Communication method, communication device, communication system and storage medium

Abstract

The present disclosure relates to a communication method, a communication device, a communication system and a storage medium. The communication method comprises: a terminal can send first information to a network device, wherein the first information is configured to indicate information of parameters supported by an artificial intelligence (AI) model, and the AI model is configured to predict channel state information (CSI). Therefore, the information of the parameters supported by the AI model can be effectively reported to the network device, so as to ensure that the AI model can use information of more rational parameters to predict the CSI, thereby supporting the improvement of the prediction performance of the AI model.

Description

Communication methods, communication equipment, communication systems and storage media Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, communication device, communication system and storage medium. Background Technology

[0002] With the development and application of Artificial Intelligence (AI) technology, AI has been widely applied to the physical layer of wireless communication. Channel state information (CSI) predictions for future moments can be derived through AI model reasoning; this method can be called an "AI prediction algorithm." Summary of the Invention

[0003] This disclosure provides a communication method, communication device, communication system, and storage medium.

[0004] The first aspect of this disclosure provides a communication method executed by a terminal, comprising: sending first information to a network device, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0005] A second aspect of this disclosure provides a communication method performed by a network device, comprising: receiving first information sent by a terminal, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0006] A third aspect of this disclosure provides a terminal, which includes a transceiver module for sending first information to a network device, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0007] A fourth aspect of this disclosure provides a network device comprising: a transceiver module for receiving first information sent by a terminal, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0008] A fifth aspect of this disclosure provides a communication device comprising: one or more processors; wherein the processors are configured to perform the method as described in the first aspect above, or to perform the method as described in the second aspect above.

[0009] A sixth aspect of this disclosure provides a communication system including a terminal and a network device, wherein the terminal is used to perform the method as described in the first aspect above, and the network device is used to perform the method as described in the second aspect above.

[0010] A seventh aspect of this disclosure provides a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method described in the first aspect above, or to perform the method described in the second aspect above.

[0011] An eighth aspect embodiment of this disclosure provides a computer program product including a computer program that, when executed by a processor, implements the method as described in the first aspect above, or implements the method as described in the second aspect above.

[0012] In the solution proposed in this disclosure, as described above, the terminal can send first information to the network device. This first information indicates the parameters supported by the artificial intelligence (AI) model, which is used by the AI ​​model to predict Channel State Information (CSI). This effectively reports the parameters supported by the AI ​​model to the network device, ensuring that the AI ​​model can predict CSI using more reasonable parameter information, thereby improving the predictive performance of the AI ​​model. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments or background art of this disclosure, the accompanying drawings used in the embodiments or background art of this disclosure will be described below.

[0014] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure;

[0015] Figure 1B is a schematic diagram of an observation window and a prediction window in an embodiment of this disclosure;

[0016] Figure 1C is a schematic diagram of another observation window and prediction window in an embodiment of this disclosure;

[0017] Figure 1D is a schematic diagram of another observation window and prediction window in an embodiment of this disclosure;

[0018] Figure 2A is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure;

[0019] Figure 2B is an interactive schematic diagram of a communication method according to another embodiment of the present disclosure;

[0020] Figure 2C is an interactive schematic diagram of a communication method according to another embodiment of the present disclosure;

[0021] Figure 3 is an interactive schematic diagram of a communication method according to yet another embodiment of the present disclosure;

[0022] Figure 4 is an interactive schematic diagram of a communication method according to yet another embodiment of the present disclosure;

[0023] Figure 5 is an interactive schematic diagram of a communication method according to another embodiment of the present disclosure;

[0024] Figure 6 is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure;

[0025] Figure 7A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure;

[0026] Figure 7B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0027] This disclosure provides communication methods, communication devices, communication systems, and storage media.

[0028] In a first aspect, embodiments of this disclosure propose a communication method executed by a terminal, comprising: sending first information to a network device, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0029] In the above embodiments, the terminal can send first information to the network device. This first information indicates the parameters supported by the artificial intelligence (AI) model, which is used by the AI ​​model to predict Channel State Information (CSI). This effectively reports the parameters supported by the AI ​​model to the network device, ensuring that the AI ​​model can predict CSI using more reasonable parameters, thereby improving the predictive performance of the AI ​​model.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the parameters include at least one of the following:

[0031] Number of sub-bands;

[0032] Number of antenna ports;

[0033] Number of measurement times, where the measurement times are used to measure CSI;

[0034] Number of prediction times, where prediction times are used to predict CSI.

[0035] In the above embodiments, the terminal can report information on the various possible parameters supported by the AI ​​model to the network device, thereby comprehensively ensuring that the AI ​​model can use more reasonable parameter information to predict CSI, and also improving application flexibility and supporting personalized communication scenarios.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

[0037] The first possible value for the number of sub-bands;

[0038] Second value information regarding the number of antenna ports;

[0039] The third value information for the number of measurement times;

[0040] The fourth value information for the number of predicted time points.

[0041] In the above embodiments, the terminal can send first information to the network device, which may include at least one of first value information, second value information, third value information, and fourth value information, to report information of at least one parameter supported by the AI ​​model to the network device in a display manner, thereby effectively improving the reporting efficiency and effect of the information of the parameters supported by the AI ​​model.

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

[0043] The first indicator field is used to determine the first value information of the number of sub-bands;

[0044] The second indication field is used to determine the second value information of the number of antenna ports;

[0045] The third indication field, wherein the third indication field is used to determine the third value information for the number of measurement times;

[0046] The fourth indicator field is used to determine the fourth value information for the number of prediction times.

[0047] In the above embodiments, the terminal can send first information to the network device, and the first information includes at least one of a first indication field, a second indication field, a third indication field, and a fourth indication field, so that the network device can determine the information of the parameters supported by the AI ​​model reported by the terminal based on the aforementioned at least one indication field. This can effectively improve the flexibility and effectiveness of reporting the information of the parameters supported by the AI ​​model.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes:

[0049] The fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following:

[0050] The first possible value for the number of sub-bands;

[0051] Second value information regarding the number of antenna ports;

[0052] The third value information for the number of measurement times;

[0053] The fourth value information for the number of predicted time points.

[0054] In the above embodiments, the terminal can send first information to the network device, and include a fifth indication field in the first information, so as to jointly indicate the combination of information of at least two parameters based on the fifth indication field. This enables the network device to determine the combination of information of at least two parameters supported by the AI ​​model reported by the terminal based on the fifth indication field. Thus, the reporting flexibility and effectiveness of the information of the parameters supported by the AI ​​model can be effectively improved.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, wherein,

[0056] The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or,

[0057] The second value information belongs to at least one second candidate value information related to the number of antenna ports; and / or,

[0058] The third value information belongs to at least one third candidate value information of the number of measurement times; and / or,

[0059] The fourth value information belongs to at least one fourth candidate value information of the number of prediction time points.

[0060] In the above embodiments, at least one candidate value information for each parameter can be predetermined. The candidate value information is used to describe the optional value information of the corresponding parameter. Then, during the reporting of the first information, an indicator field corresponding to at least one parameter can be carried in the first information, and one or more candidate value information among the at least one candidate value information of the parameter can be indicated based on the indicator field. The network device can determine the information of the parameter based on the content indicated by the indicator field corresponding to at least one parameter. Thus, the reporting flexibility and reporting effect of the information of the parameters supported by the AI ​​model can be improved, and the network device can quickly and accurately determine the information of the parameters supported by the AI ​​model.

[0061] In conjunction with some embodiments of the first aspect, in some embodiments, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

[0062] In the above embodiments, it is possible to flexibly define the information of the parameters supported by the AI ​​model, and to improve the reporting flexibility and reporting effect of the information of the parameters supported by the AI ​​model.

[0063] In conjunction with some embodiments of the first aspect, in some embodiments, the first value information includes at least one of the following:

[0064] The first possible value for the number of sub-bands;

[0065] The first set of values ​​includes: multiple first values;

[0066] The first value range, where the first value belongs to the first value range.

[0067] In the above embodiments, the terminal can effectively report the various possible values ​​of the number of subbands supported by the AI ​​model, so that the network device can accurately know the various possible values ​​of the number of subbands supported by the terminal's AI model. This facilitates the subsequent determination of a more accurate value for the number of subbands used by the AI ​​model, ensuring the predictive performance and effectiveness of the AI ​​model.

[0068] In conjunction with some embodiments of the first aspect, in some embodiments, the second value information includes at least one of the following:

[0069] The second possible value for the number of antenna ports;

[0070] The second set of values ​​includes: multiple second values;

[0071] The second value range, where the second value belongs to the second value range.

[0072] In the above embodiments, the terminal can effectively report various possible values ​​of the number of antenna ports supported by the AI ​​model, enabling the network device to accurately know the various possible values ​​of the number of antenna ports supported by the terminal's AI model. This facilitates the subsequent determination of a more accurate value for the number of antenna ports used by the AI ​​model, ensuring the predictive performance and effectiveness of the AI ​​model.

[0073] In conjunction with some embodiments of the first aspect, in some embodiments, the third value information includes at least one of the following:

[0074] The third possible value for the number of measurement moments;

[0075] The third set of values ​​includes multiple third values.

[0076] The third value range, where the third value belongs to the third value range.

[0077] In the above embodiments, the terminal can effectively report various possible values ​​of the number of measurement times supported by the AI ​​model, enabling the network device to accurately know the various possible values ​​of the number of measurement times supported by the terminal's AI model. This facilitates the subsequent determination of a more accurate value for the number of measurement times used by the AI ​​model, ensuring the predictive performance and effectiveness of the AI ​​model.

[0078] In conjunction with some embodiments of the first aspect, in some embodiments, the fourth value information includes at least one of the following:

[0079] The fourth possible value for the number of predicted time points;

[0080] The fourth set of values, wherein the fourth set of values ​​includes: multiple fourth values;

[0081] The fourth value range, where the fourth value belongs to the fourth value range.

[0082] In the above embodiments, the terminal can effectively report the various possible values ​​of the number of prediction times supported by the AI ​​model, so that the network device can accurately know the various possible values ​​of the number of prediction times supported by the terminal's AI model. This facilitates the subsequent determination of a more accurate value for the number of prediction times used by the AI ​​model, ensuring the prediction performance and effectiveness of the AI ​​model.

[0083] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes at least one of the following:

[0084] The first event has been confirmed;

[0085] Receive second information sent by a network device, wherein the second information is used to instruct the terminal to report the first information;

[0086] Receive third information sent by network devices, wherein the third information is used to query terminal capabilities.

[0087] In the above embodiments, the terminal can flexibly select the reporting method of the first information according to the actual communication scenario requirements, and can report the information of the parameters supported by the AI ​​model to the network device in a timely and flexible manner.

[0088] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0089] Determine the fourth piece of information, which is used to determine the parameters used by the AI ​​model;

[0090] CSI is predicted based on fourth information and AI models.

[0091] In the above embodiments, this enables the AI ​​model to predict CSI using more reasonable parameter information, thereby significantly improving the predictive performance of the AI ​​model.

[0092] In conjunction with some embodiments of the first aspect, in some embodiments, determining the fourth information includes at least one of the following:

[0093] The protocol predefines the fourth piece of information;

[0094] Determine the fourth information based on the first information;

[0095] Receive the fourth message sent by the network device.

[0096] In the above embodiments, the terminal can select an appropriate method to determine the fourth information based on the specific type of parameters supported by the AI ​​model. This enables the terminal to flexibly and accurately determine the fourth information and, based on the fourth information, determine the information of the parameters used by the AI ​​model, thereby improving flexibility and applicability.

[0097] Secondly, embodiments of this disclosure propose a communication method, which is executed by a network device, including: receiving first information sent by a terminal, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0098] In conjunction with some embodiments of the second aspect, in some embodiments, the parameters include at least one of the following:

[0099] Number of sub-bands;

[0100] Number of antenna ports;

[0101] Number of measurement times, where the measurement times are used to measure CSI;

[0102] Number of prediction times, where prediction times are used to predict CSI.

[0103] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following:

[0104] The first possible value for the number of sub-bands;

[0105] Second value information regarding the number of antenna ports;

[0106] The third value information for the number of measurement times;

[0107] The fourth value information for the number of predicted time points.

[0108] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following:

[0109] The first indicator field is used to determine the first value information of the number of sub-bands;

[0110] The second indication field is used to determine the second value information of the number of antenna ports;

[0111] The third indication field, wherein the third indication field is used to determine the third value information for the number of measurement times;

[0112] The fourth indicator field is used to determine the fourth value information for the number of prediction times.

[0113] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes:

[0114] The fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following:

[0115] The first possible value for the number of sub-bands;

[0116] Second value information regarding the number of antenna ports;

[0117] The third value information for the number of measurement times;

[0118] The fourth value information for the number of predicted time points.

[0119] In conjunction with some embodiments of the second aspect, in some embodiments, wherein,

[0120] The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or,

[0121] The second value information belongs to at least one second candidate value information related to the number of antenna ports; and / or,

[0122] The third value information belongs to at least one third candidate value information of the number of measurement times; and / or,

[0123] The fourth value information belongs to at least one fourth candidate value information of the number of prediction time points.

[0124] In conjunction with some embodiments of the second aspect, in some embodiments, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

[0125] In conjunction with some embodiments of the second aspect, in some embodiments, the first value information includes at least one of the following:

[0126] The first possible value for the number of sub-bands;

[0127] The first set of values ​​includes: multiple first values;

[0128] The first value range, where the first value belongs to the first value range.

[0129] In conjunction with some embodiments of the second aspect, in some embodiments, the second value information includes at least one of the following:

[0130] The second possible value for the number of antenna ports;

[0131] The second set of values ​​includes: multiple second values;

[0132] The second value range, where the second value belongs to the second value range.

[0133] In conjunction with some embodiments of the second aspect, in some embodiments, the third value information includes at least one of the following:

[0134] The third possible value for the number of measurement moments;

[0135] The third set of values ​​includes multiple third values.

[0136] The third value range, where the third value belongs to the third value range.

[0137] In conjunction with some embodiments of the second aspect, in some embodiments, the fourth value information includes at least one of the following:

[0138] The fourth possible value for the number of predicted time points;

[0139] The fourth set of values, wherein the fourth set of values ​​includes: multiple fourth values;

[0140] The fourth value range, where the fourth value belongs to the fourth value range.

[0141] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes at least one of the following:

[0142] Send a second message to the terminal, wherein the second message is used to instruct the terminal to report the first message;

[0143] Send a third piece of information to the terminal, which is used to query the terminal's capabilities.

[0144] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0145] Determine the fourth piece of information, which is used to determine the parameters used by the AI ​​model;

[0146] Send the fourth message to the terminal.

[0147] In conjunction with some embodiments of the second aspect, in some embodiments, determining the fourth information includes at least one of the following:

[0148] The protocol predefines the fourth piece of information;

[0149] The fourth information is determined based on the first information.

[0150] Thirdly, embodiments of this disclosure propose a terminal, which includes a transceiver module for sending first information to a network device, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0151] Fourthly, this disclosure provides a network device comprising: a transceiver module for receiving first information sent by a terminal, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0152] Fifthly, embodiments of this disclosure provide a communication device, which includes one or more processors; wherein the communication device is used to execute the first aspect and optional implementations of the first aspect, or to execute the second aspect and optional implementations of the second aspect.

[0153] In a sixth aspect, embodiments of this disclosure provide a communication system comprising: a terminal and a network device; wherein the terminal is configured to perform the method described in the first aspect and optional implementations thereof, and the network device is configured to perform the method described in the second aspect and optional implementations thereof.

[0154] In a seventh aspect, 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 the first aspect and its optional implementations, or to perform the method described in the second aspect and its optional implementations.

[0155] Eighthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method described in the first aspect and its optional implementations, or to perform the method described in the second aspect and its optional implementations.

[0156] In a ninth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the method as described in the first aspect and optional implementations of the first aspect, or to perform the method as described in the second aspect and optional implementations of the second aspect.

[0157] In a tenth aspect, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described according to the first aspect and optional implementations thereof, or configured to perform the method described according to the second aspect and optional implementations thereof.

[0158] It is understood that the aforementioned terminals, network devices, communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems 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.

[0159] This disclosure provides a communication method, a communication device, a communication system, and a storage medium. In some embodiments, the terms "communication method" and "information processing method," "communication control method," etc., can be used interchangeably; the terms "communication method apparatus" and "information processing apparatus," "communication control apparatus," etc., can be used interchangeably; and the terms "transmission system" and "information processing system," "communication system," etc., can be used interchangeably.

[0160] 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.

[0161] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0162] 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.

[0163] In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the aforementioned," "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 or a plural expression.

[0164] In the embodiments disclosed herein, "multiple" refers to two or more.

[0165] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0166] 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 B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0167] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); 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, C, etc.

[0168] 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.

[0169] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0170] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0171] 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”.

[0172] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0173] In some embodiments, "network" can be interpreted as devices included in the network, such as access network devices, core network devices, etc.

[0174] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)," "base station (BS)," "radio base station," or "fixed station." In some embodiments, it may also be understood as "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," or "bandwidth part (BWP)."

[0175] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "Narrow Band-Internet of Things (NB-IoT) device," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0176] 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.

[0177] 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.

[0178] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0179] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0180] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0181] As shown in Figure 1A, the communication system 100 includes a terminal 101 and a network device 102.

[0182] In some embodiments, terminal 101 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.

[0183] In some embodiments, network device 102 may include at least one of access network device and core network device.

[0184] 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, but is not limited to, 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.

[0185] 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.

[0186] 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.

[0187] 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 an evolved protocol core (EPC), a 5G core network (5GCN), or a next-generation core (NGC).

[0188] 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.

[0189] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. 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.

[0190] 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), 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), IEEE 802.16 (WiMAX), and 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).

[0191] Optionally, for medium- and high-speed mobile terminals, due to the rapid changes in channel information in the time domain, in order to improve system performance, the codebook added in Type II of some versions of the communication protocol (such as Rel-18) can be used to feed back channel state information (CSI). The codebook added in Type II predicts the downlink channel information at future times based on the downlink channel information estimated by the terminal side based on historical times, using an autoregressive or linear minimum mean square error (LMMSE) algorithm, and then calculates the corresponding precoding information for the future times based on the predicted downlink channel information.

[0192] Optionally, with the development and application of Artificial Intelligence (AI) technology, AI has been widely applied to the physical layer of wireless communication. Channel state information (CSI) prediction for future moments can be predicted using AI models; this method can be called an "AI prediction algorithm." The performance of AI prediction algorithms can outperform that of non-AI algorithms. Both AI and non-AI prediction algorithms require the use of CSI from multiple historical moments. Downlink channel information is estimated based on channel state information reference signals (CSI-RS) transmitted at multiple historical moments. The range encompassing "multiple historical moments" can be called the observation window. The range encompassing "multiple prediction moments" can be called the prediction window, where the prediction moments are used to predict future CSI. As shown in Figures 1B, 1C, and 1D, where Figure 1B is a schematic diagram of one observation window and prediction window in an embodiment of this disclosure, Figure 1C is a schematic diagram of another observation window and prediction window in an embodiment of this disclosure, and Figure 1D is a schematic diagram of yet another observation window and prediction window in an embodiment of this disclosure. Among them, CSI prediction for future times can be made based on CSI at historical times within the observation window under different parameter configurations. The definitions of each parameter are as follows:

[0193] N represents the number of times CSI-RS is transmitted within the observation window, where N can be any one of 4, 5, 8, or 10;

[0194] M represents the interval between adjacent CSI-RS, where M can be equal to D, and M can be any one of 2 slots, 2.5 slots, 4 slots, or 5 slots;

[0195] K indicates that the prediction window has K time points to predict CSI, where K can be any one of 1, 3, or 4;

[0196] D represents the CSI interval between adjacent time points predicted by the prediction window, where D can be any one of 1 slot, 2.5 slots, 4 slots, 5 slots, or 8 slots.

[0197] The length w of the prediction window d =K·D.

[0198] Optionally, if the network (NW) is configured with an aperiodic channel state information reference signal (AP CSI-RS), the value of N can be any one of 4 slots, 8 slots, or 12 slots, and M is 2 slots.

[0199] Alternatively, N can also be referred to as the number of measurement times, and K can also be referred to as the number of prediction times.

[0200] Optionally, the input data dimension of the terminal's AI model is related to the number of measurement times N within the observation window, and the output dimension of the AI ​​model is related to the number of prediction times K within the prediction window. For each measurement time, the input data dimension and / or output data dimension of the AI ​​model are also related to the number of antenna ports, the number of sub-bands, etc. If the NW does not know the number of measurement times N, the number of antenna ports, and the number of sub-bands supported by the terminal's AI model, the values ​​of the aforementioned parameters configured by the NW may cause the input data dimension and / or output data dimension input to the AI ​​model to exceed the maximum dimension value supported by the AI ​​model, thereby making the inference performance of the terminal's AI model inaccurate. If an AI model is trained for each input data dimension or output data dimension, the number of terminal AI models will increase significantly. In some embodiments, the terminal can deploy one or a small number of scalable AI models to adapt to different input data dimensions and / or output data dimensions. That is, for different values ​​of the number of antenna ports, the number of sub-bands, the number of measurement times N, or the number of prediction times K, the terminal can train an AI model to support different values ​​of these parameters. This characteristic of the AI ​​model can be called "scalability". The scalability of AI models trained on different terminals may also vary. Terminals can report information about the parameters supported by the AI ​​model to network devices to ensure that the AI ​​model can use more reasonable parameter information to predict CSI, thereby supporting improvements in the predictive performance of the AI ​​model.

[0201] Figure 2A is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2A, the embodiment of the present disclosure relates to a communication method that can be used in a communication system 100. The communication system 100 may include a terminal and a network device, which are not limited thereto. The above method includes:

[0202] In step S2101, the terminal determines that the first event has occurred and sends the first information to the network device.

[0203] The first event can be used to trigger the terminal to report first information to the network device. The first event can also be referred to as a triggering event. Optionally, in some embodiments, the first event may be, for example, a switch in the terminal's AI model and / or an update of the AI ​​model, which is used to predict CSI. Optionally, in some embodiments, the terminal may send the first information to the network device upon determining that the AI ​​model has been switched. Optionally, in some embodiments, the terminal may send the first information to the network device upon determining that the AI ​​model has been updated.

[0204] The first piece of information indicates the parameters supported by the AI ​​model, which is used to predict Channel State Information (CSI). The value information describes the possible values ​​of the parameters supported by the AI ​​model. The parameters supported by the AI ​​model can be a specific value, and / or the value information can be at least one set of values ​​containing several values, and / or the value information can be at least one range of values, which can have a maximum and a minimum value. In this case, the parameter value can belong to this range, and the parameter value can also be equal to the maximum or minimum value; there are no restrictions on this.

[0205] Optionally, in some embodiments, the parameters include at least one of the following: the number of sub-bands, the number of antenna ports, the number of measurement times, and the number of prediction times; wherein the measurement times are used to measure CSI, and the prediction times are used to predict CSI. Thus, the terminal can report information on various possible parameters supported by the AI ​​model to the network device, thereby comprehensively ensuring that the AI ​​model can use more reasonable parameter information to predict CSI, and also improving application flexibility and supporting applications suitable for personalized communication scenarios.

[0206] Optionally, in some embodiments, the "information on parameters supported by the AI ​​model" mentioned above may include at least one of the following: a first value of the number of sub-bands, a second value of the number of antenna ports, a third value of the number of measurement times, and a fourth value of the number of prediction times.

[0207] Optionally, in some embodiments, when the terminal determines that a first event has occurred, it may send first information to the network device to proactively report information about at least one parameter supported by the AI ​​model to the network device through the first information.

[0208] Optionally, in some embodiments, the first information includes at least one of the following: a first value of the number of sub-bands, a second value of the number of antenna ports, a third value of the number of measurement times, and a fourth value of the number of prediction times. That is, the terminal can send the first information to the network device, which may include at least one of the first, second, third, and fourth values, to report information about at least one parameter supported by the AI ​​model to the network device via a display method. This effectively improves the reporting efficiency and effectiveness of the information about the parameters supported by the AI ​​model.

[0209] Optionally, in some embodiments, the first information includes at least one of the following: a first indication field, a second indication field, a third indication field, and a fourth indication field; wherein the first indication field is used to determine a first value for the number of sub-bands, the second indication field is used to determine a second value for the number of antenna ports, the third indication field is used to determine a third value for the number of measurement times, and the fourth indication field is used to determine a fourth value for the number of prediction times. That is, the terminal can send the first information to the network device, and include at least one of the first, second, third, and fourth indication fields in the first information, enabling the network device to determine the information of the parameters supported by the AI ​​model reported by the terminal based on at least one of the aforementioned indication fields. This effectively improves the flexibility and effectiveness of reporting the information of the parameters supported by the AI ​​model.

[0210] Optionally, in some embodiments, the first value information belongs to at least one first candidate value information of the number of sub-bands; and / or, the second value information belongs to at least one second candidate value information of the number of antenna ports; and / or, the third value information belongs to at least one third candidate value information of the number of measurement times; and / or, the fourth value information belongs to at least one fourth candidate value information of the number of prediction times. That is, at least one candidate value information of each parameter can be predetermined. The candidate value information is used to describe the optional value information of the corresponding parameter. Then, in the process of reporting the first information, an indication field corresponding to at least one parameter can be carried in the first information, and one or more candidate value information of the at least one candidate value information of the parameter can be indicated based on the indication field. The network device can determine the information of the parameter based on the content indicated by the indication field corresponding to at least one parameter. Thus, the reporting flexibility and reporting effect of the information of the parameters supported by the AI ​​model can be improved, and the network device can quickly and accurately determine the information of the parameters supported by the AI ​​model.

[0211] Optionally, in some embodiments, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol. This enables flexible definition of the information of the parameters supported by the AI ​​model and supports improved reporting flexibility and effectiveness of the information of the parameters supported by the AI ​​model.

[0212] For example, the first information may include a first indication field, which can be used to determine the first value information of the number of subbands. The first value information may be selected from at least one first candidate value information of the number of subbands, and the at least one first candidate value information of the number of subbands may be predefined by the protocol. The terminal may send the first indication field to the network device according to the scalability of the AI ​​model, and indicate one or more first candidate value information of at least one first candidate value information based on the first indication field. The network device may combine the first indication field and the at least one first candidate value information predefined by the protocol to determine the value information of the number of subbands supported by the AI ​​model reported by the terminal.

[0213] For example, the first information may include a second indication field, which can be used to determine a second value of the number of antenna ports. The second value can be selected from at least one second candidate value of the number of antenna ports, and the at least one second candidate value of the number of antenna ports can be predefined by the protocol. The terminal can send the second indication field to the network device according to the scalability of the AI ​​model, and indicate one or more of the second candidate values ​​based on the second indication field. The network device can combine the second indication field and the at least one second candidate value predefined by the protocol to determine the value of the number of antenna ports supported by the AI ​​model reported by the terminal.

[0214] For example, the first information may include a third indication field. The third indication field is used to determine the third value information of the number of measurement times. The third value information may be selected from at least one third candidate value information of the number of measurement times. The at least one third candidate value information of the number of measurement times may be predefined by the protocol. The terminal may send the third indication field to the network device according to the scalability of the AI ​​model, and indicate one or more third candidate value information of at least one third candidate value information based on the third indication field. The network device may combine the third indication field and at least one third candidate value information predefined by the protocol to determine the value information of the number of measurement times supported by the AI ​​model reported by the terminal.

[0215] For example, the first information may include a fourth indication field. The fourth indication field is used to determine the fourth value information of the number of predicted time points. The fourth value information may be selected from at least one fourth candidate value information of the number of predicted time points. The at least one fourth candidate value information of the number of predicted time points may be predefined by the protocol. The terminal may send the fourth indication field to the network device according to the scalability of the AI ​​model, and indicate one of the at least one fourth candidate value information based on the fourth indication field. The network device may combine the fourth indication field and the at least one fourth candidate value information predefined by the protocol to determine the value information of the number of predicted time points supported by the AI ​​model reported by the terminal.

[0216] For example, the first information may include at least one of the first indication field, second indication field, third indication field, and fourth indication field mentioned above, so that the network device can determine the information of the parameters supported by the AI ​​model reported by the terminal based on at least one indication field.

[0217] Optionally, in some embodiments, the first information may include a fifth indication field, wherein the fifth indication field is used to determine a combination of information for at least two parameters, the information for which includes at least one of the following: a first value for the number of sub-bands, a second value for the number of antenna ports, a third value for the number of measurement times, and a fourth value for the number of prediction times. That is, the terminal can send the first information to the network device, including the fifth indication field, to jointly indicate a combination of information for at least two parameters based on the fifth indication field. This allows the network device to determine, based on the fifth indication field, the combination of information for at least two parameters supported by the AI ​​model reported by the terminal. This effectively improves the flexibility and effectiveness of reporting information for the parameters supported by the AI ​​model.

[0218] Optionally, in some embodiments, the first value information includes at least one of the following: a first value for the number of subbands, a first set of values, and a first value interval; wherein the first value set includes: multiple first values; and the first value belongs to the first value interval. This enables the terminal to effectively report various possible values ​​for the number of subbands supported by the AI ​​model, allowing the network device to accurately know the various possible values ​​for the number of subbands supported by the terminal's AI model. This facilitates the subsequent determination of a more accurate value for the number of subbands used by the AI ​​model, ensuring the predictive performance and effectiveness of the AI ​​model.

[0219] Optionally, in some embodiments, the second value information includes at least one of the following: a second value for the number of antenna ports, a set of second values, and a second value range; wherein the set of second values ​​includes: multiple second values; and the second value belongs to a second value range. This enables the terminal to effectively report various possible values ​​for the number of antenna ports supported by the AI ​​model, allowing network devices to accurately know the various possible values ​​for the number of antenna ports supported by the terminal's AI model. This facilitates the subsequent determination of a more accurate value for the number of antenna ports used by the AI ​​model, ensuring the predictive performance and effectiveness of the AI ​​model.

[0220] Optionally, in some embodiments, the third value information includes at least one of the following: a third value of the number of measurement times, a set of third values, and a range of third values; wherein the set of third values ​​includes: multiple third values; and the third value belongs to a range of third values. This enables the terminal to effectively report various possible values ​​of the number of measurement times supported by the AI ​​model, allowing the network device to accurately know the various possible values ​​of the number of measurement times supported by the terminal's AI model. This facilitates the subsequent determination of more accurate values ​​for the number of measurement times used by the AI ​​model, ensuring the predictive performance and effectiveness of the AI ​​model.

[0221] Optionally, in some embodiments, the fourth value information includes at least one of the following: a fourth value for the number of prediction times, a set of fourth values, and a range of fourth values; wherein the set of fourth values ​​includes: multiple fourth values; and the fourth value belongs to a range of fourth values. This enables the terminal to effectively report various possible values ​​for the number of prediction times supported by the AI ​​model, allowing the network device to accurately know the various possible values ​​for the number of prediction times supported by the terminal's AI model. This facilitates the subsequent determination of more accurate values ​​for the number of prediction times used by the AI ​​model, ensuring the prediction performance and effectiveness of the AI ​​model.

[0222] In step S2102, the network device determines the fourth piece of information.

[0223] The fourth piece of information is used to determine the parameters used by the AI ​​model. This information refers to the parameters actually used by the AI ​​model when predicting CSI.

[0224] Optionally, in some embodiments, the information of the parameters used by the AI ​​model may be determined based on the information of the parameters supported by the AI ​​model, and there is no limitation thereto.

[0225] Optionally, in some embodiments, the information of the parameters used by the AI ​​model may be the same as the information of the parameters supported by the AI ​​model, or may belong to the information of the parameters supported by the AI ​​model. For example, the information of the parameters used by the AI ​​model may be a subset of the information of the parameters supported by the AI ​​model, and there is no limitation thereto.

[0226] Optionally, in some embodiments, the fourth information may be used to configure information on the parameters used by the AI ​​model for the terminal, and / or the fourth information may be used to configure and / or indicate some reference information for determining the parameters used by the AI ​​model, without limitation.

[0227] For example, if the parameter supported by the terminal's AI model is the number of antenna ports, the network device can configure the value of the number of antenna ports used by the AI ​​model for the terminal through the fourth information; if the parameter supported by the terminal's AI model is the number of sub-bands, the network device can configure the bandwidth size (e.g., the number of resource blocks (RBs) included in the bandwidth) and the number of RBs per sub-band for the terminal through the fourth information. The terminal can obtain the bandwidth size and the number of RBs per sub-band through the fourth information, and determine the value of the number of sub-bands used by the AI ​​model based on the bandwidth size and the number of RBs per sub-band; if the parameter supported by the terminal's AI model is the number of measurement moments, then when the CSI-RS resource is a periodic resource, the network device does not need to configure the value of the number of measurement moments used by the AI ​​model. That is, without executing S2102 and S2103, the terminal can determine the value of the number of measurement moments used by the AI ​​model based on the characteristics of the periodic reference signal resource or a predefined protocol. When the RS resources are aperiodic, the network device can configure N aperiodic resources for the terminal through the fourth information. The terminal can implicitly determine the value of the number of measurement times used by the AI ​​model based on the number N of aperiodic resources. If the parameter supported by the terminal's AI model is the number of predicted times, and CSI is reported in the form of a codebook, the network device can explicitly indicate the number of predicted times to the terminal through the fourth information. Alternatively, the network device can also configure relevant codebook parameters for the terminal through the fourth information. The terminal can learn about the relevant codebook parameters through the fourth information and determine the value of the number of predicted times used by the AI ​​model based on the relevant codebook parameters.

[0228] Optionally, in some embodiments, the network device may determine the fourth information based on a predefined protocol, or based on the first information, or based on both a predefined protocol and the first information, during the process of determining the fourth information. This improves the accuracy and flexibility of determining the information used by the AI ​​model's parameters, effectively adapting to personalized parameter configurations and ensuring effective communication.

[0229] In step S2103, the network device sends the fourth information to the terminal.

[0230] Optionally, in some embodiments, after determining the fourth information, the network device may send the fourth information to the terminal, and the terminal may determine the information of the parameters used by the AI ​​model based on the fourth information.

[0231] Alternatively, in some other embodiments, it may be unnecessary to execute S2102 and S2103. The terminal may independently determine the fourth information and determine the information of the parameters used by the AI ​​model based on the fourth information. For specific examples, please refer to the above description.

[0232] In step S2104, the terminal predicts CSI based on the fourth information and the AI ​​model.

[0233] Optionally, in some embodiments, the terminal may determine the fourth information using at least one of the following methods: predefining the fourth information according to a protocol, determining the fourth information based on the first information, or receiving the fourth information sent by a network device. That is, the terminal can select an appropriate method to determine the fourth information based on the specific type of parameters supported by the AI ​​model. This allows the terminal to flexibly and accurately determine the fourth information and, based on the fourth information, determine the parameters used by the AI ​​model, improving flexibility and applicability.

[0234] Optionally, in some embodiments, steps S2102 and S2103 described above may not be necessary. The terminal may determine the fourth information based on a predefined protocol; or the terminal may determine the fourth information based on the first information; or the terminal may determine the fourth information based on both the predefined protocol and the first information. Then, the terminal may determine the parameter information used by the AI ​​model based on the fourth information.

[0235] For example, if the parameter supported by the terminal's AI model is the number of antenna ports, the terminal can receive fourth information sent by the network device. This fourth information is used to configure the value of the number of antenna ports used by the AI ​​model for the terminal. The terminal can determine the value of the number of antenna ports used by the AI ​​model based on the received fourth information. If the parameter supported by the terminal's AI model is the number of sub-bands, the terminal can receive fourth information sent by the network device. This fourth information can be used to configure the bandwidth size for the terminal (e.g., bandwidth includes resource blocks). The terminal can obtain the bandwidth and the number of RBs per sub-band through the fourth information, and determine the value of the number of sub-bands used by the AI ​​model based on the bandwidth and the number of RBs per sub-band. If the parameter supported by the terminal's AI model is the number of measurement moments, then when the CSI-RS resource is a periodic resource, the network device does not need to configure the value of the number of measurement moments used by the AI ​​model. That is to say, without executing S2102 and S2103, the terminal can determine the fourth information based on the characteristics of the periodic reference signal resource or a predefined protocol, and thus determine the value of the number of measurement moments used by the AI ​​model based on the fourth information. When the source is aperiodic resources, the terminal can receive the fourth information sent by the network device. The fourth information can be used to configure N aperiodic resources for the terminal. Based on the number N of aperiodic resources, the terminal can implicitly determine the value of the number of measurement times used by the AI ​​model. If the parameter supported by the terminal's AI model is the number of predicted times, and CSI is reported using a codebook, the terminal can receive the fourth information sent by the network device. The fourth information can be used to explicitly indicate the number of predicted times to the terminal, or it can be used to configure relevant codebook parameters for the terminal. The terminal can learn about the relevant codebook parameters through the fourth information and determine the value of the number of predicted times used by the AI ​​model based on the relevant codebook parameters.

[0236] Optionally, in some embodiments, after determining the information of the parameters used by the AI ​​model, the terminal can determine the input data dimension and / or output data dimension of the AI ​​model based on the information of the parameters used by the AI ​​model, and control the AI ​​model to predict CSI by referring to the input data dimension and / or output data dimension.

[0237] The communication method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2104. For example, steps S2101, S2102, S2103, and S2104 can each be implemented as an independent embodiment, steps S2101+S2102 can be implemented as an independent embodiment, steps S2101+S2103 can be implemented as an independent embodiment, steps S2101+S2104 can be implemented as an independent embodiment, steps S2102+S2103 can be implemented as an independent embodiment, and steps S2101+S2104 can be implemented as an independent embodiment. The embodiments can be implemented as follows: steps S2103+S2104 can be implemented as independent embodiments, steps S2101+S2102+S2103 can be implemented as independent embodiments, steps S2101+S2102+S2104 can be implemented as independent embodiments, steps S2102+S2103+S2104 can be implemented as independent embodiments, etc., but are not limited thereto.

[0238] 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.

[0239] In the embodiments disclosed herein, each step and its optional implementation can also be carried out independently.

[0240] In this embodiment, the terminal determines that a first event has occurred and sends first information to the network device. The network device determines fourth information and sends fourth information to the terminal. The terminal then predicts CSI based on the fourth information and the AI ​​model. Therefore, the terminal can report information about the parameters supported by the AI ​​model to the network device based on event triggering, ensuring that the AI ​​model can use more reasonable parameter information to predict CSI, thereby improving the predictive performance of the AI ​​model.

[0241] It should be noted that in the following embodiments, the descriptions of the same or corresponding terms and method steps as in the above embodiments can be found in the above embodiments, and will not be repeated here.

[0242] Figure 2B is an interactive schematic diagram of a communication method according to another embodiment of the present disclosure. As shown in Figure 2B, the embodiments of the present disclosure relate to a communication method that can be used in a communication system 100. The communication system 100 may include a terminal and a network device, without limitation. In this embodiment, the network device can trigger the terminal to report information on parameters supported by the AI ​​model. The method includes:

[0243] Step S2201: The network device sends the second information to the terminal.

[0244] The second information is used to instruct the terminal to report the first information. In other words, the network device can send the second information to the terminal to instruct the terminal to report the first information.

[0245] Optionally, in some embodiments, the network device may send higher-layer signaling to the terminal and carry second information in the higher-layer signaling to instruct the terminal to report the first information through the second information.

[0246] Optionally, in some embodiments, the network device may send physical layer signaling to the terminal and carry second information in the physical layer signaling to instruct the terminal to report the first information through the second information.

[0247] Optionally, in some embodiments, the network device may send radio resource control (RRC) signaling to the terminal and carry second information in the RRC signaling to instruct the terminal to report the first information through the second information.

[0248] Optionally, in some embodiments, the network device may send a media access control-control element (MAC-CE) to the terminal and carry second information in the MAC-CE to instruct the terminal to report the first information through the second information.

[0249] Optionally, in some embodiments, the network device may send downlink control information (DCI) to the terminal and carry second information in the DCI to instruct the terminal to report the first information through the second information.

[0250] The first piece of information indicates the parameters supported by the AI ​​model, which is used to predict Channel State Information (CSI). The value information describes the possible values ​​of the parameters supported by the AI ​​model. The parameters supported by the AI ​​model can be a specific value, and / or the value information can be at least one set of values ​​containing several values, and / or the value information can be at least one range of values, which can have a maximum and a minimum value. In this case, the parameter value can belong to this range, and the parameter value can also be equal to the maximum or minimum value; there are no restrictions on this.

[0251] Optionally, in some embodiments, the parameters include at least one of the following: the number of sub-bands, the number of antenna ports, the number of measurement times, and the number of prediction times; wherein the measurement times are used to measure CSI, and the prediction times are used to predict CSI. Thus, the terminal can report information on various possible parameters supported by the AI ​​model to the network device, thereby comprehensively ensuring that the AI ​​model can use more reasonable parameter information to predict CSI, and also improving application flexibility and supporting applications suitable for personalized communication scenarios.

[0252] Optionally, in some embodiments, the "information on parameters supported by the AI ​​model" mentioned above may include at least one of the following: a first value of the number of sub-bands, a second value of the number of antenna ports, a third value of the number of measurement times, and a fourth value of the number of prediction times.

[0253] In step S2202, the terminal sends the first information to the network device.

[0254] Optionally, in some embodiments, the terminal may receive second information sent by the network device and determine through the second information that the network device instructs the terminal to report first information. In response to having received the second information, the terminal may send the first information to the network device to report information on the parameters supported by the AI ​​model to the network device through the first information.

[0255] Optionally, in this embodiment, the contents described in S2102-S2104 of the above embodiments can also be executed. Please refer to the above embodiments for details, which will not be repeated here.

[0256] The communication method involved in the embodiments of this disclosure may include at least one of steps S2201 to S2202. For example, steps S2201 and S2202 may be implemented as independent embodiments, and steps S2201+S2202 may be implemented as independent embodiments, but are not limited thereto.

[0257] 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.

[0258] In the embodiments disclosed herein, each step and its optional implementation can also be carried out independently.

[0259] In this embodiment, the network device sends second information to the terminal, which instructs the terminal to report first information. The terminal receives the second information from the network device and sends the first information back to the network device, which indicates the information of the parameters supported by the AI ​​model. Thus, the terminal can report the information of the parameters supported by the AI ​​model based on the triggering of the network device, ensuring that the AI ​​model can use more reasonable parameter information to predict CSI, thereby improving the predictive performance of the AI ​​model.

[0260] Figure 2C is an interactive schematic diagram of a communication method according to another embodiment of the present disclosure. As shown in Figure 2C, the embodiments of the present disclosure relate to a communication method that can be used in a communication system 100. The communication system 100 may include a terminal and a network device, without limitation. In this embodiment, the method may involve reporting information about the parameters supported by the AI ​​model to the network device based on the terminal's capabilities. The method includes:

[0261] Step S2301: The network device sends third information to the terminal.

[0262] The third piece of information is used to query terminal capabilities.

[0263] Optionally, in some embodiments, the network device may send third information to the terminal to implicitly instruct the terminal to report the first information.

[0264] Optionally, in some embodiments, the network device may send a capability query request to the terminal and carry third information in the capability query request, so as to implicitly instruct the terminal to report the first information through the third information.

[0265] Step S2302: The terminal sends terminal capabilities to the network device, wherein the terminal capabilities include: first information.

[0266] Optionally, in some embodiments, after receiving the third information sent by the network device, the terminal can query the terminal capabilities and report the terminal capabilities to the network device. The terminal capabilities may include the first information, thereby enabling the terminal to report information on the parameters supported by the AI ​​model to the network device through the terminal capabilities.

[0267] The first piece of information indicates the parameters supported by the AI ​​model, which is used to predict Channel State Information (CSI). The value information describes the possible values ​​of the parameters supported by the AI ​​model. The parameters supported by the AI ​​model can be a specific value, and / or the value information can be at least one set of values ​​containing several values, and / or the value information can be at least one range of values, which can have a maximum and a minimum value. In this case, the parameter value can belong to this range, and the parameter value can also be equal to the maximum or minimum value; there are no restrictions on this.

[0268] Optionally, in some embodiments, the parameters include at least one of the following: the number of sub-bands, the number of antenna ports, the number of measurement times, and the number of prediction times; wherein the measurement times are used to measure CSI, and the prediction times are used to predict CSI. Thus, the terminal can report information on various possible parameters supported by the AI ​​model to the network device, thereby comprehensively ensuring that the AI ​​model can use more reasonable parameter information to predict CSI, and also improving application flexibility and supporting applications suitable for personalized communication scenarios.

[0269] Optionally, in some embodiments, the "information on parameters supported by the AI ​​model" mentioned above may include at least one of the following: a first value of the number of sub-bands, a second value of the number of antenna ports, a third value of the number of measurement times, and a fourth value of the number of prediction times.

[0270] Optionally, in this embodiment, the contents described in S2102-S2104 of the above embodiments can also be executed. Please refer to the above embodiments for details, which will not be repeated here.

[0271] The communication method involved in the embodiments of this disclosure may include at least one of steps S2301 to S2302. For example, steps S2301 and S2302 may be implemented as independent embodiments, and steps S2301+S2302 may be implemented as independent embodiments, but are not limited thereto.

[0272] 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.

[0273] In the embodiments disclosed herein, each step and its optional implementation can also be carried out independently.

[0274] In this embodiment, the network device sends third information to the terminal. This third information is used to query the terminal's capabilities. The terminal receives the third information from the network device and sends first information to the network device by reporting its capabilities. This first information indicates the parameters supported by the AI ​​model. Therefore, by reporting the parameters supported by the AI ​​model through the terminal's capabilities, it is possible to ensure that the AI ​​model can use more reasonable parameter information to predict CSI, thereby improving the predictive performance of the AI ​​model.

[0275] Figure 3 is an interactive schematic diagram illustrating a communication method according to yet another embodiment of the present disclosure. As shown in Figure 3, the embodiments of the present disclosure relate to a communication method that can be used in a terminal. The method includes:

[0276] Step S3101: Send first information to the network device, wherein the first information is used to indicate the information of the parameters supported by the artificial intelligence (AI) model, and the AI ​​model is used to predict the channel state information (CSI).

[0277] The communication method involved in the embodiments of this disclosure may include step S3101. For example, step S3101 may be implemented as a standalone embodiment, but is not limited thereto.

[0278] 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.

[0279] In the embodiments disclosed herein, each step and its optional implementation can also be carried out independently.

[0280] Optionally, in some embodiments of this disclosure, the parameters include at least one of the following:

[0281] Number of sub-bands;

[0282] Number of antenna ports;

[0283] Number of measurement times, where the measurement times are used to measure CSI;

[0284] Number of prediction times, where prediction times are used to predict CSI.

[0285] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0286] The first possible value for the number of sub-bands;

[0287] Second value information regarding the number of antenna ports;

[0288] The third value information for the number of measurement times;

[0289] The fourth value information for the number of predicted time points.

[0290] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0291] The first indicator field is used to determine the first value information of the number of sub-bands;

[0292] The second indication field is used to determine the second value information of the number of antenna ports;

[0293] The third indication field, wherein the third indication field is used to determine the third value information for the number of measurement times;

[0294] The fourth indicator field is used to determine the fourth value information for the number of prediction times.

[0295] Optionally, in some embodiments of this disclosure, the first information includes:

[0296] The fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following:

[0297] The first possible value for the number of sub-bands;

[0298] Second value information regarding the number of antenna ports;

[0299] The third value information for the number of measurement times;

[0300] The fourth value information for the number of predicted time points.

[0301] Optionally, in some embodiments of this disclosure, wherein,

[0302] The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or,

[0303] The second value information belongs to at least one second candidate value information related to the number of antenna ports; and / or,

[0304] The third value information belongs to at least one third candidate value information of the number of measurement times; and / or,

[0305] The fourth value information belongs to at least one fourth candidate value information of the number of prediction time points.

[0306] Optionally, in some embodiments of this disclosure, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

[0307] Optionally, in some embodiments of this disclosure, the first value information includes at least one of the following:

[0308] The first possible value for the number of sub-bands;

[0309] The first set of values ​​includes: multiple first values;

[0310] The first value range, where the first value belongs to the first value range.

[0311] Optionally, in some embodiments of this disclosure, the second value information includes at least one of the following:

[0312] The second possible value for the number of antenna ports;

[0313] The second set of values ​​includes: multiple second values;

[0314] The second value range, where the second value belongs to the second value range.

[0315] Optionally, in some embodiments of this disclosure, the third value information includes at least one of the following:

[0316] The third possible value for the number of measurement moments;

[0317] The third set of values ​​includes multiple third values.

[0318] The third value range, where the third value belongs to the third value range.

[0319] Optionally, in some embodiments of this disclosure, the fourth value information includes at least one of the following:

[0320] The fourth possible value for the number of predicted time points;

[0321] The fourth set of values, wherein the fourth set of values ​​includes: multiple fourth values;

[0322] The fourth value range, where the fourth value belongs to the fourth value range.

[0323] Optionally, in some embodiments of this disclosure, the method further includes at least one of the following:

[0324] The first event has been confirmed;

[0325] Receive second information sent by a network device, wherein the second information is used to instruct the terminal to report the first information;

[0326] Receive third information sent by network devices, wherein the third information is used to query terminal capabilities.

[0327] Optionally, in some embodiments of this disclosure, the method further includes:

[0328] Determine the fourth piece of information, which is used to determine the parameters used by the AI ​​model;

[0329] CSI is predicted based on fourth information and AI models.

[0330] Optionally, in some embodiments of this disclosure, determining the fourth information includes at least one of the following:

[0331] The protocol predefines the fourth piece of information;

[0332] Determine the fourth information based on the first information;

[0333] Receive the fourth message sent by the network device.

[0334] Figure 4 is an interactive schematic diagram illustrating a communication method according to yet another embodiment of the present disclosure. As shown in Figure 4, the embodiments of the present disclosure relate to a communication method that can be used in network devices. The method includes:

[0335] Step S4101: Receive first information sent by the terminal, wherein the first information is used to indicate the parameters supported by the artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0336] The communication method involved in the embodiments of this disclosure may include step S4101. For example, step S4101 may be implemented as a standalone embodiment, but is not limited thereto.

[0337] 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.

[0338] In the embodiments disclosed herein, each step and its optional implementation can also be carried out independently.

[0339] Optionally, in some embodiments of this disclosure, the parameters include at least one of the following:

[0340] Number of sub-bands;

[0341] Number of antenna ports;

[0342] Number of measurement times, where the measurement times are used to measure CSI;

[0343] Number of prediction times, where prediction times are used to predict CSI.

[0344] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0345] The first possible value for the number of sub-bands;

[0346] Second value information regarding the number of antenna ports;

[0347] The third value information for the number of measurement times;

[0348] The fourth value information for the number of predicted time points.

[0349] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0350] The first indicator field is used to determine the first value information of the number of sub-bands;

[0351] The second indication field is used to determine the second value information of the number of antenna ports;

[0352] The third indication field, wherein the third indication field is used to determine the third value information for the number of measurement times;

[0353] The fourth indicator field is used to determine the fourth value information for the number of prediction times.

[0354] Optionally, in some embodiments of this disclosure, the first information includes:

[0355] The fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following:

[0356] The first possible value for the number of sub-bands;

[0357] Second value information regarding the number of antenna ports;

[0358] The third value information for the number of measurement times;

[0359] The fourth value information for the number of predicted time points.

[0360] Optionally, in some embodiments of this disclosure, wherein,

[0361] The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or,

[0362] The second value information belongs to at least one second candidate value information related to the number of antenna ports; and / or,

[0363] The third value information belongs to at least one third candidate value information of the number of measurement times; and / or,

[0364] The fourth value information belongs to at least one fourth candidate value information of the number of prediction time points.

[0365] Optionally, in some embodiments of this disclosure, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

[0366] Optionally, in some embodiments of this disclosure, the first value information includes at least one of the following:

[0367] The first possible value for the number of sub-bands;

[0368] The first set of values ​​includes: multiple first values;

[0369] The first value range, where the first value belongs to the first value range.

[0370] Optionally, in some embodiments of this disclosure, the second value information includes at least one of the following:

[0371] The second possible value for the number of antenna ports;

[0372] The second set of values ​​includes: multiple second values;

[0373] The second value range, where the second value belongs to the second value range.

[0374] Optionally, in some embodiments of this disclosure, the third value information includes at least one of the following:

[0375] The third possible value for the number of measurement moments;

[0376] The third set of values ​​includes multiple third values.

[0377] The third value range, where the third value belongs to the third value range.

[0378] Optionally, in some embodiments of this disclosure, the fourth value information includes at least one of the following:

[0379] The fourth possible value for the number of predicted time points;

[0380] The fourth set of values, wherein the fourth set of values ​​includes: multiple fourth values;

[0381] The fourth value range, where the fourth value belongs to the fourth value range.

[0382] Optionally, in some embodiments of this disclosure, the method further includes at least one of the following:

[0383] Send a second message to the terminal, wherein the second message is used to instruct the terminal to report the first message;

[0384] Send a third piece of information to the terminal, which is used to query the terminal's capabilities.

[0385] Optionally, in some embodiments of this disclosure, the method further includes:

[0386] Determine the fourth piece of information, which is used to determine the parameters used by the AI ​​model;

[0387] Send the fourth message to the terminal.

[0388] Optionally, in some embodiments of this disclosure, determining the fourth information includes at least one of the following:

[0389] The protocol predefines the fourth piece of information;

[0390] The fourth information is determined based on the first information.

[0391] Figure 5 is an interactive schematic diagram illustrating a communication method according to another embodiment of the present disclosure. As shown in Figure 5, the embodiments of the present disclosure relate to a communication method that can be used in a communication system 100. The communication system 100 may include a terminal and a network device, which are not limited thereto. The method includes:

[0392] In step S5101, the terminal sends first information to the network device, wherein the first information is used to indicate the information of the parameters supported by the artificial intelligence (AI) model, and the AI ​​model is used to predict the channel state information (CSI).

[0393] In step S5102, the network device receives the first information sent by the terminal.

[0394] The communication method involved in the embodiments of this disclosure may include at least one of steps S5101 to S5102. For example, steps S5101, S5102, etc. may be implemented as independent embodiments, and steps S5101+S5102 may be implemented as independent embodiments, but are not limited thereto.

[0395] 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.

[0396] In the embodiments disclosed herein, each step and its optional implementation can also be carried out independently.

[0397] The following is an exemplary description of the above method.

[0398] Optionally, the following embodiments are available:

[0399] This disclosure proposes a scalability indication method for the CSI prediction model on the terminal side. The network (NW) can configure reasonable parameters such as the number of antenna ports, the number of sub-bands, the number of CSI measurement moments within the observation window (an optional example of the number of measurement moments), and the number of CSI prediction moments (an optional example of the number of prediction moments) based on the scalability of the encoder model (an optional example of the number of prediction moments) indicated by the terminal.

[0400] The example uses the UE as the terminal.

[0401] Optionally, in some embodiments, the UE can indicate the scalability of the AI ​​model for CSI prediction on the UE side through the reported UE capabilities (an optional example of the terminal capabilities mentioned above). The UE capabilities indicate that the AI ​​model supports one or more of the following scalability: different number of antenna ports, different number of sub-bands, different number of measurement times within different observation windows, and different number of prediction times within different prediction windows.

[0402] Optionally, in some embodiments, the scalability indication of the number of antenna ports supported by the UE, the number of sub-bands, the number of measurement moments within the observation window (an optional example of the number of measurement moments mentioned above), and the number of prediction moments within the prediction window (an optional example of the number of prediction moments mentioned above) can be as follows:

[0403] Optionally, in some embodiments, the UE displays one or more of the following: the number of supported antenna ports, the set or range of values ​​for the number of sub-bands, the set or range of values ​​for the number of measurement times within the observation window, and the set or range of values ​​for the number of prediction times within the prediction window.

[0404] Optionally, in some embodiments, for each scalability, multiple sets of values ​​or multiple value intervals are predefined. The reported UE capability indicates support for at least one set of values ​​or at least one value interval. Alternatively, based on the values ​​of the number of antenna ports, the number of sub-bands, the number of measurement times within the observation window, and the number of prediction times within the prediction window, two or more combinations of these values ​​are predefined, and the reported UE capability indicates at least one combination.

[0405] Optionally, in some embodiments, the UE may receive NW signaling triggering (an optional example of the second or third information described above), or the UE may actively report the scalability of the encoder model on the UE side. The UE-reported information indicates that the CSI prediction AI model supports one or more scalability values ​​or ranges for different numbers of antenna ports, sub-bands, the number of measurement moments within the observation window, and the number of prediction moments within the prediction window. The scalability indication methods for the supported number of antenna ports, sub-bands, the number of measurement moments within the observation window, and the number of prediction moments within the prediction window can be found in the examples described above.

[0406] Optionally, in some embodiments, the NW can determine the configuration of parameters such as the number of antenna ports, the number of sub-bands, the number of measurement moments within the observation window, and the number of prediction moments within the prediction window based on the scalability indicated by the received UE. Alternatively, the NW can send signaling to instruct the UE to report the supported scalability.

[0407] Optionally, in some embodiments, the number of antenna ports can be configured to 2, 4, 8, 12, 16, 24, and 32, and the number of sub-bands can be expressed as... Where W represents the configured bandwidth size in RBs, and S represents the number of RBs contained in the subband. The NW side can configure periodic, semi-persistent, or aperiodic CSI-RS resources to estimate the CSI at each moment within the observation window, such as measuring the downlink channel information corresponding to each moment as input to the CSI prediction model. For periodic or semi-persistent CSI-RS resources, the number N of moments within the observation window can be configured by the network or predefined. Optionally, the value of N is indicated by the UE reporting a value supported by the CSI prediction model. The interval between adjacent CSI-RS transmission moments is the period of the CSI-RS resource. For aperiodic CSI-RS resources, similarly, the value of N can also be determined by network configuration or predefined observation windows, as well as by UE reporting. The value of the interval M between two adjacent aperiodic CSI-RS resource transmission moments can be determined by predefinition or NW configuration. The value of the number K of compressed CSI moments within the prediction window can be determined by predefinition, NW side configuration, or UE reporting. The value of the interval D between adjacent compressed CSI moments within the prediction window can also be determined by one of the following: predefined, NW-side configuration, or UE-reported indication. If the CSI-RS resource is aperiodic, the value of D can be equal to M; if the CSI-RS resource is periodic or semi-persistent, the value of D can be equal to the period of the CSI-RS resource.

[0408] Optionally, in some embodiments, it is assumed that the UE-side CSI prediction AI model supports configurations of 16 and 32 antenna ports, 8 and 13 subbands, the number of CSI-RS transmission times within the observation window N = 4, 5, 8, or 12, and the number of CSI compression times within the prediction window K = 1, 3, or 4 future times. The UE can indicate these parameter values ​​supported by the UE-side CSI prediction AI model in its capability reporting. To indicate these parameter values, a set of parameters supported by the UE can be defined, such as the set of antenna port numbers P ∈ {16, 32}, the set of subband numbers S ∈ {8, 13, 19}, the number of CSI-RS transmission times within the observation window N ∈ {4, 5, 8, 12}, and the number of CSI compression times K ∈ {1, 3, 4}. The UE indicates these supported parameter combinations in its capability reporting, and the NW configures appropriate parameters based on these reported parameter combination values. Optionally,

[0409] Optionally, in some embodiments, the value ranges of these parameters can be defined. For example, P∈[2, 64], S∈[4, 19], N∈[4, 12], and K∈[1, 6], where [a, b] represents any integer value between a and b, including a and b themselves. In this case, the UE can indicate the minimum and maximum values ​​of each parameter in the capability reporting. Optionally, all combinations of the supported parameters can be given through predefinition. Taking P and S as examples, P∈{2, 4, 8, 12, 16, 24, 32} and S∈{8, 13, 19} are predefined. There are 21 combinations of these two parameters, so the UE can... Indicates a combination. Or, suppose the CSI predicts the AI ​​model supports two values ​​in P, two values ​​in S, and the UE through... and These indicate the number of supported antenna ports and the number of sub-bands, respectively.

[0410] Optionally, in some embodiments, the above-mentioned values ​​are the parameter values ​​supported by the reporting indication of the UE capability. Optionally, the UE reports the supported parameter values ​​based on the indication signaling from the NW side or by actively triggering the UE. The NW side can indicate the value through one or more signaling methods such as RRC, MAC-CE, and DCI, or the UE can report the indication through event triggering. The indication method is the same as described above, indicating the parameter values ​​supported by the AI ​​model predicted by CSI in the form of a single value or a range of values.

[0411] The scalability indication method provided in this disclosure can configure appropriate parameters to improve the AI-based CSI prediction performance on the UE side.

[0412] This disclosure also provides embodiments of an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., a RAN) in any of the above methods.

[0413] 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.

[0414] 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).

[0415] Figure 6 is a schematic diagram of the structure of a communication device proposed in an embodiment of this disclosure. As shown in Figure 6, the communication device 6100 may include at least one of a transceiver module 6101, a processing module 6102, etc.

[0416] In some embodiments, the communication device 6100 is a terminal, wherein...

[0417] The transceiver module 6101 is used to send first information to the network device, wherein the first information is used to indicate the parameters supported by the artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0418] Optionally, in some embodiments of this disclosure, the parameters include at least one of the following:

[0419] Number of sub-bands;

[0420] Number of antenna ports;

[0421] Number of measurement times, where the measurement times are used to measure CSI;

[0422] Number of prediction times, where prediction times are used to predict CSI.

[0423] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0424] The first possible value for the number of sub-bands;

[0425] Second value information regarding the number of antenna ports;

[0426] The third value information for the number of measurement times;

[0427] The fourth value information for the number of predicted time points.

[0428] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0429] The first indicator field is used to determine the first value information of the number of sub-bands;

[0430] The second indication field is used to determine the second value information of the number of antenna ports;

[0431] The third indication field, wherein the third indication field is used to determine the third value information for the number of measurement times;

[0432] The fourth indicator field is used to determine the fourth value information for the number of prediction times.

[0433] Optionally, in some embodiments of this disclosure, the first information includes:

[0434] The fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following:

[0435] The first possible value for the number of sub-bands;

[0436] Second value information regarding the number of antenna ports;

[0437] The third value information for the number of measurement times;

[0438] The fourth value information for the number of predicted time points.

[0439] Optionally, in some embodiments of this disclosure, wherein,

[0440] The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or,

[0441] The second value information belongs to at least one second candidate value information related to the number of antenna ports; and / or,

[0442] The third value information belongs to at least one third candidate value information of the number of measurement times; and / or,

[0443] The fourth value information belongs to at least one fourth candidate value information of the number of prediction time points.

[0444] Optionally, in some embodiments of this disclosure, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

[0445] Optionally, in some embodiments of this disclosure, the first value information includes at least one of the following:

[0446] The first possible value for the number of sub-bands;

[0447] The first set of values ​​includes: multiple first values;

[0448] The first value range, where the first value belongs to the first value range.

[0449] Optionally, in some embodiments of this disclosure, the second value information includes at least one of the following:

[0450] The second possible value for the number of antenna ports;

[0451] The second set of values ​​includes: multiple second values;

[0452] The second value range, where the second value belongs to the second value range.

[0453] Optionally, in some embodiments of this disclosure, the third value information includes at least one of the following:

[0454] The third possible value for the number of measurement moments;

[0455] The third set of values ​​includes multiple third values.

[0456] The third value range, where the third value belongs to the third value range.

[0457] Optionally, in some embodiments of this disclosure, the fourth value information includes at least one of the following:

[0458] The fourth possible value for the number of predicted time points;

[0459] The fourth set of values, wherein the fourth set of values ​​includes: multiple fourth values;

[0460] The fourth value range, where the fourth value belongs to the fourth value range.

[0461] Optionally, in some embodiments of this disclosure, wherein,

[0462] Processing module 6102 is used to determine that the first event has occurred;

[0463] The transceiver module 6101 is used to receive second information sent by the network device, wherein the second information is used to instruct the terminal to report the first information;

[0464] The transceiver module 6101 is used to receive third information sent by the network device, wherein the third information is used to query the terminal's capabilities.

[0465] Optionally, in some embodiments of this disclosure, wherein,

[0466] The processing module 6102 is used to determine the fourth information, wherein the fourth information is used to determine the parameters used by the AI ​​model, and to predict the CSI based on the fourth information and the AI ​​model.

[0467] Optionally, in some embodiments of this disclosure, the processing module 6102 is configured to perform at least one of the following:

[0468] The protocol predefines the fourth piece of information;

[0469] Determine the fourth information based on the first information;

[0470] Receive the fourth message sent by the network device.

[0471] Optionally, the transceiver module described above is used to perform at least one of the communication steps such as sending and / or receiving performed by the terminal in any of the above methods, which will not be elaborated here.

[0472] Optionally, the above processing module is used to perform at least one of the other steps executed by the terminal in any of the above methods, which will not be elaborated here.

[0473] In some embodiments, the communication device 6100 is a network device, wherein...

[0474] The transceiver module 6101 is used to receive first information sent by the terminal, wherein the first information is used to indicate the parameters supported by the artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

[0475] Optionally, in some embodiments of this disclosure, the parameters include at least one of the following:

[0476] Number of sub-bands;

[0477] Number of antenna ports;

[0478] Number of measurement times, where the measurement times are used to measure CSI;

[0479] Number of prediction times, where prediction times are used to predict CSI.

[0480] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0481] The first possible value for the number of sub-bands;

[0482] Second value information regarding the number of antenna ports;

[0483] The third value information for the number of measurement times;

[0484] The fourth value information for the number of predicted time points.

[0485] Optionally, in some embodiments of this disclosure, the first information includes at least one of the following:

[0486] The first indicator field is used to determine the first value information of the number of sub-bands;

[0487] The second indication field is used to determine the second value information of the number of antenna ports;

[0488] The third indication field, wherein the third indication field is used to determine the third value information for the number of measurement times;

[0489] The fourth indicator field is used to determine the fourth value information for the number of prediction times.

[0490] Optionally, in some embodiments of this disclosure, the first information includes:

[0491] The fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following:

[0492] The first possible value for the number of sub-bands;

[0493] Second value information regarding the number of antenna ports;

[0494] The third value information for the number of measurement times;

[0495] The fourth value information for the number of predicted time points.

[0496] Optionally, in some embodiments of this disclosure, wherein,

[0497] The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or,

[0498] The second value information belongs to at least one second candidate value information related to the number of antenna ports; and / or,

[0499] The third value information belongs to at least one third candidate value information of the number of measurement times; and / or,

[0500] The fourth value information belongs to at least one fourth candidate value information of the number of prediction time points.

[0501] Optionally, in some embodiments of this disclosure, at least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

[0502] Optionally, in some embodiments of this disclosure, the first value information includes at least one of the following:

[0503] The first possible value for the number of sub-bands;

[0504] The first set of values ​​includes: multiple first values;

[0505] The first value range, where the first value belongs to the first value range.

[0506] Optionally, in some embodiments of this disclosure, the second value information includes at least one of the following:

[0507] The second possible value for the number of antenna ports;

[0508] The second set of values ​​includes: multiple second values;

[0509] The second value range, where the second value belongs to the second value range.

[0510] Optionally, in some embodiments of this disclosure, the third value information includes at least one of the following:

[0511] The third possible value for the number of measurement moments;

[0512] The third set of values ​​includes multiple third values.

[0513] The third value range, where the third value belongs to the third value range.

[0514] Optionally, in some embodiments of this disclosure, the fourth value information includes at least one of the following:

[0515] The fourth possible value for the number of predicted time points;

[0516] The fourth set of values, wherein the fourth set of values ​​includes: multiple fourth values;

[0517] The fourth value range, where the fourth value belongs to the fourth value range.

[0518] Optionally, in some embodiments of this disclosure, the transceiver module 6101 is configured to perform at least one of the following:

[0519] Send a second message to the terminal, wherein the second message is used to instruct the terminal to report the first message;

[0520] Send a third piece of information to the terminal, which is used to query the terminal's capabilities.

[0521] Optionally, in some embodiments of this disclosure, the processing module 6102 is used to determine fourth information, wherein the fourth information is used to determine information about the parameters used by the AI ​​model;

[0522] The transceiver module 6101 is used to send fourth information to the terminal.

[0523] Optionally, in some embodiments of this disclosure, the processing module 6102 is configured to perform at least one of the following:

[0524] The protocol predefines the fourth piece of information;

[0525] The fourth information is determined based on the first information.

[0526] Optionally, the transceiver module described above is used to perform at least one of the communication steps such as sending and / or receiving performed by the network device in any of the above methods, which will not be elaborated here.

[0527] Optionally, the above processing module is used to perform at least one of the other steps performed by the network device in any of the above methods, which will not be elaborated here.

[0528] Figure 7A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure. The communication device 7100 can be the terminal described above, or it can be the network device described above. The communication device 7100 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 7100 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.

[0529] As shown in Figure 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can 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. The communication device 7100 is used to execute any of the above methods.

[0530] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may also be located outside the communication device 7100.

[0531] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceivers 7103 perform at least one of the communication steps such as sending and / or receiving in the above method, and the processor 7101 performs other steps.

[0532] In some embodiments, a transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0533] In some embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuit 7104 is connected to the memory 7102, and the interface circuit 7104 can be used to receive signals from the memory 7102 or other devices, and can be used to send signals to the memory 7102 or other devices. For example, the interface circuit 7104 can read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0534] The communication device 7100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 7100 described in this disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A. 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 and programs; (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.

[0535] Figure 7B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. For cases where the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the chip 7200 shown in Figure 7B, but it is not limited thereto.

[0536] Chip 7200 includes one or more processors 7201, which are used to perform any of the above methods.

[0537] In some embodiments, chip 7200 further includes one or more interface circuits 7202. Optionally, the interface circuit 7202 is connected to memory 7203, and the interface circuit 7202 can be used to receive signals from memory 7203 or other devices, and the interface circuit 7202 can be used to send signals to memory 7203 or other devices. For example, the interface circuit 7202 can read instructions stored in memory 7203 and send the instructions to processor 7201.

[0538] In some embodiments, the interface circuit 7202 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processor 7201 performs at least one of the other steps.

[0539] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0540] In some embodiments, chip 7200 further includes one or more memories 7203 for storing instructions. Optionally, all or part of the memories 7203 may be located outside of chip 7200.

[0541] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 7100, cause the communication device 7100 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.

[0542] This disclosure also provides a program product that, when executed by the communication device 7100, causes the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0543] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0544] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program can be transferred from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0545] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0546] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0547] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A communication method, characterized in that, The method is executed by a terminal, and the method includes: Send first information to network devices, wherein the first information is used to indicate information on parameters supported by an artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

2. The method as described in claim 1, characterized in that, The parameter includes at least one of the following: Number of sub-bands; Number of antenna ports; Number of measurement moments, wherein the measurement moments are used to measure CSI; Number of prediction times, wherein the prediction times are used to predict CSI.

3. The method as described in claim 2, characterized in that, The first information includes at least one of the following: The first value information of the number of sub-bands; The second value information regarding the number of antenna ports; The third value information of the number of measurement times; The fourth value information for the number of predicted time points.

4. The method as described in claim 2, characterized in that, The first information includes at least one of the following: A first indication field, wherein the first indication field is used to determine a first value of the number of sub-bands; The second indication field is used to determine a second value of the number of antenna ports; The third indication field is used to determine the third value information for the number of measurement times; The fourth indication field is used to determine the fourth value information for the number of predicted times.

5. The method as described in claim 2, characterized in that, The first information includes: A fifth indication field, wherein the fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following: The first value information of the number of sub-bands; The second value information regarding the number of antenna ports; The third value information of the number of measurement times; The fourth value information for the number of predicted time points.

6. The method according to any one of claims 3-5, characterized in that, in, The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or, The second value information belongs to at least one second candidate value information of the number of antenna ports; and / or, The third value information belongs to at least one third candidate value information belonging to the number of measurement times; and / or, The fourth value information belongs to at least one fourth candidate value information of the number of predicted time points.

7. The method as described in claim 6, characterized in that, At least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

8. The method according to any one of claims 3-7, characterized in that, The first value information includes at least one of the following: The first value of the number of sub-bands; A first set of values, wherein the first set of values ​​includes: a plurality of the first values; A first value range, wherein the first value belongs to the first value range.

9. The method according to any one of claims 3-8, characterized in that, The second value information includes at least one of the following: A second value for the number of antenna ports; The second set of values ​​includes: a plurality of the second values; The second value range, wherein the second value belongs to the second value range.

10. The method according to any one of claims 3-9, characterized in that, The third value information includes at least one of the following: A third value for the number of measurement moments; A third set of values, wherein the third set of values ​​includes: a plurality of the third values; The third value range, wherein the third value belongs to the third value range.

11. The method according to any one of claims 3-10, characterized in that, The fourth value information includes at least one of the following: The fourth value for the number of predicted time points; A fourth set of values, wherein the fourth set of values ​​includes: a plurality of the fourth values; The fourth value range, wherein the fourth value belongs to the fourth value range.

12. The method according to any one of claims 1-11, characterized in that, The method further includes at least one of the following: The first event has been confirmed; Receive second information sent by the network device, wherein the second information is used to instruct the terminal to report the first information; The third information sent by the network device is received, wherein the third information is used to query the terminal's capabilities.

13. The method according to any one of claims 1-12, characterized in that, The method further includes: Determine the fourth information, wherein the fourth information is used to determine the parameters used by the AI ​​model; The CSI is predicted based on the fourth information and the AI ​​model.

14. The method as described in claim 13, characterized in that, The determination of the fourth information includes at least one of the following: The protocol predefines the fourth piece of information; The fourth information is determined based on the first information; Receive the fourth information sent by the network device.

15. A communication method, characterized in that, The method is performed by a network device, and the method includes: The receiving terminal sends first information, wherein the first information is used to indicate information on parameters supported by the artificial intelligence (AI) model, and the AI ​​model is used to predict channel state information (CSI).

16. The method as described in claim 15, characterized in that, The parameter includes at least one of the following: Number of sub-bands; Number of antenna ports; Number of measurement moments, wherein the measurement moments are used to measure CSI; Number of prediction times, wherein the prediction times are used to predict CSI.

17. The method as described in claim 16, characterized in that, The first information includes at least one of the following: The first value information of the number of sub-bands; The second value information regarding the number of antenna ports; The third value information of the number of measurement times; The fourth value information for the number of predicted time points.

18. The method as described in claim 16, characterized in that, The first information includes at least one of the following: A first indication field, wherein the first indication field is used to determine a first value of the number of sub-bands; The second indication field is used to determine a second value of the number of antenna ports; The third indication field is used to determine the third value information for the number of measurement times; The fourth indication field is used to determine the fourth value information for the number of predicted times.

19. The method as described in claim 16, characterized in that, The first information includes: A fifth indication field, wherein the fifth indication field is used to determine a combination of information for at least two parameters, the information of which includes at least one of the following: The first value information of the number of sub-bands; The second value information regarding the number of antenna ports; The third value information of the number of measurement times; The fourth value information for the number of predicted time points.

20. The method according to any one of claims 17-19, characterized in that, in, The first value information belongs to at least one first candidate value information of the number of sub-bands; and / or, The second value information belongs to at least one second candidate value information of the number of antenna ports; and / or, The third value information belongs to at least one third candidate value information belonging to the number of measurement times; and / or, The fourth value information belongs to at least one fourth candidate value information of the number of predicted time points.

21. The method as described in claim 20, characterized in that, At least one of the first candidate value information, the second candidate value information, the third candidate value information, and the fourth candidate value information is predefined by the protocol.

22. The method according to any one of claims 17-21, characterized in that, The first value information includes at least one of the following: The first value of the number of sub-bands; A first set of values, wherein the first set of values ​​includes: a plurality of the first values; A first value range, wherein the first value belongs to the first value range.

23. The method according to any one of claims 17-22, characterized in that, The second value information includes at least one of the following: A second value for the number of antenna ports; The second set of values ​​includes: a plurality of the second values; The second value range, wherein the second value belongs to the second value range.

24. The method according to any one of claims 17-23, characterized in that, The third value information includes at least one of the following: A third value for the number of measurement moments; A third set of values, wherein the third set of values ​​includes: a plurality of the third values; The third value range, wherein the third value belongs to the third value range.

25. The method according to any one of claims 17-24, characterized in that, The fourth value information includes at least one of the following: The fourth value for the number of predicted time points; A fourth set of values, wherein the fourth set of values ​​includes: a plurality of the fourth values; The fourth value range, wherein the fourth value belongs to the fourth value range.

26. The method according to any one of claims 15-25, characterized in that, The method further includes at least one of the following: Send a second message to the terminal, wherein the second message is used to instruct the terminal to report the first message; Send third information to the terminal, wherein the third information is used to query the terminal's capabilities.

27. The method according to any one of claims 15-26, characterized in that, The method further includes: Determine the fourth information, wherein the fourth information is used to determine the parameters used by the AI ​​model; The fourth information is sent to the terminal.

28. The method as described in claim 27, characterized in that, The determination of the fourth information includes at least one of the following: The protocol predefines the fourth piece of information; The fourth information is determined based on the first information.

29. A communication device, characterized in that, The communication device is used to perform the method as described in any one of claims 1-14, 15-28.

30. A communication system, characterized in that, The device includes a terminal and a network device, wherein the terminal is used to perform the method as described in any one of claims 1-14, and the network device is used to perform the method as described in any one of claims 15-28.

31. 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 1-28.

32. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-28.

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