the network device on the same uplink resources.

4. The method according to any one of claims 1 to 3, characterized in that, The abandonment of a third CSI report includes at least one of the following: The third CSI report is not updated or recalculated; The third CSI report is not sent to the network device.

5. The method according to any one of claims 1 to 4, characterized in that, The type of the first CSI report or the type of the second CSI report includes at least one of the following: The CSI report corresponding to the AI / ML function or the AI / ML model derivation; The CSI report corresponding to the performance monitoring of the AI / ML function or the AI / ML model; The CSI report corresponding to the AI / ML function or the AI / ML model training data collection.

6. The method according to any one of claims 1 to 5, characterized in that, The priority of the first CSI report and / or the second CSI report is determined based on at least one of the following: First parameter; Second parameter; The third parameter; Serving cell index; Maximum number of service cells; Report configuration identifier; Maximum number of CSI reports.

7. The method according to claim 6, characterized in that, The value of the first parameter is determined based on the transmission characteristics corresponding to the first CSI report or the second CSI report, and the transmission characteristics include at least one of the following: CSI reports transmitted aperiodically on the Physical Uplink Shared Channel (PUSCH); CSI reports are continuously sent on PUSCH; CSI reports are transmitted semi-persistently on the Physical Uplink Control Channel (PUCCH). CSI reports sent periodically on PUCCH.

8. The method according to claim 6, characterized in that, The value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, the content including at least one of the following: Includes Layer 1 Reference Signal Received Power L1-RSRP or Layer 1 Signal to Interference-plus-Noise Ratio L1-SINR; Includes beam accuracy indication information; It does not include L1-RSRP or L1-SINR; It does not include beam accuracy indication information.

9. The method according to claim 8, characterized in that, The second parameter is determined based on the content contained in the L1-RSRP or L1-SINR included in the first CSI report or the second CSI report, including: The second parameter is determined based on the characteristics of L1-RSRP or L1-SINR contained in the first CSI report or the second CSI report, wherein the characteristics of L1-RSRP or L1-SINR include at least one of the following: The L1-RSRP or L1-SINR corresponds to the CSI report derived from the AI / ML function or the AI / ML model. The L1-RSRP or L1-SINR corresponds to the CSI report corresponding to the AI / ML function or the AI / ML model performance monitoring. The L1-RSRP or L1-SINR corresponds to the CSI report related to the AI / ML function or the AI / ML model training data collection.

10. The method according to claim 8 or 9, characterized in that, The beam accuracy indication information corresponds to the CSI report of the AI / ML function or the AI / ML model performance monitoring, and the beam accuracy indication information includes at least one of the following: The predicted N optimal beams include the measurement optimal beam; The predicted optimal beam is among the N best beams measured; The difference between the predicted optimal beam measurement L1-RSRP and the measured optimal beam measurement L1-RSRP; The probability that the difference between the predicted optimal beam measurement L1-RSRP and the measured optimal beam measurement L1-RSRP is within a threshold. The difference between the predicted L1-RSRP of the optimal beam and the measured L1-RSRP of the optimal beam. The probability that the difference between the predicted L1-RSRP of the optimal beam and the measured L1-RSRP of the optimal beam is within a threshold.

11. The method according to claim 6, characterized in that, The value of the third parameter is determined based on whether the first CSI report or the second CSI report is related to the AI / ML function or the AI / ML model.

12. The method according to any one of claims 6 to 11, characterized in that, The parameter corresponding to the CSI report derived by the AI / ML function or the AI / ML model is greater than the parameter corresponding to the CSI report of the performance monitoring of the AI / ML function or the AI / ML model. The parameter corresponding to the CSI report derived by the AI / ML function or the AI / ML model is smaller than the parameter corresponding to the CSI report of the performance monitoring of the AI / ML function or the AI / ML model. The parameters corresponding to the CSI report derived by the AI / ML function or the AI / ML model are smaller than the parameters corresponding to the CSI report collected from the training data of the AI / ML function or the AI / ML model. The parameter corresponding to the CSI report of the AI / ML function or the AI / ML model performance monitoring is smaller than the parameter corresponding to the CSI report of the AI / ML function or the AI / ML model training data collection; Wherein, the parameter is the first parameter, the second parameter, or the third parameter.

13. The method according to any one of claims 5, 9, and 12, characterized in that, The CSI report corresponding to the AI / ML function or the AI / ML model derivation includes the following characteristics. The terminal receives CSI report configuration information corresponding to the CSI report derived by the AI / ML function or the AI / ML model, and determines a first reference signal resource set and a second reference signal resource set based on the CSI report configuration information. The CSI report is the measurement result of the terminal based on the first reference signal resource set and the prediction result corresponding to the second reference signal resource set obtained by the AI / ML function or the AI / ML model. The first reference signal resource set and the second reference signal resource set contain at least one different reference signal resource, or the time domain positions corresponding to the first reference signal resource set and the second reference signal resource set are different.

14. The method according to any one of claims 5, 9, and 12, characterized in that, The CSI report corresponding to the AI / ML function or the AI / ML model performance monitoring includes at least one of the following characteristics: The terminal receives CSI report configuration information of the CSI report corresponding to the AI / ML function or the AI / ML model performance monitoring. The CSI configuration information includes the CSI report configuration identifier corresponding to the AI / ML function or the AI / ML model derivation corresponding to the performance monitoring and / or the CSI configuration information indicating the reporting beam accuracy indicator.

15. The method according to any one of claims 5, 9, and 12, characterized in that, The CSI report corresponding to the AI / ML function or the AI / ML model training data collection includes the following characteristics: The terminal receives CSI report configuration information of the CSI report corresponding to the AI / ML function or the AI / ML model training data collection. The CSI report configuration information includes an association identifier, which is used to reflect the beam characteristics corresponding to the reference signal resource or reference signal resource set indicated in the CSI report configuration information.

16. A communication method, characterized in that, The method is performed by a network device, and the method includes: The receiving terminal sends a fourth CSI report or a fourth CSI report and a third CSI report, wherein the fourth CSI report is a higher priority CSI report among the first CSI report and the second CSI report, and the third CSI report is a lower priority CSI report among the first CSI report and the second CSI report, and the third CSI report is an outdated CSI report, and at least one of the first CSI report and the second CSI report is related to artificial intelligence (AI) / machine learning (ML) functions or AI / ML models.

17. The method according to claim 16, characterized in that, The type of the first CSI report or the type of the second CSI report includes at least one of the following: The CSI report corresponding to the AI / ML function or the AI / ML model derivation; The CSI report corresponding to the performance monitoring of the AI / ML function or the AI / ML model; The CSI report corresponding to the AI / ML function or the AI / ML model training data collection.

18. The method according to claim 16 or 17, characterized in that, The priority of the first CSI report and / or the second CSI report is determined based on at least one of the following: First parameter; Second parameter; The third parameter; Serving cell index; Maximum number of service cells; Report configuration identifier; Maximum number of CSI reports.

19. The method according to claim 18, characterized in that, The value of the first parameter is determined based on the transmission characteristics corresponding to the first CSI report or the second CSI report, and the transmission characteristics include at least one of the following: CSI reports transmitted aperiodically on the Physical Uplink Shared Channel (PUSCH); CSI reports are continuously sent on PUSCH; CSI reports are transmitted semi-persistently on the Physical Uplink Control Channel (PUCCH). CSI reports sent periodically on PUCCH.

20. The method according to claim 18, characterized in that, The value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, the content including at least one of the following: Includes Layer 1 Reference Signal Received Power L1-RSRP or Layer 1 Signal to Interference-plus-Noise Ratio L1-SINR; Includes beam accuracy indication information; It does not include L1-RSRP or L1-SINR; It does not include beam accuracy indication information.

21. The method according to claim 20, characterized in that, The value of the second parameter is determined based on the content contained in the first CSI report or the second CSI report, including: The second parameter is determined based on the characteristics of L1-RSRP or L1-SINR contained in the first CSI report or the second CSI report, wherein the characteristics of L1-RSRP or L1-SINR include at least one of the following: The L1-RSRP or L1-SINR corresponds to the CSI report derived from the AI / ML function or the AI / ML model. The L1-RSRP or L1-SINR corresponds to the CSI report corresponding to the AI / ML function or the AI / ML model performance monitoring. The L1-RSRP or L1-SINR corresponds to the CSI report related to the AI / ML function or the AI / ML model training data collection.

22. The method according to claim 20 or 21, characterized in that, The beam accuracy indication information corresponds to the CSI report of the AI / ML function or the AI / ML model performance monitoring, and the beam accuracy indication information includes at least one of the following: The predicted N optimal beams include the measurement optimal beam; The predicted optimal beam is among the N best beams measured; The difference between the predicted optimal beam measurement L1-RSRP and the measured optimal beam measurement L1-RSRP; The probability that the difference between the predicted optimal beam measurement L1-RSRP and the measured optimal beam measurement L1-RSRP is within a threshold. The difference between the predicted L1-RSRP of the optimal beam and the measured L1-RSRP of the optimal beam. The probability that the difference between the predicted L1-RSRP of the optimal beam and the measured L1-RSRP of the optimal beam is within a threshold.

23. The method according to claim 18, characterized in that, The value of the third parameter is determined based on whether the first CSI report or the second CSI report is related to the AI / ML function or the AI / ML model.

24. The method according to any one of claims 18 to 23, characterized in that, The parameter corresponding to the CSI report derived by the AI / ML function or the AI / ML model is greater than the parameter corresponding to the CSI report of the performance monitoring of the AI / ML function or the AI / ML model. The parameter corresponding to the CSI report derived by the AI / ML function or the AI / ML model is smaller than the parameter corresponding to the CSI report of the performance monitoring of the AI / ML function or the AI / ML model. The parameters corresponding to the CSI report derived by the AI / ML function or the AI / ML model are smaller than the parameters corresponding to the CSI report collected from the training data of the AI / ML function or the AI / ML model. The parameter corresponding to the CSI report of the AI / ML function or the AI / ML model performance monitoring is smaller than the parameter corresponding to the CSI report of the AI / ML function or the AI / ML model training data collection; Wherein, the parameter is the first parameter, the second parameter, or the third parameter.

25. The method according to any one of claims 17, 21, and 24, characterized in that, The CSI report corresponding to the AI / ML function or the AI / ML model derivation includes the following characteristics. The network device sends CSI report configuration information corresponding to the CSI report derived by the AI / ML function or the AI / ML model. The CSI report configuration information is used by the terminal to determine the first reference signal resource set and the second reference signal resource set. The CSI report is the measurement result of the terminal based on the first reference signal resource set and the prediction result corresponding to the second reference signal resource set obtained by the AI / ML function or the AI / ML model. The first reference signal resource set and the second reference signal resource set contain at least one different reference signal resource, or the time domain positions corresponding to the first reference signal resource set and the second reference signal resource set are different.

26. The method according to any one of claims 17, 21, and 24, characterized in that, The CSI report corresponding to the AI / ML function or the AI / ML model performance monitoring includes at least one of the following characteristics: The network device sends CSI report configuration information for the CSI report corresponding to the AI / ML function or the AI / ML model performance monitoring. The CSI configuration information includes a CSI report configuration identifier corresponding to the AI / ML function or the AI / ML model derivation for performance monitoring and / or a CSI configuration information indicating the reporting beam accuracy.

27. The method according to any one of claims 17, 21, and 24, characterized in that, The CSI report corresponding to the AI / ML function or the AI / ML model training data collection includes the following characteristics: The network device sends CSI report configuration information for the CSI report corresponding to the AI / ML function or the AI / ML model training data collection. The CSI report configuration information includes an association identifier, which is used to reflect the beam characteristics corresponding to the reference signal resource or reference signal resource set indicated in the CSI report configuration information.

28. A communication device, characterized in that, The communication device is used to perform the method according to any one of claims 1-15 or 16-27.

29. A storage medium storing instructions, characterized in that, When the instructions are executed on the communication device, the communication device performs the method as described in any one of claims 1-15 or 16-27.

30. A program product comprising at least one of a program and instructions, characterized in that, When at least one of the programs or instructions is executed by the communication device, it implements the steps of the method according to any one of claims 1-15 or 16-27.

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