CN117581581A

CN117581581A - Communication method, terminal, network device, and communication system

Info

Publication number: CN117581581A
Application number: CN202380010877.6A
Authority: CN
Inventors: 李明菊
Original assignee: Beijing Xiaomi Mobile Software Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd
Priority date: 2023-08-30
Filing date: 2023-08-30
Publication date: 2024-02-20

Abstract

The present disclosure relates to a communication method, a terminal, a network device, and a communication system. Transmitting a first report to a network device, where beam measurement information and beam prediction information for a first set of beams are available, the first report comprising beam measurement information for the first set of beams; the beam prediction information of the first set of beams comprises at least one of: information output by the AI function; information output by the AI model. By the embodiments of the present disclosure, the accuracy of the first report during AI model or AI functional performance monitoring may be improved.

Description

Communication method, terminal, network device, and communication system

Technical Field

The 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

In the New air interface (NR), particularly in the frequency band (frequency range) 2, beam-based transmission and reception are required to secure coverage.

Disclosure of Invention

In beam management based on an artificial intelligence (Artificial Intelligence, AI) model or AI function, the model performance needs to be monitored and beam information is reported, but the beam information reporting is inaccurate, so that the communication performance is reduced.

The embodiment of the disclosure provides a communication method, a terminal, network equipment and a communication system.

According to a first aspect of an embodiment of the present disclosure, a communication method is provided, including: transmitting a first report to a network device, where beam measurement information and beam prediction information for a first set of beams are available, the first report comprising beam measurement information for the first set of beams; the beam prediction information for the first set of beams includes at least one of: an artificial intelligence AI function; information output by the AI model.

According to a second aspect of the embodiments of the present disclosure, there is provided a communication method, including: receiving a first report, the first report being transmitted by a terminal in the event that beam measurement information and beam prediction information for a first set of beams are available, the first report comprising beam measurement information for the first set of beams, the prediction information for the first set of beams comprising at least one of: information output by the AI function; information output by the AI model.

According to a third aspect of the embodiments of the present disclosure, a communication method is provided, the method including: the terminal sends a first report to the network equipment under the condition that the beam measurement information and the beam prediction information of the first beam set can be obtained, 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 components: information output by the AI function; information output by the AI model; the network device receives a first report.

According to a fourth aspect of embodiments of the present disclosure, there is provided a terminal, including: 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, where the first report includes the beam measurement information of the first beam set; the beam prediction information for the first set of beams includes at least one of: information output by the AI function; information output by the AI model.

According to a fifth aspect of embodiments of the present disclosure, there is provided a network device, comprising: for receiving a first report, the first report being transmitted by a terminal in case of obtaining beam measurement information and beam prediction information of a first set of beams, the first report comprising beam measurement information of the first set of beams, the prediction information of the first set of beams comprising at least one of: information output by the AI function; information output by the AI model.

According to a sixth aspect of the embodiments of the present disclosure, there is provided a terminal, including: one or more processors; wherein the processor is configured to perform the communication method of the first aspect.

According to a seventh aspect of embodiments of the present disclosure, there is provided a network device, including: one or more processors; wherein the processor is configured to perform the communication method of the second aspect.

According to an eighth aspect of embodiments of the present disclosure, there is provided a communication system comprising a terminal configured to implement the communication method of the first aspect and a network device configured to implement the communication method of the second aspect.

According to a ninth aspect of embodiments of the present disclosure, there is provided a storage medium storing instructions, characterized in that when the instructions are run on a communication device, the instructions cause the communication device to perform the communication method of any one of the first and second aspects.

By the embodiments of the present disclosure, the accuracy of the first report during AI model or AI functional performance monitoring may be improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following description of the embodiments refers to the accompanying drawings, which are only some embodiments of the present disclosure, and do not limit the protection scope of the present disclosure in any way.

Fig. 1 is an exemplary schematic diagram of an architecture of a communication system provided in accordance with an embodiment of the present disclosure.

Fig. 2 is an exemplary interaction diagram of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 3A is an exemplary flowchart of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 3B is an exemplary flowchart of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 3C is an exemplary flowchart of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 4A is an exemplary flowchart of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 4B is an exemplary flowchart of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 5 is an exemplary interaction diagram of a communication method provided in accordance with an embodiment of the present disclosure.

Fig. 6A is a schematic diagram of a terminal shown according to an embodiment of the present disclosure.

Fig. 6B is a schematic diagram of a network device shown in accordance with an embodiment of the present disclosure.

Fig. 7A is a schematic diagram of a communication device, according to an example embodiment.

Fig. 7B is a schematic diagram of a chip architecture according to an exemplary embodiment.

Detailed Description

In a first aspect, an embodiment of the present disclosure proposes a communication method, including: transmitting a first report to the network device, the first report including beam measurement information of the first set of beams, if the beam measurement information and the beam prediction information of the first set of beams are available; the beam prediction information of the first set of beams comprises at least one of: information output by the artificial intelligence AI function; information output by the AI model.

In the above embodiment, by adding the actual measurement information to the first report, the network device may determine the current communication status based on the actual measurement information, and avoid using inaccurate beam prediction information for communication during performance monitoring by the AI function or the model, thereby improving the communication performance of the communication based on the beam.

With reference to some embodiments of the first aspect, in some embodiments, the beam prediction information and the beam measurement information are different.

With reference to some embodiments of the first aspect, in some embodiments, where beam measurement information of the first set of beams is available, the method includes: during performance monitoring of the AI function, the AI function is in an active state; during performance monitoring of the AI model, the AI model is in an active state.

With reference to some embodiments of the first aspect, in some embodiments, the sending of the first report to the network device includes at least one of: the AI function or AI model is used for spatial 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 the beam measurement information of the first beam set; the AI function or AI model is for time domain beam prediction of the plurality of time domain instances, and after obtaining 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 a number of time domain instances output by the AI function or AI model.

In the above embodiment, based on the above scheme, the first report transmission scheme in the scenario for the AI model or AI function for the time domain and the space domain prediction is specified, and the communication method provided in this embodiment may be applied to the scenario for the AI model or AI function for the time domain and the space domain prediction.

With reference to some embodiments of the first aspect, in some embodiments, the method further includes: receiving first information, wherein the first information is used for indicating a terminal to monitor performance of at least one of the following: AI function; AI model.

In the above embodiment, based on the above scheme, it is clear that the terminal may perform performance monitoring via the network device instruction in addition to the spontaneous performance monitoring.

With reference to some embodiments of the first aspect, in some embodiments, the first information includes at least one of: configuration information of a reference signal resource set of the first beam set; configuration information of a 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 quantities of the second set of beams.

With reference to some embodiments of the first aspect, in some embodiments, the first information is carried on at least one of: radio resource control, RRC, signaling; a medium access control MAC CE activation indication; downlink control information DCI.

In the above embodiment, the signaling overhead may be reduced by performing the first information transmission based on the existing bearer resource.

With reference to some embodiments of the first aspect, in some embodiments, performance monitoring of the AI function or AI model includes: the beam prediction information of the first set of beams is compared with the beam measurement information of the first set of beams to obtain a performance value.

In the above embodiment, by comparing the predicted value with the actual value, the degree of deviation of the current predicted value from the actual value can be determined, and a technical pad is formed for the subsequent evaluation of the performance of the AI model or AI function.

With reference to some embodiments of the first aspect, in some embodiments, the method further includes: the performance value satisfies a first condition, at least one of: activating a first AI function; activating a first AI model; switching to a first AI function; switch to the first AI model.

With reference to some embodiments of the first aspect, in some embodiments, the method further includes: the performance value satisfies a second condition, at least one of: deactivating the first AI function; deactivating a first model included in the first AI function; switching to a second AI function; switching to the second AI model; returning to the non-AI model; performing a first AI function update; a first AI model update is performed.

With reference to some embodiments of the first aspect, in some embodiments, the performance value satisfies the performance monitoring index L consecutive times, wherein L is an integer greater than or equal to 1;

within the first time threshold, the performance value is M times to meet the performance monitoring index, wherein M is an integer greater than or equal to 1; the ratio of the first number of performance values satisfying the performance monitoring index is greater than a first ratio threshold.

With reference to some embodiments of the first aspect, in some embodiments, the performance value meets a performance monitoring criterion, including at least one of: the beam or beam pair prediction accuracy is greater than or equal to a first accuracy threshold; the beam or beam pair prediction accuracy of the reference signal received power L1-RSRP difference of the layer 1 within a first threshold is greater than or equal to a second accuracy threshold; the L1-RSRP differential is less than or equal to a first differential threshold; predicting that the L1-RSRP has a degree of difference less than or equal to a second degree of difference threshold; the average throughput of the terminal is greater than or equal to a 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 a second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

With reference to some embodiments of the first aspect, in some embodiments, the performance value does not meet the performance monitoring index O times in succession, wherein O is an integer greater than or equal to 1;

in the second time threshold, the performance value is P times that the performance monitoring index is not met, wherein P is an integer greater than or equal to 1; the ratio of the second number of performance values not meeting the performance monitoring criterion is greater than a second ratio threshold.

With reference to some embodiments of the first aspect, in some embodiments, the performance value does not meet a performance monitoring index, including at least one of: the beam or beam pair prediction accuracy is less than a third accuracy threshold; the beam or beam pair prediction accuracy of the L1-RSRP difference within the second threshold is less than a fourth accuracy threshold; the L1-RSRP differential degree is larger than a third differential degree threshold value; predicting that the difference degree of the L1-RSRP is larger than a fourth difference degree threshold; the average throughput of the terminal is smaller than a 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 expense threshold; the predicted delay is greater than a second delay threshold.

With reference to some embodiments of the first aspect, in some embodiments, the performance monitoring includes AI function-based performance monitoring, or AI model-based performance monitoring.

In a second aspect, an embodiment of the present disclosure provides a communication method, including: receiving a first report, the first report being transmitted by the terminal in a situation where beam measurement information and beam prediction information for a first set of beams are available, the first report comprising beam measurement information for the first set of beams, the prediction information for the first set of beams comprising at least one of: information output by the AI function; information output by the AI model.

With reference to 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 predicted based on an AI function or an AI model.

With reference to some embodiments of the second aspect, in some embodiments, where beam measurement information of the first beam set is available, the method includes: during performance monitoring of the AI function, the AI function is in an active state; during performance monitoring of the AI model, the AI model is in an active state.

With reference to some embodiments of the second aspect, in some embodiments, the receiving the first report includes at least one of: the AI function or AI model is for spatial beam prediction, or for time domain beam prediction of a single time domain instance, receiving a first report after obtaining beam measurement information of a first set of beams; the AI function or AI model is used for time domain beam prediction of a plurality of time domain instances, and after beam measurement information of N time domain instances of a first beam set is obtained, a first report is received, wherein 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.

With reference to some embodiments of the second aspect, in some embodiments, the method further comprises: sending first information, wherein the first information is used for indicating a terminal to monitor performance of at least one of the following items, and the method comprises the following steps: AI function; AI model.

With reference to some embodiments of the second aspect, in some embodiments, the first information includes at least one of: configuration information of a reference signal resource set of the first beam set; configuration information of a 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 quantities of the second set of beams.

With reference to some embodiments of the second aspect, in some embodiments, the first information is carried on at least one of: radio resource control, RRC, signaling; a medium access control MAC CE activation indication; downlink control information DCI.

With reference to some embodiments of the second aspect, in some embodiments, the AI function or AI model performs performance monitoring comprising: the beam prediction information of the first set of beams is compared with the beam measurement information of the first set of beams to obtain a performance value.

With reference to some embodiments of the second aspect, in some embodiments, the performance value satisfying the first condition is a condition that the terminal performs at least one of: the performance value continuously meets the performance monitoring index for L times, wherein L is an integer greater than or equal to 1; within the first time threshold, the performance value is M times to meet the performance monitoring index, wherein M is an integer greater than or equal to 1; the ratio of the first number of performance values meeting the performance monitoring index is greater than a first ratio threshold; the performance value satisfies a first condition, and the terminal performs at least one of the following: activating a first AI function; activating a first AI model; switching to a first AI function; switch to the first AI model.

With reference to some embodiments of the second aspect, in some embodiments, the performance value meets a performance monitoring criterion, including at least one of: the beam or beam pair prediction accuracy is greater than or equal to a first accuracy threshold; the beam or beam pair prediction accuracy of the reference signal received power L1-RSRP difference of the layer 1 within a first threshold is greater than or equal to a second accuracy threshold; the L1-RSRP differential is less than or equal to a first differential threshold; predicting that the L1-RSRP has a degree of difference less than or equal to a second degree of difference threshold; the average throughput of the terminal is greater than or equal to a 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 a second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

With reference to some embodiments of the second aspect, in some embodiments, the performance value satisfying the second condition is a condition that the terminal performs at least one of: performance values, wherein O is an integer greater than or equal to 1, are not met by continuous O times; the ratio of the second number of performance values not meeting the performance monitoring index is greater than a second ratio threshold; the performance value satisfies a second condition, and 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 the non-AI model; performing a first AI function update; a first AI model update is performed.

With reference to some embodiments of the second aspect, in some embodiments, the performance value does not meet a performance monitoring index, including at least one of: the beam or beam pair prediction accuracy is less than a third accuracy threshold; the beam or beam pair prediction accuracy of the L1-RSRP difference within the second threshold is less than a fourth accuracy threshold; the reference signal received power L1-RSRP of the layer 1 is more than a third difference threshold; predicting that the difference degree of the L1-RSRP is larger than a fourth difference degree threshold; the average throughput of the terminal is smaller than a 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 expense threshold; the predicted delay is greater than a second delay threshold.

In combination with some embodiments of the second aspect, in some embodiments, the performance monitoring comprises AI function-based performance monitoring, or AI model-based performance monitoring.

In a third aspect, an embodiment of the present disclosure provides a communication method, including: the terminal sends a first report to the network device in case of obtaining beam measurement information and beam prediction information of a first set of beams, the first report comprising the beam measurement information of the first set of beams, the beam prediction information of the first set of beams comprising at least one of: information output by the AI function; information output by the AI model; the network device receives a first report. In a fourth aspect, an embodiment of the present disclosure proposes a terminal, including: for transmitting a first report to the network device in case of obtaining beam measurement information and beam prediction information of a first set of beams, the first report comprising the beam measurement information of the first set of beams; the beam prediction information for the first set of beams includes at least one of: information output by the AI function; information output by the AI model.

In a fifth aspect, embodiments of the present disclosure provide a network device, including: for receiving a first report, the first report being transmitted by the terminal in case of obtaining beam measurement information and beam prediction information for a first set of beams, the first report comprising beam measurement information for the first set of beams, the prediction information for the first set of beams comprising at least one of: information output by the AI function; information output by the AI model.

In a sixth aspect, an embodiment of the present disclosure proposes a terminal, including: one or more processors; wherein the processor is configured to perform the communication method of the first aspect.

In a seventh aspect, embodiments of the present disclosure provide a network device, including: one or more processors; wherein the processor is configured to perform the communication method of the second aspect.

In an eighth aspect, an embodiment of the present disclosure proposes a communication system including: a terminal configured to implement the communication method of the first aspect, and a network device configured to implement the communication method of the second aspect.

In a ninth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method of any one of the first aspect and the second aspect.

It will be appreciated that the above-described terminal, network device, communication system, storage medium, program product, computer program, chip or chip system are all adapted to perform the methods set forth in the embodiments of the present disclosure. Thus, the advantages that can be achieved are referred to as advantages in the corresponding method.

The embodiment of the disclosure provides a communication method, a terminal, network equipment and a communication system. In some embodiments, terms such as a communication method and an information processing method, a communication method, and the like may be replaced with each other, terms such as a communication device and an information processing device may be replaced with each other, and terms such as an information processing system and a communication system may be replaced with each other.

The embodiments of the present disclosure are not intended to be exhaustive, but rather are exemplary of some embodiments and are not intended to limit the scope of the disclosure. In the case of no contradiction, each step in a certain embodiment may be implemented as an independent embodiment, and the steps may be arbitrarily combined, for example, a scheme in which part of the steps are removed in a certain embodiment may also be implemented as an independent embodiment, the order of the steps in a certain embodiment may be arbitrarily exchanged, and further, alternative implementations in a certain embodiment may be arbitrarily combined; furthermore, various embodiments may be arbitrarily combined, for example, some or all steps of different embodiments may be arbitrarily combined, and an embodiment may be arbitrarily combined with alternative implementations of other embodiments.

In the various embodiments of the disclosure, terms and/or descriptions of the various embodiments are consistent throughout the various embodiments and may be referenced to each other in the absence of any particular explanation or logic conflict, and features from different embodiments may be combined to form new embodiments in accordance with their inherent logic relationships.

The terminology used in the embodiments of the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure.

In the presently disclosed embodiments, elements that are referred to in the singular, such as "a," "an," "the," "said," etc., may mean "one and only one," or "one or more," "at least one," etc., unless otherwise indicated. For example, where an article (article) is used in translation, such as "a," "an," "the," etc., in english, a noun following the article may be understood as a singular expression or as a plural expression.

In the presently disclosed embodiments, "plurality" refers to two or more.

In some embodiments, terms such as "at least one of", "one or more of", "multiple of" and the like may be substituted for each other.

In some embodiments, "A, B at least one of", "a and/or B", "in one case a, in another case B", "in response to one case a", "in response to another case B", and the like, may include the following technical solutions according to circumstances: in some embodiments a (a is performed independently of B); b (B is performed independently of a) in some embodiments; in some embodiments, execution is selected from a and B (a and B are selectively executed); in some embodiments a and B (both a and B are performed). Similar to that described above when there are more branches such as A, B, C.

In some embodiments, the description modes such as "a or B" may include the following technical schemes according to circumstances: in some embodiments a (a is performed independently of B); b (B is performed independently of a) in some embodiments; in some embodiments execution is selected from a and B (a and B are selectively executed). Similar to that described above when there are more branches such as A, B, C.

The prefix words "first", "second", etc. in the embodiments of the present disclosure are only for distinguishing different description objects, and do not limit the location, order, priority, number, content, etc. of the description objects, and the statement of the description object refers to the claims or the description of the embodiment context, and should not constitute unnecessary limitations due to the use of the prefix words. For example, if the description object is a "field", the ordinal words before the "field" in the "first field" and the "second field" do not limit the position or the order between the "fields", and the "first" and the "second" do not limit whether the "fields" modified by the "first" and the "second" are in the same message or not. For another example, describing an object as "level", ordinal words preceding "level" in "first level" and "second level" do not limit priority between "levels". As another example, the number of descriptive objects is not limited by ordinal words, and may be one or more, taking "first device" as an example, where the number of "devices" may be one or more. Furthermore, objects modified by different prefix words may be the same or different, e.g., the description object is "a device", then "a first device" and "a second device" may be the same device or different devices, and the types may be the same or different; for another example, the description object is "information", and the "first information" and the "second information" may be the same information or different information, and the contents thereof may be the same or different.

In some embodiments, "comprising a", "containing a", "for indicating a", "carrying a", may be interpreted as carrying a directly, or as indicating a indirectly.

In some embodiments, terms "responsive to … …", "responsive to determination … …", "in the case of … …", "at … …", "when … …", "if … …", "if … …", and the like may be interchanged.

In some embodiments, terms "greater than", "greater than or equal to", "not less than", "more than or equal to", "not less than", "above" and the like may be interchanged, and terms "less than", "less than or equal to", "not greater than", "less than or equal to", "not more than", "below", "lower than or equal to", "no higher than", "below" and the like may be interchanged.

In some embodiments, the apparatuses and devices may be interpreted as entities, or may be interpreted as virtual, and the names thereof are not limited to those described in the embodiments, and may also be interpreted as "device (apparatus)", "device)", "circuit", "network element", "node", "function", "unit", "component (section)", "system", "network", "chip system", "entity", "body", and the like in some cases.

In some embodiments, a "network" may be interpreted as an apparatus comprised in the network, e.g. an access network device, a core network device, etc.

In some embodiments, the "access network device (access network device, AN device)" may also be referred to as a "radio access network device (radio access network device, RAN device)", "Base Station (BS)", "radio base station (radio base station)", "fixed station (fixed station)", and in some embodiments may also be referred to as a "node)", "access point (access point)", "transmission point (transmission point, TP)", "Reception Point (RP)", "transmission and/or reception point (transmission/reception point), TRP)", "panel", "antenna array", "cell", "macrocell", "microcell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier (component carrier)", bandwidth part (BWP), etc.

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

In some embodiments, the acquisition of data, information, etc. may comply with laws and regulations of the country of locale.

In some embodiments, data, information, etc. may be obtained after user consent is obtained.

Furthermore, each element, each row, or each column in the tables of the embodiments of the present disclosure may be implemented as a separate embodiment, and any combination of elements, any rows, or any columns may also be implemented as a separate embodiment.

Fig. 1 is a schematic architecture diagram of a communication system shown in accordance with an embodiment of the present disclosure. As shown in fig. 1, the communication system 100 includes a terminal (terminal) 101 and a network device 102.

In some embodiments, the terminal 101 includes at least one of a mobile phone (mobile phone), a wearable device, an internet of things device, a communication enabled car, a smart car, a tablet (Pad), a wireless transceiver enabled computer, a Virtual Reality (VR) terminal device, an augmented reality (augmented reality, AR) terminal device, a wireless terminal device in industrial control (industrial control), a wireless terminal device in unmanned (self-driving), a wireless terminal device in teleoperation (remote medical surgery), a wireless terminal device in smart grid (smart grid), a wireless terminal device in transportation security (transportation safety), a wireless terminal device in smart city (smart city), a wireless terminal device in smart home (smart home), for example, but is not limited thereto.

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

In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network, and the access network device may include at least one of an evolved NodeB (eNB), a next generation evolved NodeB (next generation eNB, ng-eNB), a next generation NodeB (next generation NodeB, gNB), a NodeB (node B, NB), a Home NodeB (HNB), a home NodeB (home evolved nodeB, heNB), a wireless backhaul device, a radio network controller (radio network controller, RNC), a base station controller (base station controller, BSC), a base transceiver station (base transceiver station, BTS), a baseband 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, an access node in a Wi-Fi system, but is not limited thereto.

In some embodiments, the technical solutions of the present disclosure may be applied to an Open RAN architecture, where an access network device or an interface in an access network device according to the embodiments of the present disclosure may become an internal interface of the Open RAN, and flow and information interaction between these internal interfaces may be implemented by using software or a program.

In some embodiments, the access network device may be composed of a Central Unit (CU) and a Distributed Unit (DU), where the CU may also be referred to as a control unit (control unit), and the structure of the CU-DU may be used to split the protocol layers of the access network device, where functions of part of the protocol layers are centrally controlled by the CU, and functions of the rest of all the protocol layers are distributed in the DU, and the DU is centrally controlled by the CU, but is not limited thereto.

In some embodiments, the core network device may be a device, including one or more network elements, or may be a plurality of devices or a device group, including all or part of one or more network elements. The network element may be virtual or physical. The core network comprises, for example, at least one of an evolved packet core (Evolved Packet Core, EPC), a 5G core network (5G Core Network,5GCN), a next generation core (Next Generation Core, NGC).

It may be understood that, the communication system described in the embodiments of the present disclosure is for more clearly describing the technical solutions of the embodiments of the present disclosure, and is not limited to the technical solutions provided in the embodiments of the present disclosure, and those skilled in the art can know that, with the evolution of the system architecture and the appearance of new service scenarios, the technical solutions provided in the embodiments of the present disclosure are applicable to similar technical problems.

The embodiments of the present disclosure described below may be applied to the communication system 100 shown in fig. 1, or a part of the main body, but are not limited thereto. The respective bodies shown in fig. 1 are examples, and the communication system may include all or part of the bodies in fig. 1, or may include other bodies than fig. 1, and the number and form of the respective bodies may be arbitrary, and the respective bodies may be physical or virtual, and the connection relationship between the respective bodies is examples, and the respective bodies may not be connected or may be connected, and the connection may be arbitrary, direct connection or indirect connection, or wired connection or wireless connection.

The embodiments of the present disclosure may be applied to long term evolution (Long Term Evolution, LTE), LTE-Advanced (LTE-a), LTE-Beyond (LTE-B), upper 3G, IMT-Advanced, fourth generation mobile communication system (4th generation mobile communication system,4G)), fifth generation mobile communication system (5th generation mobile communication system,5G), 5G New air (New Radio, NR), future wireless access (Future Radio Access, FRA), new wireless access technology (New-Radio Access Technology, RAT), new wireless (New Radio, NR), new wireless access (New Radio access, NX), future generation wireless access (Future generation Radio access, FX), global System for Mobile communications (GSM (registered trademark)), CDMA2000, ultra mobile broadband (Ultra Mobile Broadband, UMB), IEEE 802.11 (registered trademark), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, ultra WideBand (Ultra-wide bandwidth, UWB), bluetooth (Bluetooth) mobile communication network (Public Land Mobile Network, PLMN, device-D-Device, device-M, device-M, internet of things system, internet of things (internet of things), machine-2, device-M, device-M, internet of things (internet of things), system (internet of things), internet of things 2, device (internet of things), machine (internet of things), etc. In addition, a plurality of system combinations (e.g., LTE or a combination of LTE-a and 5G, etc.) may be applied.

In NR, particularly in the frequency range (frequency range) 2, since the high-frequency channel decays fast, communication based on transmission and reception of a beam (beam) is required in order to secure coverage.

For beam management, a base station configures a reference signal resource set for beam measurement, a terminal measures reference signals on reference signal resources in the reference signal resource set, and reports a plurality of reference signal resource Identifications (IDs) with stronger X (X is a positive integer) and corresponding correlation parameters (including at least one of, for example, reference signal received power (L1-Reference Signal Received Power, L1-RSRP) of layer 1 and signal-to-Interference-plus-Noise Ratio (L1-SINR) of layer 1).

In some embodiments, implementations of the AI model and/or AI function for beam prediction are provided. Among them, AI functions can be considered as one or more AI models that achieve some same function, purpose.

In some embodiments, the AI model for beam prediction may be referred to as a beam prediction model. Of course, it may also be referred to as a beam predictive AI model, a predictive beam model, and so forth. The present disclosure does not limit the names of such AI models.

In some embodiments, in case the beam prediction model is spatial prediction, the terminal measures L1-RSRP of set B and inputs it to the beam prediction model. The beam prediction model may predict the identity of the best beam/beam pair in set a and/or L1-RSRP for set a.

Wherein, the relation of set B and set A comprises the following two kinds:

the first relationship is that set B is a subset of set a. For example set a contains 32 reference signal resources (one for each beam direction), then set B contains some of the reference signal resources, for example set B contains 8 of the 32 reference signal resources.

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 one beam direction, and 32 reference signal resources cover 120 degrees of direction). And set B contains another Y reference signal resources, such as y=8. The Y reference signal resources also cover 120 degrees of direction, i.e. 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 relationship between 32/Y reference signal resources in set A and the same reference signal resource in set B is QCL Type D.

It will be appreciated that in the examples of the first and second relationships described above, only the case of transmitting beams is described. For beam pairs including a transmit beam and a receive beam, the receive beam of the terminal is also considered. For example, 32 transmit beams and 4 receive beams, set a is 32 x 4 beam pairs, and set B may be 32 beam pairs, 16 beam pairs, etc. therein.

In some embodiments, if there is no need to monitor the performance of the beam prediction model, the network device only needs to periodically send the reference signal on the reference signal resource of set B (e.g., the first period), assuming that the AI model has been trained in advance. The terminal then measures the L1-RSRP of the reference signal on the reference signal resource in set B and inputs it into the beam prediction model. I.e. the L1-RSRP of set a or the strongest one or more of the 32 reference signals in set a.

In some embodiments, the network device is required to periodically send set a reference signal resources (e.g., the second period) if it is required to monitor the performance of the beam prediction model. Then the terminal needs to measure the L1-RSRP of the reference signal on the reference signal resource of the set B on one side, and for the terminal side model, the terminal inputs the measured L1-RSRP of the reference signal on the reference signal resource of the set B into a beam prediction model to obtain predicted beam information and reports the predicted beam information to the network equipment; for the network side model, the terminal reports the measured L1-RSRP and/or the measured identifier of the reference signal on the reference signal resource of the set B to the network equipment, and the network equipment inputs the L1-RSRP and/or the identifier of the set B into the beam prediction model to obtain the predicted beam information. Meanwhile, the terminal also measures the L1-RSRP of the reference signals on all the reference signal resources in the set A, and reports the measured L1-RSRP and/or the optimal beam/beam pair identification of the reference signals on all the reference signal resources in the set A to the base station as beam information obtained by a traditional method.

For the case that set B is a subset of set a, the terminal may only report beam information of all beams or beam pairs of set a.

In some embodiments, the second period is greater than the first period, or the second period is a multiple of the first period, or the second period is greater than the first period by a greater amount, without limitation of the present disclosure. Of course, the second period may also be less than or equal to the first period, which is not limited by the present disclosure.

In some embodiments, for the case where the beam prediction model is a time domain prediction, the terminal measures the L1-RSRP of the historical time set B, and inputs the L1-RSRP to the beam prediction model to predict the L1-RSRP of the future time set A. And the relationship of set B and set a is the same as set B and set a in addition to the two.

If beam prediction is performed based on the beam prediction model, the reference signal for beam measurement at a future time may not be transmitted when performance monitoring of the beam prediction model is not required. If the beam prediction model is deployed at the terminal, the terminal needs to report to the network device based on the beam information output by the beam prediction model.

For beam measurements using conventional methods, reference signals at future times also need to be transmitted by the base station. The terminal measures reference signals at future time and reports the measured beam information to the network equipment. I.e. the terminal needs to measure and report beam information of all beams and/or beam pairs in set B, set a to the network device.

In some related art, in order to reduce the number of beam pairs measured by a terminal, an AI model is used for beam prediction. For example, the number of beam pairs that the terminal would have to measure in total is g×h. When the AI model is used for beam prediction, for airspace beam prediction, the terminal only needs to measure one part of the g×h beam pairs. For example, 1/8, 1/4 beam peering of G.times.H is measured. Then, the measured beam measurement quality of the beam pairs is input into an AI model, and the AI model can output the beam information of the g×h beam pairs. For time domain beam prediction, the terminal may measure beam measurement quality of beam pairs at historical times to predict beam information of beam pairs at future times.

Of course, for the input and output of some AI models, the beam quality or the beam identification of the beam pair may not be considered, and only the beam quality or the beam identification of the downlink transmission beam may be considered. For example, the disclosure is not limited based on the AI model of the downlink beam, but not based on the AI model of the beam pair.

It will be appreciated that the AI model is of a certain lifecycle or a certain range of applicability. Therefore, there is a need to monitor the performance of AI models in real time. When the performance of the AI model does not meet the corresponding requirement, the AI model needs to be updated, switched and other operations in time.

In some embodiments, in performing AI model performance monitoring, for example, for a terminal side model, the terminal may perform performance monitoring index calculation by itself, and then determine whether a deactivation (deactivation) model is required based on the performance monitoring index calculation result.

It can be appreciated that taking the performance monitoring index of the AI model as an example, when the calculation result of the performance monitoring index does not meet the corresponding threshold value or does not meet the preset requirement, the performance monitoring index of the AI model is determined to not meet the requirement, that is, the performance of the AI model is not good. Therefore, it is necessary to deactivate the AI model whose current performance is poor.

It can be understood that the performance monitoring index of the AI function is the same as the performance monitoring index of the AI model, and will not be described in detail herein.

In some embodiments, in performing AI model performance monitoring, for example, performance monitoring on a hybrid (hybrid) model or a network side (NW-side) model, the terminal needs to report at least one of the following, including: data for calculating performance monitoring indicators, performance values, events triggering reporting, and the like.

It will be appreciated that these performance monitoring indicators are based on a comparison of the beam information output by the model with the actually measured beam information of set a.

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

In some embodiments, the performance index is the beam prediction accuracy, and if the beam prediction is accurate, it can be understood that the performance monitoring index meets the requirement. For beam prediction accuracy, at least one of the following is included: the predicted strongest beam Identification (ID) contains the measured strongest beam ID; the predicted strongest beam pair ID comprises the measured strongest beam pair ID; the predicted strongest beam ID is contained in N beam IDs with the strongest measurement, wherein N is an integer greater than or equal to 1; the predicted strongest beam pair ID is included in the N beam pairs IDs that are measured strongest.

In some embodiments, the downlink receive beam ID is the receive beam ID (Rx beam ID) of the terminal. The beam pair ID is an ID corresponding to a combination of a downlink transmission beam and a downlink reception beam.

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.

Alternatively, the beam prediction accuracy may be the accuracy (accuracy) of the beam information derived and output a plurality of times based on the model, and may be, for example, a ratio (ratio).

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

the difference between the measured L1-RSRP of the best predicted beam and the measured L1-RSRP of the best predicted beam;

the difference between the measured L1-RSRP of the best predicted beam pair and the measured L1-RSRP of the best beam pair.

In some embodiments, the performance monitoring index is a beam or beam pair prediction accuracy with the L1-RSRP difference within a first threshold, and if the first threshold is 1dB, the difference is smaller than a certain threshold, that is, the accuracy degree meets the requirement, that is, the performance monitoring index meets the requirement. Meeting the requirements for accuracy includes: the accuracy of the difference between the predicted measured L1-RSRP of the best beam and the measured L1-RSRP of the best beam is within 1dB, or the accuracy of the difference between the predicted L1-RSRP of the best beam pair and the measured L1-RSRP of the best beam pair is within 1 dB.

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

In some embodiments, the degree of difference of the L1-RSRP comprises: the difference between the predicted L1-RSRP of the best beam and the measured L1-RSRP of the best beam or the 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.

Alternatively, the L1-RSRP differential may be a ratio of L1-RSRP differential below a threshold or a ratio of L1-RSRP differential above a threshold.

In some embodiments, the predicted difference in L1-RSRP comprises: the difference between the predicted L1-RSRP of the predicted best beam and the measured L1-RSRP of the predicted best beam's best beam, or the difference between the predicted L1-RSRP of the predicted best beam pair and the measured L1-RSRP of the predicted best beam's best beam, e.g., the average value is X dB, or the value at the cumulative distribution function 5% percentile is Y dB.

Alternatively, the predicted L1-RSRP differential may be a ratio of L1-RSRP differential below a threshold or a ratio above a threshold.

In some embodiments, the performance monitoring index is the average throughput of the terminal, and if the throughput difference is smaller than a certain threshold, it can be understood that the performance monitoring index meets the requirement. The average value for throughput meets the requirement, including: based on the predicted strongest beam and the measured strongest beam, SINR corresponding to the two beams is obtained, throughput (capability) difference is calculated based on shannon (shannon) capacity, or based on the predicted strongest beam pair and the measured strongest beam pair, SINR corresponding to the two beam pairs is obtained, and capability difference is calculated based on shannon capacity.

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

In some embodiments, in case the AI model is a network side model, the measurement results of set B all need to be reported to the network, i.e. the signaling overhead of this reporting.

In some embodiments, for AI function or model performance monitoring at the terminal side or performance monitoring of a hybrid AI function or model, the terminal does not report the actually measured beam information of set a, but calculates the corresponding performance monitoring index by itself and reports the performance monitoring index to the network, so the network does not know the actually measured beam information of set a during this time. Since the performance of the function or model is being monitored, it is not yet determined that the performance state of the function or model is good or bad, i.e., there may be a function or model with bad performance that has not yet been deactivated. In this case, the terminal also obtains information (e.g., SSB ID or CSI-RS ID, L1-RSRP, L1-SINR, etc.) about the best beam or the best beam pair based on the output of the AI model, and reports it to the network as a beam report (beam report).

It should be understood that the beam reporting in the above embodiments refers to beam reporting by CSI report based mechanisms.

Obviously, the beam report reported at this time is greatly different from the actually measured beam information of set a, so that the report result is inaccurate, thereby reducing the communication performance based on the beam.

Based on this, the embodiment of the disclosure proposes a communication method, so that when a terminal is in model performance monitoring, actual measurement information of a beam is added in a beam report, thereby improving accuracy of the beam report.

Fig. 2 is an interactive schematic diagram of a communication method shown in accordance with an embodiment of the present disclosure. As shown in fig. 2, an embodiment of the present disclosure relates to a communication method, the method including:

in step S2101, the terminal 101 performs performance monitoring.

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

In some embodiments, the AI function is to output beam prediction information for the first set of beams.

In some embodiments, the AI model is for outputting beam prediction information for the first set of beams.

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.

The AI model takes set B in the above embodiment as an input of the AI model, and outputs an output result obtained through the AI model, that is, beam prediction information of the first beam set.

In some embodiments, performance monitoring based on AI functionality or based on AI models includes: the beam prediction information of the first set of beams is compared with the beam measurement information of the first set of beams to obtain a performance value.

In some embodiments, the beam prediction information of the first set of beams is compared to the beam measurement information of the first set of beams, the beam measurement information of the first set of beams being a reference (reference) at the time of the comparison.

Illustratively, taking the comparison performance monitoring index as the difference between the predicted L1-RSRP and the actual measured L1-RSRP as an example, the comparison of the beam prediction information of the first beam set with the beam measurement information of the first beam set may be understood as the difference between the predicted L1-RSRP and the actual measured L1-RSRP.

In some embodiments, the performance value includes at least one of: beam or beam pair prediction accuracy; beam or beam pair prediction accuracy for which the L1-RSRP difference is within a first threshold; the L1-RSRP differential is less than or equal to a first differential threshold; predicting the degree of difference of L1-RSRP; average throughput of the terminal; reference signal resource overhead; row control information overhead; and predicting the time delay.

In some embodiments, the performance values satisfy different conditions, and the terminal 101 performs different processing for the function or model.

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

In some embodiments, the performance value satisfying the first condition includes at least one of:

the performance value continuously meets the performance monitoring index for L times, wherein L is an integer greater than or equal to 1;

within the first time threshold, the performance value is M times to meet the performance monitoring index, wherein M is an integer greater than or equal to 1;

the ratio of the first number of performance values satisfying the performance monitoring index is greater than a first ratio threshold.

Alternatively, the times L and M may be equal or unequal in value.

Optionally, at least one of the following is determined based on network configuration or default rules:

the value of the number of times L, the value of the number of times M, a first time threshold, a first number threshold, and a first scale threshold.

In some embodiments, the performance value meets performance monitoring criteria, including at least one of: the beam or beam pair prediction accuracy is greater than or equal to a first accuracy threshold; the beam or beam pair prediction accuracy of the L1-RSRP difference within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP differential is less than or equal to a first differential threshold; predicting that the L1-RSRP has a degree of difference less than or equal to a second degree of difference threshold; the average throughput of the terminal is greater than or equal to a 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 a second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

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

Optionally, the L1-RSRP variability is determined based on the beam prediction information and the beam measurement information.

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

Optionally, the average throughput of the terminal is based on the predicted strongest reference signal and the measured strongest reference signal, and corresponding SINR determinations of the two reference signals are obtained.

Optionally, the first threshold is determined based on a network configuration or a default rule.

The first threshold may be, for example, 1 dB.

a first accuracy threshold, a second accuracy threshold, a first variability threshold, a second variability threshold, a first throughput threshold, a first overhead threshold, a second overhead threshold, and a first latency threshold.

Alternatively, the first and second variance thresholds may or may not be equal in value.

Alternatively, the first accuracy threshold and the second accuracy threshold may or may not be equal in value.

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

In some embodiments, the L1-RSRP differential is the differential of the measured L1-RSRP of the best predicted beam or best beam pair and the measured L1-RSRP of the best beam or beam pair.

In some embodiments, the L1-RSRP differential comprises at least one of: the average value of the differences of the L1-RSRP, the cumulative distribution function of the differences of the L1-RSRP, the ratio of the differences of the L1-RSRP being less than or equal to the first difference threshold, and the ratio of the differences of the L1-RSRP being greater than the second difference threshold, wherein the difference of the L1-RSRP is the difference of the measured L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best measured beam or beam pair.

Alternatively, the first difference threshold and the second difference threshold may or may not be equal in value.

Optionally, the first difference threshold is determined based on a network configuration or a default rule.

Optionally, the second difference threshold is determined based on a network configuration or a default rule.

In some embodiments, the predicted L1-RSRP differential is the differential 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.

In some embodiments, predicting the L1-RSRP variability includes at least one of: the average value of the difference values of the predicted L1-RSRP, the cumulative distribution function of the difference values of the predicted L1-RSRP, the proportion that the difference value of the predicted L1-RSRP is smaller than or equal to a third difference threshold value, and the proportion that the difference value of the predicted L1-RSRP is larger than a fourth difference threshold value, wherein the difference value of the predicted L1-RSRP is the difference value of 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.

Optionally, the third difference threshold and the fourth difference threshold may or may not be equal in value.

Optionally, the third difference threshold is determined based on a network configuration or a default rule.

Optionally, the fourth difference threshold is determined based on a network configuration or a default rule.

In some embodiments, the performance value does not satisfy the first condition, the terminal 101 at least one of: deactivating the first AI function, deactivating the first AI function model, switching to the second AI function model, returning to the non-AI model, performing the first AI function update, performing the first AI model update.

In some embodiments, the performance value satisfies the second condition, the terminal 101 at least one of: deactivating the first AI function, deactivating the first AI function model, switching to the second AI function model, returning to the non-AI model, performing the first AI function update, performing the first AI model update.

In some embodiments, the performance threshold meeting the second condition or not meeting the first condition comprises at least one of: the performance value is continuously O times, wherein O is an integer greater than or equal to 1, and does not meet the performance monitoring index; within the second time threshold, the performance value has P times of meeting the performance monitoring index, wherein P is an integer greater than or equal to 1;

The ratio of the second number of performance values not meeting the performance monitoring criterion is greater than a second ratio threshold.

Alternatively, the times O and P may be equal or unequal in value.

the value of the number O, the value of the number P, the second number, the second threshold, the second time threshold, and the second ratio threshold.

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

Alternatively, the first number and the second number may or may not be equal in value.

Alternatively, the first proportional threshold and the second proportional threshold may or may not be equal in value.

In some embodiments, the performance value does not meet the performance monitoring criteria, including at least one of: the beam or beam pair prediction accuracy is less than a third accuracy threshold; the beam or beam pair prediction accuracy of the L1-RSRP difference within the second threshold is less than a fourth accuracy threshold; the L1-RSRP differential degree is larger than a third differential degree threshold value; predicting that the difference degree of the L1-RSRP is larger than a fourth difference degree threshold; the average throughput of the terminal is smaller than a 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 expense threshold; the predicted delay is greater than a second delay threshold.

The second threshold may be, for example, 1 dB.

a third accuracy threshold, a fourth accuracy threshold, a second throughput threshold, a third overhead threshold, and a fourth overhead threshold.

Optionally, the third accuracy threshold and the fourth accuracy threshold may or may not be equal in value.

Optionally, the first accuracy threshold, the second accuracy threshold, the third accuracy threshold, and the fourth accuracy threshold may or may not be equal in value.

Alternatively, the first throughput threshold and the second throughput threshold may or may not be equal in value.

Optionally, the third overhead threshold and the fourth overhead threshold may or may not be equal in value.

Optionally, the first overhead threshold, the second overhead threshold, the third overhead threshold, and the fourth overhead threshold may or may not be equal in value.

In some embodiments, terms such as "time of day," "point of time," "time location," and the like may be interchanged, and terms such as "duration," "period," "time window," "time," and the like may be interchanged.

In some embodiments, the terms "measure," "actual," "measured," and the like may be used interchangeably.

In some embodiments, terms such as "specific (specific)", "predetermined", "preset", "set", "indicated", "certain", "arbitrary", "first", and the like may be replaced with each other, and "specific a", "predetermined a", "preset a", "set a", "indicated a", "certain a", "arbitrary a", "first a" may be interpreted as a predetermined in a protocol or the like, may be interpreted as a obtained by setting, configuring, or indicating, or the like, may be interpreted as specific a, certain a, arbitrary a, or first a, or the like, but are not limited thereto.

It should be understood that the specific content of the performance monitoring index is described in the foregoing embodiments, and will not be described in detail herein.

In step S2102, the network device 102 transmits first information.

In some embodiments, the network device 102 transmits the first information to the terminal 101.

In some embodiments, the terminal 101 receives the first information.

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

In some embodiments, the first information includes at least one of: configuration information of a reference signal resource set of the first beam set, configuration information of a 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.

In some embodiments, the measurement configuration information for the first set of beams includes: the L1-RSRP corresponding to the first beam set, or the L1-SINR corresponding to the first beam set.

In some embodiments, the measurement configuration information for the second set of beams includes: the L1-RSRP corresponding to the second beam set or the L1-RSRP corresponding to the second beam set.

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

In some embodiments, "acquire," "obtain," "receive," "transmit," "bi-directional transmit," "send and/or receive" may be used interchangeably and may be interpreted as receiving from other principals, acquiring from protocols, acquiring from higher layers, processing itself, autonomous implementation, etc.

In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "instruction", "command", "channel", "parameter", "field", "symbol", "codebook", "code word", "code point", "bit", "data", "program", "chip", and the like may be replaced with each other.

In step S2103, the terminal 101 transmits a first report.

In some embodiments, the terminal 101 sends the first report to the network device 102.

In some embodiments, the network device 102 receives the first report.

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

Optionally, the first report is a beam report.

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.

In some embodiments, the terminal 101 sends a first report to the network device 102 comprising at least one of:

the AI function is for spatial beam prediction, and transmits a first report to the network device 102 after obtaining beam measurement information of the first beam set;

the AI model is for spatial beam prediction, and transmits a first report to the network device 102 after obtaining beam measurement information for the first set of beams;

the AI function is for time domain beam prediction of a single time domain instance, and transmits a first report to the network device 102 after obtaining beam measurement information of the first set of beams;

the AI function is for beam prediction for a plurality of time domain instances, and after obtaining beam measurement information for N time domain instances of the first set of beams, sends a first report to the network device 102;

the AI model is used for beam prediction for a plurality of time domain instances, and after obtaining beam measurement information for 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, a positive integer less than or equal to M, and M is the number of time domain instances output by the AI function or AI model.

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

In some embodiments, the terminal 101 sends a first report to the network device 102 if the beam measurement information and the beam prediction information for the first set of beams are available.

In some embodiments, the terminal 101 sends a first report to the network device 102, where the beam measurement information and the beam prediction information for the first set of beams are available, the first report comprising the beam measurement information.

In some embodiments, the cases where beam measurement information and beam prediction information for the first set of beams can be obtained include at least one of:

during performance monitoring of the AI function, the AI function is in an active state.

During performance monitoring of the AI model, the AI model is in an active state.

It should be appreciated that while the AI function or AI model is in an active state, the terminal 101 may obtain beam prediction information for the first set of beams based on the AI function or AI model. But since the AI function or model is in the performance monitoring period, the terminal 101 can also obtain beam measurement information for the first set of beams at the same time. And the beam measurement information is more accurate than the beam prediction information, in this case, the terminal 101 should report the beam measurement information of the first beam set instead of the beam prediction information.

Alternatively, the terminal 101 sends a first report to the network device 102, where the beam measurement information and the beam prediction information of the first set of beams are available, the first report comprising the beam measurement information and the beam prediction information.

It should be appreciated that while the AI function or AI model is in an active state, the terminal 101 may obtain beam prediction information for the first set of beams based on the AI function or AI model. But since the AI function or model is in the performance monitoring period, the terminal 101 can also obtain beam measurement information for the first set of beams at the same time. And the beam measurement information is more accurate than the beam prediction information, in this case, the terminal 101 may report the beam measurement information of the first beam set and report the beam prediction information, thereby providing a reference for the network device.

In some embodiments, the "time domain instance" and "predicted time instance" may be interchanged.

The communication method according to the embodiment of the present disclosure may include at least one of step S2101 to step S2103. For example, step S2103 may be implemented as a separate embodiment, step S2101 may be implemented as a separate embodiment, and step S2101+s2102+s2103 may be implemented as a separate embodiment, but is not limited thereto.

In some embodiments, steps S2101, S2102 may be performed in exchange for sequence or simultaneously, steps S2102, S2103 may be performed in exchange for sequence or simultaneously, and steps S2101, S2103 may be performed in exchange for sequence or simultaneously.

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

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

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

In some embodiments, reference may be made to alternative implementations carried before or after the description corresponding to fig. 2.

In some embodiments, fig. 3A is a flow diagram illustrating a communication method according to an embodiment of the disclosure. As shown in fig. 3A, an embodiment of the present disclosure relates to a communication method, the method including:

in step S3101, performance monitoring is performed.

Alternative implementations of step S3101 may refer to alternative implementations of step S2101 of fig. 2, and other relevant parts of the embodiment related to fig. 2, which are not described herein.

In step S3102, first information is acquired.

Alternative implementations of step S3102 may refer to alternative implementations of step S2102 in fig. 2, and other relevant parts in the embodiment related to fig. 2, which are not described herein.

In some embodiments, the terminal 101 receives the first information transmitted by the network device 102, but is not limited thereto, and may also receive the first information transmitted by other subjects.

In some embodiments, the terminal 101 obtains the first information specified by the protocol.

In some embodiments, the terminal 101 acquires the first information from an upper layer(s).

In some embodiments, the terminal 101 processes to obtain the first information.

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

Step S3103, a first report is sent.

Alternative implementations of step S3103 may refer to alternative implementations of step S2103 of fig. 2, and other relevant parts of the embodiment related to fig. 2, which are not described herein.

The communication method according to the embodiment of the present disclosure may include at least one of step S3101 to step S3103. For example, step S3101 may be implemented as a separate embodiment, step S3102 may be implemented as a separate embodiment, step S3103 may be implemented as a separate embodiment, step S3101+s3102 may be implemented as a separate embodiment, step S3101+s3103 may be implemented as a separate embodiment, and step S3101+s3102+s3103 may be implemented as a separate embodiment, but is not limited thereto.

In some embodiments, steps S3102, S3103 may be performed in exchange for sequence or simultaneously, steps S3101, S3103 may be performed in exchange for sequence or simultaneously, and steps S3102, S3103 may be performed in exchange for sequence or simultaneously.

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

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

Fig. 3B is a flow chart diagram of a communication method shown in accordance with an embodiment of the present disclosure. As shown in fig. 3B, an embodiment of the present disclosure relates to a communication method, the method including:

in step S3201, first information is acquired and performance monitoring is performed.

Alternative implementations of step S3201 may refer to step S2101, step S2102 of fig. 2, alternative implementations of step S3101 and step S3102 of fig. 3A, and other relevant parts in the embodiments related to fig. 2 and 3A, which are not described herein.

Step S3202, a first report is sent.

Alternative implementations of step S3202 may refer to alternative implementations of step S2103 of fig. 2 and step S3103 of fig. 3A, and other relevant parts in the embodiments related to fig. 2 and 3A, which are not described herein.

The communication method according to the embodiment of the present disclosure may include at least one of step S3201 to step S3202. For example, step S3201 may be implemented as a separate embodiment, and step S3202 may be implemented as a separate embodiment, but is not limited thereto.

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

In the disclosed embodiment, step S3201 may be combined with steps S3102-S3103 of fig. 3A, and step S3202 may be combined with steps S3101, S3102, S3103 of fig. 3A.

Fig. 3C is a flow chart diagram illustrating a communication method according to an embodiment of the present disclosure. As shown in fig. 3C, an embodiment of the present disclosure relates to a communication method, the method including:

in step S3301, in the first case, a first report is sent.

In some embodiments, the first case comprises: the situation of beam measurement information and beam prediction information for the first set of beams can be obtained.

Alternative implementations of step S3301 may refer to step S2103 of fig. 2, step S3103 of fig. 3A, alternative implementations of step S3202 of fig. 3B, and other relevant parts in the embodiments related to fig. 2, 3A, and 3B, which are not described herein.

In some embodiments, where beam measurement information for the first set of beams is available, the method includes: during performance monitoring of the AI function, the AI function is in an active state; during performance monitoring of the AI model, the AI model is in an active state.

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

Optionally, the first report is a beam report.

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

In some embodiments, the first report is sent to the network device, including at least one of: the AI function or AI model is used for spatial 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 the beam measurement information of the first beam set; the AI function or AI model is for time domain beam prediction of the plurality of time domain instances, and after obtaining 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 a number of time domain instances output by the AI function or AI model.

In some embodiments, the method further comprises: receiving first information, wherein the first information is used for indicating a terminal to monitor performance of at least one of the following items, and the method comprises the following steps: AI function; AI model.

In some embodiments, the first information includes at least one of: configuration information of a reference signal resource set of the first beam set; configuration information of a 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 quantities of the second set of beams.

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

In some embodiments, performance monitoring of AI functions or AI models includes: the beam prediction information of the first set of beams is compared with the beam measurement information of the first set of beams to obtain a performance value.

In some embodiments, the method further comprises: the performance value satisfies a first condition, at least one of: activating a first AI function; activating a first AI model; switching to a first AI function; switch to the first AI model.

In some embodiments, the method further comprises: the performance value satisfies a second condition, at least one of: deactivating the first AI function; deactivating a first model included in the first AI function; switching to a second AI function; switching to the second AI model; returning to the non-AI model; performing a first AI function update; a first AI model update is performed.

In some embodiments, the performance value satisfies the performance monitoring index L consecutive times, where L is an integer greater than or equal to 1;

within the first time threshold, the performance value is M times to meet the performance monitoring index, wherein 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 a first accuracy threshold, the proportion of the performance values meeting the performance monitoring index in the first number of performance values is greater than a first proportion threshold, and the first number and the first proportion threshold are determined based on network configuration or default rules.

In some embodiments, the performance value meets performance monitoring criteria, including at least one of: the beam or beam pair prediction accuracy is greater than or equal to a first accuracy threshold; the beam or beam pair prediction accuracy of the reference signal received power L1-RSRP difference of the layer 1 within a first threshold is greater than or equal to a second accuracy threshold; the L1-RSRP differential is less than or equal to a first differential threshold; predicting that the L1-RSRP has a degree of difference less than or equal to a second degree of difference threshold; the average throughput of the terminal is greater than or equal to a 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 a second overhead threshold; the predicted delay is less than or equal to the first delay threshold.

In some embodiments, the performance value, O consecutive times, does not meet the performance monitoring index, where O is an integer greater than or equal to 1;

within the second time threshold, the performance value, P times of meeting the performance monitoring index exist, wherein P is an integer greater than or equal to 1; a proportion of the second number of performance values not meeting the performance monitoring criterion is greater than a second proportion threshold, the second number and the second proportion threshold being determined based on a network configuration or a default rule.

In some embodiments, the performance value does not meet the performance monitoring criteria, including at least one of: the beam or beam pair prediction accuracy is less than a third accuracy threshold; the beam or beam pair prediction accuracy of the L1-RSRP difference within the second threshold is less than a fourth accuracy threshold; the reference signal received power L1-RSRP of the layer 1 is more than a third difference threshold; predicting that the difference degree of the L1-RSRP is larger than a fourth difference degree threshold; the average throughput of the terminal is smaller than a 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 expense threshold; the predicted delay is greater than a second delay threshold.

In some embodiments, the performance monitoring includes AI function-based performance monitoring, or AI model-based performance monitoring.

In the disclosed embodiment, step S3301 may be combined with steps S3102-S3103 of fig. 3A, and step S3301 may be combined with step S3202 of fig. 3B.

Fig. 4A is a flow diagram illustrating a communication method according to an embodiment of the present disclosure. As shown in fig. 4A, an embodiment of the present disclosure relates to a communication method, the method including:

step S4101, first information is transmitted.

In step S4102, a first report is received.

Alternative implementations of step S4101 may refer to alternative implementations of step S2102 in fig. 2, and other relevant parts in the embodiment related to fig. 2, which are not described herein.

Alternative implementations of step S4102 may refer to alternative implementations of step S2103 of fig. 2, and other relevant parts in the embodiment related to fig. 2, which are not described here again.

The communication method according to the embodiment of the present disclosure may include at least one of step S4101 to step S4102. For example, step S4101 may be implemented as a separate embodiment, and step S4102 may be implemented as a separate embodiment, but is not limited thereto.

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

Fig. 4B is a flow chart diagram of a communication method shown in accordance with an embodiment of the present disclosure. As shown in fig. 4B, an embodiment of the present disclosure relates to a communication method, the method including:

in step S4201, a first report is received.

Alternative implementations of step S4201 may refer to alternative implementations of step S2103 of fig. 2, and other relevant parts of the embodiment related to fig. 2, which are not described herein.

In some embodiments, the network device 102 acquires the first report transmitted from the terminal 101, but is not limited thereto, and may acquire the first report to other subjects.

In some embodiments, the beam prediction information and the beam measurement information are different, and the beam prediction information is predicted based on AI functions or AI models.

In some embodiments, the first report is sent by the terminal in the event that the beam measurement information and the beam prediction information for the first set of beams are available.

In some embodiments, the prediction information for the first set of beams includes: AI function or AI model output information.

In some embodiments, a first report is received comprising at least one of: the AI function or AI model is for spatial beam prediction, or for time domain beam prediction of a single time domain instance, receiving a first report after obtaining beam measurement information of a first set of beams; the AI function or AI model is used for time domain beam prediction of a plurality of time domain instances, and after beam measurement information of N time domain instances of a first beam set is obtained, a first report is received, wherein 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.

In some embodiments, the method further comprises: and sending first information, wherein the first information is used for indicating the terminal to monitor the performance of the AI function or the AI model.

In some embodiments, the AI function or AI model performs performance monitoring, including: the beam prediction information of the first set of beams is compared with the beam measurement information of the first set of beams to obtain a performance value.

In some embodiments, the performance value satisfies a first condition comprising at least one of: the performance value continuously meets the performance monitoring index for L times, wherein L is an integer greater than or equal to 1; within a first time threshold, the performance value is met with a performance monitoring index for M times, wherein M is an integer greater than or equal to 1; the ratio of the first number of performance values meeting the performance monitoring index is greater than a first ratio threshold; the performance value satisfies a first condition, which is a condition that the terminal performs at least one of: activating a first AI function; activating a first AI model; switching to a first AI function; switch to the first AI model.

In some embodiments, the performance value satisfies a second condition comprising at least one of: performance values, wherein O is an integer greater than or equal to 1, are not met by continuous O times; the ratio of the second number of performance values not meeting the performance monitoring index is greater than a second ratio threshold; the performance value satisfying the second condition is a condition that the terminal performs at least one of: 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 the non-AI model; performing a first AI function update; a first AI model update is performed.

Fig. 5 is an interactive schematic diagram of a communication method shown according to an embodiment of the disclosure. As shown in fig. 5, an embodiment of the present disclosure relates to a communication method, the method including:

in step S5101, the terminal 101 transmits, in the first case, a first report to the network device 102, the beam prediction information of the first beam set including at least one of: information output by the AI function; information output by the AI model.

Alternative implementations of step S5101 may refer to step S2101 of fig. 2, step S2102, alternative implementations of step S3101 of fig. 3A to step S3102 of step S4101 of fig. 4A, and other relevant parts in the embodiments related to fig. 2, 3A, and 4A, which are not described herein.

In step S5102, the network device 102 receives a first report.

Alternative implementations of step S5102 may refer to alternative implementations of step S2103 of fig. 2, step S3103 of fig. 3A, step S4102 of fig. 4A, and other relevant parts of the embodiments related to fig. 2, 3A, and 4A, which are not described herein.

In some embodiments, the method may include the method described in the embodiments of the communication system side, the terminal side, the network device side, and so on, which are not described herein.

The embodiment also provides a communication method, including: during performance monitoring of an AI function or model by the terminal, the first report reported by the terminal should be a report including actual measured beam information, even if the AI function or model is not deactivated, rather than reporting beam information obtained based on a model output. Improving the beam-based communication performance.

In some embodiments, the terminal receives first information, the first information being used to instruct the terminal to perform performance monitoring.

In some embodiments, the first information includes at least one of: reference signal resource configuration information for set B, reference signal resource configuration information for set a.

In some embodiments, the measurement comprises at least one of: L1-SINR or L1-RSRP.

Optionally, the measured quantity comprises at least one of: layer 1 reference signal received power L1-RSRP for a beam or beam pair in set a; the signal to interference plus noise ratio L1-SINR for layer 1 of the beam or beam pair in set a; layer 1 reference signal received power L1-RSRP for a beam or beam pair in set B; set B, the signal to interference plus noise ratio L1-SINR of layer 1 of the beam or beam pair.

In some embodiments, set B comprises a beam or beam pair corresponding to the model input, and set a comprises a beam or beam pair corresponding to the model output.

In some embodiments, the first information includes displayed performance monitoring indication information.

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

In some embodiments, performance monitoring refers to comparing the acquired beam information of set a output by the AI model with the actually measured beam information of set a to obtain at least one performance monitoring index, and when the obtained performance monitoring index meets a specified condition, a certain function or model may be activated or deactivated.

In some embodiments, performance monitoring includes: function-based model performance monitoring, or model-based model performance monitoring.

In some embodiments, activating or deactivating a function includes: a function (functionality) is activated or deactivated, whereas multiple models inside the function switch directly regardless.

In some embodiments, activating or deactivating a model includes: a certain model is directly activated or deactivated.

In some embodiments, the specified conditions include at least one of:

if the performance of the AI function or model is good for N times continuously, the AI function or model needs to be activated or switched to, wherein N is an integer greater than or equal to 1;

The AI function or model is required to be activated or switched to when the AI function or model has good performance for N times within the appointed time;

if the performance of the AI function or model is poor for M times continuously, the AI function or model needs to be deactivated, or is switched to other AI functions or models, or is returned to a non-AI model, or is updated, wherein M is an integer greater than or equal to 1;

if the performance of the AI function or model is poor for M times in the appointed time, the AI function or model needs to be deactivated, or the AI function or model is switched to other AI functions or models, or the AI function or model is returned to a non-AI model, or the function or model is updated;

comparing the ratio of each performance monitoring index with a threshold value, and if the ratio is higher than or equal to the threshold value, activating the function or the model;

in the appointed time, comparing the ratio of each performance monitoring index with a threshold value, and if the ratio is higher than or equal to the threshold value, activating the function or the model;

and comparing the ratio of each performance monitoring index with a threshold value in a specified time, and if the ratio is lower than the threshold value, deactivating the function or the model.

Illustratively, the ratio of the various performance monitoring metrics is compared to a threshold, e.g., top4 beam prediction accuracy is less than 60%, deactivating the function, and prediction accuracy is greater than 80% activating the function or model.

In some embodiments, the specified condition may be that the terminal itself performs terminal side model operation or that the terminal reports related data to the network.

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

In some embodiments, the measurement of set A is used as a reference (reference/benchmark) for performance monitoring performance comparisons.

In some embodiments, the terminal reports a beam report that includes the set a measurement (if set a measurement is different from the model output), rather than a predicted value obtained based on model output of model inference (model reference).

In some embodiments, for spatial beam prediction or time domain beam prediction that is a predicted time instance (time-instance), the beam report may be reported after each measurement set a.

In some embodiments, for time-domain beam prediction that predicts multiple predicted time instances, the beam report may be reported after each measurement set a, or after N consecutive measurements of set a, where N corresponds to the number of predicted time instances that the model outputs.

Based on this, when the terminal obtains the measurement information of set a and the prediction information of set a obtained based on the AI model output at the same time, the beam report preferentially contains the measurement information of set a. Improving the beam-based communication performance.

The embodiments of the present disclosure also provide an apparatus for implementing any of the above methods, for example, an apparatus is provided, where the apparatus includes a unit or a module for implementing each step performed by the terminal in any of the above methods. For another example, another apparatus is also proposed, which includes a unit or module configured to implement steps 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.

It should be understood that the division of each unit or module in the above apparatus is merely a division of a logic function, and may be fully or partially integrated into one physical entity or may be physically separated when actually implemented. Furthermore, units or modules in the apparatus may be implemented in the form of processor-invoked software: the device comprises, for example, a processor, the processor being connected to a memory, the memory having instructions stored therein, the processor invoking the instructions stored in the memory to perform any of the methods or to perform the functions of the units or modules of the device, wherein the processor is, for example, a general purpose processor, such as a central processing unit (Central Processing Unit, CPU) or microprocessor, and the memory is internal to the device or external to the device. Alternatively, the units or modules in the apparatus may be implemented in the form of hardware circuits, and part or all of the functions of the units or modules may be implemented by designing hardware circuits, which may be understood as one or more processors; for example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the units or modules are implemented by designing the logic relationships of elements in the circuit; for another example, in another implementation, the above hardware circuit may be implemented by a programmable logic device (programmable logic device, PLD), for example, a field programmable gate array (Field Programmable Gate Array, FPGA), which may include a large number of logic gates, and the connection relationship between the logic gates is configured by a configuration file, so as to implement the functions of some or all of the above units or modules. All units or modules of the above device may be realized in the form of invoking software by a processor, or in the form of hardware circuits, or in part in the form of invoking software by a processor, and in the rest in the form of hardware circuits.

In the disclosed embodiments, the processor is a circuit with signal processing capabilities, and in one implementation, the processor may be a circuit with instruction reading and running capabilities, such as a central processing unit (Central Processing Unit, CPU), microprocessor, graphics processor (graphics processing unit, GPU) (which may be understood as a microprocessor), or digital signal processor (digital signal processor, DSP), etc.; in another implementation, the processor may implement a function through a logical relationship of hardware circuits that are fixed or reconfigurable, e.g., a hardware circuit implemented as an application-specific integrated circuit (ASIC) or a programmable logic device (programmable logic device, PLD), such as an FPGA. In the reconfigurable hardware circuit, the processor loads the configuration document, and the process of implementing the configuration of the hardware circuit may be understood as a process of loading instructions by the processor to implement the functions of some or all of the above units or modules. Furthermore, hardware circuits designed for artificial intelligence may be used, which may be understood as ASICs, such as neural network processing units (Neural Network Processing Unit, NPU), tensor processing units (Tensor Processing Unit, TPU), deep learning processing units (Deep learning Processing Unit, DPU), etc.

Fig. 6A is a schematic structural diagram of a terminal according to an embodiment of the present disclosure. As shown in fig. 6A, the terminal 6100 may include: a transceiver module 6101. In some embodiments, the transceiver module is configured to send a first report to the network device, where the first report includes beam measurement information of the first beam set and beam prediction information of the first beam set are obtained; the beam prediction information for the first set of beams includes at least one of: information output by the AI function; information output by the AI model. Optionally, the transceiver module is configured to perform at least one of the communication steps (e.g., step S2102, step S2103, but not limited to the foregoing steps) of sending and/or receiving performed by the terminal 101 in any of the foregoing methods, which is not described herein.

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

Fig. 6B is a schematic structural diagram of a network device according to an embodiment of the present disclosure. As shown in fig. 6B, the network device 6200 may include: transceiver module 6201. In some embodiments, the transceiver module is configured to receive a first report, where the first report is sent by the terminal in a case where 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 includes at least one of: information output by the AI function; information output by the AI model. Optionally, the transceiver module is configured to perform at least one of the communication steps (e.g., step S2102, step S2103, but not limited to the foregoing steps) of sending and/or receiving performed by the terminal 101 in any of the foregoing methods, which is not described herein.

Fig. 7A is a schematic structural diagram of a communication device 7100 according to an embodiment of the present disclosure. The communication device 7100 may be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user device, etc.), a chip system, a processor, etc. that supports the network device to implement any of the above methods, or a chip, a chip system, a processor, etc. that supports the terminal to implement any of the above methods. The communication device 7100 may be used to implement the methods described in the above method embodiments, and may be referred to in particular in the description of the above method embodiments.

As shown in fig. 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 may be a general-purpose processor or a special-purpose processor, etc., and may be, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processor may be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process data for the programs. Optionally, the communication device 7100 is used to perform any of the above methods. Optionally, the one or more processors 7101 are configured to invoke instructions to cause the communication device 7100 to perform any of the methods above.

In some embodiments, the communication device 7100 also 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 (e.g., step S2102, step S2103, but not limited thereto) of the above-described method, and the processor 7101 performs at least one of the other steps (e.g., step S2101, but not limited thereto). In alternative embodiments, the transceiver may include a receiver and/or a transmitter, which may be separate or integrated. Alternatively, terms such as transceiver, transceiver unit, transceiver circuit, interface, etc. may be replaced with each other, terms such as transmitter, transmitter unit, transmitter circuit, etc. may be replaced with each other, and terms such as receiver, receiving unit, receiver, receiving circuit, etc. may be replaced with each other.

In some embodiments, the communication device 7100 also includes one or more memories 7103 for storing data. Alternatively, all or part of the memory 7103 may be external to the communication device 7100. In alternative embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuit 7104 is coupled to the memory 7103, and the interface circuit 7104 may be configured to receive data from the memory 7103 or other device and may be configured to transmit data to the memory 7103 or other device. For example, the interface circuit 7104 may read data stored in the memory 7103 and send the data to the processor 7101.

The communication device 7100 in the above embodiment description 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 a stand-alone device or may be part of a larger device. For example, the communication device may be: 1) A stand-alone integrated circuit IC, or chip, or a system-on-a-chip or subsystem; (2) A set of one or more ICs, optionally including storage means for storing data, programs; (3) an ASIC, such as a Modem (Modem); (4) modules that may be embedded within other devices; (5) A receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handset, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligent device, and the like; (6) others, and so on.

Fig. 7B is a schematic structural diagram of a chip 7200 according to an embodiment of the disclosure. For the case where the communication device 7100 may be a chip or a chip system, reference may be made to a schematic structural diagram of the chip 7200 shown in fig. 7B, but is not limited thereto.

The chip 7200 includes one or more processors 7201. Chip 7200 is used to perform any of the above methods.

In some embodiments, the chip 7200 further includes one or more interface circuits 7202. Alternatively, the terms interface circuit, interface, transceiver pin, etc. may be interchanged. In some embodiments, the chip 7200 further includes one or more memories 7203 for storing data. Alternatively, all or a portion of memory 7203 may be external to chip 7200. Optionally, an interface circuit 7202 is coupled to the memory 7203, the interface circuit 7202 may be configured to receive data from the memory 7203 or other device, and the interface circuit 7202 may be configured to transmit data to the memory 7203 or other device. For example, the interface circuit 7202 may read data stored in the memory 7203 and transmit the data to the processor 7201.

In some embodiments, the interface circuit 7202 performs at least one of the communication steps (e.g., but not limited to, step S2102, step S2103) of the above-described method of transmitting and/or receiving. The interface circuit 7202 performs the communication step of transmission and/or reception in the above-described method, for example, refers to: the interface circuit 7202 performs data interaction between the processor 7201, the chip 7200, the memory 7203, or the transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (e.g., step S2101, but is not limited thereto).

The modules and/or devices described in the embodiments of the virtual device, the physical device, the chip, etc. may be arbitrarily combined or separated according to circumstances. Alternatively, some or all of the steps may be performed cooperatively by a plurality of modules and/or devices, without limitation.

The present disclosure also proposes a storage medium having stored thereon instructions that, when executed on a communication device 7100, cause the communication device 7100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Alternatively, the storage medium described above is a computer-readable storage medium, but is not limited thereto, and it may be a storage medium readable by other devices. Alternatively, the above-described storage medium may be a non-transitory (non-transitory) storage medium, but is not limited thereto, and it may also be a transitory storage medium.

The present disclosure also proposes a program product which, when executed by a communication device 7100, causes the communication device 7100 to perform any of the above methods. Optionally, the above-described program product is a computer program product.

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

Claims

1. A method of communication, the method comprising:

transmitting a first report to a network device, where beam measurement information and beam prediction information for a first set of beams are available, the first report comprising beam measurement information for the first set of beams; the beam prediction information of the first set of beams comprises at least one of:

information output by the artificial intelligence AI function;

information output by the AI model.

2. The method of claim 1, wherein the case where beam measurement information for the first set of beams is available comprises at least one of:

during performance monitoring of the AI function, the AI function is in an active state;

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

4. A method according to any of claims 1 to 3, wherein said sending a first report to a network device comprises at least one of:

the AI function or AI model is used for space domain beam prediction or time domain beam prediction of a single time domain instance, and a first report is sent to the network equipment after beam measurement information of the first beam set is obtained;

The AI function or AI model is used for time domain beam prediction of a plurality of time domain instances, and after beam measurement information of N time domain instances of the first beam set is obtained, a first report is sent to a network device, 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.

5. The method according to any one of claims 1-4, further comprising:

receiving first information, wherein the first information is used for indicating a terminal to monitor performance of at least one of the following:

AI function;

AI model.

6. The method of claim 5, wherein the first information comprises at least one of:

configuration information of a reference signal resource set of the first beam set;

configuration information of a 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 quantities of the second set of beams.

7. The method of claim 5 or 6, wherein the first information is carried by at least one of:

radio resource control, RRC, signaling;

a medium access control MAC CE activation indication;

Downlink control information DCI.

8. The method according to any one of claims 2 to 7, wherein said performing performance monitoring comprises:

the beam prediction information of the first set of beams is compared with the beam measurement information of the first set of beams to obtain a performance value.

9. The method of claim 8, wherein the method further comprises:

the performance value satisfies a first condition, at least one of:

activating a first AI function;

activating a first AI model;

switching to a 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 a second condition, at least one of:

deactivating the first AI function;

the first AI model is deactivated and the first AI model,

switching to a second AI function;

switching to the second AI model;

returning to the non-AI model;

performing a first AI function update;

a first AI model update is performed.

11. The method of claim 9, wherein the performance value satisfying a first condition comprises at least one of:

Within a first time threshold, the performance value is met with a performance monitoring index for M times, wherein M is an integer greater than or equal to 1;

12. The method of any one of claims 11, wherein the performance value meets a performance monitoring criterion, comprising at least one of:

the beam or beam pair prediction accuracy is greater than or equal to a first accuracy threshold;

the beam or beam pair prediction accuracy of the reference signal received power L1-RSRP difference of the layer 1 within a first threshold is greater than or equal to a second accuracy threshold;

the L1-RSRP differential is less than or equal to a first differential threshold;

predicting that the L1-RSRP has a degree of difference less than or equal to a second degree of difference threshold;

the average throughput of the terminal is greater than or equal to a 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 a second overhead threshold;

the predicted delay is less than or equal to the first delay threshold.

13. The method of claim 10, wherein the performance value satisfying a second condition comprises at least one of:

The performance value is continuously not met with the performance monitoring index for O times, wherein O is an integer greater than or equal to 1;

within a second time threshold, the performance value does not meet the performance monitoring index for P times, wherein P is an integer greater than or equal to 1;

14. The method of claim 13, wherein the performance value does not meet a performance monitoring criterion, comprising at least one of:

the beam or beam pair prediction accuracy is less than a third accuracy threshold;

the beam or beam pair prediction accuracy of the L1-RSRP difference within the second threshold is less than a fourth accuracy threshold;

the reference signal received power L1-RSRP of the layer 1 is more than a third difference threshold;

predicting that the difference degree of the L1-RSRP is larger than a fourth difference degree threshold;

the average throughput of the terminal is smaller than a 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 expense threshold;

the predicted delay is greater than a second delay threshold.

15. The method of any of claims 1-14, wherein the performance monitoring comprises AI function-based performance monitoring, or AI model-based performance monitoring.

16. A method of communication, the method comprising:

receiving a first report, the first report being transmitted by the terminal in the event that beam measurement information and beam prediction information for a first set of beams are available, the first report comprising beam measurement information for the first set of beams,

the prediction information for the first set of beams includes at least one of:

information output by the AI function;

information output by the AI model.

17. The method of claim 16, wherein the case where beam measurement information for the first set of beams is available comprises at least one of:

18. The method according to claim 16 or 17, wherein the beam prediction information and the beam measurement information are different.

19. The method of any of claims 16 to 18, wherein the receiving the first report comprises at least one of:

the AI function or AI model is used for spatial beam prediction, or for time domain beam prediction of a single time domain instance, receiving a first report after obtaining beam measurement information of the first set of beams;

The AI function or AI model is used for time domain beam prediction of a plurality of time domain instances, and after beam measurement information of N time domain instances of the first beam set is obtained, a first report is received, 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.

20. The method according to any one of claims 16 to 19, further comprising:

transmitting first information, wherein the first information is used for indicating a terminal to monitor performance of at least one of the following:

AI function

AI model.

21. The method of claim 20, wherein the first information comprises at least one of:

configuration information of the measurement quantity of the first beam set;

22. The method of claim 20 or 21, wherein the first information is carried by at least one of:

radio resource control, RRC, signaling;

a medium access control MAC CE activation indication;

Downlink control information DCI.

23. The method according to any one of claims 17 to 22, wherein said performing performance monitoring comprises:

24. The method of claim 23, wherein the performance value satisfies a first condition comprising at least one of:

the ratio of the first number of performance values meeting the performance monitoring index is greater than a first ratio threshold;

the performance value satisfying a first condition is a condition that the terminal performs at least one of:

activating a first AI function;

activating a first AI model;

switching to a first AI function;

switch to the first AI model.

25. The method of claim 23 or 24, wherein the performance value meets a performance monitoring criterion, comprising at least one of:

the predicted delay is less than or equal to the first delay threshold.

26. The method of claim 23, wherein the performance value satisfies a second condition comprising at least one of:

the ratio of the second number of performance values not meeting the performance monitoring index is greater than a second ratio threshold;

the performance value satisfying the second condition is a condition that the terminal performs at least one of:

deactivating the first AI function;

the first AI model is deactivated and the first AI model,

switching to a second AI function;

switching to the second AI model;

returning to the non-AI model;

performing a first AI function update;

A first AI model update is performed.

27. The method of any one of claims 23, wherein the performance value does not meet a performance monitoring criterion, comprising at least one of:

the predicted delay is greater than a second delay threshold.

28. The method of any one of claims 16 to 27, wherein the performance monitoring comprises AI function-based performance monitoring, or AI model-based performance monitoring.

29. A method of communication, the method comprising: the terminal sends a first report to the network equipment under the condition that the beam measurement information and the beam prediction information of the first beam set can be obtained, 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 components:

Information output by the AI function;

information output by the AI model;

the network device receives a first report.

30. A terminal, 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, where the first report includes the beam measurement information of the first beam set; the beam prediction information for the first set of beams includes at least one of:

information output by the AI function;

information output by the AI model.

31. A network device, comprising:

a transceiver module for receiving a first report, the first report being transmitted by a terminal in case of obtaining beam measurement information and beam prediction information of a first set of beams, the first report comprising the beam measurement information of the first set of beams,

the prediction information for the first set of beams includes at least one of:

information output by the AI function;

information output by the AI model.

32. A terminal, comprising:

one or more processors;

wherein the processor is configured to perform the communication method of any one of claims 1 to 15.

33. A network device, comprising:

one or more processors;

wherein the processor is configured to perform the communication method of any one of claims 16 to 28.

34. A communication system comprising a terminal configured to implement the communication method of any one of claims 1 to 15 and a network device configured to implement the communication method of any one of claims 16 to 28.

35. A storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method of any one of claims 1 to 15 or 16 to 28.