WO2025043573A1 - Procédé de communication, terminal, dispositif de réseau et système de communication - Google Patents

Procédé de communication, terminal, dispositif de réseau et système de communication Download PDF

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Publication number
WO2025043573A1
WO2025043573A1 PCT/CN2023/115987 CN2023115987W WO2025043573A1 WO 2025043573 A1 WO2025043573 A1 WO 2025043573A1 CN 2023115987 W CN2023115987 W CN 2023115987W WO 2025043573 A1 WO2025043573 A1 WO 2025043573A1
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Prior art keywords
information
threshold
model
prediction
equal
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English (en)
Chinese (zh)
Inventor
李明菊
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Beijing Xiaomi Mobile Software Co Ltd
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Beijing Xiaomi Mobile Software Co Ltd
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Priority to CN202380010877.6A priority Critical patent/CN117581581A/zh
Priority to PCT/CN2023/115987 priority patent/WO2025043573A1/fr
Publication of WO2025043573A1 publication Critical patent/WO2025043573A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

  • the present disclosure relates to the field of communication technology, and in particular to a communication method, a terminal, a network device, and a communication system.
  • NR New Radio
  • beam-based transmission and reception are required to ensure coverage.
  • Beam management based on artificial intelligence (AI) models or AI functions requires monitoring model performance and reporting beam information.
  • AI artificial intelligence
  • a communication method comprising: when beam measurement information and beam prediction information of a first beam set can be obtained, sending a first report to a network device, the first report comprising the beam measurement information of the first beam set; the beam prediction information of the first beam set comprising at least one of the following: an artificial intelligence (AI) function; information output by an AI model.
  • AI artificial intelligence
  • a communication method comprising: receiving a first report, the first report being sent by a terminal when beam measurement information and beam prediction information of a first beam set can be obtained, the first report comprising beam measurement information of the first beam set, and the prediction information of the first beam set comprising at least one of the following: information output by an AI function; information output by an AI model.
  • a communication method comprising: when a terminal can obtain beam measurement information and beam prediction information of a first beam set, the terminal sends a first report to a network device, the first report comprising beam measurement information of the first beam set, and the beam prediction information of the first beam set comprising at least one of the following: information output by an AI function; information output by an AI model; the network device receives the first report.
  • a terminal comprising: a transceiver module, for sending a first report to a network device when beam measurement information and beam prediction information of a first beam set are obtained, wherein the first report includes the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model.
  • a network device comprising: for receiving a first report, wherein the first report is sent by a terminal when beam measurement information and beam prediction information of a first beam set are obtained, the first report includes beam measurement information of the first beam set, and the prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model.
  • a terminal comprising: one or more processors; wherein the processor is used to execute the communication method of the first aspect.
  • a network device comprising: one or more processors; wherein the processor is used to execute the communication method of the second aspect.
  • a communication system including a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and the network device is configured to implement the communication method of the second aspect.
  • a storage medium stores instructions, and wherein when the instructions are executed on a communication device, the communication device executes the communication method of any one of the first aspect and the second aspect.
  • the accuracy of the first report during the performance monitoring of the AI model or AI function can be improved.
  • FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.
  • FIG. 2 is an exemplary interaction diagram of a communication method provided according to an embodiment of the present disclosure.
  • FIG. 3A is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.
  • FIG. 3B is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.
  • FIG. 3C is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.
  • FIG. 4A is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.
  • FIG. 4B is an exemplary flowchart of a communication method provided according to an embodiment of the present disclosure.
  • FIG. 6B is a schematic diagram of a network device according to an embodiment of the present disclosure.
  • Fig. 7A is a schematic diagram of a communication device according to an exemplary embodiment.
  • FIG. 7B is a schematic diagram showing a chip structure according to an exemplary embodiment.
  • the embodiments of the present disclosure provide a communication method, a terminal, a network device, and a communication system.
  • an embodiment of the present disclosure proposes a communication method, comprising: when beam measurement information and beam prediction information of a first beam set can be obtained, sending a first report to a network device, the first report comprising beam measurement information of the first beam set; the beam prediction information of the first beam set comprising at least one of the following: information output by an artificial intelligence (AI) function; information output by an AI model.
  • AI artificial intelligence
  • the network device can determine the current communication status based on the actual measurement information, avoiding the use of inaccurate beam prediction information for communication during performance monitoring of the AI function or model, thereby improving the communication performance of beam-based communication.
  • the beam prediction information and the beam measurement information are different.
  • the situation in which beam measurement information of the first beam set can be obtained includes: the situation in which the AI function is in an activated state during performance monitoring of the AI function; the situation in which the AI model is in an activated state during performance monitoring of the AI model.
  • a first report is sent to a network device, including at least one of the following: the AI function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is sent to the network device after obtaining beam measurement information of the first beam set; the AI function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is sent to the network device after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and less than or equal to Q, and Q is the number of time domain instances output by the AI function or AI model.
  • the first report sending scheme for the AI model or AI function used for time domain and spatial domain prediction scenarios is clarified.
  • the communication method provided in this embodiment can be applied to scenarios where the AI model or AI function is used for time domain and spatial domain prediction.
  • the method also includes: receiving first information, the first information being used to instruct the terminal to perform performance monitoring on at least one of the following: AI function; AI model.
  • the terminal can also perform performance monitoring via instructions from the network device.
  • the first information includes at least one of the following: 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 a measurement quantity of the first beam set; configuration information of a measurement quantity of the second beam set.
  • the first information is carried by at least one of the following: radio resource control RRC signaling; media access control MAC CE activation indication; downlink control information DCI.
  • the first information is sent based on the existing bearer resources, which can reduce signaling overhead.
  • performance monitoring of the AI function or AI model includes: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.
  • the degree of deviation between the current predicted value and the actual value can be determined, laying a technical foundation for the subsequent evaluation of the performance of the AI model or AI function.
  • the method also includes: when the performance value satisfies the first condition, performing at least one of the following: activating the first AI function; activating the first AI model; switching to the first AI function; switching to the first AI model.
  • the method also includes: when the performance value satisfies the second condition, performing at least one of the following: deactivating the first AI function; deactivating the first model included in the first AI function; switching to the second AI function; switching to the second AI model; returning to the non-AI model; updating the first AI function; updating the first AI model.
  • the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1;
  • the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; and the proportion of the first number of performance values that satisfy the performance monitoring indicator is greater than a first proportion threshold.
  • the performance value satisfies the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the difference of the predicted L1-RSRP is less than or equal to the second difference threshold; the average throughput of the terminal is greater than or equal to the first Throughput threshold; reference signal resource overhead is less than or equal to the first overhead threshold; uplink control information overhead is less than or equal to the second overhead threshold; predicted delay is less than or equal to the first delay threshold.
  • the performance value fails to meet the performance monitoring indicator for O consecutive times, where O is an integer greater than or equal to 1;
  • the performance value fails to meet the performance monitoring index P times, where P is an integer greater than or equal to 1; and the proportion of the second number of performance values that fail to meet the performance monitoring index is greater than the second proportion threshold.
  • the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair whose L1-RSRP difference is within the second threshold is less than the fourth accuracy threshold; the L1-RSRP difference is greater than the third difference threshold; the predicted L1-RSRP difference is greater than the fourth difference threshold; the terminal average throughput is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.
  • performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.
  • an embodiment of the present disclosure proposes a communication method, comprising: receiving a first report, the first report being sent by a terminal when beam measurement information and beam prediction information of a first beam set can be obtained, the first report comprising beam measurement information of the first beam set, and the prediction information of the first beam set comprising at least one of the following: information output by an AI function; information output by an AI model.
  • the beam prediction information and the beam measurement information are different, and the beam prediction information is obtained based on an AI function or an AI model prediction.
  • the situation in which beam measurement information of the first beam set can be obtained includes: the situation in which the AI function is in an activated state during performance monitoring of the AI function; the situation in which the AI model is in an activated state during performance monitoring of the AI model.
  • receiving a first report includes at least one of the following: the AI function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is received after obtaining beam measurement information of a first beam set; the AI function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is received after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and less than or equal to Q, and Q is the number of time domain instances output by the AI function or AI model.
  • the method also includes: sending first information, where the first information is used to instruct the terminal to perform performance monitoring on at least one of the following items, including: AI function; AI model.
  • the first information includes at least one of the following: 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 a measurement quantity of the first beam set; configuration information of a measurement quantity of the second beam set.
  • the first information is carried by at least one of the following: radio resource control RRC signaling; media access control MAC CE activation indication; downlink control information DCI.
  • the AI function or AI model performs performance monitoring, including: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.
  • the performance value satisfying the first condition is a condition for the terminal to perform at least one of the following: the performance value satisfies the performance monitoring indicator for L consecutive times, where L is an integer greater than or equal to 1; within the first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; the proportion of the first number of performance values that satisfy the performance monitoring indicator is greater than the first proportion threshold; the performance value satisfies the first condition, and the terminal performs at least one of the following: activates the first AI function; activates the first AI model; switches to the first AI function; switches to the first AI model.
  • the performance value satisfies the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.
  • the performance value satisfies the second condition is a condition that the terminal performs at least one of the following: the performance value fails to satisfy the performance monitoring indicator for O consecutive times, where O is an integer greater than or equal to 1; the ratio of the second number of performance values that fail to satisfy the performance monitoring indicator is greater than a second ratio threshold; the performance value satisfies the 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 non-AI model; perform a first AI function update; perform a first AI model update.
  • the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair within the second threshold of the L1-RSRP difference is less than the fourth accuracy threshold; the difference of the reference signal received power L1-RSRP of layer 1 is greater than the third difference threshold; the difference of the predicted L1-RSRP is greater than the fourth difference threshold; the average throughput of the terminal is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.
  • performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.
  • an embodiment of the present disclosure proposes a communication method, comprising: when the terminal can obtain beam measurement information and beam prediction information of a first beam set, the terminal sends a first report to a network device, the first report includes beam measurement information of the first beam set, and the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model; the network device receives the first report.
  • an embodiment of the present disclosure proposes a terminal, comprising: when the beam measurement information and beam prediction information of a first beam set are obtained, the terminal sends a first report to a network device, the first report includes beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model.
  • an embodiment of the present disclosure proposes a network device, including: for receiving a first report, the first report is sent by the terminal when beam measurement information and beam prediction information of a first beam set are obtained, the first report includes beam measurement information of the first beam set, and the prediction information of the first beam set includes at least one of the following: information output by an AI function; information output by an AI model.
  • an embodiment of the present disclosure proposes a terminal, comprising: one or more processors; wherein the processor is used to execute the communication method of the first aspect.
  • an embodiment of the present disclosure proposes a network device, comprising: one or more processors; wherein the processor is used to execute the communication method of the second aspect.
  • an embodiment of the present disclosure proposes a communication system, comprising: a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and the network device is configured to implement the communication method of the second aspect.
  • an embodiment of the present disclosure proposes a storage medium storing instructions, which, when the instructions are executed on a communication device, enables the communication device to execute any one of the communication methods of the first aspect and the second aspect.
  • the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods.
  • the embodiments of the present disclosure provide a communication method, a terminal, a network device, and a communication system.
  • the terms communication method, information processing method, communication method, etc. can be replaced with each other
  • the terms communication device, information processing device, etc. can be replaced with each other
  • the terms information processing system, communication system, etc. can be replaced with each other.
  • each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined.
  • a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged.
  • the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.
  • elements expressed in the singular form such as “a”, “an”, “the”, “above”, “said”, “aforementioned”, “this”, etc., may mean “one and only one", or “one or more”, “at least one”, etc.
  • the noun after the article may be understood as a singular expression or a plural expression.
  • plurality refers to two or more.
  • the recording method of "A or B” may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed).
  • A A is executed independently of B
  • B B is executed independently of A
  • execution is selected from A and B (A and B are selectively executed).
  • the description object is a "level”
  • the ordinal number before the "level” in the “first level” and the “second level” does not limit the priority between the "levels”.
  • the number of description objects is not limited by the ordinal number, and can be one or more. Taking the "first device” as an example, the number of "devices” can be one or more.
  • the objects modified by different prefixes may be the same or different. For example, if the description object is "device”, then the “first device” and the “second device” may be the same device or different devices, and their types may be the same or different. For another example, if the description object is "information”, then the "first information” and the “second information” may be the same information or different information, and their contents may be the same or different.
  • “including A”, “comprising A”, “used to indicate A”, and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.
  • terms such as “greater than”, “greater than or equal to”, “not less than”, “more than”, “more than or equal to”, “not less than”, “higher than”, “higher than or equal to”, “not lower than”, and “above” can be replaced with each other, and terms such as “less than”, “less than or equal to”, “not greater than”, “less than”, “less than or equal to”, “no more than”, “lower than”, “lower than or equal to”, “not higher than”, and “below” can be replaced with each other.
  • devices and equipment may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as “equipment”, “device”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, “subject”, etc.
  • network can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.
  • access network device may also be referred to as “radio access network device (RAN device)", “base station (BS)”, “radio base station (radio base station)”, “fixed station” and in some embodiments may also be understood as “node”, “access point (access point)”, “transmission point (TP)”, “reception point (RP)”, “transmission and/or reception point (transmission/reception point, TRP)", “panel”, “antenna panel”, “antenna array”, “cell”, “macro cell”, “small cell”, “femto cell”, “pico cell”, “sector”, “cell group”, “serving cell”, “carrier”, “component carrier”, “bandwidth part (bandwidth part, BWP)", etc.
  • RAN device radio access network device
  • base station base station
  • RP radio base station
  • TRP transmission and/or reception point
  • terminal or “terminal device” may be referred to as "user equipment (UE)", “user terminal (user terminal)”, “mobile station (MS)”, “mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.
  • UE user equipment
  • MS mobile station
  • MT mobile terminal
  • the acquisition of data, information, etc. may comply with the laws and regulations of the country where the data is obtained.
  • each element, each row, or each column in the table of the embodiments of the present disclosure may be implemented as an independent embodiment, and the combination of any elements, any rows, or any columns may also be implemented as an independent embodiment.
  • FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.
  • a communication system 100 includes a terminal 101 and a network device 102 .
  • the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control (industrial control), a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid (smart grid), a wireless terminal device in transportation safety (transportation safety), a wireless terminal device in a smart city (smart city), and at least one of a wireless terminal device in a smart home (smart home), but is not limited to these.
  • a mobile phone a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal
  • the network device 102 may include at least one of an access network device and a core network device.
  • the access network device is, for example, a node or device that accesses a terminal to a wireless network.
  • the access network device may include an evolved Node B (eNB), a next generation evolved Node B (ng-eNB), a next generation Node B (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.
  • eNB evolved Node B
  • ng-eNB next generation evolved Node B
  • gNB next generation Node B
  • NB node
  • the technical solution of the present disclosure may be applicable to the Open RAN architecture.
  • the interfaces between access network devices or within access network devices involved in the embodiments of the present disclosure may become internal interfaces of Open RAN, and the processes and information interactions between these internal interfaces may be implemented through software or programs.
  • the access network device may be composed of a centralized unit (central unit, CU) and a distributed unit (distributed unit, DU), wherein the CU may also be called a control unit (control unit).
  • the CU-DU structure may be used to split the protocol layer of the access network device, with some functions of the protocol layer being centrally controlled by the CU, and the remaining part or all of the functions of the protocol layer being distributed in the DU, and the DU being centrally controlled by the CU, but not limited to this.
  • the core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements.
  • the network element may be virtual or physical.
  • the core network may include, for example, at least one of the Evolved Packet Core (EPC), the 5G Core Network (5GCN), and the Next Generation Core (NGC).
  • EPC Evolved Packet Core
  • 5GCN 5G Core Network
  • NGC Next Generation Core
  • the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure.
  • a person skilled in the art can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.
  • the present invention relates to wireless communication systems such as LTE, Wi-Fi (X), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) network, Device to Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle to-Everything (V2X), systems using other communication methods, and next-generation systems expanded based on them.
  • PLMN Public Land Mobile Network
  • D2D Device to Device
  • M2M Machine to Machine
  • IoT Internet of Things
  • V2X Vehicle to-Everything
  • systems using other communication methods and next-generation systems expanded based on them.
  • next-generation systems expanded based on them.
  • a combination of multiple systems for example,
  • the base station configures a reference signal resource set for beam measurement.
  • the terminal measures the reference signals on the reference signal resources in the reference signal resource set, and then reports X (X is a positive integer) stronger reference signal resource identifications (IDs) and corresponding related parameters (including at least one of the following, for example: layer 1 reference signal received power (L1-Reference Signal Received Power, L1-RSRP); layer 1 signal-to-interference-plus-Noise Ratio (L1-SINR)).
  • L1-Reference Signal Received Power L1-RSRP
  • L1-SINR layer 1 signal-to-interference-plus-Noise Ratio
  • the AI model used for beam prediction may be referred to as a beam prediction model.
  • a beam prediction AI model may also be referred to as a beam prediction AI model, a prediction AI model, a prediction beam model, etc. This disclosure does not limit the names of such AI models.
  • the terminal measures the L1-RSRP of set B and inputs it into the beam prediction model.
  • the beam prediction model can predict the L1-RSRP of set A and/or the identifier of the best beam/beam pair in set A.
  • set B and set A includes the following two types:
  • set B is a wide beam and set A is a narrow beam.
  • set A contains 32 reference signal resources (each reference signal resource corresponds to a beam direction, and the 32 reference signal resources cover a direction of 120 degrees).
  • these Y reference signal resources also cover a direction of 120 degrees, that is, the beam direction of each reference signal resource in set B covers the beam directions of multiple reference signal resources in set A.
  • the relationship between the 32/Y reference signal resources in set A and the same reference signal resource in set B is QCL Type D.
  • the receive beam of the terminal must also be considered. For example, if there are 32 transmit beams and 4 receive beams, then set A is 32*4 beam pairs, and set B can be 32 beam pairs, 16 beam pairs, and so on.
  • the network device if the performance of the beam prediction model does not need to be monitored, assuming that the AI model has been trained in advance, the network device only needs to periodically send the reference signal on the reference signal resource of set B (for example, the first period).
  • the terminal measures the L1-RSRP of the reference signal on the reference signal resource in set B and inputs it into the beam prediction model.
  • the L1-RSRP of set A can be output, or one or more reference signal resource IDs of the strongest of the 32 reference signals in set A can be output.
  • the network device if it is necessary to monitor the performance of the beam prediction model, the network device is required to periodically send the reference signal resources of set A (such as the second period). Then the terminal needs to measure the L1-RSRP of the reference signal on the reference signal resources of set B.
  • the terminal inputs the measured L1-RSRP of the reference signal on the reference signal resources of set B into the beam prediction model to obtain the predicted beam information and report it to the network device; for the network side model, the terminal reports the measured L1-RSRP and/or identifier of the reference signal on the reference signal resources of set B to the network device, and the network device inputs the L1-RSRP and/or identifier of set B into the beam prediction model to obtain the predicted beam information.
  • the terminal When set B is a subset of set A, the terminal only needs to report the beam information of all beams or beam pairs of set A.
  • the terminal measures the L1-RSRP of the historical time set B and inputs it into the beam prediction model to predict the L1-RSRP of the future time set A.
  • the terminal measures the L1-RSRP of the historical time set B and inputs it into the beam prediction model to predict the L1-RSRP of the future time set A.
  • the reference signal at the future time also needs to be sent by the base station.
  • the terminal measures the reference signal at the future time and reports the measured beam information to the network device. That is, the terminal needs to measure the beam information of all beams and/or beam pairs in set B and set A and report it to the network device.
  • an AI model is used for beam prediction.
  • the number of beam pairs that the terminal originally needs to measure is G*H.
  • the AI model is used for beam prediction, for spatial domain beam prediction, the terminal only needs to measure a part of the G*H beam pairs. For example, measure 1/8 or 1/4 of the G*H beam pairs. Then, the measured beam measurement quality of these beam pairs is input into the AI model, and the AI model can output the beam information of G*H beam pairs.
  • the terminal can measure the beam measurement quality of the beam pairs at historical time to predict the beam information of the beam pairs at future time.
  • AI models have a certain life cycle or a certain scope of application. Therefore, it is necessary to monitor the performance of AI models in real time. When the performance of AI models does not meet the corresponding requirements, it is necessary to update and switch the AI models in a timely manner.
  • the terminal can calculate the performance monitoring indicators by itself, and then determine whether the model needs to be deactivated based on the calculation results of the performance monitoring indicators.
  • these performance monitoring indicators are obtained by comparing the beam information output by the model with the beam information of set A actually measured.
  • the performance monitoring indicator includes at least one of the following: beam prediction accuracy of top 1/K beam (pair)), beam prediction accuracy of beam or beam pair within a first threshold value, L1-RSRP difference, predicted L1-RSRP difference, average UE throughput difference, reference signal resource overhead, uplink control information overhead, and predicted delay.
  • the performance indicator is the beam prediction accuracy. If the beam prediction is accurate, it can be understood that the performance monitoring indicator meets the requirements. For beam prediction accuracy, at least one of the following is included: the predicted strongest beam identification (ID) includes the measured strongest beam ID; the predicted strongest beam pair ID includes the measured strongest beam pair ID; the predicted strongest beam ID is included in the measured strongest N beam IDs, where N is an integer greater than or equal to 1; the predicted strongest beam pair ID is included in the measured strongest N beam pair IDs.
  • ID the predicted strongest beam identification
  • the downlink receive beam ID is the receive beam ID (Rx beam ID) of the terminal.
  • the beam pair ID is the ID corresponding to the combination of the downlink transmit beam and the downlink receive beam.
  • the strongest beam ID refers to the beam ID with the strongest L1-RSRP or the beam ID with the strongest L1-SINR
  • 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.
  • the beam prediction accuracy may be the accuracy of beam information derived and outputted based on multiple times of the model, for example, may be a ratio.
  • the L1-RSRP difference comprises at least one of the following:
  • the best beam ID refers to the beam ID with the best L1-RSRP or the beam ID with the best L1-SINR
  • the best beam pair ID refers to the beam pair ID with the best L1-RSRP or the beam pair ID with the best L1-SINR.
  • the L1-RSRP difference may also be a ratio of the L1-RSRP difference being lower than a threshold value, or a ratio of the L1-RSRP difference being higher than a threshold value.
  • the difference in the predicted L1-RSRP includes: the predicted L1-RSRP of the best beam and the predicted L1-RSRP of the best beam.
  • the predicted L1-RSRP difference may also be a ratio of the L1-RSRP difference being lower than a threshold value, or a ratio of the L1-RSRP difference being higher than a threshold value.
  • the performance monitoring indicator is the average throughput of the terminal. If the throughput difference is less than a certain threshold, it can be understood that the performance monitoring indicator meets the requirements.
  • the average value of the throughput to meet the requirements it includes: based on the predicted strongest beam and the measured strongest beam, the SINR corresponding to the two beams is obtained, and the throughput (capacity) and the capacity difference are calculated based on the Shannon capacity, or, based on the predicted strongest beam pair and the measured strongest beam pair, the SINR corresponding to the two beam pairs is obtained, and the capacity and the capacity difference are calculated based on the Shannon capacity.
  • the reference signal resource overhead is the number of reference signal resources required by the AI model.
  • the main influencing factors include the size of set B corresponding to the AI model input and the number of historical measurement times during time domain prediction.
  • the measurement results of set B need to be reported to the network, that is, the signaling overhead of this reporting.
  • the beam report in the above embodiment refers to the beam report based on the CSI report mechanism.
  • an embodiment of the present disclosure proposes a communication method, which enables the terminal to add actual measurement information of the beam in the beam report when the terminal is in model performance monitoring, thereby improving the accuracy of the beam report.
  • Step S2101 terminal 101 performs performance monitoring.
  • terminal 101 performs performance monitoring, including at least one of the following: performance monitoring based on artificial intelligence (AI) functions, or performance monitoring based on AI models.
  • AI artificial intelligence
  • the AI functionality is used to output beam prediction information for the first set of beams.
  • the AI model is used to output 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 the input of the AI model, and the output result obtained by the AI model output is the beam prediction information of the first beam set.
  • performance monitoring is performed based on an AI function or based on an AI model, including: comparing beam prediction information of a first beam set with beam measurement information of the first beam set to obtain a performance value.
  • the beam measurement information of the first beam set is used as a reference (or benchmark) for comparison.
  • comparing the beam prediction information of the first beam set with the beam measurement information of the first beam set can be understood as the difference between the predicted L1-RSRP and the actually measured L1-RSRP.
  • the performance value includes at least one of the following: prediction accuracy of a beam or a beam pair; prediction accuracy of a beam or a beam pair with an L1-RSRP difference within a first threshold; L1-RSRP variability less than or equal to a first variability threshold; predicted L1-RSRP variability; terminal average throughput; reference signal resource overhead; row control information overhead; and predicted latency.
  • the performance value satisfies the first condition
  • the terminal 101 performs at least one of the following: activating the first AI function, activating the first AI model, switching to the first AI function, and switching to the first AI model, etc.
  • the performance value satisfies the first condition including at least one of the following:
  • the performance value meets the performance monitoring index for L consecutive times, where L is an integer greater than or equal to 1;
  • the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1;
  • the number L and the number M may be equal or unequal in value.
  • At least one of the following is determined based on a network configuration or a default rule:
  • the value of the number L, the value of the number M, the first time threshold, the first quantity threshold, and the first ratio threshold are the first ratio threshold.
  • the performance value satisfies the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair within the first threshold of the L1-RSRP difference is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.
  • the L1-RSRP difference is determined based on beam prediction information and beam measurement information.
  • the L1-RSRP difference is determined based on beam prediction information and beam measurement information.
  • the difference of the predicted L1-RSRP is determined based on beam prediction information and beam measurement information.
  • the average throughput of the terminal is determined based on the predicted strongest reference signal and the measured strongest reference signal, and corresponding SINRs of the two reference signals are obtained.
  • the first threshold is determined based on a network configuration or a default rule.
  • the first threshold may be 1 decibel dB.
  • At least one of the following is determined based on a network configuration or a default rule:
  • the first difference threshold and the second difference threshold may be equal or unequal in value.
  • the first accuracy threshold and the second accuracy threshold may be equal or unequal in value.
  • the L1-RSRP difference is the difference between the measured L1-RSRP of the best predicted beam or the best beam pair and the measured L1-RSRP of the measured best beam or beam pair.
  • the L1-RSRP difference is the difference between the measured L1-RSRP of the best predicted beam or the best beam pair and the measured L1-RSRP of the measured best beam or the best beam pair.
  • the L1-RSRP difference includes at least one of the following: an average value of the L1-RSRP difference, a cumulative distribution function of the L1-RSRP difference, a proportion of the L1-RSRP difference less than or equal to a first difference threshold, and a proportion of the L1-RSRP difference greater than a second difference threshold, wherein the L1-RSRP difference is the difference between the measured L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best beam or beam pair.
  • the first difference threshold and the second difference threshold may be equal or unequal in value.
  • the first difference threshold is determined based on a network configuration or a default rule.
  • the second difference threshold is determined based on network configuration or a default rule.
  • the predicted L1-RSRP difference is the difference between the predicted L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best predicted beam or beam pair.
  • the predicted L1-RSRP difference includes at least one of the following: an average value of the predicted L1-RSRP difference, a cumulative distribution function of the predicted L1-RSRP difference, a proportion of the predicted L1-RSRP difference less than or equal to a third difference threshold, and a proportion of the predicted L1-RSRP difference greater than a fourth difference threshold, wherein the predicted L1-RSRP difference is the difference between the predicted L1-RSRP of the best predicted beam or beam pair and the measured L1-RSRP of the best predicted beam or beam pair.
  • the third difference threshold is determined based on network configuration or default rules.
  • the fourth difference threshold is determined based on network configuration or default rules.
  • the performance value satisfies the second condition
  • the terminal 101 performs at least one of the following: deactivating the first AI function, deactivating the first AI function model, switching to the second AI function, switching to the second AI function model, returning to the non-AI model, updating the first AI function, and updating the first AI model.
  • the second threshold may be 1 decibel dB.
  • At least one of the following is determined based on a network configuration or a default rule:
  • the third accuracy threshold and the fourth accuracy threshold may be equal or unequal in value.
  • the first accuracy threshold, the second accuracy threshold, the third accuracy threshold and the fourth accuracy threshold may be equal or unequal in value.
  • the first throughput threshold and the second throughput threshold may be equal or unequal in value.
  • the third overhead threshold and the fourth overhead threshold may be equal or unequal in value.
  • terms such as “moment”, “time point”, “time”, and “time position” can be interchangeable, and terms such as “duration”, “period”, “time window”, “window”, and “time” can be interchangeable.
  • the terms “measure”, “actual”, “actual measurement”, etc. can be used interchangeably.
  • terms such as “certain”, “preset”, “preset”, “set”, “indicated”, “some”, “any”, and “first” can be interchangeable, and "specific A”, “preset A”, “preset A”, “set A”, “indicated A”, “some A”, “any A”, and “first A” can be interpreted as A pre-defined in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., and can also be interpreted as specific A, some A, any A, or first A, etc., but is not limited to this.
  • Step S2102 the network device 102 sends first information.
  • the network device 102 sends first information to the terminal 101 .
  • terminal 101 receives first information.
  • the first information is used to instruct the terminal 101 to perform performance monitoring on at least one of the AI functions or AI models.
  • the first information includes at least one of the following: 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.
  • the measurement quantity configuration information of the first beam set includes: L1-RSRP corresponding to the first beam set, or L1-SINR corresponding to the first beam set.
  • the measurement quantity configuration information of the second beam set includes: L1-RSRP corresponding to the second beam set, or L1-RSRP corresponding to the second beam set.
  • the first information is carried by at least one of the following items: radio resource control RRC signaling, media access control MAC CE activation indication, and downlink control information DCI.
  • obtain can be interchangeable, which can be interpreted as receiving from other entities, obtaining from protocols, obtaining from high levels, obtaining by self-processing, autonomous implementation, etc.
  • the names of information and the like are not limited to the names described in the embodiments, and include “information”, “message”, “signal”, “signaling”, “report”, “configuration”, “indication”, “instruction”, “command”, “channel”, “parameter”,
  • domain “field”, “symbol”, “symbol”, “code element”, “codebook”, “codeword”, “codepoint”, “bit”, “data”, “program”, “chip” and the like are interchangeable.
  • Step S2103 Terminal 101 sends a first report.
  • terminal 101 sends a first report to network device 102 .
  • network device 102 receives a first report.
  • the first report includes beam measurement information for the first set of beams.
  • the beam prediction information for the first set of beams is different from the beam measurement information for the first set of beams.
  • the terminal 101 when the terminal 101 is able to obtain beam measurement information and beam prediction information of the first beam set, the terminal 101 sends a first report to the network device 102, where the first report includes the beam measurement information.
  • the terminal 101 can obtain the beam prediction information of the first beam set according to the AI function or AI model. However, since the AI function or model is in the performance monitoring period, the terminal 101 can also obtain the beam measurement information of the first beam set. The beam measurement information is more accurate than the beam prediction information. Therefore, in this case, the terminal 101 should report the beam measurement information of the first beam set instead of the beam prediction information.
  • the terminal 101 can obtain the beam prediction information of the first beam set according to the AI function or AI model. However, since the AI function or model is in the performance monitoring period, the terminal 101 can also obtain the beam measurement information of the first beam set. The beam measurement information is more accurate than the beam prediction information. Therefore, in this case, the terminal 101 can report the beam measurement information of the first beam set and report the beam prediction information, thereby providing a reference for the network device.
  • the communication method involved in the embodiment of the present disclosure may include at least one of steps S2101 to S2103.
  • step S2103 may be implemented as an independent embodiment
  • step S2101 may be implemented as an independent embodiment
  • steps S2101+S2102+S2103 may be implemented as independent embodiments, but are not limited thereto.
  • steps S2101 and S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.
  • steps S2101 and S2102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.
  • FIG3A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3A, an embodiment of the present disclosure relates to a communication method, and the method includes:
  • Step S3102 obtaining first information.
  • step S3102 can refer to the optional implementation of step S2102 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 , which will not be described in detail here.
  • the terminal 101 receives the first information sent by the network device 102, but is not limited thereto and may also receive the first information sent by other entities.
  • terminal 101 obtains first information from an upper layer(s).
  • terminal 101 performs processing to obtain the first information.
  • step S3102 is omitted, and the terminal 101 autonomously implements the function indicated by the first information, or the above function is default or acquiescent.
  • Step S3103 sending the first report.
  • step S3103 can refer to the optional implementation of step S2103 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 , which will not be described in detail here.
  • the communication method involved in the embodiments of the present disclosure may include at least one of steps S3101 to S3103.
  • step S3101 may be implemented as an independent embodiment
  • step S3102 may be implemented as an independent embodiment
  • step S3103 may be implemented as an independent embodiment
  • step S3101+S3102 may be implemented as an independent embodiment
  • step S3101+S3103 may be implemented as an independent embodiment
  • step S3101+S3103 may be implemented as an independent embodiment
  • step S3101+S3103 may be implemented as an independent embodiment
  • step S3101+S3103 may be implemented as an independent embodiment
  • step S3101+S3102+S3103 may be implemented as an independent embodiment, but is not limited thereto.
  • steps S3102 and S3103 may be executed in an exchanged order or simultaneously, steps S3101 and S3103 may be executed in an exchanged order or simultaneously, and steps S3102 and S3103 may be executed in an exchanged order or simultaneously.
  • steps S3101 and S3103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.
  • steps S3102 and S3103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.
  • FIG3B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the present disclosure embodiment relates to a communication method, and the method includes:
  • Step S3201 obtain first information and perform performance monitoring.
  • step S3201 can refer to step S2101 and step S2102 in Figure 2, step S3101 and step S3102 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.
  • Step S3202 sending the first report.
  • step S3202 can refer to the optional implementation of step S2103 in Figure 2, step S3103 in Figure 3A, and other related parts of the embodiments involved in Figures 2 and 3A, which will not be repeated here.
  • step S3201 may be implemented as an independent embodiment
  • step S3202 may be implemented as an independent embodiment, but is not limited thereto.
  • step S3201 is optional, and one or more of these steps may be omitted or replaced in different embodiments.
  • step S3201 can be combined with steps S3102-S3103 of FIG. 3A
  • step S3202 can be combined with steps S3101, S3102, and S3103 of FIG. 3A .
  • FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the present disclosure embodiment relates to a communication method, and the method includes:
  • Step S3301 in the first case, sending a first report.
  • the first situation includes: a situation where beam measurement information and beam prediction information of the first beam set can be obtained.
  • step S3301 can refer to the optional implementation of step S2103 in Figure 2, step S3103 in Figure 3A, step S3202 in Figure 3B, and other related parts in the embodiments involved in Figures 2, 3A, and 3B, which will not be repeated here.
  • the situations in which beam measurement information of the first beam set can be obtained include: the situation in which the AI function is in an activated state during performance monitoring of the AI function; the situation in which the AI model is in an activated state during performance monitoring of the AI model.
  • the beam prediction information of the first beam set includes information output by an AI function or an AI model.
  • the first report includes beam measurement information for the first set of beams.
  • the first report is a beam report.
  • the beam prediction information and the beam measurement information are different.
  • a first report is sent to a network device, including at least one of the following: an AI function or an AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is sent to the network device after obtaining beam measurement information of a first beam set; the AI function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is sent to the network device after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and less than or equal to Q, and Q is the number of time domain instances output by the AI function or AI model.
  • the method also includes: receiving first information, the first information is used to instruct the terminal to perform performance monitoring on at least one of the following items, including: AI function; AI model.
  • the first information includes at least one of the following: 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 a measurement quantity of the first beam set; configuration information of a measurement quantity of the second beam set.
  • the first information is carried by at least one of the following: radio resource control RRC signaling; media access control MAC CE activation indication; downlink control information DCI.
  • performance monitoring of the AI function or AI model includes: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.
  • the method further includes: when the performance value satisfies a first condition, performing at least one of the following: activating a first AI function; activating a first AI model; switching to the first AI function; switching to the first AI model.
  • the method also includes: when the performance value satisfies the second condition, performing at least one of the following: deactivating the first AI function; deactivating the first model included in the first AI function; switching to the second AI function; switching to the second AI model; returning to the non-AI model; updating the first AI function; and updating the first AI model.
  • the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1;
  • the performance value meets the performance monitoring indicator M times, where M is an integer greater than or equal to 1; the prediction accuracy corresponding to each performance value in the first number of performance values is greater than or equal to the first accuracy threshold, and the proportion of the first number of performance values that meet the performance monitoring indicator is greater than a first proportion threshold, and the first number and the first proportion threshold are determined based on network configuration or default rules.
  • the performance value satisfies the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.
  • the performance value fails to meet the performance monitoring indicator for O consecutive times, where O is an integer greater than or equal to 1;
  • performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.
  • Step S4102 receiving the first report.
  • step S4101 can refer to the optional implementation of step S2102 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 , which will not be described in detail here.
  • step S4102 can refer to the optional implementation of step S2103 in FIG. 2 and other related parts in the embodiment involved in FIG. 2 , which will not be described in detail here.
  • step S4101 may be implemented as an independent embodiment
  • step S4102 may be implemented as an independent embodiment, but is not limited thereto.
  • step S4101 is optional, and one or more of these steps may be omitted or replaced in different embodiments.
  • FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the present disclosure embodiment relates to a communication method, and the method includes:
  • Step S4201 receiving the first report.
  • the first report includes beam measurement information for the first set of beams.
  • the beam prediction information is different from the beam measurement information, and the beam prediction information is obtained based on an AI function or an AI model prediction.
  • the first report is sent by the terminal when beam measurement information and beam prediction information of the first beam set can be obtained.
  • the prediction information of the first beam set includes: information output by an AI function or an AI model.
  • the situation in which beam measurement information of the first beam set can be obtained includes: during performance monitoring of the AI function, the AI function is in an activated state; during performance monitoring of the AI model, the AI model is in an activated state.
  • receiving a first report includes at least one of the following: the AI function or AI model is used for spatial domain beam prediction, or for time domain beam prediction of a single time domain instance, and the first report is received after obtaining beam measurement information of a first beam set; the AI function or AI model is used for time domain beam prediction of multiple time domain instances, and the first report is received after obtaining beam measurement information of N time domain instances of the first beam set, where N is a positive integer greater than or equal to 1 and less than or equal to Q, and Q is the number of time domain instances output by the AI function or AI model.
  • the method also includes: sending first information, where the first information is used to instruct the terminal to perform performance monitoring on the AI function or AI model.
  • the first information includes at least one of the following: 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 a measurement quantity of the first beam set; configuration information of a measurement quantity of the second beam set.
  • the first information is carried by at least one of the following: radio resource control RRC signaling; media access control MAC CE activation indication; downlink control information DCI.
  • the AI function or AI model performs performance monitoring, including: comparing beam prediction information of the first beam set with beam measurement information of the first beam set to obtain a performance value.
  • the performance value satisfies the first condition, including at least one of the following: the performance value satisfies the performance monitoring indicator L times in a row, where L is an integer greater than or equal to 1; within the first time threshold, the performance value satisfies the performance monitoring indicator M times, where M is an integer greater than or equal to 1; the proportion of the first number of performance values that meet the performance monitoring indicator is greater than the first proportion threshold; the performance value satisfies the first condition, which is a condition for the terminal to perform at least one of the following: activating the first AI function; activating the first AI model; switching to the first AI function; switching to the first AI model.
  • the performance value satisfies the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is greater than or equal to the first accuracy threshold; the prediction accuracy of the beam or beam pair of the reference signal received power L1-RSRP difference of layer 1 within the first threshold is greater than or equal to the second accuracy threshold; the L1-RSRP difference is less than or equal to the first difference threshold; the predicted L1-RSRP The difference is less than or equal to the second difference threshold; the terminal average throughput is greater than or equal to the first throughput threshold; the reference signal resource overhead is less than or equal to the first overhead threshold; the uplink control information overhead is less than or equal to the second overhead threshold; the predicted delay is less than or equal to the first delay threshold.
  • the performance value satisfies the second condition, including at least one of the following: the performance value fails to meet the performance monitoring indicator for O consecutive times, where O is an integer greater than or equal to 1; the ratio of the second number of performance values that fail to meet the performance monitoring indicator is greater than a second ratio threshold; the performance value satisfies the second condition, which is a condition for the terminal to perform at least one of the following: deactivate the first AI function; deactivate the first AI model and switch to the second AI function; switch to the second AI model; return to the non-AI model; update the first AI function; update the first AI model.
  • the performance value does not meet the performance monitoring indicator, including at least one of the following: the prediction accuracy of the beam or beam pair is less than the third accuracy threshold; the prediction accuracy of the beam or beam pair within the second threshold of the L1-RSRP difference is less than the fourth accuracy threshold; the reference signal received power L1-RSRP difference of layer 1 is greater than the third difference threshold; the predicted L1-RSRP difference is greater than the fourth difference threshold; the terminal average throughput is less than the second throughput threshold; the reference signal resource overhead is greater than the third overhead threshold; the uplink control information overhead is greater than the fourth overhead threshold; the predicted delay is greater than the second delay threshold.
  • performance monitoring includes performance monitoring based on AI functions, or performance monitoring based on AI models.
  • FIG5 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the present disclosure embodiment relates to a communication method, and the method includes:
  • Step S5101 In a first case, the terminal 101 sends a first report to the network device 102, and the beam prediction information of the first beam set includes at least one of the following: information output by the AI function; information output by the AI model.
  • the first situation includes: a situation where beam measurement information and beam prediction information of the first beam set can be obtained.
  • step S5101 can refer to step S2101 of Figure 2, step S2102, step S3101 to step S3102 of Figure 3A, and step S4101 of Figure 4A, as well as other related parts of the embodiments involved in Figures 2, 3A, and 4A, which will not be repeated here.
  • Step S5102 the network device 102 receives the first report.
  • step S5102 can refer to the optional implementation of step S2103 in Figure 2, step S3103 in Figure 3A, step S4102 in Figure 4A, and other related parts of the embodiments involved in Figures 2, 3A, and 4A, which will not be repeated here.
  • the above method may include the method described in the above embodiments of the communication system side, terminal side, network device side, etc., which will not be repeated here.
  • This embodiment also provides a communication method, including: during the performance monitoring of the AI function or model by the terminal, even if the AI function or model is not deactivated, the first report reported by the terminal should include the beam information actually measured, rather than the beam information obtained based on the model output. The performance of beam-based communication is improved.
  • the terminal receives first information, where the first information is used to instruct the terminal to perform performance monitoring.
  • the first information includes at least one of the following: reference signal resource configuration information of set B, reference signal resource configuration information of set A.
  • the measurement quantity includes at least one of the following: L1-SINR or L1-RSRP.
  • the measurement quantity includes at least one of the following: the layer 1 reference signal received power L1-RSRP of the beam or beam pair in set A; the layer 1 signal to interference and noise ratio L1-SINR of the beam or beam pair in set A; the layer 1 reference signal received power L1-RSRP of the beam or beam pair in set B; the layer 1 signal to interference and noise ratio L1-SINR of the beam or beam pair in set B.
  • set B includes beams or beam pairs corresponding to model inputs
  • set A includes beams or beam pairs corresponding to model outputs.
  • the first information includes displayed performance monitoring indicator information.
  • the first information includes at least one of the following: RRC or MAC CE.
  • performance monitoring refers to comparing the beam information of set A obtained by the output of the AI model with the beam information of set A actually measured to obtain at least one performance monitoring indicator.
  • a certain function or model can be activated or deactivated.
  • the ratio of each performance monitoring indicator is compared with the threshold value. If it is higher than or equal to the threshold value, the function or model is activated;
  • the specified conditions may be that the terminal itself performs terminal-side model operations or the terminal reports relevant data to the network.
  • the measurements of set A are used as a reference/benchmark for performance monitoring and performance comparison.
  • the terminal reports a beam report, which includes the measurement value of set A (if the measurement value of set A is different from the model output), rather than the predicted value obtained by the model output based on model inference.
  • the beam report can be reported after each measurement of set A, or after N consecutive measurements of set A, where N corresponds to the number of prediction time instances output by the model.
  • the division of the units or modules in the above device is only a division of logical functions, which can be fully or partially integrated into one physical entity or physically separated in actual implementation.
  • the units or modules in the device can be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, instructions are stored in the memory, and the processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory inside the device or a memory outside the device.
  • CPU central processing unit
  • microprocessor a microprocessor
  • the units or modules in the device may be implemented in the form of hardware circuits, and the functions of some or all of the units or modules may be implemented by designing the hardware circuits.
  • the hardware circuits may be understood as one or more processors; for example, in one implementation, the hardware circuits are application-specific integrated circuits (ASICs), and the functions of some or all of the above units or modules may be implemented by designing the logical relationship of the components in the circuits; for another example, in another implementation, the hardware circuits may be implemented by programmable logic devices (PLDs), and Field Programmable Gate Arrays (FPGAs) may be used as an example, which may include a large number of logic gate circuits, and the connection relationship between the logic gate circuits may be configured by configuring the configuration files, thereby implementing the functions of some or all of the above units or modules. All units or modules of the above devices may be implemented in the form of software called by the processor, or in the form of hardware circuits, or in the form of software called by the processor, and the remaining part may be implemented in
  • the process of the processor loading the configuration document to realize the hardware circuit configuration can be understood as the process of the processor loading instructions to realize the functions of some or all of the above units or modules.
  • it can also be a hardware circuit designed for artificial intelligence, which can be understood as ASIC, such as Neural Network Processing Unit (NPU), Tensor Processing Unit (TPU), Deep Learning Processing Unit (DPU), etc.
  • ASIC Neural Network Processing Unit
  • NPU Neural Network Processing Unit
  • TPU Tensor Processing Unit
  • DPU Deep Learning Processing Unit
  • Figure 6A is a structural diagram of the terminal proposed in an embodiment of the present disclosure.
  • the terminal 6100 may include: a transceiver module 6101.
  • the above-mentioned transceiver module is used to send a first report to the network device when the beam measurement information and beam prediction information of the first beam set are obtained, and the first report includes the beam measurement information of the first beam set; the beam prediction information of the first beam set includes at least one of the following: information output by the AI function; information output by the AI model.
  • the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and/or receiving (for example, step S2102, step S2103, but not limited to this) performed by the terminal 101 in any of the above methods, which will not be repeated here.
  • the communication steps such as sending and/or receiving (for example, step S2102, step S2103, but not limited to this) performed by the terminal 101 in any of the above methods, which will not be repeated here.
  • Figure 6B is a structural diagram of a network device proposed in an embodiment of the present disclosure.
  • the network device 6200 may include: a transceiver module 6201.
  • the above-mentioned transceiver module is used to receive a first report, and the first report is sent by the terminal when the beam measurement information and beam prediction information of the first beam set are obtained.
  • the first report includes the beam measurement information of the first beam set, and the prediction information of the first beam set includes at least one of the following: information output by the AI function; information output by the AI model.
  • the transceiver module may include a sending module and/or a receiving module, and the sending module and the receiving module may be separate or integrated.
  • the transceiver module may be interchangeable with the transceiver.
  • FIG7A is a schematic diagram of the structure of a communication device 7100 proposed in 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.), or a terminal (e.g., a user device, etc.), or a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods.
  • the communication device 7100 may be used to implement the method described in the above method embodiment, and the details may refer to the description in the above method embodiment.
  • the communication device 7100 includes one or more processors 7101.
  • the processor 7101 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit.
  • the baseband processor may be used to process the communication protocol and the communication data
  • the central processing unit may be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute the program, and process the data of the program.
  • the communication device 7100 is used to execute any of the above methods.
  • one or more processors 7101 are used to call instructions so that the communication device 7100 executes any of the above methods.
  • the communication device 7100 further includes one or more transceivers 7102.
  • the transceiver 7102 performs at least one of the communication steps such as sending and/or receiving in the above method (for example, step S2102, step S2103, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2101, but not limited thereto).
  • the transceiver may include a receiver and/or a transmitter, and the receiver and the transmitter may be separated or integrated together.
  • the terms such as transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc. may be replaced with each other, the terms such as transmitter, transmitting unit, transmitter, transmitting circuit, etc. may be replaced with each other, and the terms such as receiver, receiving unit, receiver, receiving circuit, etc. may be replaced with each other.
  • the communication device 7100 further includes one or more memories 7103 for storing data.
  • the memories 7103 may also be outside the communication device 7100.
  • the communication device 7100 may include one or more interface circuits 7104.
  • the interface circuit 7104 is connected to the memory 7103, and the interface circuit 7104 may be used to receive data from the memory 7103 or other devices, and may be used to send data to the memory 7103 or other devices.
  • the interface circuit 7104 may read the data stored in the memory 7103 and send the data to the processor 7101.
  • the communication device 7100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A.
  • the communication device may be an independent device or may be part of a larger device.
  • the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.
  • FIG. 7B is a schematic diagram of the structure of a chip 7200 provided in an embodiment of the present disclosure.
  • the communication device 7100 may be a chip or a chip system
  • the chip 7200 includes one or more processors 7201.
  • the chip 7200 is configured to execute any of the above methods.
  • the chip 7200 further includes one or more interface circuits 7202.
  • the terms interface circuit, interface, transceiver pin, etc. can be interchangeable.
  • the chip 7200 further includes one or more memories 7203 for storing data.
  • all or part of the memory 7203 can be outside the chip 7200.
  • the interface circuit 7202 is connected to the memory 7203, and the interface circuit 7202 can be used to receive data from the memory 7203 or other devices, and the interface circuit 7202 can be used to send data to the memory 7203 or other devices.
  • the interface circuit 7202 can read the data stored in the memory 7203 and send the data to the processor 7201.
  • the interface circuit 7202 performs at least one of the communication steps such as sending and/or receiving in the above method (for example, step S2102, step S2103, but not limited thereto).
  • the interface circuit 7202 performs the communication steps such as sending and/or receiving in the above method, for example, means that the interface circuit 7202 performs data interaction between the processor 7201, the chip 7200, the memory 7203, or the transceiver device.
  • the processor 7201 performs at least one of the other steps (for example, step S2101, but not limited thereto).
  • modules and/or devices described in the embodiments such as virtual devices, physical devices, chips, etc. can be combined or separated as needed.
  • some or all steps can also be performed by multiple modules and/or devices in collaboration, which is not limited here.
  • the present disclosure also proposes a storage medium, on which instructions are stored, and when the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods.
  • the storage medium is an electronic storage medium.
  • the storage medium is a computer-readable storage medium, but is not limited to this, and it can also be a storage medium readable by other devices.
  • the storage medium can be a non-transitory storage medium, but is not limited to this, and it can also be a temporary storage medium.
  • the present disclosure also proposes a program product, which, when executed by the communication device 7100, enables the communication device 7100 to execute any of the above methods.
  • the program product is a computer program product.
  • the present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to execute any one of the above methods.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente divulgation concerne un procédé de communication, un terminal, un dispositif de réseau et un système de communication. Le procédé comprend : lorsque des informations de mesure de faisceau et des informations de prédiction de faisceau d'un premier ensemble de faisceaux peuvent être obtenues, l'envoi d'un premier rapport à un dispositif de réseau, le premier rapport comprenant les informations de mesure de faisceau du premier ensemble de faisceaux, et les informations de prédiction de faisceau du premier ensemble de faisceaux comprenant ce qui suit : des informations délivrées par une fonction d'IA et/ou des informations délivrées par un modèle d'IA. Au moyen des modes de réalisation de la présente divulgation, la précision d'un premier rapport pendant la surveillance des performances d'un modèle d'IA ou d'une fonction d'IA peut être améliorée.
PCT/CN2023/115987 2023-08-30 2023-08-30 Procédé de communication, terminal, dispositif de réseau et système de communication Pending WO2025043573A1 (fr)

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CN202380010877.6A CN117581581A (zh) 2023-08-30 2023-08-30 通信方法、终端、网络设备、以及通信系统
PCT/CN2023/115987 WO2025043573A1 (fr) 2023-08-30 2023-08-30 Procédé de communication, terminal, dispositif de réseau et système de communication

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CN121418888A (zh) * 2024-07-26 2026-01-27 华为技术有限公司 一种信息传输方法和装置
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CN121485841A (zh) * 2024-08-05 2026-02-06 维沃移动通信有限公司 波束预测性能监测方法及终端
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WO2026031225A1 (fr) * 2024-08-09 2026-02-12 北京小米移动软件有限公司 Procédé d'évaluation de performance de modèle et de mesure de faisceau, premier dispositif, second dispositif, appareil de communication, système de communication, support de stockage et produit-programme
CN121286081A (zh) * 2024-08-09 2026-01-06 北京小米移动软件有限公司 通信方法、终端、网络设备及存储介质
WO2026064937A1 (fr) * 2024-09-24 2026-04-02 Oppo广东移动通信有限公司 Procédé de communication, dispositif terminal et dispositif réseau
WO2026065114A1 (fr) * 2024-09-27 2026-04-02 壹泛尼提株式会社 Procédé et appareil de surveillance de performances, et système de communication
CN120151927B (zh) * 2025-05-14 2025-09-19 荣耀终端股份有限公司 一种确定波束集的方法及相关设备

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