WO2023206177A1 - Ai波束模型确定方法、装置 - Google Patents

Ai波束模型确定方法、装置 Download PDF

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Publication number
WO2023206177A1
WO2023206177A1 PCT/CN2022/089676 CN2022089676W WO2023206177A1 WO 2023206177 A1 WO2023206177 A1 WO 2023206177A1 CN 2022089676 W CN2022089676 W CN 2022089676W WO 2023206177 A1 WO2023206177 A1 WO 2023206177A1
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Prior art keywords
receiving
model
terminal device
receiving beam
beam model
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PCT/CN2022/089676
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English (en)
French (fr)
Inventor
李明菊
Original Assignee
北京小米移动软件有限公司
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Publication date
Application filed by 北京小米移动软件有限公司 filed Critical 北京小米移动软件有限公司
Priority to PCT/CN2022/089676 priority Critical patent/WO2023206177A1/zh
Priority to CN202280001344.7A priority patent/CN117322029A/zh
Publication of WO2023206177A1 publication Critical patent/WO2023206177A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering

Definitions

  • the present disclosure relates to the field of communication technology, and in particular to an artificial intelligence (Artificial Intelligence, AI) beam model determination method, device, equipment and storage medium.
  • AI Artificial Intelligence
  • beam pairs can be measured by using prediction methods based on artificial intelligence (AI) models.
  • AI artificial intelligence
  • the terminal device does not obtain the information of the AI model, resulting in inaccurate beam measurement quality performed by the AI model. Therefore, a method of "AI beam model determination" is urgently needed to provide the receiving beam characteristics corresponding to the AI beam model and improve the accuracy of the prediction results corresponding to the beam measurement quality of the AI beam model.
  • the present disclosure proposes an AI beam model determination method, device, equipment and storage medium to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • An embodiment of the present disclosure proposes an artificial intelligence AI beam model determination method.
  • the method is executed by a terminal device.
  • the method includes:
  • the method before receiving the AI beam model sent by the network side device, the method further includes:
  • determining the first receiving beam characteristics corresponding to the AI beam model includes:
  • Receive indication information sent by the network side device the indication information being used to indicate the first receiving beam characteristics corresponding to the AI beam model;
  • the first receiving beam characteristics corresponding to the AI beam model are determined based on default rules.
  • the first receiving beam characteristics include at least one of the following:
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • the method after receiving the AI beam model sent by the network side device, the method includes:
  • the input parameters include at least one of the following:
  • the beams corresponding to the third number of beams measure quality.
  • the beam pair characteristics include at least one of the following:
  • the beam pair ID corresponding to the beam pair
  • a reference signal ID corresponding to the beam pair wherein the reference signal includes at least one of a synchronization signal block SSB and a channel state information-reference signal CSI-RS;
  • the second angle value of the second dimensional direction angle of a second receiving beam corresponding to the beam pair is the second angle value of the second dimensional direction angle of a second receiving beam corresponding to the beam pair.
  • the beam measurement quality includes layer one reference signal received power L1-RSRP and/or layer one signal to interference plus noise ratio L1-SINR.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device.
  • the first receiving beam characteristics include multiple receiving beam numbers;
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • Another aspect of the present disclosure provides a method for determining an AI beam model.
  • the method is executed by a network side device.
  • the method includes:
  • the method before sending the AI beam model to the terminal device, the method further includes:
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • sending the AI beam model to the terminal device includes:
  • the indication information is used to indicate the first receiving beam characteristics corresponding to the AI beam model.
  • the first receiving beam characteristics include at least one of the following:
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device.
  • the first receiving beam characteristics include multiple receiving beam numbers;
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • An AI beam model determination device proposed by another aspect of the present disclosure includes:
  • the receiving module is configured to receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • An AI beam model determination device proposed by another aspect of the present disclosure includes:
  • the sending module is used to send the AI beam model to the terminal device.
  • the device includes a processor and a memory.
  • a computer program is stored in the memory.
  • the processor executes the computer program stored in the memory so that the The device performs the method proposed in the embodiment of the above aspect.
  • the device includes a processor and a memory.
  • a computer program is stored in the memory.
  • the processor executes the computer program stored in the memory so that the The device performs the method proposed in the above embodiment.
  • a communication device provided by another embodiment of the present disclosure includes: a processor and an interface circuit
  • the interface circuit is used to receive code instructions and transmit them to the processor
  • the processor is configured to run the code instructions to perform the method proposed in the embodiment of one aspect.
  • a communication device provided by another embodiment of the present disclosure includes: a processor and an interface circuit
  • the interface circuit is used to receive code instructions and transmit them to the processor
  • the processor is configured to run the code instructions to perform the method proposed in another embodiment.
  • a computer-readable storage medium provided by an embodiment of another aspect of the present disclosure is used to store instructions. When the instructions are executed, the method proposed by the embodiment of the present disclosure is implemented.
  • a computer-readable storage medium provided by an embodiment of another aspect of the present disclosure is used to store instructions. When the instructions are executed, the method proposed by the embodiment of another aspect is implemented.
  • the terminal device can receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device determines the first receiving beam characteristics corresponding to the AI beam model, thereby reducing the situation where the AI beam model does not correspond to the first receiving beam characteristics, and improving the beam prediction accuracy of the AI beam model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 1 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of an AI beam model determination method provided by another embodiment of the present disclosure.
  • FIG. 3 is a schematic flowchart of an AI beam model determination method provided by yet another embodiment of the present disclosure.
  • Figure 4 is a schematic flowchart of an AI beam model determination method provided by yet another embodiment of the present disclosure.
  • FIG. 5 is a schematic flowchart of an AI beam model determination method provided by yet another embodiment of the present disclosure.
  • Figure 6 is a schematic flowchart of an AI beam model determination method provided by yet another embodiment of the present disclosure.
  • Figure 7 is a schematic flowchart of an AI beam model determination method provided by yet another embodiment of the present disclosure.
  • Figure 8 is a schematic structural diagram of an AI beam model determination device provided by an embodiment of the present disclosure.
  • Figure 9 is a schematic structural diagram of an AI beam model determination device provided by another embodiment of the present disclosure.
  • Figure 10 is a block diagram of a terminal device provided by an embodiment of the present disclosure.
  • Figure 11 is a block diagram of a network side device provided by an embodiment of the present disclosure.
  • first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other.
  • first information may also be called second information, and similarly, the second information may also be called first information.
  • the words "if” and “if” as used herein may be interpreted as “when” or “when” or “in response to determining.”
  • Figure 1 is a schematic flowchart of an AI beam model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 1, the method may include the following steps:
  • Step 101 Receive the AI beam model sent by the network side device, and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device may be a device that provides voice and/or data connectivity to the user.
  • Terminal devices can communicate with one or more core networks via RAN (Radio Access Network).
  • Terminal devices can be IoT terminals, such as sensor devices, mobile phones (or "cellular" phones) and devices with The computer of the Internet of Things terminal, for example, can be a fixed, portable, pocket-sized, handheld, computer-built-in or vehicle-mounted device.
  • station STA
  • subscriber unit subscriber unit
  • subscriber station subscriber station
  • mobile station mobile station
  • remote station remote station
  • access terminal access terminal
  • user device user terminal
  • user agent useragent
  • the terminal device may also be a device of an unmanned aerial vehicle.
  • the terminal device may also be a vehicle-mounted device, for example, it may be a driving computer with wireless communication function, or a wireless terminal connected to an external driving computer.
  • the terminal device may also be a roadside device, for example, it may be a street light, a signal light or other roadside device with wireless communication function.
  • the terminal device before receiving the AI beam model sent by the network side device, the terminal device further includes:
  • determining the first receiving beam characteristics corresponding to the AI beam model includes:
  • the first receiving beam characteristics corresponding to the AI beam model are determined based on default rules.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • the method after receiving the AI beam model sent by the network side device, the method includes:
  • the third number of beam pairs corresponds to beam measurement quality.
  • the beam pair characteristics include at least one of the following:
  • the beam pair ID corresponding to the beam pair
  • the second angle value of the second dimension direction angle of a second receiving beam corresponding to the beam pair is the second angle value of the second dimension direction angle of a second receiving beam corresponding to the beam pair.
  • the beam measurement quality includes layer one reference signal received power L1-RSRP and/or layer one signal to interference plus noise ratio L1-SINR.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device
  • the first receiving beam characteristic includes multiple Number of receive beams
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • the terminal device can receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device determines the first receiving beam characteristics corresponding to the AI beam model, thereby reducing the situation where the AI beam model does not correspond to the first receiving beam characteristics, and improving the beam prediction accuracy of the AI beam model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 2 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 2, the method may include the following steps:
  • Step 201 Send the AI beam model request and/or the second receiving beam characteristics to the network side device;
  • Step 202 Receive the AI beam model sent by the network side device, and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device sends the AI beam model request and/or the second receiving beam characteristic to the network side device. Specifically, the terminal device first sends the AI beam model request to the network side device and then sends The second receiving beam characteristics are sent to the network side device. Alternatively, the terminal device first sends the second receiving beam characteristics to the network side device, and then sends the AI beam model request to the network side device. The terminal device can also send the second receiving beam at the same time. Features and AI beam models are requested to the network side device. When the terminal device simultaneously sends the second receiving beam characteristics and the AI beam model request to the network side device, the terminal device can specifically add the second receiving beam characteristics to the AI beam model request, that is, the terminal device can send a request carrying the second receiving beam characteristics. The AI beam model is requested to the network side device.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the first number is only used to refer to the number of first receiving beams supported by the AI beam model.
  • the first quantity does not specifically refer to a fixed quantity.
  • the first receive beam includes at least one first receive beam.
  • the first number of at least one first receiving beam supported by the AI beam model is Q, which means that the AI beam model is suitable for terminals with the number of receiving beams Q, and the value of Q is a positive integer; or, the AI beam model supports The first number of at least one first receiving beam is Q, which means that the AI beam model is suitable for terminals whose number of receiving beams is less than or equal to Q, and the value of Q is a positive integer; or, at least one supported by the AI beam model
  • the number of first receiving beams is an arbitrary value, which indicates that the AI beam model is suitable for terminals whose number of receiving beams is an arbitrary value.
  • the first receiving beam characteristic refers to the receiving beam characteristic supported by the AI beam model.
  • the first receiving beam characteristic does not specifically refer to a fixed receiving beam characteristic.
  • the first receiving beam characteristic may also change accordingly.
  • the receiving beam characteristics included in the first receiving beam characteristics may also change accordingly.
  • the first dimensional direction angle is only used to indicate any one of the multiple dimensional directions, wherein the first of the first dimensional direction angles is only used to communicate with the second dimension. direction angle.
  • the first dimension direction angle does not specifically refer to a fixed direction angle.
  • the second dimensional direction angle is only used to indicate any one of the multiple dimensional directions that is different from the first dimensional direction angle, wherein the second dimensional direction angle is not Specifically refers to a certain fixed direction angle.
  • the direction angle in the first dimension is the zenith angle
  • the direction angle in the second dimension may be the azimuth angle.
  • the first absolute value of the first dimension direction angle ranges from 0 to 2*pi.
  • the second absolute value of the second dimension direction angle ranges from 0 to 2*pi.
  • the first of the number of first dimensional direction angles corresponding to the first receiving beam is only used to distinguish from the number of second dimensional direction angles corresponding to the first receiving beam.
  • the number of first-dimensional direction angles corresponding to the first receiving beam does not specifically refer to a fixed number.
  • the number of second-dimensional direction angles corresponding to the first receiving beam does not specifically refer to a fixed number.
  • the first receive beam includes at least one first receive beam.
  • the number of first-dimensional direction angles of at least one first receiving beam supported by the AI beam model is N, which indicates that the AI beam model is suitable for the first dimension of the receiving beam.
  • the value of N is a positive integer; or, the number of direction angles in the first dimension of at least one first receiving beam supported by the AI beam model is N, which means that the AI beam model is suitable for receiving beams.
  • the value of N is a positive integer; or, the number of direction angles in the first dimension of at least one first receiving beam supported by the AI beam model is an arbitrary value, which means that the AI The beam model is suitable for terminals whose first dimension direction angle number of the receiving beam is any value.
  • the number of second-dimensional direction angles of at least one first receiving beam supported by the AI beam model is M, which means that the AI beam model is suitable for the second dimension of the receiving beam.
  • the value of M is a positive integer; or, the number of second-dimensional direction angles of at least one first receiving beam supported by the AI beam model is M, which means that the AI beam model is suitable for receiving beams.
  • the value of M is a positive integer; or, the number of direction angles in the second dimension of at least one first receiving beam supported by the AI beam model is an arbitrary value, which means that the AI The beam model is suitable for terminals whose second dimension direction angle number of the receiving beam is any value. It should be noted that in this case, the number of first receiving beams supported by the AI beam model is the product of M and N.
  • the first angle value of the first dimensional direction angle corresponding to the first receiving beam refers to the angle value of the first dimensional direction angle corresponding to the first receiving beam.
  • the second angle value of the first dimensional direction angle corresponding to the first receiving beam refers to the angle value of the second dimensional direction angle corresponding to the first receiving beam.
  • the first of the first angle values of the first dimensional direction angle corresponding to the first receiving beam is only used to distinguish from the second angle value of the first dimensional direction angle corresponding to the first receiving beam.
  • the first angle value of the first dimension direction angle corresponding to the first receiving beam does not specifically refer to a fixed angle value.
  • the first receive beam includes at least one first receive beam.
  • the first angle value of the first dimensional direction angle of at least one first receiving beam supported by the AI beam model is ⁇ 1 , ⁇ 2 ... ⁇ N , which means that This AI beam model is suitable for terminals where the number of first-dimensional direction angles of the receiving beam is N, and the first angle value of the first-dimensional direction angle is ⁇ 1 , ⁇ 2 ;
  • the first angle value of the first dimension direction angle of at least one first receiving beam supported by the AI beam model is any value, which indicates that the AI beam model is suitable for receiving beams
  • the first angle value of the first dimension direction angle is an arbitrary value of the terminal.
  • the second angle value of the second dimension direction angle of at least one first receiving beam supported by the AI beam model is ⁇ 1 , ⁇ 2 ... ⁇ M , which means that the AI beam model is suitable for the second dimension of the receiving beam.
  • the number of direction angles is M
  • the second angle value of the second dimension direction angle is ⁇ 1 , ⁇ 2 ...the terminal of ⁇ M
  • the value of ⁇ i is 0 ⁇ 2*pi
  • the value of M is a positive integer
  • the second angle value of the second dimension direction angle of at least one first receiving beam supported by the AI beam model is an arbitrary value, which means that the AI beam model is suitable for the second angle value of the second dimension direction angle of the receiving beam.
  • the receiving beam identifier is used to uniquely identify a receiving beam.
  • different receiving beams correspond to different receiving beam identities.
  • the first receive beam includes at least one first receive beam.
  • the first beam identifier is used to represent the identifier of at least one first receiving beam.
  • the first receiving beam characteristics include the first beam identifier corresponding to the first receiving beam in the AI beam model being the first beam identifier ID#1 and the first beam identifier ID#2, which indicates that the AI beam model is suitable for the receiving beam.
  • the first beam identifier is the terminal with the first beam identifier ID#1 and the first beam identifier ID#2, or it is also used to indicate that the input of beam prediction using the AI beam model contains the measurement quality of at least one first beam pair,
  • the receiving beam identifiers corresponding to the at least one first beam pair are the first beam identifier ID#1 and the first beam identifier ID#2.
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • the second receiving beam characteristic refers to the receiving beam characteristic supported by the terminal device.
  • the second receive beam characteristic does not specifically refer to a fixed receive beam characteristic.
  • the second receiving beam characteristic may also change accordingly.
  • the second receiving beam characteristics included in the second receiving beam characteristics may also change accordingly.
  • the first of the number of first dimensional direction angles corresponding to the second receiving beam is only used to distinguish from the number of second dimensional direction angles corresponding to the second receiving beam.
  • the number of first-dimensional direction angles corresponding to the second receiving beam does not specifically refer to a fixed number.
  • the number of second dimension direction angles corresponding to the second receiving beam does not specifically refer to a fixed number.
  • the second receive beam includes at least one second receive beam.
  • the number of first-dimensional direction angles of the second receiving beam supported by the terminal device is A
  • the number of second-dimensional direction angles is B, where the values of A and B are both positive integers. It should be noted that in this case, the number of second receiving beams supported by the terminal device is the product of A and B.
  • the first angle value of the first dimensional direction angle corresponding to the second receiving beam refers to the angle value of the first dimensional direction angle corresponding to the second receiving beam.
  • the second angle value of the first-dimensional direction angle corresponding to the second receiving beam refers to the angle value of the second-dimensional direction angle corresponding to the second receiving beam.
  • the first of the first angle values of the first dimensional direction angle corresponding to the second receiving beam is only used to distinguish from the second angle value of the first dimensional direction angle corresponding to the second receiving beam.
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam does not specifically refer to a fixed angle value.
  • the second receive beam includes at least one second receive beam.
  • the terminal of A the value of ⁇ i is 0 ⁇ 2*pi, and the value of A is a positive integer.
  • the second angle value of the second dimensional direction angle of the second receiving beam supported by the terminal equipment is ⁇ 1 , ⁇ 2 ... ⁇ B , which indicates the second dimensional direction angle of the second receiving beam supported by the terminal equipment.
  • the quantity is B
  • the second angle value of the second dimension direction angle is ⁇ 1 , ⁇ 2 ...the terminal of ⁇ B
  • the value of ⁇ i is 0 ⁇ 2*pi
  • the value of B is a positive integer.
  • the second number is only used to refer to the number of second receiving beams supported by the terminal device.
  • the second quantity does not specifically refer to a fixed quantity.
  • the second receive beam includes at least one second receive beam. For example, if the number of receiving beams supported by the terminal device is W, then the second number is W, and the value of W is a positive integer. Among them, the number of receiving beams is Rxbeamnumber.
  • the receiving beam identifier is used to uniquely identify a receiving beam.
  • different receiving beams correspond to different receiving beam identities.
  • the second receive beam includes at least one second receive beam.
  • the second beam identifier is used to represent the identifier of at least one second receiving beam. For example, if the terminal includes two receiving beams, and the identifiers are ID#1 and ID#2 respectively, then the second receiving beam identifiers are the second beam identifier ID#1 and the second beam identifier ID#2.
  • the terminal device may receive beam configuration information of the network side device, and the beam configuration information is used to indicate the transmission configuration indication state TCI state.
  • TCI state includes at least one quasi-co-located type QCL Type.
  • QCL includes QCL Type A, QCL Type B, QCL Type C, and QCL Type D.
  • QCL Type D is used to indicate reception parameter information
  • QCL Type A includes Doppler frequency shift parameters.
  • QCL Type B includes Doppler frequency shift parameters, Doppler spread parameters, average delay parameters and delay spread related parameters.
  • At least one of QCL Type C includes at least one of Doppler frequency shift parameters, Doppler spread parameters, average delay parameters and delay spread related parameters.
  • the terminal device can receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device after the terminal device sends the AI beam model request and/or the second receiving beam characteristics to the network side device, it can determine the first receiving beam characteristics corresponding to the AI beam model, and improve the relationship between the second receiving beam characteristics and The matching degree of the AI beam model improves the beam prediction accuracy of the AI beam model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 3 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 3, the method may include the following steps:
  • Step 301 Receive the AI beam model sent by the network side device
  • Step 302 Receive indication information sent by the network side device, where the indication information is used to indicate the first receiving beam characteristics corresponding to the AI beam model; or
  • the first receiving beam characteristics corresponding to the AI beam model are determined based on default rules.
  • the terminal device when the terminal device determines the first receiving beam characteristics corresponding to the AI beam model, the terminal device can receive the indication information sent by the network side device, because the indication information is used to indicate the AI beam model corresponding to The first receiving beam characteristic is the first receiving beam characteristic, so the terminal device can obtain the first receiving beam characteristic corresponding to the AI beam model according to the indication information.
  • the terminal device can determine the first receiving beam characteristics corresponding to the AI beam model based on default rules.
  • the default rule may specifically indicate that the AI beam model is applicable to a terminal whose number of receiving beams is an arbitrary value.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the terminal device can receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device obtains the first receiving beam characteristics corresponding to the AI beam model by receiving the instruction information sent by the network side device or determines the first receiving beam characteristics based on the default rules, thereby improving the accuracy of determining the first receiving beam characteristics and improving the AI beam Beam prediction accuracy of the model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 4 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 4, the method may include the following steps:
  • Step 401 Receive the AI beam model sent by the network side device, and determine the first receiving beam characteristics corresponding to the AI beam model;
  • Step 402 Determine the input parameters of the AI beam model, and input the input parameters into the AI beam model to obtain prediction results of beam measurement quality.
  • the input parameters refer to parameters that can be used to correspond to the AI beam model.
  • the AI beam model may be a model corresponding to only a certain receiving beam characteristic, or may be a model corresponding to multiple receiving beam characteristics.
  • the AI beam model may be an AI beam model corresponding only to the number of receiving beams, where the input parameter corresponding to the AI beam model is the number of receiving beams.
  • the AI model may also be an AI beam model corresponding to all received beam characteristics.
  • the input parameters include at least one of the following:
  • the third number of beam pairs corresponds to beam measurement quality.
  • the third quantity refers to the number of beam pairs.
  • the third quantity does not specifically refer to a fixed quantity.
  • the third number is the number of beam pairs that the terminal device needs to measure.
  • the third number of beam pairs that the terminal needs to measure is M*N, where M is the number of transmit beams of the network side device, each transmit beam corresponds to a reference signal ID, and N is the number of receive beams of the terminal device. M and N are positive integers respectively.
  • the input parameters may include, for example, the second receiving beam characteristics, the beam pair characteristics corresponding to the third number of beam pairs, and the beam measurement quality corresponding to the third number of beam pairs.
  • the terminal device can input the second receiving beam characteristics, the beam pair characteristics corresponding to the third number of beam pairs, and the beam measurement quality corresponding to the third number of beam pairs into the AI beam model to obtain a prediction result of the beam measurement quality.
  • the beam pair characteristics include at least one of the following:
  • the beam pair ID corresponding to the beam pair
  • the second angle value of the second dimension direction angle of a second receiving beam corresponding to the beam pair is the second angle value of the second dimension direction angle of a second receiving beam corresponding to the beam pair.
  • the first angle value is only used to indicate the angle value of the first dimensional direction angle of a second receiving beam corresponding to the beam pair.
  • the second angle value is only used to indicate the angle value of the second dimensional direction angle of a second receiving beam corresponding to the beam pair.
  • the first of the first angle values is only used to distinguish it from the second angle value, and the first angle value does not specifically refer to a fixed angle value.
  • the first dimensional direction angle is only used to indicate any one of the multiple dimensional directions, wherein the first of the first dimensional direction angles is only used to indicate the second dimensional direction. angle to differentiate.
  • the first dimension direction angle does not specifically refer to a fixed direction angle.
  • the second dimensional direction angle is only used to indicate any one of the multiple dimensional directions that is different from the first dimensional direction angle, where the second dimensional direction angle does not specifically refer to a certain fixed direction angle.
  • the direction angle in the first dimension is the zenith angle
  • the direction angle in the second dimension may be the azimuth angle.
  • the beam measurement quality includes layer one reference signal received power L1-RSRP and/or layer one signal to interference plus noise ratio L1-SINR.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device
  • the first receiving beam characteristic includes multiple Number of receive beams
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature, indicating that the number of receive beams supported by the AI model is less than or equal to the terminal device. Number of receive beams supported.
  • the terminal device can receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device determines the input parameters of the AI beam model and inputs the input parameters into the AI beam model to obtain the prediction results of the beam measurement quality, improve the matching between the input parameters and the AI model, and improve the AI beam model.
  • the accuracy of obtaining the corresponding prediction results This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 5 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 5, the method may include the following steps:
  • Step 501 Send the AI beam model to the terminal device.
  • the terminal device before sending the AI beam model to the terminal device, it also includes:
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • sending the AI beam model to the terminal device includes:
  • the indication information is used to indicate the first receiving beam characteristics corresponding to the AI beam model.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device
  • the first receiving beam characteristic includes multiple receiving beams. number of beams
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • the network side device can send the AI beam model to the terminal device.
  • the network side device can improve the matching between the input parameters determined by the terminal device and the AI model by indicating the first receiving beam characteristics corresponding to the AI beam model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 6 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 6, the method may include the following steps:
  • Step 601 Receive the AI beam model request and/or the second receiving beam characteristics sent by the terminal device;
  • Step 602 Send the AI beam model to the terminal device.
  • the network side device receives the AI beam model request and/or the second reception beam characteristic sent by the terminal device, and the network side device can determine the AI beam model corresponding to the second reception beam characteristic, And send the AI beam model to the terminal device.
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device
  • the first receiving beam characteristic includes multiple Number of receive beams
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature, indicating that the number of receive beams supported by the AI model is less than or equal to the terminal device. Number of receive beams supported.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device.
  • the value of the number of receiving beams supported by the terminal device may be, for example, 2 to 8.
  • the network side device can send the AI beam model to the terminal device.
  • the network side device can determine the AI beam model based on the second receive beam feature by receiving the AI beam model request and/or the second receive beam feature sent by the terminal device, which can improve the input parameters determined by the terminal device. Matching with AI model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • Figure 7 is a schematic flowchart of a method for determining an AI beam model provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 7, the method may include the following steps:
  • Step 701 Send the AI beam model to the terminal device
  • Step 702 Send indication information to the terminal device, where the indication information is used to indicate the first receiving beam characteristics corresponding to the AI beam model.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the execution order of steps 701 and 702 is not limited. That is to say, the network side device can first perform step 701 and send the AI beam model to the terminal device; and then perform step 702. , send the instruction information to the terminal device; you can also first step 702, send the instruction information to the terminal device; then perform step 701, send the AI beam model to the terminal device; you can also notify the execution step 701, send the AI beam model to the terminal device and the steps 702. Send instruction information to the terminal device.
  • the network side device can send the AI beam model to the terminal device.
  • the network side device can improve the matching between the input parameters determined by the terminal device and the AI model by sending the instruction information and the AI beam model to the terminal device.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • FIG 8 is a schematic structural diagram of an AI beam model determination device provided by an embodiment of the present disclosure. As shown in Figure 8, the device 800 may include:
  • the receiving module 801 is configured to receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device can receive the AI beam model sent by the network side device and determine the first receiving beam characteristics corresponding to the AI beam model.
  • the terminal device determines the first receiving beam characteristics corresponding to the AI beam model, thereby reducing the situation where the AI beam model does not correspond to the first receiving beam characteristics, and improving the beam prediction accuracy of the AI beam model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • the receiving module 801 is also configured to send an AI beam model request and/or a second receive beam characteristic to the network side device before receiving the AI beam model sent by the network side device. .
  • the receiving module 801 is used to determine the first receiving beam characteristics corresponding to the AI beam model, specifically for:
  • the first receiving beam characteristics corresponding to the AI beam model are determined based on default rules.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • the receiving module 801 is also configured to determine the AI model input parameters after receiving the AI beam model sent by the network side device, and input the input parameters to the AI beam model to obtain Prediction results of beam measurement quality, where the input parameters include at least one of the following:
  • the beam pair ID corresponding to the third number of beam pairs
  • the third number of beam pairs corresponds to beam measurement quality.
  • the beam pair ID corresponds to a reference signal ID and a second receiving beam identity, where the reference signal includes synchronization signal block SSB, channel state information-reference signal CSI-RS. at least one of.
  • the beam measurement quality includes layer one reference signal received power L1-RSRP and/or layer one signal to interference plus noise ratio L1-SINR.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device
  • the first receiving beam characteristic includes a plurality of receiving beams. Number of receiving beams
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • Figure 9 is a schematic structural diagram of an AI beam model determination device provided by an embodiment of the present disclosure. As shown in Figure 9, the device 900 may include:
  • the sending module 901 is used to send the AI beam model to the terminal device.
  • the network side device can send the AI beam model to the terminal device.
  • the network side device can improve the matching between the parameters input by the terminal device and the AI model by indicating the first receiving beam characteristics corresponding to the AI beam model.
  • This disclosure provides a processing method for the situation of "AI beam model determination method" to provide receiving beam characteristics corresponding to the AI beam model and improve the accuracy of obtaining prediction results corresponding to the beam measurement quality of the AI beam model.
  • the AI beam model request and/or the second reception beam characteristic sent by the terminal device are received.
  • the second receiving beam characteristics include at least one of the following:
  • the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device is the first angle value of the first dimension direction angle corresponding to the second receiving beam in the terminal device
  • the second beam identifier corresponding to the second receiving beam in the terminal device is the second beam identifier corresponding to the second receiving beam in the terminal device.
  • the sending module 901 is used to send the AI beam model to the terminal device, specifically for:
  • the indication information is used to indicate the first receiving beam characteristics corresponding to the AI beam model.
  • the first receiving beam characteristics include at least one of the following:
  • the first number of first receive beams supported by the AI beam model is the first number of first receive beams supported by the AI beam model
  • the first beam identifier corresponding to the first receiving beam in the AI beam model is the first beam identifier corresponding to the first receiving beam in the AI beam model.
  • the first receiving beam characteristic is the number of receiving beams supported by the AI beam model
  • the second receiving beam characteristic is the number of receiving beams supported by the terminal device
  • the first receiving beam characteristic includes a plurality of receiving beams. Number of receiving beams
  • the number of receive beams included in the first receive beam feature is less than or equal to the number of receive beams included in the second receive beam feature.
  • Figure 10 is a block diagram of a terminal device UE1000 provided by an embodiment of the present disclosure.
  • the UE1000 can be a mobile phone, a computer, a digital broadcast terminal device, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.
  • UE 1000 may include at least one of the following components: a processing component 1002 , a memory 1004 , a power supply component 1006 , a multimedia component 1008 , an audio component 1010 , an input/output (I/O) interface 1012 , a sensor component 1014 , and a communication component. 1016.
  • Processing component 1002 generally controls the overall operations of UE 1000, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 1002 may include at least one processor 1020 to execute instructions to complete all or part of the steps of the above method. Additionally, processing component 1002 may include at least one module to facilitate interaction between processing component 1002 and other components. For example, processing component 1002 may include a multimedia module to facilitate interaction between multimedia component 1008 and processing component 1002.
  • Memory 1004 is configured to store various types of data to support operations at UE 1000. Examples of this data include instructions for any application or method operating on the UE1000, contact data, phonebook data, messages, pictures, videos, etc.
  • Memory 1004 may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EEPROM), Programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EEPROM erasable programmable read-only memory
  • EPROM Programmable read-only memory
  • PROM programmable read-only memory
  • ROM read-only memory
  • magnetic memory flash memory, magnetic or optical disk.
  • Power supply component 1006 provides power to various components of UE 1000.
  • Power supply components 1006 may include a power management system, at least one power supply, and other components associated with generating, managing, and distributing power to UE 1000.
  • Multimedia component 1008 includes a screen that provides an output interface between the UE 1000 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes at least one touch sensor to sense touches, slides, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or sliding operation, but also detect the wake-up time and pressure related to the touch or sliding operation.
  • multimedia component 1008 includes a front-facing camera and/or a rear-facing camera. When UE1000 is in an operating mode, such as shooting mode or video mode, the front camera and/or rear camera can receive external multimedia data.
  • Each front-facing camera and rear-facing camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.
  • Audio component 1010 is configured to output and/or input audio signals.
  • audio component 1010 includes a microphone (MIC) configured to receive external audio signals when UE 1000 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1004 or sent via communications component 1016 .
  • audio component 1010 also includes a speaker for outputting audio signals.
  • the I/O interface 1012 provides an interface between the processing component 1002 and a peripheral interface module.
  • the peripheral interface module may be a keyboard, a click wheel, a button, etc. These buttons may include, but are not limited to: Home button, Volume buttons, Start button, and Lock button.
  • Sensor component 1014 includes at least one sensor for providing various aspects of status assessment for UE 1000 .
  • the sensor component 1014 can detect the open/closed state of the device 1000, the relative positioning of components, such as the display and keypad of the UE 1000, the sensor component 1014 can also detect the position change of the UE 1000 or a component of the UE 1000, the user The presence or absence of contact with the UE1000, the orientation or acceleration/deceleration of the UE1000 and the temperature change of the UE1000.
  • Sensor assembly 1014 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • Sensor assembly 1014 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 1014 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 1016 is configured to facilitate wired or wireless communication between UE 1000 and other devices.
  • UE1000 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, or a combination thereof.
  • the communication component 1016 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communications component 1016 also includes a near field communications (NFC) module to facilitate short-range communications.
  • NFC near field communications
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • UE 1000 may be configured by at least one application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array ( FPGA), controller, microcontroller, microprocessor or other electronic component implementation for executing the above method.
  • ASIC application specific integrated circuit
  • DSP digital signal processor
  • DSPD digital signal processing device
  • PLD programmable logic device
  • FPGA field programmable gate array
  • controller microcontroller, microprocessor or other electronic component implementation for executing the above method.
  • Figure 11 is a block diagram of a network side device 1100 provided by an embodiment of the present disclosure.
  • the network side device 1100 may be provided as a network side device.
  • the network side device 1100 includes a processing component 1122, which further includes at least one processor, and a memory resource represented by a memory 1132 for storing instructions, such as application programs, that can be executed by the processing component 1122.
  • An application stored in memory 1132 may include one or more modules, each of which corresponds to a set of instructions.
  • the processing component 1122 is configured to execute instructions to perform any of the foregoing methods applied to the network side device, for example, the method shown in FIG. 1 .
  • the network side device 1100 may also include a power supply component 1126 configured to perform power management of the network side device 1100, a wired or wireless network interface 1150 configured to connect the network side device 1100 to the network, and an input/output (I/O). O) Interface 1158.
  • the network side device 1100 may operate based on an operating system stored in the memory 1132, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM or similar.
  • the methods provided by the embodiments of the present disclosure are introduced from the perspectives of network side equipment and UE respectively.
  • the network side device and the UE may include a hardware structure and a software module to implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module.
  • a certain function among the above functions can be executed by a hardware structure, a software module, or a hardware structure plus a software module.
  • the methods provided by the embodiments of the present disclosure are introduced from the perspectives of network side equipment and UE respectively.
  • the network side device and the UE may include a hardware structure and a software module to implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module.
  • a certain function among the above functions can be executed by a hardware structure, a software module, or a hardware structure plus a software module.
  • the communication device may include a transceiver module and a processing module.
  • the transceiver module may include a sending module and/or a receiving module.
  • the sending module is used to implement the sending function
  • the receiving module is used to implement the receiving function.
  • the transceiving module may implement the sending function and/or the receiving function.
  • the communication device may be a terminal device (such as the terminal device in the foregoing method embodiment), a device in the terminal device, or a device that can be used in conjunction with the terminal device.
  • the communication device may be a network device, a device in a network device, or a device that can be used in conjunction with the network device.
  • the communication device may be a network device, or may be a terminal device (such as the terminal device in the foregoing method embodiment), or may be a chip, chip system, or processor that supports the network device to implement the above method, or may be a terminal device that supports A chip, chip system, or processor that implements the above method.
  • the device can be used to implement the method described in the above method embodiment. For details, please refer to the description in the above method embodiment.
  • a communications device may include one or more processors.
  • the processor may be a general-purpose processor or a special-purpose processor, etc.
  • it can be a baseband processor or a central processing unit.
  • the baseband processor can be used to process communication protocols and communication data
  • the central processor can be used to control and execute communication devices (such as network side equipment, baseband chips, terminal equipment, terminal equipment chips, DU or CU, etc.)
  • a computer program processes data for a computer program.
  • the communication device may also include one or more memories, on which a computer program may be stored, and the processor executes the computer program, so that the communication device performs the method described in the above method embodiment.
  • data may also be stored in the memory.
  • the communication device and the memory can be provided separately or integrated together.
  • the communication device may also include a transceiver and an antenna.
  • the transceiver can be called a transceiver unit, a transceiver, or a transceiver circuit, etc., and is used to implement transceiver functions.
  • the transceiver can include a receiver and a transmitter.
  • the receiver can be called a receiver or a receiving circuit, etc., and is used to implement the receiving function;
  • the transmitter can be called a transmitter or a transmitting circuit, etc., and is used to implement the transmitting function.
  • one or more interface circuits may also be included in the communication device.
  • Interface circuitry is used to receive code instructions and transmit them to the processor.
  • the processor executes the code instructions to cause the communication device to perform the method described in the above method embodiment.
  • the communication device is a terminal device (such as the terminal device in the foregoing method embodiment): the processor is configured to execute the method shown in any one of Figures 1-4.
  • the communication device is a network-side device: the processor is used to execute the method shown in any one of Figures 5-7.
  • a transceiver for implementing receiving and transmitting functions may be included in the processor.
  • the transceiver may be a transceiver circuit, an interface, or an interface circuit.
  • the transceiver circuits, interfaces or interface circuits used to implement the receiving and transmitting functions can be separate or integrated together.
  • the above-mentioned transceiver circuit, interface or interface circuit can be used for reading and writing codes/data, or the above-mentioned transceiver circuit, interface or interface circuit can be used for signal transmission or transfer.
  • the processor may store a computer program, and the computer program runs on the processor, which can cause the communication device to perform the method described in the above method embodiment.
  • the computer program may be embedded in the processor, in which case the processor may be implemented in hardware.
  • the communication device may include a circuit, and the circuit may implement the functions of sending or receiving or communicating in the foregoing method embodiments.
  • the processors and transceivers described in this disclosure may be implemented on integrated circuits (ICs), analog ICs, radio frequency integrated circuits (RFICs), mixed signal ICs, application specific integrated circuits (ASICs), printed circuit boards ( printed circuit board (PCB), electronic equipment, etc.
  • the processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductor (CMOS), n-type metal oxide-semiconductor (NMOS), P-type Metal oxide semiconductor (positive channel metal oxide semiconductor, PMOS), bipolar junction transistor (BJT), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.
  • CMOS complementary metal oxide semiconductor
  • NMOS n-type metal oxide-semiconductor
  • PMOS P-type Metal oxide semiconductor
  • BJT bipolar junction transistor
  • BiCMOS bipolar CMOS
  • SiGe silicon germanium
  • GaAs gallium arsenide
  • the communication device described in the above embodiments may be a network device or a terminal device (such as the terminal device in the foregoing method embodiment), but the scope of the communication device described in the present disclosure is not limited thereto, and the structure of the communication device may not be limited to limits.
  • the communication device may be a stand-alone device or may be part of a larger device.
  • the communication device may be:
  • the IC collection may also include storage components for storing data and computer programs;
  • the communication device may be a chip or a system on a chip
  • the chip includes a processor and an interface.
  • the number of processors may be one or more, and the number of interfaces may be multiple.
  • the chip also includes a memory for storing necessary computer programs and data.
  • the present disclosure also provides a readable storage medium on which instructions are stored, and when the instructions are executed by a computer, the functions of any of the above method embodiments are implemented.
  • the present disclosure also provides a computer program product, which, when executed by a computer, implements the functions of any of the above method embodiments.
  • the above embodiments it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof.
  • software it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer programs.
  • the computer program When the computer program is loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present disclosure are generated in whole or in part.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
  • the computer program may be stored in or transferred from one computer-readable storage medium to another, for example, the computer program may be transferred from a website, computer, server, or data center Transmission to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more available media integrated.
  • the usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVD)), or semiconductor media (e.g., solid state disks, SSD)) etc.
  • magnetic media e.g., floppy disks, hard disks, magnetic tapes
  • optical media e.g., high-density digital video discs (DVD)
  • DVD digital video discs
  • semiconductor media e.g., solid state disks, SSD
  • At least one in the present disclosure can also be described as one or more, and the plurality can be two, three, four or more, and the present disclosure is not limited.
  • the technical feature is distinguished by “first”, “second”, “third”, “A”, “B”, “C” and “D” etc.
  • the technical features described in “first”, “second”, “third”, “A”, “B”, “C” and “D” are in no particular order or order.

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Abstract

本公开提出一种AI波束模型确定方法、装置、设备及存储介质，属于通信技术领域。方法包括接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征（101）。本公开针对于"AI波束模型确定方法"这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。

Description

AI波束模型确定方法、装置 技术领域
本公开涉及通信技术领域，尤其涉及一种人工智能(Artificial Intelligence，AI)波束模型确定方法、装置、设备及存储介质。
背景技术
在通信系统中，由于高频信道的衰减较快，为了保证新的无线技术(New Radio，NR)的覆盖范围，需要使用基于波束(beam)的发送和接收。
在相关技术的波束管理过程中，通过使用基于人工智能(Artificial Intelligence，AI)模型的预测方法，可以对波束对进行测量。但是相关技术中，终端设备并未获取到AI模型的信息，从而出现AI模型进行波束测量质量不准确的情况。因此，亟需一种“AI波束模型确定”的方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
发明内容
本公开提出的一种AI波束模型确定方法、装置、设备及存储介质，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
本公开一方面实施例提出的一种人工智能AI波束模型确定方法，所述方法由终端设备执行，所述方法包括：
接收网络侧设备发送的AI波束模型，确定所述AI波束模型对应的第一接收波束特征。
可选地，在本公开的一个实施例之中，在所述接收网络侧设备发送的AI波束模型之前，还包括：
发送AI波束模型请求和/或第二接收波束特征至网络侧设备。
可选地，在本公开的一个实施例之中，所述确定所述AI波束模型对应的第一接收波束特征，包括：
接收网络侧设备发送的指示信息，所述指示信息用于指示所述AI波束模型对应的第一接收波束特征；或
基于默认规则确定所述AI波束模型对应的第一接收波束特征。
可选地，在本公开的一个实施例之中，所述第一接收波束特征包括以下至少一项：
所述AI波束模型支持的第一接收波束的第一数量；
所述AI波束模型中所述第一接收波束对应的第一维度方向角数量；
所述AI波束模型中所述第一接收波束对应的第一维度方向角的第一角度值；
所述AI波束模型中所述第一接收波束对应的第二维度方向角数量；
所述AI波束模型中所述第一接收波束对应的第二维度方向角的第二角度值；
所述AI波束模型中所述第一接收波束对应的第一波束标识。
可选地，在本公开的一个实施例之中，所述第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
所述终端设备中所述第二接收波束对应的第一维度方向角数量；
所述终端设备中所述第二接收波束对应的第一维度方向角的第一角度值；
所述终端设备中所述第二接收波束对应的第二维度方向角数量；
所述终端设备中所述第二接收波束对应的第二维度方向角的第二角度值；
所述终端设备中所述第二接收波束对应的第二波束标识。
可选地，在本公开的一个实施例之中，在所述接收网络侧设备发送的AI波束模型之后，包括：
确定AI波束模型输入参数，并将所述输入参数输入至所述AI波束模型，获得波束测量质量的预测结果，其中所述输入参数包括以下至少一项：
第二接收波束特征；
第三数量的波束对对应的波束对特征；
所述第三数量的波束对对应的波束测量质量。
可选地，在本公开的一个实施例之中，所述波束对特征包括以下至少一项：
所述波束对对应的波束对ID；
所述波束对对应的一个参考信号ID，其中，所述参考信号包括同步信号块SSB、信道状态信息-参考信号CSI-RS中的至少一个；
所述波束对对应的一个第二接收波束对应的第二波束标识；
所述波束对对应的一个第二接收波束的第一维度方向角的第一角度值；
所述波束对对应的一个第二接收波束的第二维度方向角的第二角度值。
可选地，在本公开的一个实施例之中，所述波束测量质量包括层一参考信号接收功率L1-RSRP和/或层一信号与干扰加噪声比L1-SINR。
可选地，在本公开的一个实施例之中，所述第一接收波束特征为所述AI波束模型支持的接收波束数量，所述第二接收波束特征为所述终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
本公开另一方面实施例提出的一种AI波束模型确定方法，所述方法由网络侧设备执行，所述方法包括：
发送AI波束模型至终端设备。
可选地，在本公开的一个实施例之中，在所述发送AI波束模型至终端设备之前，还包括：
接收终端设备发送的AI波束模型请求和/或第二接收波束特征。
可选地，在本公开的一个实施例之中，所述第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
所述终端设备中所述第二接收波束对应的第一维度方向角数量；
所述终端设备中所述第二接收波束对应的第一维度方向角的第一角度值；
所述终端设备中所述第二接收波束对应的第二维度方向角数量；
所述终端设备中所述第二接收波束对应的第二维度方向角的第二角度值；
所述终端设备中所述第二接收波束对应的第二波束标识。
可选地，在本公开的一个实施例之中，所述发送AI波束模型至终端设备，包括：
发送指示信息至所述终端设备，所述指示信息用于指示所述AI波束模型对应的第一接收波束特征。
可选地，在本公开的一个实施例之中，所述第一接收波束特征包括以下至少一项：
所述AI波束模型支持的第一接收波束的第一数量；
所述AI波束模型中所述第一接收波束对应的第一维度方向角数量；
所述AI波束模型中所述第一接收波束对应的第一维度方向角的第一角度值；
所述AI波束模型中所述第一接收波束对应的第二维度方向角数量；
所述AI波束模型中所述第一接收波束对应的第二维度方向角的第二角度值；
所述AI波束模型中所述第一接收波束对应的第一波束标识。
可选地，在本公开的一个实施例之中，所述第一接收波束特征为所述AI波束模型支持的接收波束数量，所述第二接收波束特征为所述终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
本公开又一方面实施例提出的一种AI波束模型确定装置，包括：
接收模块，用于接收网络侧设备发送的AI波束模型，确定所述AI波束模型对应的第一接收波束特征。
本公开又一方面实施例提出的一种AI波束模型确定装置，包括：
发送模块，用于发送AI波束模型至终端设备。
本公开又一方面实施例提出的一种通信装置，所述装置包括处理器和存储器，所述存储器中存储有 计算机程序，所述处理器执行所述存储器中存储的计算机程序，以使所述装置执行如上一方面实施例提出的方法。
本公开又一方面实施例提出的一种通信装置，所述装置包括处理器和存储器，所述存储器中存储有计算机程序，所述处理器执行所述存储器中存储的计算机程序，以使所述装置执行如上另一方面实施例提出的方法。
本公开又一方面实施例提出的通信装置，包括：处理器和接口电路；
所述接口电路，用于接收代码指令并传输至所述处理器；
所述处理器，用于运行所述代码指令以执行如一方面实施例提出的方法。
本公开又一方面实施例提出的通信装置，包括：处理器和接口电路；
所述接口电路，用于接收代码指令并传输至所述处理器；
所述处理器，用于运行所述代码指令以执行如另一方面实施例提出的方法。
本公开又一方面实施例提出的计算机可读存储介质，用于存储有指令，当所述指令被执行时，使如一方面实施例提出的方法被实现。
本公开又一方面实施例提出的计算机可读存储介质，用于存储有指令，当所述指令被执行时，使如另一方面实施例提出的方法被实现。
综上所述，在本公开实施例之中，终端设备可接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。在本公开实施例之中，终端设备通过确定AI波束模型对应的第一接收波束特征，减少AI波束模型与第一接收波束特征不对应的情况，提高AI波束模型的波束预测准确性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
附图说明
本公开上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为本公开一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图2为本公开另一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图3为本公开再一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图4为本公开又一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图5为本公开又一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图6为本公开又一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图7为本公开又一个实施例所提供的一种AI波束模型确定方法的流程示意图；
图8为本公开一个实施例所提供的一种AI波束模型确定装置的结构示意图；
图9为本公开另一个实施例所提供的一种AI波束模型确定装置的结构示意图；
图10是本公开一个实施例所提供的一种终端设备的框图；
图11为本公开一个实施例所提供的一种网络侧设备的框图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开实施例相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本公开实施例的一些方面相一致的装置和方法的例子。
在本公开实施例使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本公开实施例。在本公开实施例和所附权利要求书中所使用的单数形式的“一种”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。还应当理解，本文中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。
应当理解，尽管在本公开实施例可能采用术语第一、第二、第三等来描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如，在不脱离本公开实施例范围的情况下，第一信息也可以被称为第二信息，类似地，第二信息也可以被称为第一信息。取决于语境，如在此所使用的词语“如果”及“若”可以被解释成为“在……时”或“当……时”或“响应于确定”。
下面参考附图对本公开实施例所提供的一种AI波束模型确定方法、装置、设备及存储介质进行详细描述。
图1为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由终端设备执行，如图1所示，该方法可以包括以下步骤：
步骤101、接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。
需要说明的是，在本公开的一个实施例之中，终端设备可以是指向用户提供语音和/或数据连通性的设备。终端设备可以经RAN(Radio Access Network，无线接入网)与一个或多个核心网进行通信，终端设备可以是物联网终端，如传感器设备、移动电话(或称为“蜂窝”电话)和具有物联网终端的计算机，例如，可以是固定式、便携式、袖珍式、手持式、计算机内置的或者车载的装置。例如，站(Station，STA)、订户单元(subscriber unit)、订户站(subscriber station)，移动站(mobile station)、移动台(mobile)、远程站(remote station)、接入点、远程终端(remoteterminal)、接入终端(access terminal)、用户装置(user terminal)或用户代理(useragent)。或者，终端设备也可以是无人飞行器的设备。或者，终端设备也可以是车载设备，比如，可以是具有无线通信功能的行车电脑，或者是外接行车电脑的无线终端。或者，终端设备也可以是路边设备，比如，可以是具有无线通信功能的路灯、信号灯或者其它路边设备等。
其中，在本公开的一个实施例之中，终端设备在接收网络侧设备发送的AI波束模型之前，还包括：
发送AI波束模型请求和/或第二接收波束特征至网络侧设备。
以及，在本公开的一个实施例之中，确定AI波束模型对应的第一接收波束特征，包括：
接收网络侧设备发送的指示信息，指示信息用于指示AI波束模型对应的第一接收波束特征；或
基于默认规则确定AI波束模型对应的第一接收波束特征。
以及，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
以及，在本公开的一个实施例之中，第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
终端设备中第二接收波束对应的第一维度方向角数量；
终端设备中第二接收波束对应的第一维度方向角的第一角度值；
终端设备中第二接收波束对应的第二维度方向角数量；
终端设备中第二接收波束对应的第二维度方向角的第二角度值；
终端设备中第二接收波束对应的第二波束标识。
进一步地，在本公开的一个实施例之中，在接收网络侧设备发送的AI波束模型之后，包括：
确定AI波束模型输入参数，并将输入参数输入至AI波束模型，获得波束测量质量的预测结果，其中输入参数包括以下至少一项：
第二接收波束特征；
第三数量的波束对对应的波束对特征；
第三数量的波束对对应的波束测量质量。
以及，在本公开的一个实施例之中，波束对特征包括以下至少一项：
波束对对应的波束对ID；
波束对对应的一个参考信号ID，其中，参考信号包括同步信号块SSB、信道状态信息-参考信号CSI-RS中的至少一个；
波束对对应的一个第二接收波束对应的第二波束标识；
波束对对应的一个第二接收波束的第一维度方向角的第一角度值；
波束对对应的一个第二接收波束的第二维度方向角的第二角度值。
以及，在本公开的一个实施例之中，波束测量质量包括层一参考信号接收功率L1-RSRP和/或层一信号与干扰加噪声比L1-SINR。
示例地，在本公开的一个实施例之中，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
综上所述，在本公开实施例之中，终端设备可接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。在本公开实施例之中，终端设备通过确定AI波束模型对应的第一接收波束特征，减少AI波束模型与第一接收波束特征不对应的情况，提高AI波束模型的波束预测准确性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图2为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由终端设备执行，如图2所示，该方法可以包括以下步骤：
步骤201、发送AI波束模型请求和/或第二接收波束特征至网络侧设备；
步骤202、接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。
其中，在本公开的一个实施例之中，终端设备发送AI波束模型请求和/或第二接收波束特征至网络侧设备具体可以为终端设备先发送AI波束模型请求至网络侧设备后，再发送第二接收波束特征至网络侧设备，还可以是终端设备先发送第二接收波束特征至网络侧设备后，再发送AI波束模型请求至网络侧设备，还可以是终端设备同时发送第二接收波束特征和AI波束模型请求至网络侧设备。终端设备同时发送第二接收波束特征和AI波束模型请求至网络侧设备时，终端设备具体可以将第二接收波束特征添加至AI波束模型请求中，即终端设备可以发送携带有第二接收波束特征的AI波束模型请求至网络侧设备。
以及，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
以及，在本公开的一个实施例之中，第一数量仅用于是指AI波束模型支持的第一接收波束的数量。该第一数量并不特指某一固定数量。第一接收波束包含至少一个第一接收波束。比如，AI波束模型支持的至少一个第一接收波束的第一数量为Q，即表明该AI波束模型适用于接收波束数量为Q的终端，Q的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第一数量为Q，即表明该AI波束模型适用于接收波束数量为小于或等于Q的终端，Q的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的数量为任意值，即表明该AI波束模型适用于接收波束数量为任意值的终端。
以及，在本公开的一个实施例之中，第一接收波束特征是指AI波束模型支持的接收波束特征。该第一接收波束特征并不特指某一固定接收波束特征。例如，当第一接收波束特征包括的特征数量发生变化时，该第一接收波束特征也可以相应变化。例如，当第一接收波束特征包括的接收波束特征发生变化时，该第一接收波束特征也可以相应变化。
以及，在本公开的一个实施例之中，第一维度方向角仅仅用于指示多个维度方向中的任意一个方向 角，其中，第一维度方向角中的第一仅用于与第二维度方向角进行区别。该第一维度方向角并不特指某一固定方向角。
示例地，在本公开的一个实施例之中，第二维度方向角仅仅用于指示多个维度方向中不同于第一维度方向角的任意一个方向角，其中，该第二维度方向角并不特指某一固定方向角。例如，第一维度方向角为天顶角时，第二维度方向角可以是方位角。其中，第一维度方向角的第一绝对值的取值范围为0～2*pi。其中，第二维度方向角的第二绝对值的取值范围为0～2*pi。
以及，在本公开的一个实施例之中，第一接收波束对应的第一维度方向角数量中的第一仅用于与第一接收波束对应的第二维度方向角数量进行区别。该第一接收波束对应的第一维度方向角数量并不特指某一固定数量。该第一接收波束对应的第二维度方向角数量并不特指某一固定数量。第一接收波束包含至少一个第一接收波束。
进一步地，在本公开的一个实施例之中，例如，AI波束模型支持的至少一个第一接收波束的第一维度方向角数量为N，即表明该AI波束模型适用于接收波束的第一维度方向角数量为N的终端，N的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第一维度方向角数量为N，即表明该AI波束模型适用于接收波束的第一维度方向角数量为小于或等于N的终端，N的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第一维度方向角数量为任意值，即表明该AI波束模型适用于接收波束的第一维度方向角数量为任意值的终端。
示例地，在本公开的一个实施例之中，例如，AI波束模型支持的至少一个第一接收波束的第二维度方向角数量为M，即表明该AI波束模型适用于接收波束的第二维度方向角数量为M的终端，M的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第二维度方向角数量为M，即表明该AI波束模型适用于接收波束的第二维度方向角数量为小于或等于M的终端，M的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第二维度方向角数量为任意值，即表明该AI波束模型适用于接收波束的第二维度方向角数量为任意值的终端。需要说明的是，这种情况下，AI波束模型支持的第一接收波束数量为M和N的乘积。
示例地，第一接收波束对应的第一维度方向角的第一角度值是指第一接收波束对应的第一维度方向角的角度值。第一接收波束对应的第一维度方向角的第二角度值是指第一接收波束对应的第二维度方向角的角度值。该第一接收波束对应的第一维度方向角的第一角度值中的第一仅用于与第一接收波束对应的第一维度方向角的第二角度值进行区别。该第一接收波束对应的第一维度方向角的第一角度值并不特指某一固定角度值。第一接收波束包含至少一个第一接收波束。
进一步地，在本公开的一个实施例之中，例如，AI波束模型支持的至少一个第一接收波束的第一维度方向角的第一角度值为θ 1，θ 2……θ N,即表明该AI波束模型适用于接收波束的第一维度方向角数量为N，且第一维度方向角的第一角度值为θ 1，θ 2……θ N的终端，θ i的取值为0～2*pi，N的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第一维度方向角的第一角度值为任意值，即表明该AI波束模型适用于接收波束的第一维度方向角的第一角度值为任意值的终端。
又比如，AI波束模型支持的至少一个第一接收波束的第二维度方向角的第二角度值为θ 1，θ 2……θ M,即表明该AI波束模型适用于接收波束的第二维度方向角数量为M，且第二维度方向角的第二角度值为θ 1，θ 2……θ M的终端，θ i的取值为0～2*pi，M的取值为正整数；或，AI波束模型支持的至少一个第一接收波束的第二维度方向角的第二角度值为任意值，即表明该AI波束模型适用于接收波束的第二维度方向角的第二角度值为任意值的终端。
进一步地，在本公开的一个实施例之中，接收波束标识用于唯一标识一个接收波束。也就是说，不同的接收波束对应的接收波束标识不同。第一接收波束包含至少一个第一接收波束。第一波束标识用于表示至少一个第一接收波束的标识。比如，第一接收波束特征包括AI波束模型中第一接收波束对应的第一波束标识为第一波束标识ID#1和第一波束标识ID#2，即表明该AI波束模型适用于接收波束的第一波束标识为第一波束标识ID#1和第一波束标识ID#2的终端，或者也用于指示利用该AI波束模型进行波束预测是的输入包含至少一个第一波束对的测量质量，而至少一个第一波束对对应的接收波束标识为第一波束标识ID#1和第一波束标识ID#2。
以及，在本公开的一个实施例之中，第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
终端设备中第二接收波束对应的第一维度方向角数量；
终端设备中第二接收波束对应的第一维度方向角的第一角度值；
终端设备中第二接收波束对应的第二维度方向角数量；
终端设备中第二接收波束对应的第二维度方向角的第二角度值；
终端设备中第二接收波束对应的第二波束标识。
以及，在本公开的一个实施例之中，第二接收波束特征是指终端设备支持的接收波束特征。该第二接收波束特征并不特指某一固定接收波束特征。例如，当第二接收波束特征包括的特征数量发生变化时，该第二接收波束特征也可以相应变化。例如，当第二接收波束特征包括的接收波束特征发生变化时，该第二接收波束特征也可以相应变化。
以及，在本公开的一个实施例之中，第二接收波束对应的第一维度方向角数量中的第一仅用于与第二接收波束对应的第二维度方向角数量进行区别。该第二接收波束对应的第一维度方向角数量并不特指某一固定数量。该第二接收波束对应的第二维度方向角数量并不特指某一固定数量。第二接收波束包含至少一个第二接收波束。比如，终端设备支持的第二接收波束的第一维度方向角数量为A，第二维度方向角的数量为B，其中A和B的取值均为正整数。需要说明的是，这种情况下，终端设备支持的第二接收波束数量为A和B的乘积。
示例地，第二接收波束对应的第一维度方向角的第一角度值是指第二接收波束对应的第一维度方向角的角度值。第二接收波束对应的第一维度方向角的第二角度值是指第二接收波束对应的第二维度方向角的角度值。该第二接收波束对应的第一维度方向角的第一角度值中的第一仅用于与第二接收波束对应的第一维度方向角的第二角度值进行区别。该第二接收波束对应的第一维度方向角的第一角度值并不特指某一固定角度值。第二接收波束包含至少一个第二接收波束。
比如，终端设备支持的第二接收波束的第一维度方向角的第一角度值为θ 1，θ 2……θ A,即表明该终端设备支持的第二接收波束的第一维度方向角数量为A，且第一维度方向角的第一角度值为θ 1，θ 2……θ A的终端，θ i的取值为0～2*pi，A的取值为正整数。
又比如，终端设备支持的第二接收波束的第二维度方向角的第二角度值为θ 1，θ 2……θ B,即表明该终端设备支持的第二接收波束的第二维度方向角数量为B，且第二维度方向角的第二角度值为θ 1，θ 2……θ B的终端，θ i的取值为0～2*pi，B的取值为正整数。
以及，在本公开的一个实施例之中，第二数量仅用于是指终端设备支持的第二接收波束的数量。该第二数量并不特指某一固定数量。第二接收波束包含至少一个第二接收波束。比如终端设备支持的接收波束数量为W，则第二数量为W，W的取值为正整数。其中，接收波束数量为Rxbeamnumber。
进一步地，在本公开的一个实施例之中，接收波束标识用于唯一标识一个接收波束。也就是说，不同的接收波束对应的接收波束标识不同。第二接收波束包含至少一个第二接收波束。第二波束标识用于表示至少一个第二接收波束的标识。比如，终端包含两个接收波束，标识分别为ID#1和ID#2，则第二接收波束标识为第二波束标识ID#1和第二波束标识ID#2。
进一步地，在本公开的一个实施例之中，终端设备可以接收网络侧设备的波束配置信息，波束配置信息用于指示传输配置指示状态TCI state。
其中，在本公开的一个实施例之中，TCI state包括至少一种准共址类型QCL Type。
以及，在本公开的一个实施例之中，QCL包括QCL Type A、QCL Type B、QCL Type C和QCL Type D，QCL Type D用于指示接收参数信息，QCL Type A包括多普勒频移参数、多普勒扩展参数、平均时延参数和时延扩展相关参数中的至少一个，QCL Type B包括多普勒频移参数、多普勒扩展参数、平均时延参数和时延扩展相关参数中的至少一个，QCL Type C包括多普勒频移参数、多普勒扩展参数、平均时延参数和时延扩展相关参数中的至少一个。
综上所述，在本公开实施例之中，终端设备可接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。在本公开实施例之中，终端设备通过发送AI波束模型请求和/或第二接 收波束特征至网络侧设备之后，可以确定AI波束模型对应的第一接收波束特征，提高第二接收波束特征与AI波束模型的匹配度，提高AI波束模型的波束预测准确性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图3为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由终端设备执行，如图3所示，该方法可以包括以下步骤：
步骤301、接收网络侧设备发送的AI波束模型；
步骤302、接收网络侧设备发送的指示信息，指示信息用于指示AI波束模型对应的第一接收波束特征；或
基于默认规则确定AI波束模型对应的第一接收波束特征。
其中，在本公开的一个实施例中，终端设备在确定AI波束模型对应的第一接收波束特征时，终端设备可以接收网络侧设备发送的指示信息，由于指示信息是用于指示AI波束模型对应的第一接收波束特征，因此终端设备可以根据该指示信息获取AI波束模型对应的第一接收波束特征。
以及，在本公开的一个实施例中，终端设备可以基于默认规则确定AI波束模型对应的第一接收波束特征。
以及，在本公开的一个实施例中，第一接收波束特征例如为接收波束数量时，默认规则具体可以是指示该AI波束模型适用于接收波束数量为任意值的终端。
以及，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
综上所述，在本公开实施例之中，终端设备可接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。在本公开实施例之中，终端设备通过接收网络侧设备发送的指示信息获取或基于默认规则确定AI波束模型对应的第一接收波束特征，提高第一接收波束特征确定的准确性，提高AI波束模型的波束预测准确性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图4为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由终端设备执行，如图4所示，该方法可以包括以下步骤：
步骤401、接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征；
步骤402、确定AI波束模型输入参数，并将输入参数输入至AI波束模型，获得波束测量质量的预测结果。
其中，在本公开的一个实施例中，输入参数是指可以用于与AI波束模型对应的参数。AI波束模型可以是仅与某一接收波束特征对应的模型，还可以是与多个接收波束特征对应的模型。例如，AI波束模型可以是仅与接收波束数量对应的AI波束模型，其中，该AI波束模型对应的输入参数为接收波束数量。例如，该AI模型还可以是与所有接收波束特征对应的AI波束模型。
示例地，在本公开的一个实施例之中，输入参数包括以下至少一项：
第二接收波束特征；
第三数量的波束对对应的波束对特征；
第三数量的波束对对应的波束测量质量。
以及，在本公开的一个实施例之中，第三数量是指波束对的数量。该第三数量并不特指某一固定数量。该第三数量为终端设备需要测量的波束对的数量。例如，终端需要测量的波束对的第三数量为M*N， 其中，M为网络侧设备的发送波束数量，每个发送波束对应一个参考信号ID，N为终端设备的接收波束数量。M和N分别为正整数。
以及，在本公开的一个实施例之中，输入参数例如可以包括第二接收波束特征，第三数量的波束对对应的波束对特征，以及第三数量的波束对对应的波束测量质量。终端设备可以将第二接收波束特征，第三数量的波束对对应的波束对特征，以及第三数量的波束对对应的波束测量质量输入值AI波束模型，获得波束测量质量的预测结果。
以及，在本公开的一个实施例之中，波束对特征包括以下至少一项：
波束对对应的波束对ID；
波束对对应的一个参考信号ID，其中，参考信号包括同步信号块SSB、信道状态信息-参考信号CSI-RS中的至少一个；
波束对对应的一个第二接收波束对应的第二波束标识；
波束对对应的一个第二接收波束的第一维度方向角的第一角度值；
波束对对应的一个第二接收波束的第二维度方向角的第二角度值。
以及，在本公开的一个实施例中，第一角度值仅用于指示波束对对应的一个第二接收波束的第一维度方向角的角度值。第二角度值仅用于指示波束对对应的一个第二接收波束的第二维度方向角的角度值。该第一角度值中的第一仅用于与第二角度值进行区别，第一角度值并不特指某一固定角度值。
以及，在本公开的一个实施例中，第一维度方向角仅仅用于指示多个维度方向中的任意一个方向角，其中，第一维度方向角中的第一仅用于与第二维度方向角进行区别。该第一维度方向角并不特指某一固定方向角。
进一步地，第二维度方向角仅仅用于指示多个维度方向中不同于第一维度方向角的任意一个方向角，其中，该第二维度方向角并不特指某一固定方向角。例如，第一维度方向角为天顶角时，第二维度方向角可以是方位角。
示例地，在本公开的一个实施例之中，波束测量质量包括层一参考信号接收功率L1-RSRP和/或层一信号与干扰加噪声比L1-SINR。
进一步地，在本公开的一个实施例之中，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
以及，在本公开的一个实施例之中，第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量用于表示AI模型支持的接收波束数量小于或者等于终端设备支持的接收波束数量。
综上所述，在本公开实施例之中，终端设备可接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。在本公开实施例之中，终端设备通过确定AI波束模型输入参数，并将输入参数输入至AI波束模型，获得波束测量质量的预测结果，提高输入参数和AI模型的匹配性，提高AI波束模型对应的预测结果的获取准确性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图5为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由网络侧设备执行，如图5所示，该方法可以包括以下步骤：
步骤501、发送AI波束模型至终端设备。
其中，在本公开的一个实施例之中，在发送AI波束模型至终端设备之前，还包括：
接收终端设备发送的AI波束模型请求和/或第二接收波束特征。
其次，在本公开的一个实施例之中，第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
终端设备中第二接收波束对应的第一维度方向角数量；
终端设备中第二接收波束对应的第一维度方向角的第一角度值；
终端设备中第二接收波束对应的第二维度方向角数量；
终端设备中第二接收波束对应的第二维度方向角的第二角度值；
终端设备中第二接收波束对应的第二波束标识。
示例地，在本公开的一个实施例之中，发送AI波束模型至终端设备，包括：
发送指示信息至终端设备，指示信息用于指示AI波束模型对应的第一接收波束特征。
进一步地，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
其次，在本公开的一个实施例之中，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
综上所述，在本公开实施例之中，网络侧设备可以发送AI波束模型至终端设备。在本公开实施例之中，网络侧设备通过指示该AI波束模型对应的第一接收波束特征，可以提高终端设备确定的输入参数和AI模型的匹配性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图6为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由网络侧设备执行，如图6所示，该方法可以包括以下步骤：
步骤601、接收终端设备发送的AI波束模型请求和/或第二接收波束特征；
步骤602、发送AI波束模型至终端设备。
其次，在本公开的一个实施例中，网络侧设备接收终端设备发送的AI波束模型请求和/或第二接收波束特征，网络侧设备可以确定与该第二接收波束特征对应的AI波束模型，并将该AI波束模型发送至终端设备。
其次，在本公开的一个实施例之中，第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
终端设备中第二接收波束对应的第一维度方向角数量；
终端设备中第二接收波束对应的第一维度方向角的第一角度值；
终端设备中第二接收波束对应的第二维度方向角数量；
终端设备中第二接收波束对应的第二维度方向角的第二角度值；
终端设备中第二接收波束对应的第二波束标识。
其中，关于第二接收波束特征的其他详细介绍可以参考上述实施例描述，本公开实施例在此不做赘述。
进一步地，在本公开的一个实施例之中，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
以及，在本公开的一个实施例之中，第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量用于表示AI模型支持的接收波束数量小于或者等于终端设备支持的接收波束数量。
示例地，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，终端设备支持的接收波束数量的取值例如可以是2～8。
综上所述，在本公开实施例之中，网络侧设备可以发送AI波束模型至终端设备。在本公开实施例之中，网络侧设备通过接收终端设备发送的AI波束模型请求和/或第二接收波束特征，可基于第二接收 波束特征确定AI波束模型，可以提高终端设备确定的输入参数和AI模型的匹配性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图7为本公开实施例所提供的一种AI波束模型确定方法的流程示意图，该方法由网络侧设备执行，如图7所示，该方法可以包括以下步骤：
步骤701、发送AI波束模型至终端设备；
步骤702、发送指示信息至终端设备，指示信息用于指示AI波束模型对应的第一接收波束特征。
其次，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
其中，关于第一接收波束特征的其他详细介绍可以参考上述实施例描述，本公开实施例在此不做赘述。
以及，在本公开的一个实施例之中，针对步骤701和步骤702的执行顺序并不做限定，也就是说网络侧设备可以先执行步骤701、发送AI波束模型至终端设备；再执行步骤702、发送指示信息至终端设备；还可以先步骤702、发送指示信息至终端设备；再执行步骤701、发送AI波束模型至终端设备；还可以通知执行步骤701、发送AI波束模型至终端设备和步骤702、发送指示信息至终端设备。
综上所述，在本公开实施例之中，网络侧设备可以发送AI波束模型至终端设备。在本公开实施例之中，网络侧设备通过发送指示信息和AI波束模型至终端设备，可以提高终端设备确定的输入参数和AI模型的匹配性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
图8为本公开实施例所提供的一种AI波束模型确定装置的结构示意图，如图8所示，该装置800可以包括：
接收模块801，用于接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。
综上所述，在本公开实施例的AI波束模型确定装置之中，终端设备可接收网络侧设备发送的AI波束模型，确定AI波束模型对应的第一接收波束特征。在本公开实施例之中，终端设备通过确定AI波束模型对应的第一接收波束特征，减少AI波束模型与第一接收波束特征不对应的情况，提高AI波束模型的波束预测准确性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
可选地，在本公开的一个实施例之中，接收模块801，还用于在接收网络侧设备发送的AI波束模型之前，发送AI波束模型请求和/或第二接收波束特征至网络侧设备。
可选地，在本公开的一个实施例之中，接收模块801，用于确定AI波束模型对应的第一接收波束特征时，具体用于：
接收网络侧设备发送的指示信息，指示信息用于指示AI波束模型对应的第一接收波束特征；或
基于默认规则确定AI波束模型对应的第一接收波束特征。
可选地，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
可选地，在本公开的一个实施例之中，第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
终端设备中第二接收波束对应的第一维度方向角数量；
终端设备中第二接收波束对应的第一维度方向角的第一角度值；
终端设备中第二接收波束对应的第二维度方向角数量；
终端设备中第二接收波束对应的第二维度方向角的第二角度值；
终端设备中第二接收波束对应的第二波束标识。
可选地，在本公开的一个实施例之中，接收模块801，还用于在接收网络侧设备发送的AI波束模型之后，确定AI模型输入参数，并将输入参数输入至AI波束模型，获得波束测量质量的预测结果，其中，输入参数包括以下至少一项：
第二接收波束特征；
第三数量的波束对对应的波束对ID；
第三数量的波束对对应的波束测量质量。
可选地，在本公开的一个实施例之中，波束对ID对应一个参考信号ID和一个第二接收波束标识，其中，参考信号包括同步信号块SSB、信道状态信息-参考信号CSI-RS中的至少一个。
可选地，在本公开的一个实施例之中，波束测量质量包括层一参考信号接收功率L1-RSRP和/或层一信号与干扰加噪声比L1-SINR。
可选地，在本公开的一个实施例之中，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
图9为本公开实施例所提供的一种AI波束模型确定装置的结构示意图，如图9所示，该装置900可以包括：
发送模块901，用于发送AI波束模型至终端设备。
综上所述，在本公开实施例的AI波束模型确定装置之中，网络侧设备可以发送AI波束模型至终端设备。在本公开实施例之中，网络侧设备通过指示该AI波束模型对应的第一接收波束特征，可以提高终端设备所输入参数和AI模型的匹配性。本公开针对于“AI波束模型确定方法”这一情形提供了一种处理方法，以提供AI波束模型对应的接收波束特征，提高AI波束模型的波束测量质量对应的预测结果获取的准确性。
可选地，在本公开的一个实施例之中，接收终端设备发送的AI波束模型请求和/或第二接收波束特征。
可选地，在本公开的一个实施例之中，第二接收波束特征包括以下至少一项：
终端设备支持的第二接收波束的第二数量；
终端设备中第二接收波束对应的第一维度方向角数量；
终端设备中第二接收波束对应的第一维度方向角的第一角度值；
终端设备中第二接收波束对应的第二维度方向角数量；
终端设备中第二接收波束对应的第二维度方向角的第二角度值；
终端设备中第二接收波束对应的第二波束标识。
可选地，在本公开的一个实施例之中，发送模块901，用于发送AI波束模型至终端设备时，具体用于：
发送指示信息至终端设备，指示信息用于指示AI波束模型对应的第一接收波束特征。
可选地，在本公开的一个实施例之中，第一接收波束特征包括以下至少一项：
AI波束模型支持的第一接收波束的第一数量；
AI波束模型中第一接收波束对应的第一维度方向角数量；
AI波束模型中第一接收波束对应的第一维度方向角的第一角度值；
AI波束模型中第一接收波束对应的第二维度方向角数量；
AI波束模型中第一接收波束对应的第二维度方向角的第二角度值；
AI波束模型中第一接收波束对应的第一波束标识。
可选地，在本公开的一个实施例之中，第一接收波束特征为AI波束模型支持的接收波束数量，第二接收波束特征为终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
图10是本公开一个实施例所提供的一种终端设备UE1000的框图。例如，UE1000可以是移动电话，计算机，数字广播终端设备，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等。
参照图10，UE1000可以包括以下至少一个组件：处理组件1002，存储器1004，电源组件1006，多媒体组件1008，音频组件1010，输入/输出(I/O)的接口1012，传感器组件1014，以及通信组件1016。
处理组件1002通常控制UE1000的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件1002可以包括至少一个处理器1020来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件1002可以包括至少一个模块，便于处理组件1002和其他组件之间的交互。例如，处理组件1002可以包括多媒体模块，以方便多媒体组件1008和处理组件1002之间的交互。
存储器1004被配置为存储各种类型的数据以支持在UE1000的操作。这些数据的示例包括用于在UE1000上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器1004可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件1006为UE1000的各种组件提供电力。电源组件1006可以包括电源管理系统，至少一个电源，及其他与为UE1000生成、管理和分配电力相关联的组件。
多媒体组件1008包括在所述UE1000和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括至少一个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的唤醒时间和压力。在一些实施例中，多媒体组件1008包括一个前置摄像头和/或后置摄像头。当UE1000处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件1010被配置为输出和/或输入音频信号。例如，音频组件1010包括一个麦克风(MIC)，当UE1000处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器1004或经由通信组件1016发送。在一些实施例中，音频组件1010还包括一个扬声器，用于输出音频信号。
I/O接口1012为处理组件1002和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件1014包括至少一个传感器，用于为UE1000提供各个方面的状态评估。例如，传感器组件1014可以检测到设备1000的打开/关闭状态，组件的相对定位，例如所述组件为UE1000的显示器和小键盘，传感器组件1014还可以检测UE1000或UE1000的一个组件的位置改变，用户与UE1000接触的存在或不存在，UE1000方位或加速/减速和UE1000的温度变化。传感器组件1014可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件1014还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件1014还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件1016被配置为便于UE1000和其他设备之间有线或无线方式的通信。UE1000可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件1016 经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件1016还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，UE1000可以被至少一个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
图11是本公开实施例所提供的一种网络侧设备1100的框图。例如，网络侧设备1100可以被提供为一网络侧设备。参照图11，网络侧设备1100包括处理组件1122，其进一步包括至少一个处理器，以及由存储器1132所代表的存储器资源，用于存储可由处理组件1122的执行的指令，例如应用程序。存储器1132中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1122被配置为执行指令，以执行上述方法前述应用在所述网络侧设备的任意方法，例如，如图1所示方法。
网络侧设备1100还可以包括一个电源组件1126被配置为执行网络侧设备1100的电源管理，一个有线或无线网络接口1150被配置为将网络侧设备1100连接到网络，和一个输入/输出(I/O)接口1158。网络侧设备1100可以操作基于存储在存储器1132的操作系统，例如Windows Server TM，Mac OS XTM，Unix TM,Linux TM，Free BSDTM或类似。
上述本公开提供的实施例中，分别从网络侧设备、UE的角度对本公开实施例提供的方法进行了介绍。为了实现上述本公开实施例提供的方法中的各功能，网络侧设备和UE可以包括硬件结构、软件模块，以硬件结构、软件模块、或硬件结构加软件模块的形式来实现上述各功能。上述各功能中的某个功能可以以硬件结构、软件模块、或者硬件结构加软件模块的方式来执行。
上述本公开提供的实施例中，分别从网络侧设备、UE的角度对本公开实施例提供的方法进行了介绍。为了实现上述本公开实施例提供的方法中的各功能，网络侧设备和UE可以包括硬件结构、软件模块，以硬件结构、软件模块、或硬件结构加软件模块的形式来实现上述各功能。上述各功能中的某个功能可以以硬件结构、软件模块、或者硬件结构加软件模块的方式来执行。
本公开实施例提供的一种通信装置。通信装置可包括收发模块和处理模块。收发模块可包括发送模块和/或接收模块，发送模块用于实现发送功能，接收模块用于实现接收功能，收发模块可以实现发送功能和/或接收功能。
通信装置可以是终端设备(如前述方法实施例中的终端设备)，也可以是终端设备中的装置，还可以是能够与终端设备匹配使用的装置。或者，通信装置可以是网络设备，也可以是网络设备中的装置，还可以是能够与网络设备匹配使用的装置。
本公开实施例提供的另一种通信装置。通信装置可以是网络设备，也可以是终端设备(如前述方法实施例中的终端设备)，也可以是支持网络设备实现上述方法的芯片、芯片系统、或处理器等，还可以是支持终端设备实现上述方法的芯片、芯片系统、或处理器等。该装置可用于实现上述方法实施例中描述的方法，具体可以参见上述方法实施例中的说明。
通信装置可以包括一个或多个处理器。处理器可以是通用处理器或者专用处理器等。例如可以是基带处理器或中央处理器。基带处理器可以用于对通信协议以及通信数据进行处理，中央处理器可以用于对通信装置(如，网络侧设备、基带芯片，终端设备、终端设备芯片，DU或CU等)进行控制，执行计算机程序，处理计算机程序的数据。
可选地，通信装置中还可以包括一个或多个存储器，其上可以存有计算机程序，处理器执行所述计算机程序，以使得通信装置执行上述方法实施例中描述的方法。可选地，所述存储器中还可以存储有数据。通信装置和存储器可以单独设置，也可以集成在一起。
可选地，通信装置还可以包括收发器、天线。收发器可以称为收发单元、收发机、或收发电路等，用于实现收发功能。收发器可以包括接收器和发送器，接收器可以称为接收机或接收电路等，用于实现接收功能；发送器可以称为发送机或发送电路等，用于实现发送功能。
可选地，通信装置中还可以包括一个或多个接口电路。接口电路用于接收代码指令并传输至处理器。 处理器运行所述代码指令以使通信装置执行上述方法实施例中描述的方法。
通信装置为终端设备(如前述方法实施例中的终端设备)：处理器用于执行图1-图4任一所示的方法。
通信装置为网络侧设备：处理器用于执行图5-图7任一所示的方法。
在一种实现方式中，处理器中可以包括用于实现接收和发送功能的收发器。例如该收发器可以是收发电路，或者是接口，或者是接口电路。用于实现接收和发送功能的收发电路、接口或接口电路可以是分开的，也可以集成在一起。上述收发电路、接口或接口电路可以用于代码/数据的读写，或者，上述收发电路、接口或接口电路可以用于信号的传输或传递。
在一种实现方式中，处理器可以存有计算机程序，计算机程序在处理器上运行，可使得通信装置执行上述方法实施例中描述的方法。计算机程序可能固化在处理器中，该种情况下，处理器可能由硬件实现。
在一种实现方式中，通信装置可以包括电路，所述电路可以实现前述方法实施例中发送或接收或者通信的功能。本公开中描述的处理器和收发器可实现在集成电路(integrated circuit，IC)、模拟IC、射频集成电路RFIC、混合信号IC、专用集成电路(application specific integrated circuit，ASIC)、印刷电路板(printed circuit board，PCB)、电子设备等上。该处理器和收发器也可以用各种IC工艺技术来制造，例如互补金属氧化物半导体(complementary metal oxide semiconductor，CMOS)、N型金属氧化物半导体(nMetal-oxide-semiconductor，NMOS)、P型金属氧化物半导体(positive channel metal oxide semiconductor，PMOS)、双极结型晶体管(bipolar junction transistor，BJT)、双极CMOS(BiCMOS)、硅锗(SiGe)、砷化镓(GaAs)等。
以上实施例描述中的通信装置可以是网络设备或者终端设备(如前述方法实施例中的终端设备)，但本公开中描述的通信装置的范围并不限于此，而且通信装置的结构可以不受的限制。通信装置可以是独立的设备或者可以是较大设备的一部分。例如所述通信装置可以是：
(1)独立的集成电路IC，或芯片，或，芯片系统或子系统；
(2)具有一个或多个IC的集合，可选地，该IC集合也可以包括用于存储数据，计算机程序的存储部件；
(3)ASIC，例如调制解调器(Modem)；
(4)可嵌入在其他设备内的模块；
(5)接收机、终端设备、智能终端设备、蜂窝电话、无线设备、手持机、移动单元、车载设备、网络设备、云设备、人工智能设备等等；
(6)其他等等。
对于通信装置可以是芯片或芯片系统的情况，芯片包括处理器和接口。其中，处理器的数量可以是一个或多个，接口的数量可以是多个。
可选地，芯片还包括存储器，存储器用于存储必要的计算机程序和数据。
本领域技术人员还可以了解到本公开实施例列出的各种说明性逻辑块(illustrative logical block)和步骤(step)可以通过电子硬件、电脑软件，或两者的结合进行实现。这样的功能是通过硬件还是软件来实现取决于特定的应用和整个系统的设计要求。本领域技术人员可以对于每种特定的应用，可以使用各种方法实现所述的功能，但这种实现不应被理解为超出本公开实施例保护的范围。
本公开还提供一种可读存储介质，其上存储有指令，该指令被计算机执行时实现上述任一方法实施例的功能。
本公开还提供一种计算机程序产品，该计算机程序产品被计算机执行时实现上述任一方法实施例的功能。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机程序。在计算机上加载和执行所述计算机程序时，全部或部分地产生按照本公开实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机程序可以 存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机程序可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(digital subscriber line，DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质(例如，软盘、硬盘、磁带)、光介质(例如，高密度数字视频光盘(digital video disc，DVD))、或者半导体介质(例如，固态硬盘(solid state disk，SSD))等。
本领域普通技术人员可以理解：本公开中涉及的第一、第二等各种数字编号仅为描述方便进行的区分，并不用来限制本公开实施例的范围，也表示先后顺序。
本公开中的至少一个还可以描述为一个或多个，多个可以是两个、三个、四个或者更多个，本公开不做限制。在本公开实施例中，对于一种技术特征，通过“第一”、“第二”、“第三”、“A”、“B”、“C”和“D”等区分该种技术特征中的技术特征，该“第一”、“第二”、“第三”、“A”、“B”、“C”和“D”描述的技术特征间无先后顺序或者大小顺序。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本发明的其它实施方案。本公开旨在涵盖本发明的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本发明的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由下面的权利要求指出。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。

Claims (23)

  1. 一种人工智能AI波束模型确定方法，其特征在于，所述方法由终端设备执行，所述方法包括：
    接收网络侧设备发送的AI波束模型，确定所述AI波束模型对应的第一接收波束特征。
  2. 根据权利要求1所述的方法，其特征在于，在所述接收网络侧设备发送的AI波束模型之前，还包括：
    发送AI波束模型请求和/或第二接收波束特征至网络侧设备。
  3. 根据权利要求1所述的方法，其特征在于，所述确定所述AI波束模型对应的第一接收波束特征，包括：
    接收网络侧设备发送的指示信息，所述指示信息用于指示所述AI波束模型对应的第一接收波束特征；或
    基于默认规则确定所述AI波束模型对应的第一接收波束特征。
  4. 根据权利要求1-3任一所述的方法，其特征在于，所述第一接收波束特征包括以下至少一项：
    所述AI波束模型支持的第一接收波束的第一数量；
    所述AI波束模型中所述第一接收波束对应的第一维度方向角数量；
    所述AI波束模型中所述第一接收波束对应的第一维度方向角的第一角度值；
    所述AI波束模型中所述第一接收波束对应的第二维度方向角数量；
    所述AI波束模型中所述第一接收波束对应的第二维度方向角的第二角度值；
    所述AI波束模型中所述第一接收波束对应的第一波束标识。
  5. 根据权利要求2所述的方法，其特征在于，所述第二接收波束特征包括以下至少一项：
    终端设备支持的第二接收波束的第二数量；
    所述终端设备中所述第二接收波束对应的第一维度方向角数量；
    所述终端设备中所述第二接收波束对应的第一维度方向角的第一角度值；
    所述终端设备中所述第二接收波束对应的第二维度方向角数量；
    所述终端设备中所述第二接收波束对应的第二维度方向角的第二角度值；
    所述终端设备中所述第二接收波束对应的第二波束标识。
  6. 根据权利要求1-5任一所述的方法，其特征在于，在所述接收网络侧设备发送的AI波束模型之后，包括：
    确定AI波束模型输入参数，并将所述输入参数输入至所述AI波束模型，获得波束测量质量的预测结果，其中所述输入参数包括以下至少一项：
    第二接收波束特征；
    第三数量的波束对对应的波束对特征；
    所述第三数量的波束对对应的波束测量质量。
  7. 根据权利要求6所述的方法，其特征在于，所述波束对特征包括以下至少一项：
    所述波束对对应的波束对ID；
    所述波束对对应的一个参考信号ID，其中，所述参考信号包括同步信号块SSB、信道状态信息-参考信号CSI-RS中的至少一个；
    所述波束对对应的一个第二接收波束对应的第二波束标识；
    所述波束对对应的一个第二接收波束的第一维度方向角的第一角度值；
    所述波束对对应的一个第二接收波束的第二维度方向角的第二角度值。
  8. 根据权利要求6所述的方法，其特征在于，所述波束测量质量包括层一参考信号接收功率L1-RSRP和/或层一信号与干扰加噪声比L1-SINR。
  9. 根据权利要求1-8任一所述的方法，其特征在于，所述第一接收波束特征为所述AI波束模型支持的接收波束数量，所述第二接收波束特征为所述终端设备支持的接收波束数量，第一接收波束特征包括多个接收波束数量；
    或第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
  10. 一种AI波束模型确定方法，其特征在于，所述方法由网络侧设备执行，所述方法包括：
    发送AI波束模型至终端设备。
  11. 根据权利要求10所述的方法，其特征在于，在所述发送AI波束模型至终端设备之前，还包括：
    接收终端设备发送的AI波束模型请求和/或第二接收波束特征。
  12. 根据权利要求11所述的方法，其特征在于，所述第二接收波束特征包括以下至少一项：
    终端设备支持的第二接收波束的第二数量；
    所述终端设备中所述第二接收波束对应的第一维度方向角数量；
    所述终端设备中所述第二接收波束对应的第一维度方向角的第一角度值；
    所述终端设备中所述第二接收波束对应的第二维度方向角数量；
    所述终端设备中所述第二接收波束对应的第二维度方向角的第二角度值；
    所述终端设备中所述第二接收波束对应的第二波束标识。
  13. 根据权利要求11所述的方法，其特征在于，所述发送AI波束模型至终端设备，包括：
    发送指示信息至所述终端设备，所述指示信息用于指示所述AI波束模型对应的第一接收波束特征。
  14. 根据权利要求13所述的方法，其特征在于，所述第一接收波束特征包括以下至少一项：
    所述AI波束模型支持的第一接收波束的第一数量；
    所述AI波束模型中所述第一接收波束对应的第一维度方向角数量；
    所述AI波束模型中所述第一接收波束对应的第一维度方向角的第一角度值；
    所述AI波束模型中所述第一接收波束对应的第二维度方向角数量；
    所述AI波束模型中所述第一接收波束对应的第二维度方向角的第二角度值；
    所述AI波束模型中所述第一接收波束对应的第一波束标识。
  15. 根据权利要求10-14所述的方法，其特征在于，所述第一接收波束特征为所述AI波束模型支持的接收波束数量，所述第二接收波束特征为所述终端设备支持的接收波束数量，
    其中，第一接收波束特征包括多个接收波束数量；或
    第一接收波束特征包括的接收波束数量小于或等于第二接收波束特征包括的接收波束数量。
  16. 一种AI波束模型确定装置，其特征在于，包括：
    接收模块，用于接收网络侧设备发送的AI波束模型，确定所述AI波束模型对应的第一接收波束特征。
  17. 一种AI波束模型确定装置，其特征在于，包括：
    发送模块，用于发送AI波束模型至终端设备。
  18. 一种通信装置，其特征在于，所述装置包括处理器和存储器，其中，所述存储器中存储有计算机程序，所述处理器执行所述存储器中存储的计算机程序，以使所述装置执行如权利要求1至9中任一项所述的方法。
  19. 一种通信装置，其特征在于，所述装置包括处理器和存储器，其中，所述存储器中存储有计算机程序，所述处理器执行所述存储器中存储的计算机程序，以使所述装置执行如权利要求10至15中任一项所述的方法。
  20. 一种通信装置，其特征在于，包括：处理器和接口电路，其中
    所述接口电路，用于接收代码指令并传输至所述处理器；
    所述处理器，用于运行所述代码指令以执行如权利要求1至9中任一项所述的方法。
  21. 一种通信装置，其特征在于，包括：处理器和接口电路，其中
    所述接口电路，用于接收代码指令并传输至所述处理器；
    所述处理器，用于运行所述代码指令以执行如权利要求10至15中任一项所述的方法。
  22. 一种计算机可读存储介质，其特征在于，用于存储有指令，当所述指令被执行时，使如权利要求1至9中任一项所述的方法被实现。
  23. 一种计算机可读存储介质，其特征在于，用于存储有指令，当所述指令被执行时，使如权利要求10至15中任一项所述的方法被实现。
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