Communication method, terminal, network device, and communication system

Abstract

The present disclosure relates to a communication method, a terminal, a network device, and a communication system. The method comprises: when beam measurement information and beam prediction information of a first beam set can be obtained, sending a first report to a network device, wherein the first report comprises the beam measurement information of the first beam set, and the beam prediction information of the first beam set comprises at least one of the following: information output by an AI function; and information output by an AI model. By means of the embodiments of the present disclosure, the accuracy of a first report during performance monitoring of an AI model or an AI function can be improved.

Description

Communication method, terminal, network device, and communication system Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to a communication method, a terminal, a network device, and a communication system. Background Art

[0002] In New Radio (NR), especially in operating frequency range 2, beam-based transmission and reception are required to ensure coverage.

[0003] Summary of the Invention

[0004] Beam management based on artificial intelligence (AI) models or AI functions requires monitoring model performance and reporting beam information. However, there is a phenomenon that inaccurate beam information reporting leads to reduced communication performance.

[0005] The embodiments of the present disclosure provide a communication method, a terminal, a network device, and a communication system.

[0006] According to a first aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: when beam measurement information and beam prediction information of a first beam set can be obtained, sending a first report to a network device, wherein the first report includes the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: artificial intelligence (AI) function; information output by an AI model.

[0007] According to the second aspect of an embodiment of the present disclosure, a communication method is proposed, which includes: receiving a first report, where the first report is sent by a terminal when beam measurement information and beam prediction information of a first beam set can be obtained, the first report including beam measurement information of the first beam set, and the prediction information of the first beam set including at least one of the following: information output by an AI function; information output by an AI model.

[0008] According to a third aspect of an embodiment of the present disclosure, a communication method is proposed, which includes: when a terminal can obtain beam measurement information and beam prediction information of a first beam set, the terminal sends a first report to a network device, wherein the first report includes the beam measurement information of the first beam set, and the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model; and the network device receives the first report.

[0009] According to the fourth aspect of an embodiment of the present disclosure, a terminal is proposed, comprising: a transceiver module, configured to send a first report to a network device when beam measurement information and beam prediction information of a first beam set are obtained, wherein the first report includes the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model.

[0010] According to a fifth aspect of an embodiment of the present disclosure, a network device is proposed, comprising: receiving a first report, wherein the first report is sent by a terminal when beam measurement information and beam prediction information of a first beam set are obtained, the first report including beam measurement information of the first beam set, and the prediction information of the first beam set including at least one of the following: information output by an AI function; information output by an AI model.

[0011] According to a sixth aspect of an embodiment of the present disclosure, a terminal is proposed, comprising: one or more processors; wherein the processor is configured to execute the communication method of the first aspect.

[0012] According to a seventh aspect of an embodiment of the present disclosure, a network device is proposed, comprising: one or more processors; wherein the processor is used to execute the communication method of the second aspect.

[0013] According to an eighth aspect of an embodiment of the present disclosure, a communication system is provided, including a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and the network device is configured to implement the communication method of the second aspect.

[0014] According to a ninth aspect of an embodiment of the present disclosure, a storage medium is provided, wherein the storage medium stores instructions, and wherein when the instructions are executed on a communication device, the communication device executes the communication method of any one of the first and second aspects.

[0015] Through the embodiments of the present disclosure, the accuracy of the first report during the performance monitoring of the AI ​​model or AI function can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0017] FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0018] FIG2 is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.

[0019] FIG3A is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.

[0020] FIG3B is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.

[0021] FIG3C is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.

[0022] FIG4A is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.

[0023] FIG4B is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.

[0024] FIG5 is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.

[0025] FIG6A is a schematic diagram of a terminal according to an embodiment of the present disclosure.

[0026] FIG6B is a schematic diagram of a network device according to an embodiment of the present disclosure.

[0027] Fig. 7A is a schematic diagram showing a communication device according to an exemplary embodiment.

[0028] FIG7B is a schematic diagram showing a chip structure according to an exemplary embodiment. DETAILED DESCRIPTION

[0029] The embodiments of the present disclosure provide a communication method, a terminal, a network device, and a communication system.

[0030] In a first aspect, an embodiment of the present disclosure proposes a communication method, which includes: when beam measurement information and beam prediction information of a first beam set can be obtained, sending a first report to a network device, the first report including the beam measurement information of the first beam set; the beam prediction information of the first beam set including at least one of the following: information output by an artificial intelligence (AI) function; information output by an AI model.

[0031] In the above embodiment, by adding actual measurement information to the first report, the network device can determine the current communication status based on the actual measurement information, avoiding the use of inaccurate beam prediction information for communication during performance monitoring of the AI ​​function or model, thereby improving the communication performance of beam-based communication.

[0032] In combination with some embodiments of the first aspect, in some embodiments, the beam prediction information and the beam measurement information are different.

[0033] In combination with some embodiments of the first aspect, in some embodiments, the situations in which beam measurement information of the first beam set can be obtained include: the situation in which the AI ​​function is in an activated state during performance monitoring of the AI ​​function; and the situation in which the AI ​​model is in an activated state during performance monitoring of the AI ​​model.

[0034] In combination with some embodiments of the first aspect, in some embodiments, a first report is sent to a network device, including at least one of the following: an AI function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is sent to the network device after obtaining beam measurement information of the first beam set; the AI ​​function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is sent to the network device after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and a positive integer less than or equal to Q, and Q is the number of time domain instances output by the AI ​​function or AI model.

[0035] In the above embodiment, based on the above scheme, the first report sending scheme for the AI ​​model or AI function used for time domain and spatial domain prediction scenarios is clarified. The communication method provided in this embodiment can be applied to scenarios where the AI ​​model or AI function is used for time domain and spatial domain prediction.

[0036] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: receiving first information, where the first information is used to instruct the terminal to perform performance monitoring on at least one of the following: AI function; AI model.

[0037] In the above embodiment, based on the above solution, it is clarified that in addition to performing spontaneous performance monitoring, the terminal can also perform performance monitoring via instructions from the network device.

[0038] In combination with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following: configuration information of the reference signal resource set of the first beam set; configuration information of the reference signal resource set of the second beam set; configuration information of the measurement quantity of the first beam set; configuration information of the measurement quantity of the second beam set.

[0039] In combination with some embodiments of the first aspect, in some embodiments, the first information is carried by at least one of the following: radio resource control RRC signaling; media access control MAC CE activation indication; downlink control information DCI.

[0040] In the above embodiment, the first information is sent based on the existing bearer resources, which can reduce signaling overhead.

[0041] In combination with some embodiments of the first aspect, in some embodiments, performance monitoring of the AI ​​function or AI model includes: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.

[0042] In the above embodiment, by comparing the predicted value with the actual value, the degree of deviation between the current predicted value and the actual value can be determined, laying a technical foundation for the subsequent evaluation of the performance of the AI ​​model or AI function.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: when the performance value satisfies the first condition, performing at least one of the following: activating the first AI function; activating the first AI model; switching to the first AI function; switching to the first AI model.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: when the performance value satisfies the second condition, performing at least one of the following: deactivating the first AI function; deactivating the first model included in the first AI function; switching to the second AI function; switching to the second AI model; returning to the non-AI model; updating the first AI function; and updating the first AI model.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1;

[0046] Within a first time threshold, the performance value meets the performance monitoring indicator M times, where M is an integer greater than or equal to 1; and the proportion of the first number of performance values ​​that meet the performance monitoring indicator is greater than a first proportion threshold.

[0047] In combination with some embodiments of the first aspect, in some embodiments, the performance value satisfies the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the performance value fails to meet the performance monitoring indicator for 0 consecutive times, where 0 is an integer greater than or equal to 1;

[0049] Within the second time threshold, the performance value fails to meet the performance monitoring indicator P times, where P is an integer greater than or equal to 1; and the proportion of the second number of performance values ​​that fail to meet the performance monitoring indicator is greater than the second proportion threshold.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair whose L1-RSRP difference is within the second threshold is less than the fourth accuracy threshold; the L1-RSRP difference is greater than the third difference threshold; the predicted L1-RSRP difference is greater than the fourth difference threshold; the terminal average throughput is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.

[0051] In combination with some embodiments of the first aspect, in some embodiments, performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.

[0052] In second aspect, an embodiment of the present disclosure proposes a communication method, which includes: receiving a first report, where the first report is sent by the terminal when beam measurement information and beam prediction information of the first beam set can be obtained, the first report including the beam measurement information of the first beam set, and the prediction information of the first beam set including at least one of the following: information output by the AI ​​function; information output by the AI ​​model.

[0053] In combination with some embodiments of the second aspect, in some embodiments, the beam prediction information and the beam measurement information are different, and the beam prediction information is obtained based on AI function or AI model prediction.

[0054] In combination with some embodiments of the second aspect, in some embodiments, the situation in which the beam measurement information of the first beam set can be obtained includes: the situation in which the AI ​​function is in an activated state during performance monitoring of the AI ​​function; the situation in which the AI ​​model is in an activated state during performance monitoring of the AI ​​model.

[0055] In combination with some embodiments of the second aspect, in some embodiments, receiving a first report includes at least one of the following: the AI ​​function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is received after obtaining beam measurement information of the first beam set; the AI ​​function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is received after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and a positive integer less than or equal to Q, and Q is the number of time domain instances output by the AI ​​function or AI model.

[0056] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: sending first information, where the first information is used to instruct the terminal to perform performance monitoring on at least one of the following items, including: AI function; AI model.

[0057] In combination with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following: configuration information of the reference signal resource set of the first beam set; configuration information of the reference signal resource set of the second beam set; configuration information of the measurement quantity of the first beam set; configuration information of the measurement quantity of the second beam set.

[0058] In combination with some embodiments of the second aspect, in some embodiments, the first information is carried by at least one of the following: radio resource control RRC signaling; media access control MAC CE activation indication; downlink control information DCI.

[0059] In combination with some embodiments of the second aspect, in some embodiments, the AI ​​function or AI model performs performance monitoring, including: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.

[0060] In combination with some embodiments of the second aspect, in some embodiments, the performance value satisfying the first condition is a condition for the terminal to perform at least one of the following: the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1; within a first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; the proportion of the first number of performance values ​​that satisfy the performance monitoring indicator is greater than a first proportion threshold; when the performance value satisfies the first condition, the terminal performs at least one of the following: activating the first AI function; activating the first AI model; switching to the first AI function; switching to the first AI model.

[0061] In combination with some embodiments of the second aspect, in some embodiments, the performance value satisfies the performance monitoring indicators, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

[0062] In combination with some embodiments of the second aspect, in some embodiments, when the performance value satisfies the second condition, the terminal performs at least one of the following: the performance value fails to meet the performance monitoring indicator for 0 consecutive times, where 0 is an integer greater than or equal to 1; the ratio of a second number of performance values ​​that fail to meet the performance monitoring indicator is greater than a second ratio threshold; when the performance value satisfies the second condition, the terminal performs at least one of the following: deactivating the first AI function; deactivating the first AI model and switching to the second AI function; switching to the second AI model; returning to a non-AI model; updating the first AI function; or updating the first AI model.

[0063] In combination with some embodiments of the second aspect, in some embodiments, the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair within the second threshold of the L1-RSRP difference is less than the fourth accuracy threshold; the difference of the reference signal received power L1-RSRP of layer 1 is greater than the third difference threshold; the difference of the predicted L1-RSRP is greater than the fourth difference threshold; the average throughput of the terminal is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.

[0064] In combination with some embodiments of the second aspect, in some embodiments, performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.

[0065] In a third aspect, an embodiment of the present disclosure proposes a communication method, comprising: when the terminal can obtain beam measurement information and beam prediction information of a first beam set, the terminal sends a first report to a network device, the first report including the beam measurement information of the first beam set, and the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model; the network device receives the first report. In a fourth aspect, an embodiment of the present disclosure proposes a terminal, comprising: when the beam measurement information and beam prediction information of a first beam set are obtained, the terminal sends a first report to a network device, the first report including the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model.

[0066] In a fifth aspect, an embodiment of the present disclosure proposes a network device, including: a device for receiving a first report, the first report being sent by a terminal when beam measurement information and beam prediction information of a first beam set are obtained, the first report including beam measurement information of the first beam set, and the prediction information of the first beam set including at least one of the following: information output by an AI function; information output by an AI model.

[0067] In a sixth aspect, an embodiment of the present disclosure proposes a terminal, comprising: one or more processors; wherein the processor is used to execute the communication method of the first aspect.

[0068] In a seventh aspect, an embodiment of the present disclosure proposes a network device, comprising: one or more processors; wherein the processor is used to execute the communication method of the second aspect.

[0069] In an eighth aspect, an embodiment of the present disclosure proposes a communication system, comprising: a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and the network device is configured to implement the communication method of the second aspect.

[0070] In a ninth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, which, when the instructions are executed on a communication device, enables the communication device to execute any one of the communication methods of the first aspect and the second aspect.

[0071] It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to execute the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods.

[0072] The present disclosure provides a communication method, a terminal, a network device, and a communication system. In some embodiments, the terms "communication method" and "information processing method" are interchangeable, the terms "communication device" and "information processing device" are interchangeable, and the terms "information processing system" and "communication system" are interchangeable.

[0073] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0074] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0075] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0076] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0077] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0078] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0079] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0080] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0081] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0082] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0083] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0084] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0085] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0086] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0087] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)", etc.

[0088] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0089] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0090] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0091] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0092] FIG1 is a schematic diagram illustrating the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , a communication system 100 includes a terminal 101 and a network device 102 .

[0093] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0094] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0095] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0096] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0097] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0098] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0099] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0100] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0101] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0102] In NR, especially in frequency range 2, since high-frequency channels attenuate quickly, communication based on beam transmission and reception is required to ensure coverage.

[0103] For beam management, the base station configures a reference signal resource set for beam measurement. The terminal measures the reference signals on the reference signal resources in the reference signal resource set and then reports X (X is a positive integer) of the stronger reference signal resource identifiers (IDs) and corresponding related parameters (including at least one of the following, for example: Layer 1 Reference Signal Received Power (L1-RSRP); Layer 1 Signal-to-Interference-plus-Noise Ratio (L1-SINR)).

[0104] In some embodiments, an AI model and / or AI function is provided to implement beam prediction, wherein an AI function can be considered as one or more AI models that implement the same function and purpose.

[0105] In some embodiments, the AI ​​model used for beam prediction can be referred to as a beam prediction model. Of course, it can also be referred to as a beam prediction AI model, a prediction AI model, a predicted beam model, etc. This disclosure does not limit the names of such AI models.

[0106] In some implementations, when the beam prediction model is spatial prediction, the terminal measures the L1-RSRP of set B and inputs it into the beam prediction model. The beam prediction model can predict the L1-RSRP of set A and / or the identifier of the best beam / beam pair in set A.

[0107] The relationship between set B and set A includes the following two types:

[0108] The first relationship is that set B is a subset of set A. For example, if set A contains 32 reference signal resources (each reference signal resource corresponds to a beam direction), then set B contains some of them, for example, set B contains 8 of the 32 reference signal resources.

[0109] The second relationship is that set B is a wide beam and set A is a narrow beam. For example, set A contains 32 reference signal resources (each reference signal resource corresponds to a beam direction, and the 32 reference signal resources cover a 120-degree direction). Set B contains another Y reference signal resources, for example, Y = 8. These Y reference signal resources also cover a 120-degree direction, that is, the beam direction of each reference signal resource in set B covers the beam directions of multiple reference signal resources in set A. It can be understood that the 32 / Y reference signal resources in set A and the same reference signal resource in set B are in a QCL Type D relationship.

[0110] It should be understood that the examples of the first and second relationships described above only describe the transmit beam case. When considering beam pairs consisting of transmit and receive beams, the terminal's receive beams must also be considered. For example, if there are 32 transmit beams and 4 receive beams, then set A consists of 32*4 beam pairs, and set B can consist of 32 beam pairs, 16 beam pairs, and so on.

[0111] In some embodiments, if the performance of the beam prediction model does not need to be monitored, assuming that the AI ​​model has been trained in advance, the network device only needs to periodically send reference signals on the reference signal resources of set B (for example, the first period). The terminal then measures the L1-RSRP of the reference signals on the reference signal resources in set B and inputs it into the beam prediction model. This outputs the L1-RSRP of set A or the strongest reference signal resource ID or IDs of one or more of the 32 reference signals in set A.

[0112] In some embodiments, if it is necessary to monitor the performance of the beam prediction model, the network device is required to periodically send the reference signal resources of set A (for example, the second period). Then the terminal needs to measure the L1-RSRP of the reference signal on the reference signal resources of set B. For the terminal side model, the terminal inputs the measured L1-RSRP of the reference signal on the reference signal resources of set B into the beam prediction model, obtains the predicted beam information, and reports it to the network device; for the network side model, the terminal reports the measured L1-RSRP and / or identifier of the reference signal on the reference signal resources of set B to the network device, and the network device inputs the L1-RSRP and / or identifier of set B into the beam prediction model to obtain the predicted beam information. At the same time, the terminal also measures the L1-RSRP of the reference signal on all reference signal resources in set A, and reports the measured L1-RSRP of the reference signal on all reference signal resources in set A and / or the best beam / beam pair identifier as the beam information obtained by the traditional method to the base station.

[0113] If set B is a subset of set A, the terminal may only report the beam information of all beams or beam pairs of set A.

[0114] In some embodiments, the second period is greater than the first period, or the second period is a multiple of the first period, or how much greater the second period is than the first period is not limited in this disclosure. Of course, the second period can also be less than or equal to the first period, which is not limited in this disclosure.

[0115] In some implementations, when the beam prediction model uses time-domain prediction, the terminal measures the L1-RSRP at a historical time point, set B, and inputs this into the beam prediction model to predict the L1-RSRP at a future time point, set A. In addition to the two aforementioned relationships between set B and set A, there is another relationship in which set B and set A are identical.

[0116] If beam prediction is performed based on a beam prediction model, the reference signal for future beam measurement may not be sent if performance monitoring of the model is not required. If the beam prediction model is deployed on the terminal, the terminal must report the beam information output by the model to the network device.

[0117] For traditional beam measurement methods, the base station also needs to transmit reference signals for future time periods. The terminal measures the reference signals for future time periods and reports the measured beam information to the network device. This means the terminal needs to measure and report the beam information for all beams and / or beam pairs in Set B and Set A to the network device.

[0118] In some related technologies, in order to reduce the number of beam pairs measured by the terminal, an AI model is used for beam prediction. For example, the terminal originally needs to measure a total of G*H beam pairs. When using the AI ​​model for beam prediction, for spatial domain beam prediction, the terminal only needs to measure a part of the G*H beam pairs. For example, measure 1 / 8, 1 / 4 of G*H beam pairs, etc. Then, the beam measurement quality of these beam pairs measured is input into the AI ​​model, and the AI ​​model can output the beam information of G*H beam pairs. For time domain beam prediction, the terminal can measure the beam measurement quality of beam pairs at historical times to predict the beam information of beam pairs at future times.

[0119] Of course, for the input and output of some AI models, it is also possible to not consider the beam quality or beam identification of the beam pair, but only consider the beam quality or beam identification of the downlink transmit beam. For example, an AI model based on a downlink beam, rather than an AI model based on a beam pair, is not limited in this disclosure.

[0120] It's clear that AI models have a specific lifecycle or scope of application. Therefore, real-time monitoring of AI model performance is necessary. When AI model performance falls short of expectations, timely updates or switching of the model are necessary.

[0121] In some embodiments, during the AI ​​model performance monitoring process, for example, for a terminal-side model, the terminal can calculate the performance monitoring indicators on its own, and then determine whether the model needs to be deactivated based on the calculation results of the performance monitoring indicators.

[0122] It is clear that, for example, when the calculated results of an AI model's performance monitoring indicator fail to meet the corresponding threshold or preset requirements, it will be considered that the AI ​​model's performance monitoring indicator does not meet the requirements, that is, the AI ​​model's performance is poor. Therefore, it is necessary to deactivate the current AI model with poor performance.

[0123] It is clear that the performance monitoring indicators of AI functions are the same as those of the above-mentioned AI models, and will not be detailed here.

[0124] In some embodiments, during the AI ​​model performance monitoring process, for example, performance monitoring of a hybrid model or a network-side (NW-side) model, the terminal needs to report at least one of the following items, including: data used to calculate performance monitoring indicators, performance monitoring indicators, performance values, events that trigger reporting, etc.

[0125] It is clear that these performance monitoring indicators are obtained by comparing the beam information output by the model with the beam information of set A actually measured.

[0126] In some embodiments, the performance monitoring indicators include at least one of the following: beam prediction accuracy of top 1 / K beam (pair)), beam prediction accuracy of the beam or beam pair within a first threshold value, L1-RSRP difference, predicted L1-RSRP difference, average UE throughput difference, reference signal resource overhead, uplink control information overhead, and predicted delay.

[0127] In some embodiments, the performance indicator is beam prediction accuracy. If the beam prediction is accurate, it can be understood that the performance monitoring indicator meets the requirement. Accurate beam prediction includes at least one of the following: the predicted strongest beam identification (ID) includes the measured strongest beam ID; the predicted strongest beam pair ID includes the measured strongest beam pair ID; the predicted strongest beam ID is included in the N strongest measured beam IDs, where N is an integer greater than or equal to 1; and the predicted strongest beam pair ID is included in the N strongest measured beam pair IDs.

[0128] In some embodiments, the downlink receive beam ID is the receive beam ID (Rx beam ID) of the terminal. The beam pair ID is the ID corresponding to the combination of the downlink transmit beam and the downlink receive beam.

[0129] It should be understood that the strongest beam ID refers to the beam ID with the strongest L1-RSRP or the beam ID with the strongest L1-SINR, and the strongest beam pair ID refers to the beam pair ID with the strongest L1-RSRP or the beam pair ID with the strongest L1-SINR.

[0130] Optionally, the beam prediction accuracy may be the accuracy of beam information derived and outputted based on multiple generations of the model, for example, it may be a ratio.

[0131] In some embodiments, for a beam or beam pair prediction accuracy where the L1-RSRP difference is within a first threshold, the L1-RSRP difference comprises at least one of the following:

[0132] The difference between the measured L1-RSRP of the best predicted beam and the measured L1-RSRP of the best beam;

[0133] The difference between the measured L1-RSRP of the best predicted beam pair and the measured L1-RSRP of the best measured beam pair.

[0134] In some embodiments, the performance monitoring indicator is the accuracy of beam or beam pair predictions for which the L1-RSRP difference is within a first threshold. If the first threshold is 1 dB, and the difference is less than a certain threshold, the accuracy requirement is met, which can be understood as the performance monitoring indicator meeting the requirement. Meeting the accuracy requirement includes: the accuracy of the difference between the measured L1-RSRP of the predicted best beam and the measured L1-RSRP of the measured best beam being within 1 dB, or the accuracy of the difference between the L1-RSRP of the predicted best beam pair and the measured L1-RSRP of the measured best beam pair being within 1 dB.

[0135] It should be understood that the best beam ID refers to the beam ID with the best L1-RSRP or the beam ID with the best L1-SINR, and the best beam pair ID refers to the beam pair ID with the best L1-RSRP or the beam pair ID with the best L1-SINR.

[0136] In some embodiments, the L1-RSRP difference includes: a difference between the predicted L1-RSRP of the best beam and the measured L1-RSRP of the measured best beam, or a difference between the predicted measured L1-RSRP of the best beam pair and the measured L1-RSRP of the best beam pair. For example, the average value is X dB, or the value at the 5% percentile of the cumulative distribution function is Y dB.

[0137] Optionally, the L1-RSRP difference may also be a ratio of the L1-RSRP difference being lower than a threshold value, or a ratio of the L1-RSRP difference being higher than a threshold value.

[0138] In some embodiments, the predicted L1-RSRP difference includes: a difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the best beam of the predicted best beam, or a difference between the predicted L1-RSRP of the predicted best beam pair and the measured L1-RSRP of the best beam of the predicted best beam pair, such as an average value of X dB or a value at the 5% percentile of the cumulative distribution function of Y dB.

[0139] Optionally, the predicted L1-RSRP difference may also be a ratio of the L1-RSRP difference being lower than a threshold value, or a ratio of the L1-RSRP difference being higher than a threshold value.

[0140] In some embodiments, when the performance monitoring indicator is the average terminal throughput, if the throughput difference is less than a certain threshold, it can be understood that the performance monitoring indicator meets the requirement. The average throughput meets the requirement, including: obtaining the SINR corresponding to the two beams based on the predicted strongest beam and the measured strongest beam, calculating the throughput (capacity) and the capacity difference based on Shannon capacity, or obtaining the SINR corresponding to the two beam pairs based on the predicted strongest beam pair and the measured strongest beam pair, and calculating the capacity and the capacity difference based on Shannon capacity.

[0141] In some embodiments, the reference signal resource overhead is the number of reference signal resources required by the AI ​​model, and the main influencing factors include the size of set B corresponding to the AI ​​model input and the number of historical measurement times during time domain prediction.

[0142] In some embodiments, when the AI ​​model is a network-side model, the measurement results of set B need to be reported to the network, that is, the signaling overhead of this reporting.

[0143] In some embodiments, for the performance monitoring of AI functions or models on the terminal side or the performance monitoring of hybrid AI functions or models, the terminal will not report the beam information of set A actually measured, but will calculate the corresponding performance monitoring indicators by itself and then report them to the network, so the network does not know the beam information of set A actually measured during this period. However, since the function or model is performing performance monitoring, it has not yet been determined whether the performance status of the function or model is good or bad, that is, there may be functions or models with poor performance that have not yet been deactivated. In this case, the terminal will also obtain relevant information about the best beam or the best beam pair (for example, SSB ID or CSI-RS ID, L1-RSRP, L1-SINR, etc.) based on the output of the AI ​​model, and report it to the network as a beam report.

[0144] It should be understood that the beam reporting in the above embodiment refers to beam reporting based on the CSI report mechanism.

[0145] Obviously, there is a large difference between the beam report reported at this time and the actually measured beam information of set A, so the reporting result is not accurate, thereby reducing the performance of beam-based communication.

[0146] Based on this, an embodiment of the present disclosure proposes a communication method, which enables the terminal to add actual measurement information of the beam to the beam report when it is in model performance monitoring, thereby improving the accuracy of the beam report.

[0147] FIG2 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG2 , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0148] In step S2101, terminal 101 performs performance monitoring.

[0149] In some embodiments, the terminal 101 performs performance monitoring, including at least one of the following: performance monitoring based on artificial intelligence (AI) functions, or performance monitoring based on AI models.

[0150] In some embodiments, the AI ​​functionality is configured to output beam prediction information for the first set of beams.

[0151] In some embodiments, the AI ​​model is used to output beam prediction information for the first set of beams.

[0152] Illustratively, the first beam set is set A in the above embodiment. The beam prediction information of the first beam set is the beam prediction information of set A in the above embodiment.

[0153] Exemplarily, the AI ​​model uses set B in the above embodiment as the input of the AI ​​model, and the output result obtained by the AI ​​model output is the beam prediction information of the first beam set.

[0154] In some embodiments, the performance monitoring is performed based on the AI ​​function or based on the AI ​​model, including: comparing the beam prediction information of the first beam set with the beam measurement information of the first beam set to obtain a performance value.

[0155] In some embodiments, when the beam prediction information of the first beam set is compared with the beam measurement information of the first beam set, the beam measurement information of the first beam set serves as a reference (or benchmark) for the comparison.

[0156] For example, taking the performance monitoring indicator for comparison as the difference in predicted L1-RSRP as an example, comparing the beam prediction information of the first beam set with the beam measurement information of the first beam set can be understood as the difference between the predicted L1-RSRP and the actually measured L1-RSRP.

[0157] In some embodiments, the performance value includes at least one of the following: beam or beam pair prediction accuracy; beam or beam pair prediction accuracy with L1-RSRP difference within a first threshold; L1-RSRP variability less than or equal to a first variability threshold; predicted L1-RSRP variability; terminal average throughput; reference signal resource overhead; row control information overhead; predicted delay.

[0158] In some embodiments, the performance value satisfies different conditions, and the terminal 101 performs different processing according to the function or model.

[0159] In some embodiments, the performance value satisfies the first condition, and the terminal 101 performs at least one of the following: activating the first AI function, activating the first AI model, switching to the first AI function, switching to the first AI model, etc.

[0160] In some embodiments, the performance value meeting the first condition includes at least one of the following:

[0161] The performance value meets the performance monitoring indicator for L consecutive times, where L is an integer greater than or equal to 1;

[0162] Within the first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1;

[0163] The proportion of the first number of performance values ​​that meets the performance monitoring indicator is greater than a first proportion threshold.

[0164] Optionally, the number L and the number M may be equal or unequal in value.

[0165] Optionally, at least one of the following is determined based on a network configuration or a default rule:

[0166] The value of the number of times L, the value of the number of times M, the first time threshold, the first quantity threshold, and the first ratio threshold.

[0167] In some embodiments, the performance value satisfies the performance monitoring indicators, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair whose L1-RSRP difference is within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

[0168] Optionally, the L1-RSRP difference is determined based on beam prediction information and beam measurement information.

[0169] Optionally, the L1-RSRP difference is determined based on beam prediction information and beam measurement information.

[0170] Optionally, the difference of the predicted L1-RSRP is determined based on the beam prediction information and the beam measurement information.

[0171] Optionally, the average throughput of the terminal is determined based on the predicted strongest reference signal and the measured strongest reference signal, and corresponding SINRs of the two reference signals are obtained.

[0172] Optionally, the first threshold is determined based on network configuration or default rules.

[0173] Exemplarily, the first threshold may be 1 decibel dB.

[0174] Optionally, at least one of the following is determined based on a network configuration or a default rule:

[0175] A first accuracy threshold, a second accuracy threshold, a first difference threshold, a second difference threshold, a first throughput threshold, a first overhead threshold, a second overhead threshold, and a first latency threshold.

[0176] Optionally, the first difference threshold and the second difference threshold may be equal or unequal in value.

[0177] Optionally, the first accuracy threshold and the second accuracy threshold may be equal or unequal in value.

[0178] In some embodiments, the L1-RSRP difference is the difference between the measured L1-RSRP of the best predicted beam or the best beam pair and the measured L1-RSRP of the best beam or the best beam pair.

[0179] In some embodiments, the L1-RSRP difference is the difference between the measured L1-RSRP of the best predicted beam or the best beam pair and the measured L1-RSRP of the measured best beam or beam pair.

[0180] In some embodiments, the L1-RSRP difference includes at least one of the following: an average value of the L1-RSRP difference, a cumulative distribution function of the L1-RSRP difference, a proportion of the L1-RSRP difference less than or equal to a first difference threshold, and a proportion of the L1-RSRP difference greater than a second difference threshold, wherein the L1-RSRP difference is the difference between the measured L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best beam or beam pair.

[0181] Optionally, the first difference threshold and the second difference threshold may be equal or unequal in value.

[0182] Optionally, the first difference threshold is determined based on network configuration or default rules.

[0183] Optionally, the second difference threshold is determined based on network configuration or default rules.

[0184] In some embodiments, the predicted L1-RSRP difference is the difference between the predicted L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best predicted beam or beam pair.

[0185] In some embodiments, the predicted L1-RSRP difference includes at least one of the following: the average value of the predicted L1-RSRP difference, the cumulative distribution function of the predicted L1-RSRP difference, the proportion of the predicted L1-RSRP difference less than or equal to the third difference threshold, and the proportion of the predicted L1-RSRP difference greater than the fourth difference threshold, wherein the predicted L1-RSRP difference is the difference between the predicted L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best predicted beam or beam pair.

[0186] Optionally, the third difference threshold and the fourth difference threshold may be equal or unequal in value.

[0187] Optionally, the third difference threshold is determined based on network configuration or default rules.

[0188] Optionally, the fourth difference threshold is determined based on network configuration or default rules.

[0189] In some embodiments, if the performance value does not meet the first condition, the terminal 101 performs at least one of the following: deactivating the first AI function, deactivating the first AI function model, switching to the second AI function, switching to the second AI function model, returning to the non-AI model, updating the first AI function, and updating the first AI model.

[0190] In some embodiments, the performance value satisfies the second condition, and the terminal 101 performs at least one of the following: deactivating the first AI function, deactivating the first AI function model, switching to the second AI function, switching to the second AI function model, returning to the non-AI model, updating the first AI function, and updating the first AI model.

[0191] In some embodiments, the performance threshold satisfies the second condition or does not satisfy the first condition, including at least one of the following: the performance value fails to satisfy the performance monitoring indicator for 0 consecutive times, where 0 is an integer greater than or equal to 1; within the second time threshold, the performance value satisfies the performance monitoring indicator for P times, where P is an integer greater than or equal to 1;

[0192] The proportion of the second number of performance values ​​that do not meet the performance monitoring indicator is greater than a second proportion threshold.

[0193] Optionally, the number of times O and the number of times P may be equal or unequal in value.

[0194] Optionally, at least one of the following is determined based on a network configuration or a default rule:

[0195] The value of the number of times O, the value of the number of times P, the second quantity, the second threshold, the second time threshold and the second ratio threshold.

[0196] Optionally, the first time threshold and the second time threshold are the same or different in value.

[0197] Optionally, the first quantity and the second quantity may be equal or unequal in value.

[0198] Optionally, the first ratio threshold and the second ratio threshold may be equal or unequal in value.

[0199] In some embodiments, the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair within the second threshold of the L1-RSRP difference is less than the fourth accuracy threshold; the L1-RSRP difference is greater than the third difference threshold; the predicted L1-RSRP difference is greater than the fourth difference threshold; the terminal average throughput is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.

[0200] Exemplarily, the second threshold may be 1 decibel dB.

[0201] Optionally, at least one of the following is determined based on a network configuration or a default rule:

[0202] A third accuracy threshold, a fourth accuracy threshold, a second throughput threshold, a third overhead threshold, and a fourth overhead threshold.

[0203] Optionally, the third accuracy threshold and the fourth accuracy threshold may be equal or unequal in value.

[0204] Optionally, the first accuracy threshold, the second accuracy threshold, the third accuracy threshold, and the fourth accuracy threshold may be equal or unequal in value.

[0205] Optionally, the first throughput threshold and the second throughput threshold may be equal or unequal in value.

[0206] Optionally, the third overhead threshold and the fourth overhead threshold may be equal or different in value.

[0207] Optionally, the first overhead threshold, the second overhead threshold, the third overhead threshold, and the fourth overhead threshold may be equal or unequal in value.

[0208] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0209] In some embodiments, the terms "measurement", "actual", "actual measurement", etc. can be used interchangeably.

[0210] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0211] It should be understood that the specific contents of the performance monitoring indicators are as introduced in the previous embodiments and will not be repeated here.

[0212] Step S2102: The network device 102 sends first information.

[0213] In some embodiments, the network device 102 sends first information to the terminal 101 .

[0214] In some embodiments, terminal 101 receives first information.

[0215] In some embodiments, the first information is used to instruct the terminal 101 to perform performance monitoring on at least one of the AI ​​functions or AI models.

[0216] In some embodiments, the first information includes at least one of the following: configuration information of the reference signal resource set of the first beam set, configuration information of the reference signal resource set of the second beam set, measurement quantity configuration information of the first beam set, and measurement quantity configuration information of the second beam set.

[0217] In some embodiments, the measurement quantity configuration information of the first beam set includes: L1-RSRP corresponding to the first beam set, or L1-SINR corresponding to the first beam set.

[0218] In some embodiments, the measurement quantity configuration information of the second beam set includes: L1-RSRP corresponding to the second beam set, or L1-RSRP corresponding to the second beam set.

[0219] In some embodiments, the first information is carried on at least one of the following items: radio resource control RRC signaling, media access control MAC CE activation indication, and downlink control information DCI.

[0220] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0221] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0222] Step S2103: Terminal 101 sends a first report.

[0223] In some embodiments, terminal 101 sends a first report to network device 102 .

[0224] In some embodiments, network device 102 receives a first report.

[0225] In some embodiments, the first report includes beam measurement information for the first set of beams.

[0226] Optionally, the first report is a beam report.

[0227] In some embodiments, the beam prediction information of the first set of beams is different from the beam measurement information of the first set of beams.

[0228] In some embodiments, the terminal 101 sends a first report to the network device 102, including at least one of the following:

[0229] The AI ​​function is used for spatial beam prediction and sends a first report to the network device 102 after obtaining beam measurement information of the first beam set;

[0230] The AI ​​model is used for spatial beam prediction and sends a first report to the network device 102 after obtaining beam measurement information of the first beam set;

[0231] The AI ​​function is used for time domain beam prediction of a single time domain instance, and sends a first report to the network device 102 after obtaining beam measurement information of a first beam set;

[0232] The AI ​​model is used for spatial beam prediction and sends a first report to the network device 102 after obtaining beam measurement information of the first beam set;

[0233] The AI ​​function is used for beam prediction for multiple time domain instances, and after obtaining beam measurement information of N time domain instances of a first beam set, a first report is sent to the network device 102;

[0234] The AI ​​model is used for beam prediction for multiple time domain instances. After obtaining beam measurement information of N time domain instances of the first beam set, a first report is sent to the network device 102, where N is a positive integer greater than or equal to 1 and less than or equal to M, and M is the number of time domain instances output by the AI ​​function or AI model.

[0235] In some embodiments, after obtaining the beam measurement information of multiple time domain instances, the terminal 101 may report the beam measurement information of the multiple time domain instances to the network device 102 through the same first report.

[0236] In some embodiments, the terminal 101 sends a first report to the network device 102 when the beam measurement information and beam prediction information of the first beam set can be obtained.

[0237] In some embodiments, when the terminal 101 is able to obtain beam measurement information and beam prediction information of the first beam set, the terminal 101 sends a first report to the network device 102, where the first report includes the beam measurement information.

[0238] In some embodiments, the situation in which beam measurement information and beam prediction information of the first beam set can be obtained includes at least one of the following:

[0239] During the performance monitoring of the AI ​​function, the AI ​​function is activated.

[0240] During the performance monitoring of the AI ​​model, the AI ​​model is in an active state.

[0241] It should be understood that although the AI ​​function or AI model is activated, terminal 101 can obtain beam prediction information of the first beam set based on the AI ​​function or AI model. However, because the AI ​​function or model is in the performance monitoring period, terminal 101 can also obtain beam measurement information of the first beam set. Beam measurement information is more accurate than beam prediction information. Therefore, in this case, terminal 101 should report the beam measurement information of the first beam set instead of the beam prediction information.

[0242] Optionally, when the terminal 101 can obtain the beam measurement information and beam prediction information of the first beam set, the terminal 101 sends a first report to the network device 102, where the first report includes the beam measurement information and the beam prediction information.

[0243] It should be understood that although the AI ​​function or AI model is activated, the terminal 101 can obtain beam prediction information of the first beam set based on the AI ​​function or AI model. However, because the AI ​​function or model is in the performance monitoring period, the terminal 101 can also obtain beam measurement information of the first beam set. Beam measurement information is more accurate than beam prediction information. Therefore, in this case, the terminal 101 can report the beam measurement information of the first beam set and the beam prediction information, thereby providing a reference for the network device.

[0244] In some embodiments, “time domain instance” and “prediction time instance” can be used interchangeably.

[0245] The communication method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2103. For example, step S2103 may be implemented as an independent embodiment, step S2101 may be implemented as an independent embodiment, and steps S2101+S2102+S2103 may be implemented as independent embodiments, but are not limited thereto.

[0246] In some embodiments, steps S2101 and S2102 may be executed in an exchanged order or simultaneously, steps S2102 and S2103 may be executed in an exchanged order or simultaneously, and steps S2101 and S2103 may be executed in an exchanged order or simultaneously.

[0247] In some embodiments, steps S2101 and S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0248] In some embodiments, steps S2102 and S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0249] In some embodiments, steps S2101 and S2102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0250] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2 .

[0251] In some embodiments, FIG3A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3A , an embodiment of the present disclosure relates to a communication method, and the method includes:

[0252] Step S3101, perform performance monitoring.

[0253] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0254] Step S3102, obtaining first information.

[0255] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0256] In some embodiments, the terminal 101 receives the first information sent by the network device 102, but is not limited thereto and may also receive the first information sent by other entities.

[0257] In some embodiments, terminal 101 obtains first information specified by a protocol.

[0258] In some embodiments, terminal 101 obtains the first information from upper layer(s).

[0259] In some embodiments, the terminal 101 performs processing to obtain the first information.

[0260] In some embodiments, step S3102 is omitted, and the terminal 101 autonomously implements the function indicated by the first information, or the above function is default or by default.

[0261] Step S3103: Send the first report.

[0262] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0263] The communication method involved in the embodiments of the present disclosure may include at least one of steps S3101 to S3103. For example, step S3101 can be implemented as an independent embodiment, step S3102 can be implemented as an independent embodiment, step S3103 can be implemented as an independent embodiment, steps S3101+S3102 can be implemented as an independent embodiment, steps S3101+S3103 can be implemented as an independent embodiment, steps S3101+S3103 can be implemented as an independent embodiment, and steps S3101+S3102+S3103 can be implemented as an independent embodiment, but are not limited thereto.

[0264] In some embodiments, steps S3102 and S3103 may be executed in an exchanged order or simultaneously, steps S3101 and S3103 may be executed in an exchanged order or simultaneously, and steps S3102 and S3103 may be executed in an exchanged order or simultaneously.

[0265] In some embodiments, steps S3101 and S3103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0266] In some embodiments, steps S3102 and S3103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0267] FIG3B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0268] Step S3201: Acquire first information and perform performance monitoring.

[0269] The optional implementation of step S3201 can be found in step S2101 and step S2102 of Figure 2, the optional implementation of step S3101 and step S3102 of Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0270] Step S3202: Send the first report.

[0271] The optional implementation of step S3202 can refer to the optional implementation of step S2103 in Figure 2, the optional implementation of step S3103 in Figure 3A, and other related parts of the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0272] The communication method involved in the embodiment of the present disclosure may include at least one of steps S3201 and S3202. For example, step S3201 may be implemented as an independent embodiment, and step S3202 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0273] In some embodiments, step S3201 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0274] In the embodiment of the present disclosure, step S3201 can be combined with steps S3102-S3103 of FIG. 3A , and step S3202 can be combined with steps S3101, S3102, and S3103 of FIG. 3A .

[0275] FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a communication method, which includes:

[0276] Step S3301: In the first case, a first report is sent.

[0277] In some embodiments, the first situation includes: a situation where beam measurement information and beam prediction information of the first beam set can be obtained.

[0278] The optional implementation of step S3301 can refer to the optional implementation of step S2103 in Figure 2, step S3103 in Figure 3A, step S3202 in Figure 3B, and other related parts in the embodiments involved in Figures 2, 3A, and 3B, which will not be repeated here.

[0279] In some embodiments, the situations in which beam measurement information of the first beam set can be obtained include: the situation in which the AI ​​function is in an activated state during performance monitoring of the AI ​​function; and the situation in which the AI ​​model is in an activated state during performance monitoring of the AI ​​model.

[0280] In some embodiments, the beam prediction information of the first beam set includes information output by an AI function or an AI model.

[0281] In some embodiments, the first report includes beam measurement information for the first set of beams.

[0282] Optionally, the first report is a beam report.

[0283] In some embodiments, the beam prediction information and the beam measurement information are different.

[0284] In some embodiments, a first report is sent to a network device, including at least one of the following: an AI function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is sent to the network device after obtaining beam measurement information of a first beam set; the AI ​​function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is sent to the network device after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and a positive integer less than or equal to Q, and Q is the number of time domain instances output by the AI ​​function or AI model.

[0285] In some embodiments, the method further includes: receiving first information, where the first information is used to instruct the terminal to perform performance monitoring on at least one of the following items: AI function; AI model.

[0286] In some embodiments, the first information includes at least one of the following: configuration information of the reference signal resource set of the first beam set; configuration information of the reference signal resource set of the second beam set; configuration information of the measurement quantity of the first beam set; configuration information of the measurement quantity of the second beam set.

[0287] In some embodiments, the first information is carried in at least one of the following: radio resource control RRC signaling; medium access control MAC CE activation indication; downlink control information DCI.

[0288] In some embodiments, performance monitoring of the AI ​​function or AI model includes comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.

[0289] In some embodiments, the method further includes: when the performance value satisfies a first condition, performing at least one of the following: activating a first AI function; activating a first AI model; switching to the first AI function; or switching to the first AI model.

[0290] In some embodiments, the method further includes: when the performance value satisfies a second condition, performing at least one of the following: deactivating the first AI function; deactivating the first model included in the first AI function; switching to the second AI function; switching to the second AI model; returning to the non-AI model; updating the first AI function; or updating the first AI model.

[0291] In some embodiments, the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1;

[0292] Within a first time threshold, the performance value meets the performance monitoring indicator M times, where M is an integer greater than or equal to 1; the prediction accuracy corresponding to each performance value in the first number of performance values ​​is greater than or equal to the first accuracy threshold, and the proportion of the first number of performance values ​​that meet the performance monitoring indicator is greater than the first proportion threshold, and the first number and the first proportion threshold are determined based on network configuration or default rules.

[0293] In some embodiments, the performance value satisfies the performance monitoring indicators, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

[0294] In some embodiments, the performance value fails to meet the performance monitoring indicator for 0 consecutive times, where 0 is an integer greater than or equal to 1;

[0295] Within the second time threshold, the performance value meets the performance monitoring indicator P times, where P is an integer greater than or equal to 1; the proportion of the second number of performance values ​​that do not meet the performance monitoring indicator is greater than the second proportion threshold, and the second number and the second proportion threshold are determined based on network configuration or default rules.

[0296] In some embodiments, the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair within the second threshold is less than the fourth accuracy threshold; the difference of the reference signal received power L1-RSRP of layer 1 is greater than the third difference threshold; the difference of the predicted L1-RSRP is greater than the fourth difference threshold; the average throughput of the terminal is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.

[0297] In some embodiments, performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.

[0298] In the embodiment of the present disclosure, step S3301 may be combined with steps S3102-S3103 of FIG. 3A , and step S3301 may be combined with step S3202 of FIG. 3B .

[0299] FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0300] Step S4101, sending the first information.

[0301] Step S4102, receiving the first report.

[0302] The optional implementation of step S4101 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0303] The optional implementation of step S4102 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0304] The communication method involved in the embodiment of the present disclosure may include at least one of steps S4101 to S4102. For example, step S4101 may be implemented as an independent embodiment, and step S4102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0305] In some embodiments, step S4101 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0306] FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0307] Step S4201, receiving the first report.

[0308] The optional implementation of step S4201 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0309] In some embodiments, the network device 102 obtains the first report sent from the terminal 101, but is not limited thereto and may also obtain the first report from other entities.

[0310] In some embodiments, the first report includes beam measurement information for the first set of beams.

[0311] In some embodiments, the beam prediction information is different from the beam measurement information, and the beam prediction information is predicted based on an AI function or an AI model.

[0312] In some embodiments, the first report is sent by the terminal when beam measurement information and beam prediction information of the first beam set can be obtained.

[0313] In some embodiments, the prediction information of the first beam set includes: information output by an AI function or an AI model.

[0314] In some embodiments, the situation in which beam measurement information of the first beam set can be obtained includes: during performance monitoring of the AI ​​function, the AI ​​function is in an activated state; during performance monitoring of the AI ​​model, the AI ​​model is in an activated state.

[0315] In some embodiments, receiving a first report includes at least one of the following: the AI ​​function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is received after obtaining beam measurement information of the first beam set; the AI ​​function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is received after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and a positive integer less than or equal to Q, and Q is the number of time domain instances output by the AI ​​function or AI model.

[0316] In some embodiments, the method further includes: sending first information, where the first information is used to instruct the terminal to perform performance monitoring on the AI ​​function or AI model.

[0317] In some embodiments, the first information includes at least one of the following: configuration information of the reference signal resource set of the first beam set; configuration information of the reference signal resource set of the second beam set; configuration information of the measurement quantity of the first beam set; configuration information of the measurement quantity of the second beam set.

[0318] In some embodiments, the first information is carried in at least one of the following: radio resource control RRC signaling; medium access control MAC CE activation indication; downlink control information DCI.

[0319] In some embodiments, the AI ​​function or AI model performs performance monitoring, including: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.

[0320] In some embodiments, the performance value satisfies the first condition, including at least one of the following: the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1; within a first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; the proportion of the first number of performance values ​​that meet the performance monitoring indicator is greater than a first proportion threshold; the performance value satisfies the first condition, which is a condition for the terminal to perform at least one of the following: activating the first AI function; activating the first AI model; switching to the first AI function; switching to the first AI model.

[0321] In some embodiments, the performance value satisfies the performance monitoring indicators, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

[0322] In some embodiments, the performance value satisfies the second condition, including at least one of the following: the performance value fails to meet the performance monitoring indicator for 0 consecutive times, where 0 is an integer greater than or equal to 1; the ratio of a second number of performance values ​​that fail to meet the performance monitoring indicator is greater than a second ratio threshold; the performance value satisfies the second condition, which is a condition for the terminal to perform at least one of the following: deactivate the first AI function; deactivate the first AI model and switch to the second AI function; switch to the second AI model; return to the non-AI model; update the first AI function; and update the first AI model.

[0323] In some embodiments, the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair within the second threshold is less than the fourth accuracy threshold; the difference of the reference signal received power L1-RSRP of layer 1 is greater than the third difference threshold; the difference of the predicted L1-RSRP is greater than the fourth difference threshold; the average throughput of the terminal is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.

[0324] In some embodiments, performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.

[0325] Figure 5 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 5, the embodiment of the present disclosure relates to a communication method, which includes:

[0326] In step S5101, the terminal 101 sends a first report to the network device 102 in a first case, where the beam prediction information of the first beam set includes at least one of the following: information output by the AI ​​function; information output by the AI ​​model.

[0327] In some embodiments, the first situation includes: a situation where beam measurement information and beam prediction information of the first beam set can be obtained.

[0328] The optional implementation of step S5101 can refer to step S2101 in Figure 2, step S2102 in Figure 3A, step S3101 to step S3102 in Figure 4A, and other related parts of the embodiments involved in Figures 2, 3A, and 4A, which will not be repeated here.

[0329] Step S5102, the network device 102 receives the first report.

[0330] The optional implementation of step S5102 can refer to the optional implementation of step S2103 in Figure 2, step S3103 in Figure 3A, step S4102 in Figure 4A, and other related parts of the embodiments involved in Figures 2, 3A, and 4A, which will not be repeated here.

[0331] In some embodiments, the above method may include the method described in the above embodiments of the communication system side, terminal side, network device side, etc., which will not be repeated here.

[0332] This embodiment also provides a communication method, including: during performance monitoring of an AI function or model, even if the AI ​​function or model is not deactivated, the terminal's first report should include actual measured beam information, rather than beam information obtained based on model output. This improves beam-based communication performance.

[0333] In some embodiments, the terminal receives first information, where the first information is used to instruct the terminal to perform performance monitoring.

[0334] In some embodiments, the first information includes at least one of the following: reference signal resource configuration information of set B, reference signal resource configuration information of set A.

[0335] In some embodiments, the measurement quantity includes at least one of the following: L1-SINR or L1-RSRP.

[0336] Optionally, the measurement quantity includes at least one of the following: the layer 1 reference signal received power L1-RSRP of the beam or beam pair in set A; the signal interference and noise ratio L1-SINR of layer 1 of the beam or beam pair in set A; the layer 1 reference signal received power L1-RSRP of the beam or beam pair in set B; the signal interference and noise ratio L1-SINR of layer 1 of the beam or beam pair in set B.

[0337] In some embodiments, set B includes beams or beam pairs corresponding to model inputs, and set A includes beams or beam pairs corresponding to model outputs.

[0338] In some embodiments, the first information includes displayed performance monitoring indicator information.

[0339] Optionally, the first information includes at least one of the following: RRC or MAC CE.

[0340] In some embodiments, performance monitoring refers to comparing the beam information of set A obtained by the output of the AI ​​model with the beam information of set A actually measured to obtain at least one performance monitoring indicator. When the obtained performance monitoring indicator meets the specified conditions, a certain function or model can be activated or deactivated.

[0341] In some embodiments, performance monitoring includes function-based model performance monitoring, or model-based model performance monitoring.

[0342] In some embodiments, activating or deactivating a function includes activating or deactivating a function (functionality) while directly switching multiple models within the function.

[0343] In some embodiments, activating or deactivating a model includes: directly activating or deactivating the model.

[0344] In some embodiments, the specified condition includes at least one of the following:

[0345] If the AI ​​function or model performs well for N consecutive times, the AI ​​function or model needs to be activated or switched to, where N is an integer greater than or equal to 1;

[0346] If the AI ​​function or model performs well N times within a specified time, you need to activate the AI ​​function or model, or switch to the AI ​​function or model;

[0347] If the AI ​​function or model has poor performance M times in a row, it is necessary to deactivate the AI ​​function or model, switch to another AI function or model, fall back to a non-AI model, or update the function or model, where M is an integer greater than or equal to 1.

[0348] If the AI ​​function or model has poor performance M times within a specified time, it is necessary to deactivate the AI ​​function or model, switch to another AI function or model, fall back to a non-AI model, or update the function or model.

[0349] The ratio of each performance monitoring indicator is compared with the threshold value. If it is higher than or equal to the threshold value, the function or model is activated;

[0350] Within a specified time, the ratio of each performance monitoring indicator is compared with the threshold value. If it is higher than or equal to the threshold value, the function or model is activated;

[0351] Within a specified time, the ratio of each performance monitoring indicator is compared with the threshold value. If it is lower than the threshold value, the function or model is deactivated.

[0352] Exemplarily, the ratio of each performance monitoring indicator is compared with a threshold value. For example, if the beam prediction accuracy of the Top 4 is lower than 60%, the function is deactivated, and if the prediction accuracy is higher than 80%, the function or model is activated.

[0353] In some embodiments, the specified conditions may include the terminal performing terminal-side model operations itself or the terminal reporting relevant data to the network.

[0354] In some embodiments, the terminal measures at least one of: L1-RSRP corresponding to the reference signal resources of set A; L1-SINR corresponding to the reference signal resources of set A; L1-RSRP corresponding to the reference signal resources of set B; L1-SINR corresponding to the reference signal resources of set B.

[0355] In some embodiments, the measured values ​​of set A are used as a reference or benchmark for performance monitoring and comparison.

[0356] In some embodiments, the terminal reports a beam report, which includes the measured value of set A (if the measured value of set A is different from the model output), rather than the predicted value obtained by the model output based on model inference.

[0357] In some embodiments, for spatial domain beam prediction or time domain beam prediction for one prediction time-instance, a beam report may be reported after each measurement of set A.

[0358] In some embodiments, for time domain beam prediction that predicts multiple prediction time instances, the beam report can be reported after each measurement of set A, or after N consecutive measurements of set A, where N corresponds to the number of prediction time instances output by the model.

[0359] Based on this, when the terminal obtains both the measurement information of set A and the prediction information of set A based on the AI ​​model output, the beam report preferentially includes the measurement information of set A. This improves the performance of beam-based communications.

[0360] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0361] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0362] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0363] Figure 6A is a structural diagram of the terminal proposed in an embodiment of the present disclosure. As shown in Figure 6A, the terminal 6100 may include: a transceiver module 6101. In some embodiments, the above-mentioned transceiver module is used to send a first report to the network device when the beam measurement information and beam prediction information of the first beam set are obtained, and the first report includes the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: information output by the AI ​​function; information output by the AI ​​model. Optionally, the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2102, step S2103, but not limited to this) performed by the terminal 101 in any of the above methods, which will not be repeated here.

[0364] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0365] Figure 6B is a structural diagram of the network device proposed in an embodiment of the present disclosure. As shown in Figure 6B, the network device 6200 may include: a transceiver module 6201. In some embodiments, the above-mentioned transceiver module is used to receive a first report, and the first report is sent by the terminal when the beam measurement information and beam prediction information of the first beam set are obtained. The first report includes the beam measurement information of the first beam set, and the prediction information of the first beam set includes at least one of the following: information output by the AI ​​function; information output by the AI ​​model. Optionally, the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2102, step S2103, but not limited to this) performed by the terminal 101 in any of the above methods, which will not be repeated here.

[0366] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0367] Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0368] As shown in Figure 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. Optionally, the communication device 7100 is used to perform any of the above methods. Optionally, one or more processors 7101 are used to call instructions to enable the communication device 7100 to perform any of the above methods.

[0369] In some embodiments, the communication device 7100 further includes one or more transceivers 7102. When the communication device 7100 includes one or more transceivers 7102, the transceiver 7102 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2102 and step S2103, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2101, but not limited thereto). In an optional embodiment, the transceiver may include a receiver and / or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be interchangeable, the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be interchangeable, and the terms receiver, receiving unit, receiver, and receiving circuit may be interchangeable.

[0370] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Alternatively, all or part of the memories 7103 may be located outside the communication device 7100. In alternative embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuits 7104 are connected to the memories 7103 and may be configured to receive data from the memories 7103 or other devices, or to send data to the memories 7103 or other devices. For example, the interface circuits 7104 may read data stored in the memories 7103 and send the data to the processor 7101.

[0371] The communication device 7100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0372] 7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

[0373] The chip 7200 includes one or more processors 7201. The chip 7200 is configured to execute any of the above methods.

[0374] In some embodiments, chip 7200 further includes one or more interface circuits 7202. Alternatively, terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 7200 further includes one or more memories 7203 for storing data. Alternatively, all or part of memory 7203 may be located external to chip 7200. Optionally, interface circuit 7202 is connected to memory 7203 and may be used to receive data from memory 7203 or other devices, or may be used to send data to memory 7203 or other devices. For example, interface circuit 7202 may read data stored in memory 7203 and send the data to processor 7201.

[0375] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (e.g., step S2102 and step S2103, but not limited thereto) of the aforementioned method. The interface circuit 7202 performing the communication steps (e.g., step S2102 and step S2103, but not limited thereto) of the aforementioned method means, for example, that the interface circuit 7202 performs data exchange between the processor 7201, chip 7200, memory 7203, or a transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (e.g., step S2101, but not limited thereto).

[0376] The modules and / or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0377] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0378] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0379] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A communication method, characterized in that: The method comprises: In a case where beam measurement information and beam prediction information of the first beam set can be obtained, a first report is sent to a network device, where the first report includes the beam measurement information of the first beam set; and the beam prediction information of the first beam set includes at least one of the following: Information output by artificial intelligence (AI) functions; Information output by the AI ​​model.

2. The method according to claim 1, characterized in that The situation in which the beam measurement information of the first beam set can be obtained includes at least one of the following: During the performance monitoring of the AI ​​function, the AI ​​function is activated; The situation in which the AI ​​model is activated during performance monitoring of the AI ​​model.

3. The method according to claim 1 or 2, characterized in that: The beam prediction information is different from the beam measurement information.

4. The method according to any one of claims 1 to 3, characterized in that The sending of the first report to the network device includes at least one of the following: The AI ​​function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and sends a first report to the network device after obtaining beam measurement information of the first beam set; The AI ​​function or AI model is used for time domain beam prediction of multiple time domain instances. After obtaining the beam measurement information of N time domain instances of the first beam set, a first report is sent to the network device, where N is a positive integer greater than or equal to 1 and less than or equal to Q, and Q is the number of time domain instances output by the AI ​​function or AI model.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Receive first information, where the first information is used to instruct the terminal to perform performance monitoring on at least one of the following: AI capabilities; AI model.

6. The method according to claim 5, characterized in that The first information includes at least one of the following: Configuration information of a reference signal resource set of a first beam set; Configuration information of a reference signal resource set of a second beam set; Configuration information of the measurement quantity of the first beam set; Configuration information of the measurement quantity of the second beam set.

7. The method according to claim 5 or 6, characterized in that The first information is carried by at least one of the following: Radio Resource Control (RRC) signaling; Media Access Control MAC CE activation indication; Downlink control information DCI.

8. The method according to any one of claims 2 to 7, characterized in that The performance monitoring includes: The beam prediction information of the first beam set is compared with the beam measurement information of the first beam set to obtain a performance value.

9. The method according to claim 8, characterized in that The method further comprises: The performance value satisfies the first condition, and at least one of the following is performed: Activate the first AI function; Activate the first AI model; Switch to the first AI function; Switch to the first AI model.

10. The method according to claim 8 or 9, characterized in that: The method further comprises: The performance value satisfies the second condition, and at least one of the following is performed: Deactivate the first AI function; To activate the first AI model, Switch to the second AI function; Switch to the second AI model; Falling back to a non-AI model; Carry out the first AI function update; Perform the first AI model update.

11. The method according to claim 9, characterized in that The performance value satisfies the first condition including at least one of the following: The performance value meets the performance monitoring indicator for L consecutive times, where L is an integer greater than or equal to 1; Within the first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; The proportion of the first number of performance values ​​that meets the performance monitoring indicator is greater than a first proportion threshold.

12. The method according to any one of claims 11, characterized in that: The performance value meets the performance monitoring index, including at least one of the following: The prediction accuracy of the beam or beam pair is greater than or equal to a first accuracy threshold; The prediction accuracy of the beam or beam pair for which the reference signal received power L1-RSRP difference of layer 1 is within the first threshold is greater than or equal to the second accuracy threshold; The L1-RSRP difference is less than or equal to the first difference threshold; The difference of the predicted L1-RSRP is less than or equal to the second difference threshold; The average throughput of the terminal is greater than or equal to the first throughput threshold; The reference signal resource overhead is less than or equal to a first overhead threshold; The uplink control information overhead is less than or equal to the second overhead threshold; The predicted delay is less than or equal to a first delay threshold.

13. The method according to claim 10, characterized in that The performance value satisfies the second condition including at least one of the following: The performance value fails to meet the performance monitoring index for O consecutive times, where O is an integer greater than or equal to 1; Within the second time threshold, the performance value fails to meet the performance monitoring indicator for P times, where P is an integer greater than or equal to 1; The proportion of the second number of performance values ​​that do not satisfy the performance monitoring indicator is greater than a second proportion threshold.

14. The method according to claim 13, characterized in that The performance value does not meet the performance monitoring index, including at least one of the following: The prediction accuracy of the beam or beam pair is less than a third accuracy threshold; The prediction accuracy of the beam or beam pair for which the L1-RSRP difference is within the second threshold is less than a fourth accuracy threshold; The reference signal received power L1-RSRP difference of layer 1 is greater than the third difference threshold; The difference of the predicted L1-RSRP is greater than a fourth difference threshold; The average throughput of the terminal is less than the second throughput threshold; The reference signal resource overhead is greater than a third overhead threshold; The uplink control information overhead is greater than a fourth overhead threshold; The predicted delay is greater than the second delay threshold.

15. The method according to any one of claims 1 to 14, characterized in that: The performance monitoring includes performance monitoring based on AI functions or performance monitoring based on AI models.

16. A communication method, characterized in that: The method comprises: receiving a first report, where the first report is sent by the terminal when beam measurement information and beam prediction information of the first beam set can be obtained, and the first report includes the beam measurement information of the first beam set; The prediction information of the first beam set includes at least one of the following: Information output by AI functions; Information output by the AI ​​model.

17. The method according to claim 16, characterized in that The situation in which the beam measurement information of the first beam set can be obtained includes at least one of the following: During the performance monitoring of the AI ​​function, the AI ​​function is activated; The situation in which the AI ​​model is activated during performance monitoring of the AI ​​model.

18. The method according to claim 16 or 17, characterized in that The beam prediction information is different from the beam measurement information.

19. The method according to any one of claims 16 to 18, characterized in that The receiving of the first report includes at least one of the following: The AI ​​function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and receives a first report after obtaining beam measurement information of the first beam set; The AI ​​function or AI model is used for time domain beam prediction of multiple time domain instances. After obtaining beam measurement information of N time domain instances of the first beam set, a first report is received, where N is a positive integer greater than or equal to 1 and less than or equal to Q, and Q is the number of time domain instances output by the AI ​​function or AI model.

20. The method according to any one of claims 16 to 19, characterized in that The method further comprises: Sending first information, where the first information is used to instruct the terminal to perform performance monitoring on at least one of the following: AI Features AI model.

21. The method according to claim 20, characterized in that The first information includes at least one of the following: Configuration information of a reference signal resource set of a first beam set; Configuration information of a reference signal resource set of a second beam set; Configuration information of the measurement quantity of the first beam set; Configuration information of the measurement quantity of the second beam set.

22. The method according to claim 20 or 21, characterized in that The first information is carried by at least one of the following: Radio Resource Control (RRC) signaling; Media Access Control MAC CE activation indication; Downlink control information DCI.

23. The method according to any one of claims 17 to 22, characterized in that The performance monitoring includes: The beam prediction information of the first beam set is compared with the beam measurement information of the first beam set to obtain a performance value.

24. The method according to claim 23, characterized in that The performance value satisfies the first condition, including at least one of the following: The performance value meets the performance monitoring indicator for L consecutive times, where L is an integer greater than or equal to 1; Within the first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; The proportion of the first number of performance values ​​that meets the performance monitoring indicator is greater than a first proportion threshold; The performance value satisfies the first condition that the terminal performs at least one of the following conditions: Activate the first AI function; Activate the first AI model; Switch to the first AI function; Switch to the first AI model.

25. The method according to claim 23 or 24, characterized in that The performance value meets the performance monitoring index, including at least one of the following: The prediction accuracy of the beam or beam pair is greater than or equal to a first accuracy threshold; The prediction accuracy of the beam or beam pair for which the reference signal received power L1-RSRP difference of layer 1 is within the first threshold is greater than or equal to the second accuracy threshold; The L1-RSRP difference is less than or equal to the first difference threshold; The difference of the predicted L1-RSRP is less than or equal to the second difference threshold; The average throughput of the terminal is greater than or equal to the first throughput threshold; The reference signal resource overhead is less than or equal to a first overhead threshold; The uplink control information overhead is less than or equal to the second overhead threshold; The predicted delay is less than or equal to a first delay threshold.

26. The method according to claim 23, characterized in that The performance value satisfies the second condition, including at least one of the following: The performance value fails to meet the performance monitoring index for O consecutive times, where O is an integer greater than or equal to 1; The ratio of the second number of performance values ​​that do not meet the performance monitoring indicator is greater than a second ratio threshold; The performance value satisfies the second condition when the terminal performs at least one of the following: Deactivate the first AI function; To activate the first AI model, Switch to the second AI function; Switch to the second AI model; Falling back to a non-AI model; Carry out the first AI function update; Perform the first AI model update.

27. The method according to any one of claims 23, characterized in that The performance value does not meet the performance monitoring index, including at least one of the following: The prediction accuracy of the beam or beam pair is less than a third accuracy threshold; The prediction accuracy of the beam or beam pair for which the L1-RSRP difference is within the second threshold is less than a fourth accuracy threshold; The reference signal received power L1-RSRP difference of layer 1 is greater than the third difference threshold; The difference of the predicted L1-RSRP is greater than a fourth difference threshold; The average throughput of the terminal is less than the second throughput threshold; The reference signal resource overhead is greater than a third overhead threshold; The uplink control information overhead is greater than a fourth overhead threshold; The predicted delay is greater than the second delay threshold.

28. The method according to any one of claims 16 to 27, characterized in that The performance monitoring includes performance monitoring based on AI functions or performance monitoring based on AI models.

29. A communication method, characterized in that: The method includes: when the terminal can obtain beam measurement information and beam prediction information of a first beam set, sending a first report to a network device, the first report includes the beam measurement information of the first beam set, and the beam prediction information of the first beam set includes at least one of the following: Information output by AI functions; Information output by the AI ​​model; The network device receives the first report.

30. A terminal, characterized in that: include: A transceiver module, configured to send a first report to a network device when beam measurement information and beam prediction information of a first beam set are obtained, wherein the first report includes the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: Information output by AI functions; Information output by the AI ​​model.

31. A network device, characterized in that: include: a transceiver module, configured to receive a first report, where the first report is sent by the terminal when beam measurement information and beam prediction information of the first beam set are obtained, and the first report includes the beam measurement information of the first beam set; The prediction information of the first beam set includes at least one of the following: Information output by AI functions; Information output by the AI ​​model.

32. A terminal, characterized in that: include: one or more processors; The processor is used to execute the communication method according to any one of claims 1 to 15.

33. A network device, characterized in that: include: one or more processors; The processor is used to execute the communication method described in any one of claims 16 to 28.

34. A communication system, characterized in that: The invention comprises a terminal and a network device, wherein the terminal is configured to implement the communication method according to any one of claims 1 to 15, and the network device is configured to implement the communication method according to any one of claims 16 to 28.

35. A storage medium storing instructions, characterized in that: When the instruction is executed on a communication device, the communication device is caused to execute the communication method according to any one of claims 1 to 15 or 16 to 28.

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