Communication method and related device

Abstract

The present application provides a communication method and a related device. The method comprises: transmitting first information, wherein the first information is used for determining a first moment, and the first moment is the moment when a first report is transmitted or the moment when a first inference result is acquired; receiving second information, wherein the second information is used for determining a second moment, and the second moment is equal to or later than the first moment; and transmitting a second report at the second moment, wherein the second report is used for indicating a first monitoring result, and the first monitoring result is used for determining whether the first inference result is trustworthy or the trustworthiness of the first monitoring result. In the method, because the moment when the terminal device transmits a monitoring result is not earlier than the moment when the first inference result is acquired or the moment when the first report is transmitted, the terminal device can acquire a correct monitoring result on the basis of a correct first inference result, so as to report the correct monitoring result to a network device, thereby monitoring a first AI model.

Description

A communication method and related devices

[0001] This application claims priority to the Chinese patent application No. 202411392853.4, filed on September 30, 2024, with the State Intellectual Property Office of China, with the title of “A communication method and related devices”, the entire content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of communications, and more particularly, to a communication method, a communication device, a computer readable storage medium, a chip and a computer program product. BACKGROUND

[0003] In an artificial intelligence (AI) based beam management scenario, to save measurement overhead, a terminal device performs measurement according to each reference signal resource in a reference signal resource set B, and obtains a measurement value of channel state information corresponding to each reference signal resource in the reference signal resource set B. The terminal device utilizes the measurement value of channel state information corresponding to each reference signal resource in the reference signal resource set B and an AI model to predict a predicted value of channel state information corresponding to each reference signal resource in a reference signal resource set A, and reports one or more reference signal resources in the reference signal resource set A corresponding to a larger predicted value of channel state information. Alternatively, the terminal device utilizes the measurement value of channel state information corresponding to each reference signal resource in the reference signal resource set B and an AI model to predict a probability that each reference signal resource in the reference signal resource set A is the best reference signal resource in the reference signal resource set A, and thus reports identification information of one or more reference signal resources in the reference signal resource set A corresponding to a larger probability to a network device. The measurement value of channel state information corresponding to the best reference signal resource in the reference signal resource set A is greater than or equal to the measurement value of channel state information corresponding to other reference signal resources in the reference signal resource set A except the best reference signal resource. That is, the best reference signal resource in the reference signal resource set A is the reference signal resource in the reference signal resource set A corresponding to the best signal transmission effect. The measurement value can include one or more of a measured reference signal received power (RSRP), a signal to interference plus noise ratio (SINR) or other possible estimated values representing channel state. Each reference signal resource in the reference signal resource set is used to carry a reference signal, which includes a synchronizing signal block (SSB) and / or a channel state information reference signal (CSI-RS), etc. The reference signal resource is, for example, a beam. The network device determines a resource for communication with the terminal device according to the received one or more reference signal resources. Since the one or more reference signal resources are determined based on the AI model, the AI model needs to be monitored, so as to facilitate determination of a suitable communication resource.

[0004] However, in the current scheme, the monitoring result may have a problem of low accuracy, and how to report the correct monitoring result of the AI model to monitor the AI model becomes a problem to be solved. SUMMARY

[0005] The application provides a communication method, a communication device, a computer readable storage medium, a chip and a computer program product to improve the accuracy of monitoring results, so as to monitor the AI model.

[0006] In a first aspect, a communication method is provided. The method includes: sending first information, the first information being used for determining a first time, the first time being a sending time of a first report or a time of obtaining a first inference result, the first report being used for indicating the first inference result, the first inference result including a predicted value of channel state information corresponding to at least one reference signal resource in a first reference signal resource set (set A) and / or identification information of the at least one reference signal resource; receiving second information, the second information being used for determining a second time, the second time being equal to or later than the first time; at the second time, sending a second report, the second report being used for indicating a first monitoring result, the first monitoring result being used for determining whether the first inference result is reliable or a reliability of the first inference result.

[0007] It can be understood that the channel state information corresponding to the reference signal resource in each aspect of the application can be replaced by RSRP corresponding to the reference signal resource, RSRQ corresponding to the reference signal resource, SINR corresponding to the reference signal resource, received power corresponding to the reference signal resource, or received quality corresponding to the reference signal resource.

[0008] In some implementations, the first inference result is obtained by a device deployed with the first AI model using the first AI model. The device deployed with the first AI model is a terminal-side device, such as the terminal device itself or a device or server connected to the terminal device.

[0009] For example, the first inference result is obtained after inputting the first input data into the first AI model. The first input data is determined based on a measured value of channel state information corresponding to each reference signal resource in a second reference signal resource set (set B).

[0010] In some implementations, the first monitoring result is a monitoring result of the first AI model. For example, the first monitoring result is determined based on a measured value obtained by actual measurement and the first inference result obtained by using the first AI model.

[0011] In the embodiments of the present application, the terminal device reports to the network device the time of obtaining the first inference result or the time of sending the first report, so as to determine the time of sending the first monitoring result according to the indication of the network device. Since the time of sending the monitoring result by the terminal device is not earlier than the time of obtaining the first inference result or the time of sending the first report, the terminal device can obtain the correct monitoring result according to the correct first inference result, and thus report the correct monitoring result to the network device, so as to monitor the first AI model.

[0012] In combination with the first aspect, in some implementations, in the case that the first time is the time of sending the first report, the first information includes the first time; or in the case that the first time is the time of obtaining the first inference result, the first information includes a first time length, the first time length including a time length required for obtaining the first inference result.

[0013] In some embodiments, the first time is the time of obtaining the first inference result by using the first AI model. The first time length includes a time length required for obtaining the first inference result by using the first AI model.

[0014] In the embodiments of the present application, in the case that the terminal device reports the first report to the network device, the terminal device directly reports to the network device the time of sending the first report. In the case that the terminal device does not report the first report to the network device, the terminal device directly reports to the network device the first time length.

[0015] In combination with the first aspect, in some implementations, the first indication information is received, the first indication information being used to indicate the time of sending the first report or a sending period of the first report; or the second indication information is received, the second indication information being used to indicate the first time length; or the first time and / or the first time length are determined according to a second reference signal resource set (set B), a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set (set B) being used to obtain the first inference result.

[0016] In the embodiments of the present application, the terminal device determines the sending time or sending period of the first report according to the indication of the network device. Alternatively, the terminal device determines the first time length according to radio resource control (RRC) high-layer configuration signaling. Alternatively, the terminal device determines the first time length according to the indication of the device deploying the AI model. Alternatively, the terminal device determines the first time length and / or the first time according to the computing capability of the device deploying the AI model, the data amount of the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and the inference capability of the first AI model. Alternatively, the terminal device determines the first time length and / or the first time according to the computing capability of the device used to determine the first inference result, the data amount of the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and the inference process corresponding to the first inference result.

[0017] In combination with the first aspect, in some implementations, in the case that the first time is the time for obtaining the first inference result, the method further includes: determining the first time according to a third time and the first time length. The third time is the receiving time of the first configuration information, and the first configuration information is used to configure the second reference signal resource set (set B). Alternatively, the third time is the starting time of the inference for obtaining the first inference result. The measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set (set B) is used to obtain the first inference result. Alternatively, the third time is preconfigured information.

[0018] In some embodiments, the third time is the starting time of the inference using the first AI model to obtain the first inference result.

[0019] In the embodiments of the present application, in the case that the terminal device does not report the first report to the network device, the terminal device can also determine the time for obtaining the first inference result according to the time of receiving the first configuration information and the first time length, or according to the time of starting the inference and the first time length.

[0020] In combination with the first aspect, in some implementations, the second information includes a second time.

[0021] In the embodiments of the present application, the network device directly indicates the sending time (i.e., the second time) of the second report to the terminal device, so that the terminal device sends the second report to the network device at the second time, thereby ensuring that the sending time of the second report is not earlier than the sending time of the first report or the time for obtaining the first inference result.

[0022] In some implementations, in a case where the prediction of the channel state information corresponding to each reference signal resource in the first set of reference signal resources (set A) is determined, the first inference result includes the prediction of the channel state information corresponding to at least one reference signal resource in the first set of reference signal resources (set A) and / or identification information of the at least one reference signal resource, and the first monitoring result is determined based on a measurement value of the channel state information corresponding to at least one first reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A) and the prediction of the channel state information corresponding to at least one second reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A).

[0023] In some embodiments, the at least one first reference signal resource and the at least one second reference signal resource are the same. Alternatively, at least one of the at least one first reference signal resource and the at least one second reference signal resource is different.

[0024] In some embodiments, the prediction of the channel state information corresponding to the at least one reference signal resource included in the first inference result is greater than or equal to the prediction of the channel state information corresponding to the reference signal resources other than the at least one reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A).

[0025] In the embodiments of the present application, in a case where the prediction of the channel state information corresponding to at least one reference signal resource in the set of prediction reference signal resources is determined, the terminal device can ensure that the time of sending the monitoring result is not earlier than the time of obtaining the inference result or the time of sending the first report, so as to report the monitoring result to the network device according to the correct inference result, so as to monitor the first AI model.

[0026] In some implementations, in a case where the probability that each reference signal resource in the first set of reference signal resources (set A) is the best reference signal resource in the first set of reference signal resources (set A) is determined, the first inference result includes identification information of at least one predicted best reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A), and the first monitoring result is determined based on at least one measured best reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A) and at least one predicted best reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A).

[0027] The probability corresponding to the at least one predicted best reference signal resource in the full set of the first reference signal resource set (set A) is greater than or equal to the probability corresponding to the reference signal resource other than the at least one predicted best reference signal resource in the first reference signal resource set (set A). The probability corresponding to the at least one predicted best reference signal resource in the subset (set M) of the first reference signal resource set (set A) is greater than or equal to the probability corresponding to the reference signal resource other than the at least one predicted best reference signal resource in the subset (set M). The measurement value of the channel state information corresponding to the at least one measured best reference signal resource in the full set of the first reference signal resource set (set A) is greater than or equal to the measurement value of the channel state information corresponding to the reference signal resource other than the at least one measured best reference signal resource in the first reference signal resource set (set A). The measurement value of the channel state information corresponding to the at least one measured best reference signal resource in the subset (set M) of the first reference signal resource set (set A) is greater than or equal to the measurement value of the channel state information corresponding to the reference signal resource other than the at least one measured best reference signal resource in the subset (set M). The probability corresponding to the reference signal resource is the probability that the reference signal resource is the best reference signal resource in the first reference signal resource set (set A).

[0028] In some embodiments, the measurement value of the channel state information of the best reference signal resource in the first reference signal resource set (set A) is greater than or equal to the measurement value of the channel state information of the other reference signal resource in the first reference signal resource set (set A) other than the best reference signal resource. The measurement value of the channel state information of the best reference signal resource in the subset (set M) of the first reference signal resource set (set A) is greater than or equal to the measurement value of the channel state information of the other reference signal resource in the subset (set M) other than the best reference signal resource.

[0029] In the embodiments of the present application, in the case of predicting the probability that each reference signal resource in the first reference signal resource set is the best reference signal resource in the first reference signal resource set, the terminal device can ensure that the time of sending the monitoring result is not earlier than the time of obtaining the inference result or the time of sending the first report, so as to report the monitoring result to the network device according to the correct inference result, so as to monitor the first AI model.

[0030] In some implementations of the first aspect, a second reference signal resource set (set B) is configured, the second reference signal resource set including at least one reference signal resource; a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set is determined; and the first inference result is determined using the measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set.

[0031] In the embodiments of the present application, the terminal device performs measurement according to the configured second reference signal resource set, thereby obtaining the inference result according to the measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set.

[0032] In the second aspect, a communication method is provided. The method includes: receiving first information, the first information being used to determine a first time, the first time being a time of sending a first report or a time of obtaining a first inference result, the first report being used to indicate the first inference result, the first inference result including a predicted value of channel state information corresponding to at least one reference signal resource in a first reference signal resource set (set A) and / or identification information of the at least one reference signal resource; sending second information, the second information being used to determine a second time, the second time being equal to or later than the first time; and receiving a second report, the second report being sent at the second time, the second report being used to indicate a first monitoring result, the first monitoring result being used to determine whether the first inference result is reliable or a reliability of the first inference result.

[0033] In some implementations, the first inference result is obtained by a device deploying the first AI model using the first AI model. The device deploying the first AI model is a terminal-side device, such as the terminal device itself or a device or server connected to the terminal device.

[0034] For example, the first inference result is obtained after inputting first input data into the first AI model. The first input data is determined based on the measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set (set B).

[0035] In some implementations, the first monitoring result is a monitoring result of the first AI model. For example, the first monitoring result is determined based on an actual measurement value and the first inference result obtained using the first AI model.

[0036] In the embodiments of the application, the network device determines the time at which the terminal device obtains the first inference result or the time at which the terminal device sends the first report according to the information reported by the terminal device, and thus indicates to the terminal device the time at which the monitoring result is sent. Since the time at which the terminal device sends the monitoring result is not earlier than the time at which the terminal device obtains the first inference result or the time at which the terminal device sends the first report, the terminal device can obtain the correct monitoring result according to the correct first inference result, so that the network device obtains the correct monitoring result to monitor the first AI model.

[0037] In some implementations in combination with the second aspect, in the case where the first time is the time at which the first report is sent, the first information includes the first time; or in the case where the first time is the time at which the first inference result is obtained, the first information includes a first time length, the first time length including a time length required for obtaining the first inference result.

[0038] In some embodiments, the first time is the time at which the first inference result is obtained by using the first AI model. The first time length includes a time length required for obtaining the first inference result by using the first AI model.

[0039] In the embodiments of the application, in the case where the terminal device reports the first report to the network device, the network device obtains the sending time of the first report reported by the terminal device. In the case where the terminal device does not report the first report to the network device, the network device obtains the first time length reported by the terminal device.

[0040] In some implementations in combination with the second aspect, the first indication information is sent, and the first indication information is used to indicate the sending time of the first report or the sending period of the first report.

[0041] In the embodiments of the application, the network device indicates to the terminal device the sending time of the first report or the sending period of the first report, so as to facilitate the terminal device to determine the first time and report to the network device.

[0042] In some implementations in combination with the second aspect, in the case where the first time is the time at which the first inference result is obtained, the method further includes: determining the first time according to a third time and a first time length. The third time is a receiving time of first configuration information, and the first configuration information is used to configure a second reference signal resource set (set B). Or, the third time is a starting time of inference to obtain the first inference result. A measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set (set B) is used to obtain the first inference result. Or, the third time is preconfigured information.

[0043] In some embodiments, the third time is a starting time of inference by using the first AI model to obtain the first inference result.

[0044] In the embodiments of the present application, in the case that the terminal device does not report the first report to the network device, the network device determines the time at which the terminal device obtains the first inference result according to the first time length reported by the terminal device, thereby facilitating the determination of the second time and the sending to the terminal device.

[0045] In combination with the second aspect, in some implementations, the second information includes the second time.

[0046] In the embodiments of the present application, the network device directly indicates the sending time (i.e., the second time) of the second report to the terminal device, so that the terminal device sends the second report to the network device at the second time, thereby ensuring that the sending time of the second report is not earlier than the sending time of the first report or the time at which the first inference result is obtained.

[0047] In combination with the second aspect, in some implementations, in the case that the predicted values of the channel state information corresponding to each reference signal resource in the first reference signal resource set (set A) are determined, the first inference result includes the predicted values of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set (set A) and / or the identification information of the at least one reference signal resource, and the first monitoring result is determined based on the measured values of the channel state information corresponding to at least one first reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) and the predicted values of the channel state information corresponding to at least one second reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A).

[0048] In some embodiments, the at least one first reference signal resource and the at least one second reference signal resource are the same. Alternatively, at least one reference signal resource in the at least one first reference signal resource and the at least one second reference signal resource is different.

[0049] In some embodiments, the predicted value of the channel state information corresponding to the at least one reference signal resource included in the first inference result is greater than or equal to the predicted values of the channel state information corresponding to the reference signal resources other than the at least one reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A).

[0050] In the embodiments of the present application, in the case that the predicted values of the channel state information corresponding to at least one reference signal resource in the prediction reference signal resource set are predicted, the terminal device ensures that the time at which the monitoring result is sent is not earlier than the time at which the inference result is obtained or the time at which the first report is sent according to the indication of the network device, thereby enabling the network device to obtain correct monitoring results to monitor the first AI model.

[0051] In some implementations, in a case where it is determined that each reference signal resource in the first set of reference signal resources (set A) is the best reference signal resource in the first set of reference signal resources (set A) with a probability, the first inference result includes identification information of at least one predicted best reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A), and the first monitoring result is determined based on at least one measured best reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A) and the at least one predicted best reference signal resource in the full set or the subset (set M) of the first set of reference signal resources (set A).

[0052] In some implementations, the probability corresponding to the at least one predicted best reference signal resource in the full set of the first set of reference signal resources (set A) is greater than or equal to the probability corresponding to a reference signal resource other than the at least one predicted best reference signal resource in the first set of reference signal resources (set A). The probability corresponding to the at least one predicted best reference signal resource in the subset (set M) of the first set of reference signal resources (set A) is greater than or equal to the probability corresponding to a reference signal resource other than the at least one predicted best reference signal resource in the subset (set M). The measurement value of the channel state information corresponding to the at least one measured best reference signal resource in the full set of the first set of reference signal resources (set A) is greater than or equal to the measurement value of the channel state information corresponding to a reference signal resource other than the at least one measured best reference signal resource in the first set of reference signal resources (set A). The measurement value of the channel state information corresponding to the at least one measured best reference signal resource in the subset (set M) of the first set of reference signal resources (set A) is greater than or equal to the measurement value of the channel state information corresponding to a reference signal resource other than the at least one measured best reference signal resource in the subset (set M). The probability corresponding to a reference signal resource is the probability that the reference signal resource is the best reference signal resource in the first set of reference signal resources (set A).

[0053] In some embodiments, the measurement value of the channel state information of the best reference signal resource in the first set of reference signal resources (set A) is greater than or equal to the measurement value of the channel state information of another reference signal resource in the first set of reference signal resources (set A) other than the best reference signal resource. The measurement value of the channel state information of the best reference signal resource in the subset (set M) of the first set of reference signal resources (set A) is greater than or equal to the measurement value of the channel state information of another reference signal resource in the subset (set M) other than the best reference signal resource.

[0054] In the embodiments of the application, in the case of predicting that each reference signal resource in the first reference signal resource set is the best reference signal resource in the first reference signal resource set, the terminal device ensures that the time of sending the monitoring result is not earlier than the time of obtaining the inference result or the time of sending the first report according to the indication of the network device, so that the network device obtains the correct monitoring result, so as to monitor the first AI model.

[0055] In a third aspect, a communication apparatus is provided. The apparatus includes modules or units for implementing the first aspect or any possible implementation manner of the first aspect.

[0056] In a fourth aspect, a communication apparatus is provided. The apparatus includes modules or units for implementing the second aspect or any possible implementation manner of the second aspect.

[0057] In a fifth aspect, a communication apparatus is provided. The communication apparatus includes at least one processor and a communication interface, the communication interface is used for the communication apparatus to exchange information with other communication apparatuses, and when program instructions are executed in the at least one processor, the communication apparatus performs the method as described in the first aspect or any possible implementation manner of the first aspect.

[0058] In a sixth aspect, a communication apparatus is provided. The communication apparatus includes at least one processor and a communication interface, the communication interface is used for the communication apparatus to exchange information with other communication apparatuses, and when program instructions are executed in the at least one processor, the communication apparatus performs the method as described in the second aspect or any possible implementation manner of the second aspect.

[0059] In a seventh aspect, a communication system is provided. The communication system includes the communication apparatus as described in the third aspect and the communication apparatus as described in the fourth aspect, or includes the communication apparatus as described in the fifth aspect and the communication apparatus as described in the sixth aspect.

[0060] In an eighth aspect, a computer-readable storage medium is provided, which stores program codes for execution by a device, and when the program codes are executed, the method as described in any one of the above first aspect or second aspect or any possible implementation manner of any one of the aspects is performed.

[0061] In a ninth aspect, a chip is provided, which includes at least one processor, and when program instructions are executed in the at least one processor, the method as described in any one of the above first aspect or second aspect or any possible implementation manner of any one of the aspects is performed.

[0062] In a tenth aspect, a computer program product is provided, which comprises program instructions, which, when the computer program product is run on a communication device, cause the communication device to perform the method according to any of the first aspect or the second aspect or any possible implementation thereof. BRIEF DESCRIPTION OF DRAWINGS

[0063] Fig. 1 is a schematic structural diagram of a communication system according to an embodiment of the present application.

[0064] Fig. 2 is a schematic structural diagram of a communication system according to another embodiment of the present application.

[0065] Fig. 3 is a schematic structural diagram of a communication system according to another embodiment of the present application.

[0066] Fig. 4 is a schematic diagram of wide beam and narrow beam.

[0067] Fig. 5 is a schematic flow chart of a communication method according to an embodiment of the present application.

[0068] Fig. 6 is a schematic flow chart of a communication method according to another embodiment of the present application.

[0069] Fig. 7 is a schematic flow chart of a communication method according to another embodiment of the present application.

[0070] Fig. 8 is a schematic structural block diagram of a communication device according to an embodiment of the present application.

[0071] Fig. 9 is a schematic structural block diagram of a communication device according to another embodiment of the present application. DETAILED DESCRIPTION

[0072] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0073] Embodiments of the present application will present various aspects, embodiments or features around a system comprising a plurality of devices, components, modules, etc. It should be understood and appreciated that each system can comprise additional devices, components, modules, etc., and / or can not comprise all of the devices, components, modules, etc. discussed in conjunction with the accompanying drawings. In addition, combinations of the solutions can also be used.

[0074] In addition, in the embodiments of the present application, the words "example", "for example", etc. are used to mean serving as an example, instance or illustration. Any embodiment or design solution described as "example" in the embodiments of the present application should not be interpreted as being more preferred or having more advantages than other embodiments or design solutions. Rather, the word "example" is used to present the concept in a specific manner.

[0075] The service scenarios described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. It can be known by those skilled in the art that, with the evolution of technology and the appearance of new service scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0076] In this specification, the phrase "one embodiment" or "some embodiments" etc. means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Therefore, the phrases "in one embodiment", "in some embodiments", "in other some embodiments", "in yet some embodiments" etc. appearing in different places in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprise", "include", "have" and their conjugates mean "including but not limited to", unless otherwise specifically emphasized.

[0077] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can represent the following cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0078] The technical solutions provided in the embodiments of the present application can be applied to various communication systems, for example, a 5th generation (5G) or new radio (NR) system, a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a wireless local area network (WLAN) system, a satellite communication system, a future communication network, such as a fusion system of multiple systems, and the like. The technical solutions provided in the present application can also be applied to device to device (D2D) communication, vehicle-to-everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and an internet of things (IoT) communication system or other communication systems.

[0079] A device in the communication system in the embodiments of the present application can send a signal to another device or receive a signal from another device. The signal can include information, signaling, data, and the like. The device can also be replaced by an entity, a network entity, a communication device, a communication module, a node, a communication node, a network element, and the like. The device is taken as an example for description in the embodiments of the present application. For example, the communication system can include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. It can be understood that the terminal device in the embodiments of the present application can be replaced by a first device, and the network device can be replaced by a second device, both of which perform the corresponding communication method in the present disclosure.

[0080] The method provided in the embodiments of the present application can be executed by a terminal-side device or a network-side device. The terminal-side device can refer to a terminal device itself, a component (for example, a processor, a chip, or a chip system, etc.) in the terminal device, an AI entity serving the terminal device side, for example, a server, such as an over the top (OTT) server or a cloud server, or a logical module or software capable of realizing all or part of the functions of the terminal device. The network-side device can refer to a network device itself, a component (for example, a processor, a chip, or a chip system, etc.) in the network device, an AI entity serving the network device side, for example, a RAN intelligent controller (RIC), an operation administration and maintenance (OAM), or a server, such as an OTT server or a cloud server, or a logical module or software capable of realizing all or part of the functions of the network device. The communication between the terminal-side device and the network-side device can be realized through a communication link between the terminal device and the network device, or through a communication link between servers, or through other communication devices other than servers, or through a wired link. Hereinafter, the terminal device or the network device is taken as an example for description. It can be understood that the terminal device can be replaced by a terminal-side device, such as one or more combinations of the foregoing terminal-side devices, and the network device can be replaced by a network-side device, such as one or more combinations of the foregoing network-side devices.

[0081] In a wireless communication network, for example, in a mobile communication network, the services supported by the network are more and more diverse, and thus the needs to be met are more and more diverse. For example, the network needs to be able to support ultra-high rates, ultra-low latencies, and / or ultra-large connections. This feature makes network planning, network configuration, and / or resource scheduling more and more complex. In addition, as the functions of the network become more and more powerful, for example, the supported spectrum becomes higher and higher, high-order multiple input multiple output (MIMO) technology is supported, beamforming and / or beam management are supported, and / or new technologies such as new technologies are supported, network energy saving has become a hot research topic. These new needs, new scenarios, and new features bring unprecedented challenges to network planning, operation and maintenance, and efficient operation. In order to meet this challenge, artificial intelligence technology can be introduced into the wireless communication network, thereby realizing network intelligentization. In order to support AI technology in the wireless network, an AI node can also be introduced into the network.

[0082] FIG. 1 is a schematic diagram of a communication system applicable to a communication method according to an embodiment of the present application. As shown in FIG. 1, the communication system 100 can include at least one network device, such as the network device 110 shown in FIG. 1. The communication system 100 can also include at least one terminal device, such as the terminal device 120 and the terminal device 130 shown in FIG. 1. The network device 110 and the terminal devices (such as the terminal device 120 and the terminal device 130) can communicate with each other through wireless links. The communication devices in the communication system, such as the network device 110 and the terminal device 120, can communicate with each other through multi-antenna technology.

[0083] In some embodiments, the communication system 100 further includes an AI network element 140. The AI network element 140 is configured to perform AI-related operations, such as constructing a training data set or training an AI model.

[0084] In a possible implementation, the network device 110 can send data related to the training of the AI model to the AI network element 140, and the AI network element 140 can construct a training data set and train an AI model. For example, the data related to the training of the AI model can include data reported by the terminal device. The AI network element 140 can send the result of the AI model-related operation to the network device 110 and forward it to the terminal device through the network device 110. For example, the result of the AI model-related operation can include at least one of the following: a trained AI model, an evaluation result or a test result of the model, etc. For example, part of the trained AI model can be deployed on the network device 110, and the other part can be deployed on the terminal device. Alternatively, the trained AI model can be deployed on the network device 110. Or, the trained AI model can be deployed on the terminal device.

[0085] It should be understood that FIG. 1 only illustrates the case where the AI network element 140 is directly connected to the network device 110, and in other scenarios, the AI network element 140 can also be connected to the terminal device. Alternatively, the AI network element 140 can be connected to both the network device 110 and the terminal device. Alternatively, the AI network element 140 can also be connected to the network device 110 through a third-party network element. The embodiments of the present application do not limit the connection relationship between the AI network element and other network elements.

[0086] The AI network element 140 can also be configured as a module in the network device and / or the terminal device, such as the network device 110 or the terminal device shown in FIG. 1.

[0087] It should be noted that FIG. 1 is a simplified schematic diagram for ease of understanding, for example, the communication system can further include other devices, such as wireless relay devices and / or wireless backhaul devices, etc., which are not shown in FIG. 1. In actual application, the communication system can include multiple network devices, and can also include multiple terminal devices. Embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0088] In embodiments of the present application, the terminal device can also be referred to as a user equipment (UE), an access terminal, a user unit, a user station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent or a user apparatus.

[0089] The terminal device can be a device providing voice / data, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. At present, some examples of terminals are: a mobile phone, a tablet computer, a notebook computer, a palm computer, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a wearable device, a terminal device in a 5G network, or a terminal device in a future evolved public land mobile network (PLMN), etc. Embodiments of the present application do not limit this.

[0090] By way of example and not limitation, in embodiments of the present application, the terminal device can also be a wearable device. The wearable device can also be referred to as a wearable smart device, which is a general term for devices that are designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, clothing, and shoes. The wearable device is a portable device that is directly worn on the body or integrated into the clothes or accessories of the user. The wearable device is not only a hardware device, but also has strong functions through software support and data interaction and cloud interaction. The general wearable smart device includes a full function, a large size, and can realize complete or partial functions without relying on a smart phone, such as a smart watch or smart glasses, and focuses on a certain application function and needs to be used in cooperation with other devices, such as a smart phone, such as various smart wristbands and smart jewelry for monitoring vital signs.

[0091] In embodiments of the present application, the device for implementing the function of the terminal device can be a terminal device, or a device capable of supporting the terminal device to implement the function, such as a chip system, which can be installed in the terminal device or used in matching with the terminal device. In embodiments of the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices. In embodiments of the present application, only the device for implementing the function of the terminal device is taken as an example for description, and the present application is not limited to the scheme.

[0092] The network device in the embodiments of the present application can be a device for communicating with a terminal device, which can include an access network device (i.e., an access network node) or a radio access network device, such as a base station. The radio access network device in the embodiments of the present application can refer to a radio access network (RAN) node (or device) that accesses a terminal device to a wireless network. The base station can broadly cover various names in the following or be replaced by the following names, such as: Node B (NodeB), evolved Node B (eNB), next generation Node B (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), primary station, secondary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node, or the like, or a combination thereof. The base station can also refer to a communication module, modem, or chip for being disposed in the foregoing device or apparatus. The base station can also be a mobile switching center and a device assuming a base station function in D2D, V2X, M2M communication, a network side device in a future communication network, a device assuming a base station function in a future communication network, etc. The base station can support networks of the same or different access technologies. Alternatively, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU). The embodiments of the present application do not limit the specific technology and specific device form of the network device.

[0093] A base station can be fixed, or mobile. For example, a helicopter or unmanned aerial vehicle can be configured to function as a mobile base station, one or more cells can move according to the location of the mobile base station. In other examples, a helicopter or unmanned aerial vehicle can be configured to function as a device that communicates with another base station.

[0094] In some deployments, the network device mentioned in the embodiments of the present application can be a device including a CU, or a DU, or a device including a CU and a DU, or a control plane CU node (central unit-control plane (CU-CP)) and a user plane CU node (central unit-user plane (CU-UP)) and a DU node. For example, the network device can include a gNB-CU-CP, a gNB-CU-UP and a gNB-DU.

[0095] In some deployments, a plurality of RAN nodes cooperate to assist a terminal to implement wireless access, and different RAN nodes respectively implement part of the functions of a base station. For example, the RAN node can be a CU, a DU, a CU-CP, a CU-UP, or an RU, etc. The CU and the DU can be separately arranged, or can also be included in the same network element, for example, in a BBU. The RU can be included in a radio frequency device or a radio frequency unit, for example, included in an RRU, an AAU or an RRH.

[0096] The RAN node can support one or more types of fronthaul interfaces, respectively corresponding to DUs and RUs with different functionalities. If the fronthaul interface between the DU and the RU is common public radio interface (CPRI), the DU is configured to implement one or more of baseband functions, and the RU is configured to implement one or more of radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, which, compared with CPRI, moves one or more of partial baseband functions of the downlink and / or uplink, such as, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / add cyclic prefix (CP), from the DU to the RU for implementation, and for the uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / remove cyclic prefix (CP), from the DU to the RU for implementation. In a possible implementation, the interface can be enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the split between the DU and the RU is different, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0097] Taking eCPRI Cat A as an example, for downlink transmission, with layer mapping as the cut, the DU is configured to implement layer mapping and one or more functions (i.e., one or more of encoding, rate matching, scrambling, modulation, layer mapping) before layer mapping, while other functions (e.g., one or more of resource element (RE) mapping, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) after layer mapping are implemented in the RU. For uplink transmission, with de-RE mapping as the cut, the DU is configured to implement de-mapping and one or more functions (i.e., one or more of decoding, de-rate matching, de-scrambling, de-modulation, inverse discrete Fourier transform (IDFT), channel equalization, de-RE mapping) before de-mapping, while other functions (e.g., one or more of digital BF or fast Fourier transform (FFT) / CP removal) after de-mapping are implemented in the RU. It can be understood that the function description of the DU and the RU corresponding to various types of eCPRI can refer to the eCPRI protocol, which is not described here.

[0098] In a possible design, the processing unit in the BBU for implementing baseband functions is referred to as a base band high (BBH) unit, and the processing unit in the RRU / AAU / RRH for implementing baseband functions is referred to as a base band low (BBL) unit.

[0099] In different systems, the CU (or CU-CP and CU-UP), DU or RU can also have different names, but those skilled in the art can understand their meanings. For example, in an open radio access network (open RAN, ORAN / O-RAN) system, the CU can also be referred to as an O-CU (open CU), the DU can also be referred to as an O-DU, the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. Any of the CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0100] In the embodiments of the present application, the apparatus for implementing the function of the network device can be a network device, or can be an apparatus capable of supporting the network device to implement the function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. The apparatus can be installed in the network device or used in matching with the network device. In the embodiments of the present application, only the apparatus for implementing the function of the network device is taken as an example for description, and the scheme of the embodiments of the present application is not limited.

[0101] The network device and / or the terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on water surface; and can also be deployed on airplanes, balloons and satellites in the air. The scenario where the network device and the terminal device are located is not limited in the embodiments of the present application. In addition, the terminal device and the network device can be hardware devices, or can be software functions running on special hardware, software functions running on general hardware, such as virtualized functions instantiated on a platform (for example, a cloud platform), or entities including special or general hardware devices and software functions. The specific form of the terminal device and the network device is not limited in the present application.

[0102] Optionally, the AI node can be deployed in one or more of the following positions in the communication system: an access network device, a terminal device, or a core network device, and the like, or the AI node can also be deployed separately, for example, deployed in a position other than the above-mentioned any device, such as a host or a cloud server of an over the top (OTT) system. The AI node can communicate with other devices in the communication system, which can be one or more of the following: a network device, a terminal device, or a network element of a core network, and the like.

[0103] It can be understood that the number of AI nodes is not limited in the present application. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes responsible for different functions.

[0104] It can also be understood that the AI node can be an AI network element or an AI module. The AI node can be a device independent of each other, or can be integrated in the same device to implement different functions, or can be a network element in a hardware device, or can be a software function running on special hardware, or can be a virtualized function instantiated on a platform (for example, a cloud platform), and the specific form of the AI node is not limited in the present application.

[0105] FIG. 2 is a schematic diagram of a possible application framework in a communication system. As shown in FIG. 2, network elements in the communication system are connected through interfaces (e.g., NG, Xn) or air interfaces. One or more AI modules (only one is shown in FIG. 2 for clarity) are deployed in one or more of the network element nodes, such as a core network device, an access network node (RAN node), a terminal device, or one or more devices in operation administration and maintenance (OAM). The access network node can be a single RAN node or can include multiple RAN nodes, e.g., including a CU and a DU. The CU and / or the DU can also be provided with one or more AI modules. Optionally, the CU can be further split into a CU-CP and a CU-UP. One or more AI models are deployed in the CU-CP and / or the CU-UP. Exemplarily, the CU and the DU are connected through an Fl interface. The CUs are connected through an Xn interface.

[0106] The AI module is used to implement a corresponding AI function. The AI modules deployed in different network elements can be the same or different. The AI module can implement different functions according to different parameter configurations of the model of the AI module. The model of the AI module can be configured based on one or more of the following parameters: a structural parameter (e.g., at least one of a number of neural network layers, a width of a neural network, a connection relationship between layers, a weight of a neuron, an activation function of a neuron, or a bias in the activation function), an input parameter (e.g., a type of the input parameter and / or a dimension of the input parameter), or an output parameter (e.g., a type of the output parameter and / or a dimension of the output parameter). The bias in the activation function can also be referred to as a bias of the neural network.

[0107] One AI module can have one or more models. One model can infer an output including one parameter or multiple parameters. The learning process, the training process, or the inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0108] The network device can be a network device provided with one or more AI modules. The network device can be one or more of the core network device, the access network node (RAN node), or the OAM shown in FIG. 2. For example, the AI module can be the RIC shown in FIG. 3, such as a near-real-time RIC or a non-real-time RIC. For example, the near-real-time RIC is provided in the RAN node (for example, in the CU, the DU), and the non-real-time RIC is provided in the OAM, the cloud server, the core network device, or other network devices. The RIC can obtain a subset of data from multiple terminal devices from the RAN node (for example, the CU, the CU-CP, the CU-UP, the DU, and / or the RU), reorganize it into a training data set #2, and train based on the training data set #2. For example, the near-real-time RIC and the non-real-time RIC can be provided as a network element, respectively. The network device can be the near-real-time RIC or the non-real-time RIC.

[0109] FIG. 3 is a schematic diagram of a possible application framework in a communication system. As shown in FIG. 3, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI module shown in FIG. 2, which is used to implement AI-related functions. The RIC includes a near-real-time RIC (near-RT RIC) and a non-real-time RIC (Non-RT RIC). The non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to latency, which can be on the order of seconds. The near-RT RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, which can be on the order of tens of milliseconds.

[0110] The near-RT RIC is used for model training and inference. For example, it is used to train an AI model and perform inference using the AI model. The near-RT RIC can obtain network-side and / or terminal-side information from the RAN node (for example, the CU, the CU-CP, the CU-UP, the DU, and / or the RU) and / or the terminal. This information can be used as training data or inference data. Optionally, the near-RT RIC can deliver the inference result to the RAN node and / or the terminal. Optionally, the CU and the DU, and / or the DU and the RU can exchange inference results. For example, the near-RT RIC delivers the inference result to the DU, and the DU sends it to the RU.

[0111] The non-real-time RIC is also used for model training and inference. For example, the non-real-time RIC is used for training an AI model, and inference is performed using the model. The non-real-time RIC can obtain network-side and / or terminal-side information from the RAN node (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or the terminal. The information can be used as training data or inference data, and the inference result can be delivered to the RAN node and / or the terminal. Alternatively, the inference result can be exchanged between the CU and the DU, and / or between the DU and the RU, for example, the non-real-time RIC delivers the inference result to the DU, and the DU delivers the inference result to the RU.

[0112] The near-real-time RIC and the non-real-time RIC can also be separately provided as a network element, respectively. Alternatively, the near-real-time RIC and the non-real-time RIC can also be part of other devices, for example, the near-real-time RIC is provided in the RAN node (e.g., CU, DU), and the non-real-time RIC is provided in the OAM, the cloud server, the core network device, or other network devices.

[0113] Beam management refers to a process in which a terminal device and a network device periodically identify an optimal beam. The optimal beam can refer to a beam that maximizes reception or transmission energy. For example, the receiving end uses different reception beams to receive reference signals, and the optimal beam can include a beam in which the measurement value of the corresponding reference signal is the largest among multiple different reception beams. For another example, the transmitting end uses different transmission beams to transmit signals, and the optimal beam can include a beam in which the measurement value of the corresponding reference signal measured by the receiving end is the largest among multiple transmission beams when the reference signal transmitted by the transmission beam reaches the receiving end. The measurement value of the reference signal can be, for example, a measured reference signal received power (RSRP), a signal to interference plus noise ratio (SINR), or other possible estimated values.

[0114] A beam is a kind of communication resource. The embodiment of a beam in the NR protocol can be a spatial filter, or a spatial filter or spatial parameter. A beam used for transmitting a signal can be referred to as a transmission beam (Tx beam), or a spatial domain transmit filter or a spatial domain transmit parameter; a beam used for receiving a signal can be referred to as a reception beam (Rx beam), or a spatial domain receiver filter or a spatial domain receive parameter. A transmission beam can refer to the distribution of signal strength in different directions in space after a signal is transmitted by an antenna, and a reception beam can refer to the distribution of signal strength in different directions in space of a wireless signal received by an antenna. A beam can be identified by an identifier (ID) of the beam. For example, the ID of a beam can be a channel state information reference signal resource indicator (CSI-RS resource indicator, CRI), or the ID of a beam can be a bit corresponding to the beam in a bitmap. For example, the bitmap includes a number of bits equal to the number of all beams associated with the network device in an inference task of one beam management. A beam can be divided into a wide beam and a narrow beam. A wide beam refers to a beam in which the radiation range of a transmitting or receiving antenna is relatively large when transmitting or receiving a signal. A wide beam is usually used in application scenarios that require broadcasting signals to a larger area or a wider coverage range. A wide beam can provide a wider coverage area, but the signal strength is relatively weak. A narrow beam refers to a beam in which the radiation range of a transmitting or receiving antenna is relatively small. A narrow beam is usually used in application scenarios that require concentrating signals to a specific target or area. A narrow beam can provide higher signal strength and higher directivity, but the coverage range is relatively small.

[0115] The above measurement values can also be referred to as information characterizing the channel state, that is, the measurement values can be referred to as channel state information.

[0116] In this application, the channel state information can include one or more of rank indication (RI) information, channel quality indicator (CQI) information, precoding matrix indicator (PMI), or reference signal receiver power (RSRP), such as layer 1 reference signal receiver power (L1-RSRP), reference signal receiver quality (RSRQ), signal to interference plus noise ratio (SINR), and the like.

[0117] In this application, the reference signal resource can include a beam, or have a corresponding relationship with the beam. Further, the reference signal resource can also include a time domain resource and / or a frequency domain resource corresponding to the beam, such as a time-frequency resource. Wherein the beam can also be referred to as a spatial domain resource.

[0118] Optionally, the beam (beam) can also be replaced by the first signal, the downlink beam, the transmission beam, the transmitting beam, the fine beam, the narrow beam, the spatial filter, the spatial filter, the spatial parameter, the spatial transmission filter, the port, and the like.

[0119] In this application, the information used to indicate the beam used for transmission can be referred to as beam indication information. The beam indication information can be one or more of the following: beam number (or number, index, identity (ID), etc.), uplink signal resource number, downlink signal resource number, absolute index of the beam, relative index of the beam, logical index of the beam, index of the antenna port corresponding to the beam, antenna port group index corresponding to the beam, index of the downlink signal corresponding to the beam, time index of the downlink synchronization signal block corresponding to the beam, beam pair link (BPL) information, transmission parameter (Tx parameter) corresponding to the beam, reception parameter (Rx parameter) corresponding to the beam, transmission weight corresponding to the beam, weight matrix corresponding to the beam, weight vector corresponding to the beam, reception weight corresponding to the beam, index of the transmission weight corresponding to the beam, index of the weight matrix corresponding to the beam, index of the weight vector corresponding to the beam, index of the reception weight corresponding to the beam, reception codebook corresponding to the beam, transmission codebook corresponding to the beam, index of the reception codebook corresponding to the beam, index of the transmission codebook corresponding to the beam. The beam indication information can also be embodied as a transmission configuration index (TCI) or a TCI state. One TCI state includes one or more quasi co-location (QCL) information, and each QCL information includes the ID of one reference signal (or synchronization signal block) and one QCL type. For example: the terminal device can need to determine the beam for receiving the physical downlink shared channel (PDSCH) according to the TCI state indicated by the network device (usually carried by the physical downlink control channel (PDCCH)). In this application, the index information of the beam, i.e. the beam ID, is a typical example of beam indication information, and the index of the beam can be replaced by other beam indication information that can indicate the beam.

[0120] In this application, the prediction information (i.e. the predicted value) refers to the prediction result directly output by the AI model, or the result obtained after data processing of the prediction result directly output by the AI model. One prediction information refers to the prediction result directly output by the AI model in one prediction process, or refers to the result obtained after data processing of the prediction result. The AI model can be deployed on the terminal device, or on the OTT device on the terminal device side. When the AI model is deployed on the OTT device, the terminal device can receive the prediction result output by the AI model from the OTT device.

[0121] To achieve beam management, one possible solution is to reduce the overhead of beam sweeping by hierarchical sweeping or the like, i.e., first sweeping wide beams, and then sweeping a small part of narrow beams under the wide beams, so as to achieve the goal of reducing the overhead. The schematic diagram of wide beams and narrow beams is shown in FIG. 4. As shown in FIG. 4, compared with narrow beams, wide beams have wider beam angles and can transmit signals in a wider direction. Compared with wide beams, narrow beams have narrower beam angles and can transmit signals in a smaller direction. In the case that the sum of the beam angles of a set of wide beams is the same as the sum of the beam angles of a set of narrow beams, the number of beams included in the set of wide beams is less than the number of beams included in the set of narrow beams.

[0122] The selection of beams is mainly completed by reference signals and corresponding beam measurement. Specifically, the reference signals can include one or more of SSB or CSI-RS. SSB can be a cell broadcast signal, containing a primary synchronization signal (PSS), a secondary synchronization signal (SSS), a physical broadcast channel (PBCH), and a demodulation reference signal (DMRS). SSB is periodically transmitted according to cell configuration, and SSB signal can be considered as a wide beam signal. Correspondingly, the CSI-RS signal can be a user-level signal, and it can be understood that the CSI-RS signal is a narrow beam signal.

[0123] The AI technology can also be applied to beam sweeping, thereby reducing overhead. In AI model-based beam management, the AI model includes a regression model and a classification model. When the AI model is a regression model, the terminal device and / or the network device take, as input of the AI model, a measured value of channel state information corresponding to each reference signal resource in a reference signal resource set B (set B), and take, as output of the AI model, a predicted value of channel state information corresponding to each reference signal resource in a reference signal resource set A (set A). When the AI model is a classification model, the terminal device and / or the network device take, as input of the AI model, a measured value of channel state information corresponding to each reference signal resource in the reference signal resource set B (set B), and take, as output of the AI model, a probability that each reference signal resource in the reference signal resource set A (set A) is the best reference signal resource in the reference signal resource set A. The reference signal resource in the reference signal resource set A is a real resource (i.e., an actually configured resource) or a virtual resource (i.e., a resource not actually configured). The reference signal resource set B is a subset of the reference signal resource set A. Alternatively, each reference signal resource in the reference signal resource set B corresponds to a signal angle greater than a signal angle corresponding to each reference signal resource in the reference signal resource set A. For example, each reference signal resource in the reference signal resource set B is a wide beam, and each reference signal resource in the reference signal resource set A is a narrow beam. The channel state information corresponding to the reference signal resource is, for example, reference signal received power (RSRP) or signal to interference plus noise ratio (SINR), etc. When the AI model is a regression model, the terminal device reports, from the reference signal resource set A, one or more reference signal resources corresponding to a predicted value of channel state information greater than a predicted value of channel state information of other reference signal resources, for example, reports identification (ID) of the one or more reference signal resources and / or the predicted value of channel state information corresponding to the one or more reference signal resources. When the AI model is a classification model, the terminal device reports, to the network device, identification information of one or more reference signal resources in the reference signal resource set A corresponding to a probability greater than a probability of other reference signal resources.

[0124] In AI model-based beam management, a network device and / or a terminal device need to monitor the AI model to ensure the quality of beam management. When the AI model is a regression model, the network device and / or the terminal device measure according to a full set or a subset (set M) of a reference signal resource set A, determine a measurement value of channel state information corresponding to at least one reference signal resource in the full set or the subset (set M) of the reference signal resource set A, and then determine a monitoring result of the AI model according to the measurement value of channel state information corresponding to at least one first reference signal resource in the full set or the subset (set M) of the reference signal resource set A and a predicted value of channel state information corresponding to at least one second reference signal resource in the full set or the subset (set M) of the reference signal resource set A. The predicted value of channel state information corresponding to the at least one second reference signal resource is obtained by using the first AI model. The at least one first reference signal resource and the at least one second reference signal resource are the same, or at least one reference signal resource in the at least one first reference signal resource and the at least one second reference signal resource is different. When the AI model is a classification model, the network device and / or the terminal device determine a monitoring result of the AI model according to at least one reference signal resource in the full set or the subset (set M) of the reference signal resource set A with a larger measurement value of corresponding channel state information and one or more reference signal resources in the full set or the subset (set M) of the reference signal resource set A with a larger probability determined by using the AI model. The monitoring result of the AI model is used to determine whether an inference result obtained by using the AI model is credible or the credibility of the inference result.

[0125] When the AI model is deployed on a terminal-side device, the terminal device sends a monitoring result of the AI model to a network device at a preset time. The preset time is a preconfigured time or a time indicated by the network device, or the preset time is determined according to a preset period, and the preset period is a preconfigured period or a period indicated by the network device. However, before the preset time, if the AI model has not completed inference, that is, the terminal device has not obtained an inference result of the AI model, the monitoring result of the AI model reported at the preset time has a problem, that is, the monitoring result of the AI model sent at the preset time is an incorrect result. Therefore, in the embodiments of the present application, a communication method is provided to ensure that a correct monitoring result is reported to monitor the AI model.

[0126] The meaning of "time" in the embodiments of the present application is not limited, for example, the time can be understood as at least one of the following: time slot, subframe, frame, orthogonal frequency division multiplexing (OFDM) symbol, millisecond, second, minute, hour, etc. The sending time of the report or information can be understood as, for example: any time slot (such as the first or last time slot) of the time domain resource for sending the report or information, any subframe (such as the first or last subframe) of the time domain resource for sending the report or information, any frame (such as the first or last frame) of the time domain resource for sending the report or information, any OFDM symbol (such as the first or last OFDM symbol) of the time domain resource for sending the report or information, any millisecond (such as the first or last millisecond) within the time period for sending the report or information, any second (such as the first or last second) within the time period for sending the report or information, any minute (such as the first or last minute) within the time period for sending the report or information, etc. The receiving time of the report or information can be understood as, for example: any time slot (such as the first or last time slot) of the time domain resource for receiving the report or information, any subframe (such as the first or last subframe) of the time domain resource for receiving the report or information, any frame (such as the first or last frame) of the time domain resource for receiving the report or information, any OFDM symbol (such as the first or last OFDM symbol) of the time domain resource for receiving the report or information, any millisecond (such as the first or last millisecond) within the time period for receiving the report or information, any second (such as the first or last second) within the time period for receiving the report or information, any minute (such as the first or last minute) within the time period for receiving the report or information, etc. The time for obtaining the inference result can be understood as, for example: the last millisecond within the time period for obtaining the inference result, the last second within the time period for obtaining the inference result, the last minute within the time period for obtaining the inference result, etc. The start time of the inference for obtaining the inference result can be understood as, for example: the first millisecond of starting the inference, the first second of starting the inference, the first minute of starting the inference, etc. Wherein, the foregoing inference can be an inference performed by an AI model.

[0127] FIG. 5 is a schematic flowchart of a communication method provided by the embodiments of the present application. The method in FIG. 5 is applied to a communication system, for example, the communication system shown in FIG. 1, FIG. 2 or FIG. 3. The network device in FIG. 5 is, for example, the network device 110 in FIG. 1, the core network device in FIG. 2, the access network node in FIG. 3. The terminal device in FIG. 5 is, for example, the terminal device in FIG. 1, FIG. 2 or FIG. 3. The method in FIG. 5 includes the following steps.

[0128] 510, sending the first information to the network device.

[0129] The terminal device sends the first information to the network device. Correspondingly, the network device receives the first information from the terminal device. The first information is used for determining the first time. The first time is the time of obtaining the first inference result. Alternatively, the first time is the time of sending the first report, and the first report is used to indicate the first inference result. The first inference result includes the predicted value of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set (set A) and / or the identification information of the at least one reference signal resource. The first reference signal resource set includes at least one reference signal resource.

[0130] It can be understood that the channel state information corresponding to the reference signal resource in the present application can be replaced by the RSRP corresponding to the reference signal resource, the RSRQ corresponding to the reference signal resource, the SINR corresponding to the reference signal resource, the received power corresponding to the reference signal resource, or the received quality corresponding to the reference signal resource.

[0131] In some embodiments, the first inference result is obtained by a device deploying the first AI model using the first AI model. The device deploying the first AI model is a terminal-side device, such as the terminal device itself or a device or server connected to the terminal device.

[0132] In some embodiments, the first AI model is a regression model or a classification model.

[0133] When the first AI model is a regression model, the first AI model is used to determine the predicted value of the channel state information corresponding to each reference signal resource in the first reference signal resource set (set A) according to the measured value of the channel state information corresponding to each reference signal resource in the second reference signal resource set (set B). The first inference result includes the predicted value of the channel state information corresponding to part or all of the reference signal resources in the first reference signal resource set and / or the identification information of part or all of the reference signal resources. In the case where the first inference result includes the predicted value of the channel state information corresponding to part of the reference signal resources in the first reference signal resource set and / or the identification information of the part of the reference signal resources, the predicted value of the channel state information corresponding to the part of the reference signal resources is greater than or equal to the predicted value of the channel state information corresponding to the reference signal resources in the first reference signal resource set other than the part of the reference signal resources.

[0134] When the first AI model is a classification model, the first AI model is configured to determine, according to a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set (set B), a probability that each reference signal resource in the first reference signal resource set (set A) is the best reference signal resource in the first reference signal resource set. The first inference result includes identification information of at least one predicted best reference signal resource in the full set or the subset (set M) of the first reference signal resource set (set A). The probability corresponding to the at least one predicted best reference signal resource in the first reference signal resource set (set A) is greater than or equal to the probability corresponding to the reference signal resource in the first reference signal resource set except the at least one predicted best reference signal resource. The probability corresponding to the at least one predicted best reference signal resource in the subset (set M) of the first reference signal resource set (set A) is greater than or equal to the probability corresponding to the reference signal resource in the subset (set M) except the at least one predicted best reference signal resource. The probability corresponding to the reference signal resource is the probability that the reference signal resource is the best reference signal resource in the first reference signal resource set.

[0135] In some embodiments, the measurement value of channel state information of the best reference signal resource in the first reference signal resource set is greater than or equal to the measurement value of channel state information of the other reference signal resource in the first reference signal resource set except the best reference signal resource. The measurement value of channel state information of the best reference signal resource in the subset (set M) of the first reference signal resource set is greater than or equal to the measurement value of channel state information of the other reference signal resource in the subset (set M) except the best reference signal resource.

[0136] In some embodiments, the reference signal resource in the first reference signal resource set is a real resource (i.e., an actually configured resource) or a virtual resource (i.e., a resource not actually configured).

[0137] In some embodiments, the second reference signal resource set is a subset of the first reference signal resource set. Alternatively, the signal angle corresponding to each reference signal resource in the second reference signal resource set is greater than the signal angle corresponding to each reference signal resource in the first reference signal resource set. For example, each reference signal resource in the second reference signal resource set is a wide beam, and each reference signal resource in the first reference signal resource set is a narrow beam.

[0138] In some embodiments, the first information includes a first time point, which is a sending time point of the first report. Alternatively, the first information includes a first time length, which includes a time length required for obtaining the first inference result.

[0139] Exemplarily, the first time length comprises a time length required for obtaining the first inference result by using the first AI model.

[0140] In some embodiments, before step 510, the terminal device receives first indication information, the first indication information being used for indicating a sending time of the first report or a sending period of the first report. Alternatively, before step 510, the terminal device receives second indication information, the second indication information being used for indicating the first time length. Alternatively, before step 510, the terminal device determines the first time and / or the first time length according to the second reference signal resource set.

[0141] Exemplarily, the first indication information is from the network device. That is, the network device sends the first indication information to the terminal device. In the case where the first indication information is used for indicating the sending period of the first report, the terminal device determines the time of sending the first report next time according to the time of sending the first report last time and the sending period of the first report, that is, determines the first time. Alternatively, in the case where the first indication information is used for indicating the sending period of the first report, the terminal device determines the time of sending the first report next time according to the current time and the sending period of the first report, that is, determines the first time.

[0142] Exemplarily, the second indication information is carried in radio resource control (RRC) high layer configuration signaling. For example, in the case where the configuration of the reference signal resource set is an aperiodic configuration, or the generation or reporting of the inference result is an aperiodic generation or reporting, the first time length comprises Z ref or Z' ref . The Z ref comprises a time delay between the time of receiving the first downlink control information (DCI) and the time of sending the first report, the first DCI being used for instructing the terminal device to perform measurement and reporting of channel state information. The Z' ref comprises a time delay between the time of receiving the first configuration information and the time of sending the first report, the first configuration information being used for configuring the second reference signal resource set, a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set being used for obtaining the first inference result. That is, the Z ref or Z' refThe first time length can be used to represent a time length required for obtaining an inference result by using an AI model. In a case where a configuration of the reference signal resource set is periodic or semi-persistent, or in a case where generation or reporting of the inference result is periodic or semi-persistent, the first time length includes a time offset for calculating a predicted value of channel state information corresponding to the reference signal resource.

[0143] The second indication information is exemplarily from a first device, which is a device in which the first AI model is deployed, such as a device connected to the terminal device or a server, etc. That is, the first device sends the second indication information to the terminal device. The first device is used to obtain the first inference result. The second indication information is used to indicate the first time length and / or a time point at which the first inference result is obtained.

[0144] The terminal device determines the first time length required for inputting the first input data into the first AI model to obtain the first inference result according to a computing capability of the device in which the first AI model is deployed, a data amount of the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and an inference capability of the first AI model. The first input data is determined based on the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set. For example, the first input data includes the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set. Alternatively, the first input data is determined according to the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set, such as data obtained by performing normalization processing on the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set, or data obtained by performing filtering processing on the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set. The filtering processing on the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set includes, for example, removing one or more smaller measurement values in the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set. The device in which the first AI model is deployed is a terminal-side device, such as the terminal device itself or a device connected to the terminal device or a server, etc. In other words, the terminal device determines the first time length required for performing inference by using the first input data to obtain the first inference result according to a computing capability of the device used to obtain the first inference result, a data amount of the measurement values of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and an inference process corresponding to the first inference result.

[0145] Exemplarily, the terminal device determines the first time according to a third time and a first duration. The first time is at least separated from the third time by the first duration. The third time is a time of receiving first configuration information, the first configuration information being used for configuring a second reference signal resource set (set B). Alternatively, the third time is a start time of reasoning to obtain a first reasoning result. Alternatively, the third time is preconfigured information.

[0146] Exemplarily, the third time being a start time of reasoning to obtain a first reasoning result includes that the third time is a start time of a device deployed with a first AI model using the first AI model to perform reasoning to obtain a first reasoning result.

[0147] In some embodiments, the network device sends first configuration information to the terminal device, the first configuration information being used for configuring a second reference signal resource set (set B). The terminal device performs measurement according to the second reference signal resource set to obtain a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set.

[0148] Exemplarily, the channel state information corresponding to the reference signal resource is, for example, RSRP or SINR, etc.

[0149] 520, sending second information to the terminal device.

[0150] The network device sends second information to the terminal device. Correspondingly, the terminal device receives the second information from the network device. The second information is used to determine a second time. The second time is a time of sending a second report, and the second time is equal to or later than the first time.

[0151] In some embodiments, the second information includes the second time.

[0152] Optionally, before step 520, the network device determines the first time according to the first information. Exemplarily, when the first information includes the first time, the network device directly obtains the first time. When the first information includes a first duration, the network device determines the first time according to a third time and the first duration. The first time is at least separated from the third time by the first duration. The third time is a time of receiving first configuration information, the first configuration information being used for configuring a second reference signal resource set. Alternatively, the third time is a start time of reasoning to obtain a first reasoning result. Alternatively, the third time is preconfigured information.

[0153] In some embodiments, before step 520, the network device determines a third time. In a case that the third time is a time of receiving the first configuration information, the network device estimates the time of receiving the first configuration information by the terminal device according to a time of sending the first configuration information to the terminal device. In a case that the third time is a time of starting the inference to obtain the first inference result, the network device receives third indication information from the terminal device, the third indication information being used to indicate the time of starting the inference to obtain the first inference result. Alternatively, the third time is pre-configured information.

[0154] Optionally, before step 520, the network device determines a second time according to the first time. The second time is not earlier than the first time.

[0155] 530, at the second time, sending a second report.

[0156] The terminal device sends a second report to the network device at the second time. That is, the second time is a time of sending the second report. Correspondingly, the network device receives the second report from the terminal device. The second report is used to indicate a first monitoring result, the first monitoring result being used to determine whether the first inference result is credible or a credibility of the first inference result. The first monitoring result is determined based on a measurement value of channel state information corresponding to at least one reference signal resource in the first reference signal resource set and the first inference result.

[0157] In some embodiments, before step 530, the terminal device determines the first monitoring result according to the measurement value of channel state information corresponding to at least one reference signal resource in the first reference signal resource set and the first inference result.

[0158] When the first AI model is a regression model, the first monitoring result is determined based on a measured value of channel state information corresponding to at least one first reference signal resource in the full set or the subset (set M) of the first reference signal resource set (set A) and a predicted value of channel state information corresponding to at least one second reference signal resource in the full set or the subset (set M) of the first reference signal resource set (set A). The predicted value of channel state information corresponding to the at least one second reference signal resource is obtained by using the first AI model. The at least one first reference signal resource and the at least one second reference signal resource are the same, or at least one reference signal resource in the at least one first reference signal resource and the at least one second reference signal resource is different. For example, the first monitoring result includes the measured value of channel state information corresponding to the at least one first reference signal resource and the predicted value of channel state information corresponding to the at least one second reference signal resource. Alternatively, the first monitoring result is a first confidence degree determined according to the measured value of channel state information corresponding to the at least one first reference signal resource and the predicted value of channel state information corresponding to the at least one second reference signal resource. The first confidence degree is used to indicate whether the first inference result is reliable, or the first confidence degree is used to indicate the reliability of the first inference result. The embodiments of the present application do not limit the specific implementation manner of determining the first confidence degree, for example, the first confidence degree is a difference value, a mean value, a variance value or a standard deviation value of the measured value of channel state information corresponding to the at least one first reference signal resource and the predicted value of channel state information corresponding to the at least one second reference signal resource, and the number of the at least one first reference signal resource and the number of the at least one second reference signal resource are the same.

[0159] When the first AI model is a classification model, the first monitoring result is determined based on at least one of the best reference signal resources predicted in the first reference signal resource set (set A) or the best reference signal resources measured in the full set or the subset (set M) of the first reference signal resource set (set A). The at least one of the best reference signal resources predicted is described in step 510. The measured value of the channel state information corresponding to the at least one of the best reference signal resources measured in the full set of the first reference signal resource set (set A) is greater than or equal to the measured value of the channel state information corresponding to the reference signal resource other than the at least one of the best reference signal resources measured in the full set of the first reference signal resource set (set A). The measured value of the channel state information corresponding to the at least one of the best reference signal resources measured in the subset (set M) of the first reference signal resource set (set A) is greater than or equal to the measured value of the channel state information corresponding to the reference signal resource other than the at least one of the best reference signal resources measured in the subset (set M) of the first reference signal resource set (set A). The number of the at least one of the best reference signal resources measured is the same as the number of the at least one of the best reference signal resources predicted. Exemplarily, the first monitoring result includes the identification information of the at least one of the best reference signal resources predicted and the identification information of the at least one of the best reference signal resources measured. Alternatively, the first monitoring result is a second confidence determined according to the at least one of the best reference signal resources predicted and the at least one of the best reference signal resources measured. The second confidence is used to indicate whether the first inference result is credible, or the second confidence is used to indicate the credibility of the first inference result. The embodiments of the present application do not limit the specific implementation manner of determining the second confidence, for example, the second confidence is determined according to the ratio of the number of the same reference signal resource in the at least one of the best reference signal resources predicted and the at least one of the best reference signal resources measured and the number of the at least one of the best reference signal resources predicted, etc.

[0160] Exemplarily, when the first AI model is a classification model, the first monitoring result is determined based on at least one of the predicted optimal reference signal resource in the first reference signal resource set (set A) and at least one of the measured optimal reference signal resource. Or, the first monitoring result is determined based on at least one of the predicted optimal reference signal resource in the subset (set M) of the first reference signal resource set (set A) and at least one of the measured optimal reference signal resource. Or, the first monitoring result is determined based on at least one of the predicted optimal reference signal resource in the first reference signal resource set (set A) and at least one of the measured optimal reference signal resource in the subset (set M) of the first reference signal resource set (set A). Or, the first monitoring result is determined based on at least one of the predicted optimal reference signal resource in the subset (set M) of the first reference signal resource set (set A) and at least one of the measured optimal reference signal resource in the first reference signal resource set (set A).

[0161] It should be understood that the above-described manner of determining the first confidence or the second confidence is only exemplary, and any method for determining the confidence of the inference result of an AI model in the prior art can be applied to the embodiments of the present application.

[0162] In some embodiments, before obtaining the first inference result, the network device configures a second reference signal resource set (set B) for the terminal device, and the second reference signal resource set includes at least one reference signal resource. The terminal device performs measurement according to the second reference signal resource set, determines the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and thus determines the first input data. The terminal device inputs the first input data into the first AI model to obtain the first inference result. The first input data is described in step 510.

[0163] In some embodiments, before obtaining the first inference result, the network device configures a first reference signal resource set (set A) for the terminal device, and the first reference signal resource set includes at least one reference signal resource. The terminal device performs measurement according to one or more reference signal resources in the first reference signal resource set, and determines the measurement value of the channel state information corresponding to the one or more reference signal resources in the first reference signal resource set.

[0164] In some embodiments, in the case where the first device determines the first inference result, the first device determines the first time length and / or the time point at which the first inference result is obtained. The first device is a device in which the first AI model is deployed, for example, a device connected with the terminal device or a server, etc.

[0165] Exemplarily, the terminal device sends fourth indication information to the first device, where the fourth indication information is used to indicate a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set (set B). The first device determines the first input data according to the measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set. The first device inputs the first input data into the first AI model to obtain the first inference result. For specific implementation manners, refer to the description in step 510.

[0166] Exemplarily, the first device determines the first time length required for inputting the first input data into the first AI model to obtain the first inference result according to the computing capability of the first device, the data amount of the measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set, and the inference capability of the first AI model. In other words, the first device determines the first time length required for inference with the first input data to obtain the first inference result according to the computing capability of the first device, the data amount of the measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set, and the inference process corresponding to the first inference result.

[0167] Exemplarily, the first device determines the time point of obtaining the first inference result according to the first time length and a third time point. The third time point is a starting time point of inference of the first device to obtain the first inference result. The time point of obtaining the first inference result is at least separated from the third time point by the first time length.

[0168] Exemplarily, the first device determines the time point of obtaining the first inference result according to the first time length and a receiving time point of the fourth indication information. The time point of obtaining the first inference result is at least separated from the receiving time point of the fourth indication information by the first time length. The receiving time point of the fourth indication information can be approximately equal to the starting time point of inference of the first device to obtain the first inference result.

[0169] Exemplarily, the first device sends second indication information to the terminal device, where the second indication information is used to indicate the first time length and / or the time point of obtaining the first inference result.

[0170] Exemplarily, in a case where the second indication information is used to indicate the time point of obtaining the first inference result, the terminal device determines the sending time point of the first report according to the time point of obtaining the first inference result. The sending time point of the first report is not earlier than (i.e., equal to or later than) the time point of obtaining the first inference result.

[0171] In a case where the terminal device sends the fourth indication information to the first device, and the second indication information is used to indicate the first time length, the terminal device estimates a receiving time of the fourth indication information according to a sending time of the fourth indication information. The terminal device determines a time of obtaining the first inference result according to the receiving time of the fourth indication information and the first time length. The time of obtaining the first inference result is at least separated from the receiving time of the fourth indication information by the first time length. The terminal device determines a sending time of the first report according to the time of obtaining the first inference result. The sending time of the first report is not earlier than (i.e., equal to or later than) the time of obtaining the first inference result.

[0172] In a case where the second indication information is used to indicate the first time length, the first device sends fifth indication information to the terminal device, where the fifth indication information is used to indicate a starting time (i.e., a third time) of the inference performed by the first device to obtain the first inference result. The terminal device determines the time of obtaining the first inference result according to the third time and the first time length, and determines the sending time of the first report. The time of obtaining the first inference result is at least separated from the third time by the first time length. The sending time of the first report is not earlier than (i.e., equal to or later than) the time of obtaining the first inference result. Alternatively, the terminal device sends third indication information to the network device after receiving the fifth indication information. The third indication information is used to indicate the starting time of the inference performed by the first device to obtain the first inference result.

[0173] In the method of FIG. 5, the terminal device reports the time of obtaining the first inference result or the time of sending the first report to the network device, so as to determine the sending time of the monitoring result according to the indication of the network device. Since the sending time of the monitoring result of the terminal device is not earlier than the time of obtaining the first inference result or the time of sending the first report, the terminal device can obtain the correct monitoring result according to the correct first inference result, and thus reports the correct monitoring result to the network device, so as to monitor the first AI model.

[0174] For example, in a case where the terminal device reports the first report to the network device, an implementation manner of the method in FIG. 5 is shown in FIG. 6. FIG. 6 is a schematic flowchart of a communication method according to an embodiment of the present application. The method in FIG. 6 is applied to a communication system, for example, the communication system shown in FIG. 1, FIG. 2 or FIG. 3. The network device in FIG. 6 is, for example, the network device 110 in FIG. 1, the core network device in FIG. 2, the access network node in FIG. 3 or the like. The terminal device in FIG. 6 is, for example, the terminal device in FIG. 1, FIG. 2 or FIG. 3. The method in FIG. 6 includes the following steps.

[0175] 601, sending first configuration information to the terminal device.

[0176] The network device sends first configuration information to the terminal device, and correspondingly, the terminal device receives the first configuration information from the network device. The first configuration information is used to configure a second reference signal resource set (set B), and the second reference signal resource set includes at least one reference signal resource. The second reference signal resource set is described with reference to FIG. 5.

[0177] Exemplarily, the reference signal resource is, for example, a beam. The reference signal resource in the second reference signal resource set is, for example, a wide beam or a narrow beam, which is described with reference to FIG. 4.

[0178] Optionally, after the terminal device configures the second reference signal resource set, the terminal device performs measurement according to the second reference signal resource set to obtain a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set.

[0179] Exemplarily, the channel state information corresponding to the reference signal resource is, for example, RSRP or SINR, etc.

[0180] 602, the network device sends first indication information to the terminal device.

[0181] The network device sends first indication information to the terminal device, and correspondingly, the terminal device receives the first indication information from the network device. The first indication information is used to indicate a sending time of a first report or a sending period of the first report. The first report is used to indicate a first inference result.

[0182] In some embodiments, the first inference result is obtained by a device deploying a first AI model by using the first AI model. The device deploying the first AI model is a terminal-side device, for example, the terminal device itself or a device or a server connected to the terminal device, etc. The first inference result and the first AI model are described with reference to step 510.

[0183] In a case where the first report is a periodic report or a semi-persistent report, the first indication information is used to indicate a sending period of the first report. In a case where the first report is an aperiodic report, the first indication information is used to indicate a sending time of the first report.

[0184] 603, the terminal device sends first information to the network device, and the first information includes a first time.

[0185] The terminal device sends first information to the network device, and correspondingly, the network device receives the first information from the terminal device. The first information includes a first time, and the first time is a sending time of a first report.

[0186] Optionally, before step 630, the terminal device determines the first time.

[0187] In some embodiments, the terminal device determines the first time according to the first indication information. For example, when the first indication information is used to indicate the sending period of the first report, the terminal device determines the time of sending the next first report, i.e., the first time, according to the time of sending the last first report and the sending period of the first report, or according to the current time and the sending period of the first report.

[0188] In some embodiments, the terminal device determines the first time according to the second indication information. The second indication information is from the first device, which is a device deploying the first AI model, such as a device or a server connected to the terminal device, etc. That is, the first device sends the second indication information to the terminal device. The second indication information is used to indicate the time when the first device obtains the first inference result. The sending time of the first report is not earlier than (i.e., equal to or later than) the time when the first device obtains the first inference result.

[0189] In some embodiments, the terminal device determines the first time according to the first time length. The first time length is indicated by the first device, or is pre-configured, or is determined by the terminal device according to the second reference signal resource set.

[0190] For example, before step 630, the terminal device determines the time length required for inputting the first input data into the first AI model to obtain the first inference result, i.e., the first time length, according to the computing capability of the device deploying the first AI model, the data amount of the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and the inference capability of the first AI model. The first input data is described in step 510.

[0191] For example, the terminal device determines the first time according to the third time and the first time length. The first time is at least separated from the third time by the first time length. The third time is the receiving time of the first configuration information used to configure the second reference signal resource set. Or, the third time is the starting time of inference to obtain the first inference result. Or, the third time is pre-configured information.

[0192] For example, the third time being the starting time of inference to obtain the first inference result includes that the third time is the starting time of the device deploying the first AI model to perform inference using the first AI model to obtain the first inference result. When the first device is the device deploying the first AI model, the terminal device receives the fifth indication information from the first device, and the fifth indication information is used to indicate the third time. Or, when the terminal device deploys the first AI model, the terminal device determines the third time by itself.

[0193] 604, determine the second information according to the first time.

[0194] In a case that the first information comprises the first time point, the network device determines the second information according to the first time point. The second information is used to determine the second time point, which is not earlier than (i.e. equal to or later than) the first time point.

[0195] In some embodiments, the second information comprises the second time point. In other words, the network device determines the second time point which is not earlier than the first time point according to the first time point, thereby determining the second information.

[0196] 605, the network device sends the second information to the terminal device.

[0197] The network device sends the second information to the terminal device, and correspondingly, the terminal device receives the second information from the network device. The terminal device obtains the second time point according to the second information.

[0198] 606, the network device sends the second configuration information to the terminal device.

[0199] The network device sends the second configuration information to the terminal device, and correspondingly, the terminal device receives the second configuration information from the network device. The second configuration information is used to configure the first reference signal resource set (set A), which comprises at least one reference signal resource. The first reference signal resource set is described in FIG. 5.

[0200] In some embodiments, the second reference signal resource set is a subset of the first reference signal resource set. Alternatively, each reference signal resource in the second reference signal resource set corresponds to a signal angle which is greater than the signal angle corresponding to each reference signal resource in the first reference signal resource set. For example, each reference signal resource in the second reference signal resource set is a wide beam, and each reference signal resource in the first reference signal resource set is a narrow beam.

[0201] Optionally, after the terminal device configures the first reference signal resource set, the terminal device performs measurement according to the first reference signal resource set to obtain the measurement value of the channel state information corresponding to part or all of the reference signal resources in the first reference signal resource set.

[0202] Optionally, steps 601, 602 and 606 are optional steps, which can be executed or not executed.

[0203] 607, using the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set and the first AI model, the first inference result is obtained.

[0204] The terminal device determines first input data according to the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, inputs the first input data into the first AI model, and obtains a first inference result. The first input data is described in step 510. It should be understood that step 607 can be performed after obtaining the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and the specific execution time of step 607 is not limited by the embodiments of the present application.

[0205] 608, at the first time, sending a first report to the network device.

[0206] After the terminal device obtains the first inference result, at the first time, the terminal device sends a first report to the network device. The first report is used to indicate the first inference result. Correspondingly, the network device receives the first report. That is, the first time is the sending time of the first report.

[0207] 609, obtaining a first monitoring result according to the measurement value of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set and the first inference result.

[0208] After the terminal device obtains the first inference result, the terminal device determines a first monitoring result according to the measurement value of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set and the first inference result. The specific obtaining method of the first monitoring result is described in step 530.

[0209] 610, at the second time, sending a second report to the network device.

[0210] After the terminal device obtains the first monitoring result, at the second time, the terminal device sends a second report to the network device. The second report is used to indicate the first monitoring result. Correspondingly, the network device receives the second report. That is, the second time is the sending time of the second report.

[0211] In the method of FIG. 6, when the terminal device reports the first report (i.e. the inference result) to the network device, the terminal device reports the sending time of the first report to the network device, so as to determine the sending time of the second report (i.e. the monitoring result) according to the indication of the network device. Since the terminal device sends the monitoring result at a time not earlier than the sending time of the first report, the terminal device can obtain the correct monitoring result according to the correct inference result, so as to report the correct monitoring result to the network device, so as to monitor the first AI model.

[0212] It should be understood that the method in the embodiments of the present application can not only be applied to the scene of beam management based on an AI model, but also be applied to the scene of prediction or reporting of channel state information (CSI). Exemplarily, the first report is, for example, a CSI report.

[0213] In the case where the first report is a CSI report, the first report includes at least one of the following: identification information of at least one reference signal resource in the first reference signal resource set (set A), a predicted value of channel state information corresponding to the at least one reference signal resource in the first reference signal resource set (set A), a channel quality indication (CQI) corresponding to the at least one reference signal resource, a precoding matrix indicator (PMI) corresponding to the at least one reference signal resource, a rank indicator (RI) corresponding to the at least one reference signal resource, a CSI-RS resource indicator (CRI) corresponding to the at least one reference signal resource, a layer indicator (LI) corresponding to the at least one reference signal resource, and the like.

[0214] In the case where the first report includes the identification information of at least one reference signal resource in the first reference signal resource set (set A) and / or the predicted value of channel state information corresponding to the at least one reference signal resource, the first AI model, the first inference result, and the first monitoring result are described with reference to FIG. 5.

[0215] In the case where the first report includes the CQI corresponding to the at least one reference signal resource, the first AI model is configured to determine, according to the CQI corresponding to each reference signal resource in at least one third reference signal resource determined by measurement in a first time period, the CQI corresponding to each reference signal resource in at least one fourth reference signal resource in a second time period. The first time period is a time period before the start time of the inference of the first AI model to obtain the first inference result. The second time period is a time period after the start time of the inference of the first AI model to obtain the first inference result. The first inference result includes the CQI corresponding to each fourth reference signal resource in the at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The first monitoring result is determined based on the CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by measurement in the second time period, and the CQI corresponding to each reference signal resource in the at least one fourth reference signal resource in the second time period determined by prediction using the first AI model.

[0216] Exemplarily, the first time period includes a start time of the inference of the first AI model to obtain the first inference result. Alternatively, an end time of the first time period is x1 time points away from the start time of the inference of the first AI model to obtain the first inference result. x1 is a positive integer.

[0217] Exemplarily, the second time period includes a start time of the inference of the first AI model to obtain the first inference result. Alternatively, a start time of the second time period is x2 time points away from the start time of the inference of the first AI model to obtain the first inference result. x2 is a positive integer.

[0218] Exemplarily, the at least one third reference signal resource and the at least one fourth reference signal resource are the same. Alternatively, at least one reference signal resource in the at least one third reference signal resource and the at least one fourth reference signal resource is different.

[0219] Exemplarily, the first monitoring result includes a CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by measurement in the second time period, and a CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by prediction using the first AI model in the second time period. Alternatively, the first monitoring result includes a third confidence degree. The third confidence degree is used to indicate whether the first inference result is credible, or the third confidence degree is used to indicate a credibility of the first inference result. The third confidence degree is determined based on the CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by measurement in the second time period, and the CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by prediction using the first AI model in the second time period. Embodiments of the present application do not limit the specific implementation of determining the third confidence degree, for example, the third confidence degree is a generalized cosine similarity (GCS), a square generalized cosine similarity (SGCS), a mean square error (MSE) or a normalized mean square error (NMSE) and the like of a first vector and a second vector. The first vector includes the CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by measurement in the second time period, and the second vector includes the CQI corresponding to each reference signal resource in the at least one fourth reference signal resource determined by prediction using the first AI model in the second time period.

[0220] When the first report includes the PMI corresponding to at least one reference signal resource, the first AI model is configured to determine the PMI corresponding to each of at least one fourth reference signal resource in a second time period according to the PMI corresponding to each of at least one third reference signal resource determined by measurement in the first time period. The first inference result includes the PMI corresponding to each of at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The first monitoring result is determined based on the PMI corresponding to each of at least one fourth reference signal resource determined by measurement in the second time period and the PMI corresponding to each of at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The specific implementation manner is similar to that when the first report includes the CQI corresponding to at least one reference signal resource, which is not described here again.

[0221] When the first report includes the RI corresponding to at least one reference signal resource, the first AI model is configured to determine the RI corresponding to each of at least one fourth reference signal resource in a second time period according to the RI corresponding to each of at least one third reference signal resource determined by measurement in the first time period. The first inference result includes the RI corresponding to each of at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The first monitoring result is determined based on the RI corresponding to each of at least one fourth reference signal resource determined by measurement in the second time period and the RI corresponding to each of at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The specific implementation manner is similar to that when the first report includes the CQI corresponding to at least one reference signal resource, which is not described here again.

[0222] When the first report includes the CRI corresponding to at least one reference signal resource, the first AI model is configured to determine the CRI corresponding to each of at least one fourth reference signal resource in a second time period according to the CRI corresponding to each of at least one third reference signal resource determined by measurement in the first time period. The first inference result includes the CRI corresponding to each of at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The first monitoring result is determined based on the CRI corresponding to each of at least one fourth reference signal resource determined by measurement in the second time period and the CRI corresponding to each of at least one fourth reference signal resource in the second time period determined by prediction using the first AI model. The specific implementation manner is similar to that when the first report includes the CQI corresponding to at least one reference signal resource, which is not described here again.

[0223] In a case where the first report comprises the LI corresponding to at least one reference signal resource, the first AI model is used to determine the LI corresponding to each of at least one third reference signal resource in a second time period according to the LI corresponding to each of the at least one third reference signal resource determined in the first time period. The first inference result comprises the LI corresponding to each of the at least one fourth reference signal resource in the second time period determined by using the first AI model to make a prediction. The first monitoring result is determined based on the LI corresponding to each of the at least one fourth reference signal resource in the second time period determined in the second time period and the LI corresponding to each of the at least one fourth reference signal resource in the second time period determined by using the first AI model to make a prediction. The specific implementation manner is similar to that in a case where the first report comprises the CQI corresponding to at least one reference signal resource, which is not described herein again.

[0224] In a case where the first report comprises the predicted value of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set (set A), the information included in the first report can be compressed to reduce the number of bits included in the first report. The specific implementation manner of the compression is not limited in the embodiments of the present application. For example, the predicted value of the channel state information corresponding to the at least one reference signal resource comprises the maximum predicted value in the predicted value of the channel state information corresponding to the at least one reference signal resource and the difference between each predicted value in the predicted value of the channel state information corresponding to the at least one reference signal resource and the maximum predicted value.

[0225] Exemplarily, in a case where the terminal device does not report the first report to the network device, one implementation manner of the method in FIG. 5 is shown in FIG. 7. FIG. 7 is a schematic flowchart of a communication method according to an embodiment of the present application. The method in FIG. 7 is applied to a communication system, for example, the communication system shown in FIG. 1, FIG. 2 or FIG. 3. The network device in FIG. 7 is, for example, the network device 110 in FIG. 1, the core network device in FIG. 2, the access network node in FIG. 3 or the access network node in FIG. 3. The terminal device in FIG. 7 is, for example, the terminal device in FIG. 1, FIG. 2 or FIG. 3. The method in FIG. 7 comprises the following steps.

[0226] 701, sending the first configuration information to the terminal device. Step 701 is similar to step 601, which is not described herein again.

[0227] 702, sending the first information to the network device, the first information indicating the first time length.

[0228] The terminal device sends first information to the network device, and the network device correspondingly receives the first information from the terminal device. The first information is used to determine a first time point, which is a sending time point of the first report. The first information includes a first time length, which includes a time length required for obtaining the first inference result.

[0229] In some embodiments, the first inference result is obtained by a device deploying the first AI model by using the first AI model. The device deploying the first AI model is a terminal-side device, for example, the terminal device itself or a device or server connected to the terminal device, etc. The first AI model and the first inference result are described in step 510.

[0230] Optionally, before step 702, the terminal device determines the first time length. The first time length is indicated by the device deploying the first AI model, or is pre-configured, or is determined by the terminal device according to the second reference signal resource set.

[0231] For example, before step 702, the terminal device determines the time length required for inputting the first input data into the first AI model to obtain the first inference result according to the computing capability of the device deploying the first AI model, the data amount of the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set, and the inference capability of the first AI model, i.e., determines the first time length. The first input data is described in step 510.

[0232] 703, sending third indication information to the network device.

[0233] The terminal device sends third indication information to the network device, and the network device correspondingly receives the third indication information from the terminal device. The third indication information is used to indicate a starting time point of the inference by the device deploying the first AI model by using the first AI model to obtain the first inference result.

[0234] In some embodiments, the third indication information and the first information are carried in the same message, or the third indication information and the first information are carried in different messages.

[0235] 704, determining second information according to the third time point and the first time length.

[0236] In the case where the first information includes the first time length, the network device determines the first time point according to the third time point and the first time length, and thus determines the second information according to the first time point. The second information is used to determine a second time point, which is not earlier than (i.e., equal to or later than) the first time point.

[0237] In some embodiments, the second information includes the second time point.

[0238] In some embodiments, the first time interval is at least a first time length between the first time point and the third time point.

[0239] In some embodiments, the third time point is a receiving time point of the terminal device receiving the first configuration information. Alternatively, the third time point is a starting time point of reasoning to obtain the first reasoning result. Alternatively, the third time point is preconfigured information.

[0240] Optionally, before step 704, the network device determines the third time point. For example, the network device determines the third time point according to the third indication information. Alternatively, the network device estimates a receiving time point of the terminal device receiving the first configuration information according to a sending time point of the network device sending the first configuration information to the terminal device. Alternatively, the third time point is preconfigured information.

[0241] 705, sending the second information to the terminal device. The implementation of step 705 is similar to that of step 605, which will not be described here.

[0242] 706, sending the second configuration information to the terminal device. The implementation of step 706 is similar to that of step 606.

[0243] Optionally, steps 701, 703 and 706 are optional steps, which can be executed or not executed.

[0244] 707, obtaining the first reasoning result by using the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set and the first AI model. The implementation of step 707 is similar to that of step 607.

[0245] 708, obtaining the first monitoring result according to the measurement value of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set and the first reasoning result. The implementation of step 708 is similar to that of step 609.

[0246] 709, sending the second report to the network device at the second time point. The implementation of step 709 is similar to that of step 610.

[0247] In the method of FIG. 7, when the terminal device does not report the first report to the network device, the terminal device reports the time length required for generating the reasoning result to the network device, so as to determine the sending time point of the second report (i.e. the monitoring result) according to the indication of the network device. Since the sending time point of the terminal device sending the monitoring result is not earlier than the time point of generating the reasoning result, the terminal device can obtain the correct monitoring result according to the correct reasoning result, so as to report the correct monitoring result to the network device, so as to monitor the first AI model.

[0248] FIG. 8 and FIG. 9 are structural schematic diagrams of possible communication apparatuses provided by the embodiments of the present application. The communication apparatuses can be used to implement the functions of the terminal device or the network device in the above-mentioned method embodiments, and thus can also achieve the beneficial effects possessed by the above-mentioned method embodiments. In the embodiments of the present application, the communication apparatus can be the network device 110 shown in FIG. 1, the core network device, the access network node in FIG. 2, the access network node in FIG. 3, the network device in FIG. 5 to FIG. 7, or the terminal device in FIG. 1 to FIG. 7.

[0249] As shown in FIG. 8, the communication apparatus 800 includes a receiving unit 810 and a sending unit 820. The communication apparatus 800 is used to implement the functions of the network device in the above-mentioned method embodiments shown in FIG. 5 to FIG. 7.

[0250] When the communication apparatus 800 is used to implement the functions of the terminal device in the method embodiment shown in FIG. 5, the sending unit 820 is used to send the first information and send the second report at the second time. The sending unit 820 is used to perform steps 510 and 530 in FIG. 5. The receiving unit 810 is used to receive the second information.

[0251] When the communication apparatus 800 is used to implement the functions of the network device in the method embodiment shown in FIG. 5, the receiving unit 810 is used to receive the first information and receive the second report. The sending unit 820 is used to send the second information. The sending unit 820 is used to perform step 520 in FIG. 5.

[0252] When the communication apparatus 800 is used to implement the functions of the terminal device in the method embodiment shown in FIG. 6, the receiving unit 810 is used to: receive the first configuration information; receive the first indication information; receive the second information; and receive the second configuration information. The sending unit 820 is used to: send the first information; send the first report at the first time; and send the second report at the second time. The sending unit 820 is used to perform steps 603, 608 and 610 in FIG. 6.

[0253] When the communication apparatus 800 is used to implement the functions of the terminal device in the method embodiment shown in FIG. 6, the communication apparatus 800 further includes a processing unit (not shown in the figure). The processing unit is used to: obtain a first inference result by using a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set (set B) and the first AI model; and obtain a first monitoring result according to the measurement value of channel state information corresponding to at least one reference signal resource in the first reference signal resource set (set A) and the first inference result. The processing unit is used to perform steps 607 and 609 in FIG. 6.

[0254] When the communication apparatus 800 is configured to implement the functions of the network device in the method embodiment shown in FIG. 6, the sending unit 820 is configured to: send the first configuration information; send the first indication information; send the second information; and send the second configuration information. The sending unit 820 is configured to perform steps 601, 602, 605 and 606 in FIG. 6. The receiving unit 810 is configured to: receive the first information; receive the first report; and receive the second report.

[0255] When the communication apparatus 800 is configured to implement the functions of the network device in the method embodiment shown in FIG. 6, the communication apparatus 800 further includes a processing unit (not shown in the figure). The processing unit is configured to: determine the second information according to the first time. The processing unit is configured to perform step 604 in FIG. 6.

[0256] When the communication apparatus 800 is configured to implement the functions of the terminal device in the method embodiment shown in FIG. 7, the receiving unit 810 is configured to: receive the first configuration information; receive the second information; and receive the second configuration information. The sending unit 820 is configured to: send the first information; send the third indication information; and send the second report at the second time. The sending unit 820 is configured to perform steps 702, 703 and 709 in FIG. 7.

[0257] When the communication apparatus 800 is configured to implement the functions of the terminal device in the method embodiment shown in FIG. 7, the communication apparatus 800 further includes a processing unit (not shown in the figure). The processing unit is configured to: obtain the first inference result by using the measurement value of the channel state information corresponding to each reference signal resource in the second reference signal resource set (set B) and the first AI model; and obtain the first monitoring result according to the measurement value of the channel state information corresponding to at least one reference signal resource in the first reference signal resource set (set A) and the first inference result. The processing unit is configured to perform steps 707 and 708 in FIG. 7.

[0258] When the communication apparatus 800 is configured to implement the functions of the network device in the method embodiment shown in FIG. 7, the sending unit 820 is configured to: send the first configuration information; send the second information; and send the second configuration information. The sending unit 820 is configured to perform steps 701, 705 and 706 in FIG. 7. The receiving unit 810 is configured to: receive the first information; receive the third indication information; and receive the second report.

[0259] When the communication apparatus 800 is configured to implement the functions of the network device in the method embodiment shown in FIG. 7, the communication apparatus 800 further includes a processing unit (not shown in the figure). The processing unit is configured to: determine the second information according to the third time and the first time length. The processing unit is configured to perform step 704 in FIG. 7.

[0260] More details of the receiving unit 810 and the sending unit 820 can be referred to the related description in the method embodiments shown in FIG. 5 to FIG. 7.

[0261] As shown in FIG. 9, the communication apparatus 900 includes a processing circuit 910. Further, the communication apparatus 900 can also include the processing circuit 910 and a communication circuit 920. The processing circuit 910 and the communication circuit 920 are coupled to each other. The processing circuit can be one or more processors, or all or part of one or more processors used for control or processing functions. It can be understood that, when the communication apparatus 900 is a network device or a terminal device, the communication circuit 920 can be a transceiver, a transceiver, or an input / output interface. When the communication apparatus 900 is a chip for a network device or a terminal device, the communication circuit 920 can be an input / output interface or an input / output circuit. Optionally, the communication apparatus 900 can also include a memory 930, used to store instructions executed by the processor 910 or input data required by the processor 910 to run instructions or data generated after the processor 910 runs instructions.

[0262] When the communication apparatus 900 is used to implement the methods shown in FIG. 5 to FIG. 7, the processing circuit 910 is used to implement the functions of the processing unit, and the communication circuit 920 is used to implement the functions of the receiving unit and / or the sending unit.

[0263] When the above communication apparatus is a chip applied to a terminal, the terminal chip implements the functions of the terminal in the above method embodiments. The terminal chip receives information from other modules (such as a radio frequency module or an antenna) in the terminal, and the information is sent by the base station to the terminal; or the terminal chip sends information to other modules (such as a radio frequency module or an antenna) in the terminal, and the information is sent by the terminal to the base station.

[0264] When the above communication apparatus is a module applied to a base station (or network device), the base station module implements the functions of the base station in the above method embodiments. The base station module receives information from other modules (such as a radio frequency module or an antenna) in the base station, and the information is sent by the terminal to the base station; or the base station module sends information to other modules (such as a radio frequency module or an antenna) in the base station, and the information is sent by the base station to the terminal. The base station module can be a baseband chip of the base station, or a DU or other module, and the DU can be a DU under the open radio access network (O-RAN) architecture.

[0265] It can be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), graphics processing units, neural processing units, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0266] The method steps in the embodiments of the present application can be implemented in hardware or in software instructions executable by a processor. The software instructions can be composed of corresponding software modules, which can be stored in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, a register, a hard disk, a mobile hard disk, a compact disc read-only memory (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from and write information to the storage medium. The storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a base station or a terminal. The processor and the storage medium can also exist as discrete components in the base station or the terminal.

[0267] The embodiments of the present application also provide a communication system, which includes the network device and the terminal device described in the embodiments of the present application.

[0268] The embodiments of the present application further provide a computer readable storage medium. The computer readable storage medium can be any available media or data storage device that can store the program codes or instructions. The computer readable storage medium can be a magnetic disk, an optical disk, a semiconductor memory, or the like. The computer readable storage medium includes the program codes or instructions, which, when executed on a computing device, cause the computing device to perform the method provided above.

[0269] The embodiments of the present application further provide a computer program product. The computer program product can be software or program codes containing instructions, which can be executed on a computing device or stored in any available medium. When the instructions are executed on the computing device, the computing device performs the method provided above, or the computing device implements the functions of the apparatus provided above.

[0270] The embodiments of the present application further provide a chip. The chip includes at least one processor, which, when program instructions are executed, causes the at least one processor to perform the method provided above.

[0271] Those skilled in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized 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 the present application.

[0272] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, apparatus and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0273] In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0274] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0275] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0276] If the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0277] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A communication method characterized by comprising: The method comprises: sending first information, the first information being used for determination of a first time, the first time being a time of sending a first report or a time of obtaining a first inference result, the first report being used for indicating the first inference result, the first inference result comprising a predicted value of channel state information corresponding to at least one reference signal resource in a first reference signal resource set (set A) and / or identification information of the at least one reference signal resource; receiving second information, the second information being used for determination of a second time, the second time being equal to or later than the first time; at the second time, sending a second report, the second report being used for indicating a first monitoring result, the first monitoring result being used for determination of whether the first inference result is credible or a credibility of the first inference result.

2. The method of claim 1, wherein, in a case where the first time is the time of sending the first report, the first information indicates the first time; or, in a case where the first time is the time of obtaining the first inference result, the first information indicates a first time length, the first time length comprising a time length required for obtaining the first inference result.

3. The method of claim 2, wherein, The method further comprises: receiving first indication information, the first indication information being used for indicating the time of sending the first report or a sending period of the first report; or receiving second indication information, the second indication information being used for indicating the first time length; or determining the first time and / or the first time length according to a second reference signal resource set (set B), a measured value of channel state information corresponding to each reference signal resource in the second reference signal resource set being used for obtaining the first inference result.

4. The method according to claim 2 or 3, characterized in that, in a case where the first time is the time of determining the first inference result, the method further comprises: determining the first time according to a third time and the first time length, the third time being a receiving time of first configuration information, the first configuration information being used for configuring a second reference signal resource set (set B), or the third time being a starting time of inference for obtaining the first inference result; wherein a measured value of channel state information corresponding to each reference signal resource in the second reference signal resource set is used for obtaining the first inference result.

5. The method according to any one of claims 1 to 4, characterized in that, The second information indicates the second time.

6. The method according to any one of claims 1 to 5, characterized in that, in a case where the predicted value of channel state information corresponding to each reference signal resource in the first reference signal resource set (set A) is determined, the first inference result comprises the predicted value of channel state information corresponding to the at least one reference signal resource in the first reference signal resource set (set A) and / or the identification information of the at least one reference signal resource, and the first monitoring result is determined based on a measured value of channel state information corresponding to at least one first reference signal resource in a full set or a subset (set M) of the first reference signal resource set (set A) and a predicted value of channel state information corresponding to at least one second reference signal resource in the full set or the subset (set M) of the first reference signal resource set (set A); or In a case where a probability of each reference signal resource in the first reference signal resource set (set A) being the best reference signal resource in the first reference signal resource set (set A) is determined, the first inference result includes identification information of at least one predicted best reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A), the first monitoring result is determined based on at least one measured best reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) and at least one predicted best reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A), the probability corresponding to the at least one predicted best reference signal resource is greater than or equal to the probability corresponding to a reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) except the at least one predicted best reference signal resource, the probability corresponding to the reference signal resource is a probability of the reference signal resource being the best reference signal resource in the first reference signal resource set, and a measured value of channel state information corresponding to the at least one measured best reference signal resource is greater than or equal to a measured value of channel state information corresponding to a reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) except the at least one measured best reference signal resource.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: configuring a second reference signal resource set (set B) including at least one reference signal resource; determining a measured value of channel state information corresponding to each reference signal resource in the second reference signal resource set; determining the first inference result by using the measured value of channel state information corresponding to each reference signal resource in the second reference signal resource set.

8. A communication method characterized by comprising: includes: receiving first information for determining a first time, the first time being a sending time of a first report or a time of obtaining a first inference result, the first report being used for indicating the first inference result, the first inference result including a predicted value of channel state information corresponding to at least one reference signal resource in a first reference signal resource set (set A) and / or identification information of the at least one reference signal resource; sending second information for determining a second time, the second time being equal to or later than the first time; receiving a second report, the second report being sent at the second time, the second report being used for indicating a first monitoring result, the first monitoring result being used for determining whether the first inference result is credible or a credibility of the first inference result.

9. The method of claim 8, wherein, In a case where the first time is the sending time of the first report, the first information includes the first time; or In a case where the first time instant is an instant at which the first inference result is obtained, the first information comprises a first time length, the first time length comprising a time length required for obtaining the first inference result.

10. The method of claim 9, wherein, The method further comprises: sending first indication information, the first indication information being used for indicating a sending instant of the first report or a sending period of the first report.

11. The method according to claim 9 or 10, characterized in that, In a case where the first time instant is an instant at which the first inference result is obtained, the method further comprises: determining the first time instant according to a third time instant and the first time length, the third time instant being a receiving instant of first configuration information, the first configuration information being used for configuring a second reference signal resource set (set B), or the third time instant being a starting instant of inference for obtaining the first inference result; wherein a measurement value of channel state information corresponding to each reference signal resource in the second reference signal resource set is used for obtaining the first inference result.

12. The method according to any one of claims 9 to 11, characterized in that, The second information comprises the second time instant.

13. The method according to any one of claims 9 to 12, characterized in that, In a case where a predicted value of channel state information corresponding to each reference signal resource in the first reference signal resource set (set A) is determined, the first inference result comprises a predicted value of channel state information corresponding to at least one reference signal resource in the first reference signal resource set (set A) and / or identification information of the at least one reference signal resource, and the first monitoring result is determined based on a measurement value of channel state information corresponding to at least one first reference signal resource in a full set or a subset (set M) of the first reference signal resource set (set A) and a predicted value of channel state information corresponding to at least one second reference signal resource in the full set or the subset (set M) of the first reference signal resource set (set A); or, In a case where the probability that each reference signal resource in the first reference signal resource set (set A) is the best reference signal resource in the first reference signal resource set (set A) is determined, the first inference result includes identification information of at least one predicted best reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A), the first monitoring result is determined based on at least one measured best reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) and at least one predicted best reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A), the probability corresponding to the at least one predicted best reference signal resource is greater than or equal to the probability corresponding to the reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) except the at least one predicted best reference signal resource, the probability corresponding to the reference signal resource is the probability that the reference signal resource is the best reference signal resource in the first reference signal resource set, and the measured value of the channel state information corresponding to the at least one measured best reference signal resource is greater than or equal to the measured value of the channel state information corresponding to the reference signal resource in the full set or subset (set M) of the first reference signal resource set (set A) except the at least one measured best reference signal resource.

14. A communications device, characterized by A module for performing the method of any one of claims 1 to 7 or any one of claims 8 to 13.

15. A communications device, characterized by The communication device includes at least one processor and a communication interface for information interaction between the communication device and other communication devices, and when program instructions are executed in the at least one processor, the communication device performs the method of any one of claims 1 to 7 or any one of claims 8 to 13.

16. A computer-readable storage medium, characterized in that, The computer readable storage medium stores program code for execution by a device, and when the program code is executed, the method of any one of claims 1 to 7 or any one of claims 8 to 13 is performed.

17. A chip, characterized by The chip includes at least one processor, and when program instructions are executed in the at least one processor, the method of any one of claims 1 to 7 or any one of claims 8 to 13 is performed.

18. A computer program product, characterised in that, The program instructions are executed when the computer program product is run on a computer, and the method of any one of claims 1 to 7 or any one of claims 8 to 13 is performed.

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