[go: up one dir, main page]

WO2024169797A1 - Procédé d'indication de modèle d'ia et dispositif de communication - Google Patents

Procédé d'indication de modèle d'ia et dispositif de communication Download PDF

Info

Publication number
WO2024169797A1
WO2024169797A1 PCT/CN2024/076413 CN2024076413W WO2024169797A1 WO 2024169797 A1 WO2024169797 A1 WO 2024169797A1 CN 2024076413 W CN2024076413 W CN 2024076413W WO 2024169797 A1 WO2024169797 A1 WO 2024169797A1
Authority
WO
WIPO (PCT)
Prior art keywords
model
models
association relationship
same
indication information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2024/076413
Other languages
English (en)
Chinese (zh)
Inventor
贾承璐
杨昂
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Vivo Mobile Communication Co Ltd
Original Assignee
Vivo Mobile Communication Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Vivo Mobile Communication Co Ltd filed Critical Vivo Mobile Communication Co Ltd
Publication of WO2024169797A1 publication Critical patent/WO2024169797A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to an AI model indication method and communication equipment.
  • AI models are usually introduced to perform some tasks to improve network throughput, latency, and user capacity.
  • a positioning model can be used to predict the location information of a terminal
  • a channel measurement model can be used to perform channel estimation.
  • other communication devices can indicate or transmit the AI model to the communication device, and when indicating or transmitting the AI model, all the information of the AI model will be indicated or transmitted so that the communication device can obtain the AI model.
  • this method usually consumes more transmission resources, resulting in a waste of transmission resources.
  • the embodiments of the present application provide an AI model indication method and a communication device, which can solve the problem that a large amount of transmission resources are consumed when transmitting or indicating an AI model, resulting in a waste of transmission resources.
  • an AI model indication method which is performed by a communication device, and the method includes:
  • the communication device sends or receives indication information, where the indication information is used to indicate the association relationship between multiple artificial intelligence AI models.
  • an AI model indication device comprising:
  • a communication module is used to send or receive indication information, where the indication information is used to indicate the association relationship between multiple artificial intelligence AI models.
  • a communication device comprising a processor and a memory, wherein the memory stores a program or instruction that can be executed on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.
  • a communication device comprising a processor and a communication interface, wherein the communication interface is used to send or receive indication information, and the indication information is used to indicate an association relationship between multiple artificial intelligence AI models.
  • a readable storage medium on which a program or instruction is stored.
  • the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.
  • a chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.
  • a computer program/program product is provided, wherein the computer program/program product is stored in a storage medium, and the program/program product is executed by at least one processor to implement the steps of the AI model indication method as described in the first aspect.
  • the association relationship between multiple AI models can be indicated through the indication information, so that the model information that needs to be indicated can be determined through the association relationship between the AI models, thereby realizing the indication of the AI model.
  • the traditional model indication since it is not necessary to indicate all the model information, it is possible to reduce transmission resources and avoid the waste of transmission resources.
  • FIG1 is a schematic diagram of a wireless communication system according to an embodiment of the present application.
  • FIG2 is a schematic flow chart of an AI model indication method according to an embodiment of the present application.
  • FIG3 is a schematic diagram of the structure of an AI model indication device according to an embodiment of the present application.
  • FIG4 is a schematic diagram of the structure of a communication device according to an embodiment of the present application.
  • FIG5 is a schematic diagram of the structure of a communication device according to an embodiment of the present application.
  • FIG6 is a schematic diagram of the structure of a communication device according to an embodiment of the present application.
  • first, second, etc. of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by “first” and “second” are generally of one type, and the number of objects is not limited, for example, the first object can be one or more.
  • “or” in the present application represents at least one of the connected objects.
  • “A or B” covers three schemes, namely, Scheme 1: including A but not including B; Scheme 2: including B but not including A; Scheme 3: including both A and B.
  • the character "/" generally indicates that the objects associated with each other are in an "or” relationship.
  • indication in this application can be a direct indication (or explicit indication) or an indirect indication (or implicit indication).
  • a direct indication can be understood as the sender explicitly informing the receiver of specific information, operations to be performed, or request results in the sent indication;
  • an indirect indication can be understood as the receiver determining the corresponding information according to the indication sent by the sender, or making a judgment and determining the operation to be performed or the request result according to the judgment result.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution-Advanced
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • NR New Radio
  • 6G 6th Generation
  • FIG1 shows a block diagram of a wireless communication system applicable to an embodiment of the present application.
  • the wireless communication system includes a terminal 11 and a network side device 12 .
  • the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, an ultra-mobile personal computer (Ultra-mobile Personal Computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (Augmented Reality, AR), a virtual reality (Virtual Reality, VR) device, a robot, a wearable device (Wearable Device), a flight vehicle (flight vehicle), a vehicle user equipment (VUE), a shipborne equipment, a pedestrian terminal (Pedestrian User Equipment, PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), a game console, a personal computer (
  • Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc.
  • the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application.
  • the network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit.
  • the access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AS) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc.
  • WLAN wireless Local Area Network
  • AS Access Point
  • WiFi wireless Fidelity
  • the base station can be called Node B (Node B, NB), Evolved Node B (Evolved Node B, eNB), the next generation Node B (the next generation Node B, gNB), New Radio Node B (New Radio Node B, NR Node B), access point, Relay Base Station (Relay Base Station, RBS), Serving Base Station (Serving Base Station, SBS), Base Transceiver Station (Base Transceiver Station, BTS), radio base station, radio transceiver, base
  • the base station is not limited to specific technical terms as long as the same technical effect is achieved. It should be noted that in the embodiments of the present application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.
  • the embodiment of the present application provides an AI model indication method and communication device.
  • the association relationship between multiple AI models can be indicated through indication information.
  • the model information that needs to be indicated can be determined through the association relationship between the AI models, thereby realizing the indication of the AI model.
  • transmission resources can be reduced and waste of transmission resources can be avoided.
  • AI artificial intelligence
  • the artificial intelligence (AI) models in the embodiments of the present application include but are not limited to neural networks, decision trees, support vector machines, Bayesian classifiers, etc.
  • an embodiment of the present application provides an AI model indication method 200, which can be executed by a communication device.
  • the AI model indication method can be executed by software or hardware installed in the communication device.
  • the AI model indication method includes the following steps.
  • the communication device sends or receives indication information, where the indication information is used to indicate the association relationship between multiple artificial intelligence AI models.
  • the communication device can send indication information to other communication devices to indicate the association relationship between multiple AI models to other communication devices, and then the other communication devices determine the model information indicated by the communication device based on the association relationship.
  • the communication device can receive indication information sent by other communication devices to determine the association relationship between multiple AI models based on the indication information, and then determine the model information indicated by other communication devices based on the association relationship.
  • “multiple" refers to two or more.
  • the communication device may be a terminal or a network side device.
  • the communication device sending the indication information may be sending the indication information to other terminals, and the communication device receiving the indication information may be receiving the indication information from other terminals or network side devices.
  • the communication device sending the indication information may be sending the indication information to a terminal or other network side device, and the communication device receiving the indication information may be receiving the indication information from other network side devices.
  • the multiple AI models may be models to be indicated, or some may have been indicated (or pre-configured) and others may not have been indicated, or all may have been indicated (or pre-configured).
  • By indicating the association relationship between the multiple AI models it is convenient for the communication device that receives the indication information to determine (or construct) the multiple AI models, or some of the multiple AI models, or other models related to the multiple AI models according to the association relationship.
  • multiple AI models may be models that the communication device needs to indicate to other communication devices.
  • the same model information can be avoided from being repeatedly transmitted when the multiple AI models are subsequently indicated, thereby reducing transmission resources.
  • Other communication devices Multiple AI models can be constructed according to the indication information; or, multiple AI models may be that some of the AI models have been indicated to other communication devices (or have been pre-configured in other communication devices), and the other AI models have not yet been indicated.
  • the same model information can be avoided from being repeatedly transmitted when the other part of the AI model is subsequently transmitted, thereby reducing transmission resources.
  • the other communication devices can construct another part of the AI model according to the indication information and the indicated (or configured) model; or, multiple AI models may be that all of them have been indicated to other communication devices (or have been pre-configured in other communication devices).
  • the other communication devices can construct a new model according to the indication information and the multiple AI models that have been indicated (or configured). In this way, the communication device does not need to indicate the new model, thereby reducing transmission resources.
  • the association relationship between multiple AI models can be the association relationship between the model features of multiple AI models.
  • association relationship between AI models it can be known which features between AI models are associated.
  • association relationship between AI models it is also possible to indicate the association relationship between the tasks/functions associated with the AI models.
  • association relationship between the multiple AI models may include at least one of the following ten association relationships:
  • a first association relationship where the first association relationship indicates that the model inputs of multiple AI models are the same.
  • the same model input may include the same type of model input and/or the same format of model input.
  • the type of model input may be the specific information of the model input, such as a time domain channel impulse response.
  • the format of the model input may be the arrangement of the model input, etc.
  • the format of the model input may be the number of carrier to interference ratio (CIR) sampling points associated with each transmission and reception point (TRP), the arrangement of CIR, the arrangement of time domain channel impulse responses of different TRP IDs, the number of sampling points of the time domain channel impulse response, etc.
  • CIR carrier to interference ratio
  • Model A is a positioning model
  • the input is the channel impulse response of multiple TRPs
  • the output is the UE position.
  • Model B is used to supervise the effectiveness of Model A
  • the input is also the channel impulse response of multiple TRPs
  • the output is the confidence that Model A is valid under the current input. Therefore, the model inputs of Model A and Model B are the same.
  • the model inputs of other AI models can be determined based on the model inputs of one of the AI models. In this way, there is no need to indicate the model inputs of other AI models, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A and model B with the same model input to device 2, then device 1 can indicate model A to device 2 and indicate that the model inputs of model A and model B are the same.
  • Device 2 can determine the model input of model A based on model B. In this way, device 1 does not need to indicate the model input of model B, thereby avoiding repeated transmission of the same model input, reducing transmission resources, and avoiding waste of transmission resources.
  • the indication information when the indication information indicates the first association relationship, that is, when indicating that the model inputs of the multiple AI models are the same, the indication information may further indicate the difference between other model information of the multiple AI models except the model inputs, so that the model information of one of the AI models and the model information of the multiple AI models can be used to distinguish the differences between the model information of the multiple AI models.
  • the distinction between the model information determines the model information of the remaining AI models.
  • the same model output may include the same type of model output and/or the same format of model output.
  • the type of model output may be what information the model output specifically is, such as position coordinates, etc.
  • the format of model output may be the dimension of model output, etc. For example, when the model output is a coordinate position, the format of the model output may be that the first coordinate position represents the first dimension, and the second coordinate position represents the second dimension.
  • model A is the positioning model of scene A
  • model B is the positioning model of scene B
  • the input of model A is the CIR information of N TRPs in scene A, and the output is the position.
  • the input of model B is the CIR information of M TRPs in scene B, and the output is also the position. Therefore, the model outputs of model A and model B are the same.
  • the model outputs of other AI models can be determined based on the model output of one of the AI models. In this way, there is no need to indicate the model outputs of other AI models, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • model B has been pre-configured in device 2, and device 1 wants to indicate model A to device 2, and the model output of model A is the same as the model output of model B. Then, when device 1 indicates model A to device 2, it can indicate that the model outputs of model A and model B are the same, and device 2 can determine the model output of model A based on model B. In this way, device 1 does not need to indicate the model output of model A, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information when the indication information indicates a second association relationship, that is, indicating that the model outputs of multiple AI models are the same, the indication information may further indicate the difference between other model information of the multiple AI models except the model outputs, so that the model information of the remaining AI models can be determined based on the model information of one of the AI models and the difference between the model information of multiple AI models.
  • the third association relationship represents that the model input and model output of multiple AI models are the same.
  • the same model inputs may include the same model input types and/or the same model input formats.
  • the same model outputs may include the same model output types and/or the same model output formats.
  • Model A and Model B are two positioning models of different complexity.
  • the structures of the middle layers of the two models are different, but the input is the CIR information of N TRPs, and the output is the location information. Therefore, the model input and model output of Model A and Model B are the same.
  • the model inputs and outputs of other AI models can be determined based on the model inputs and outputs of one of the AI models. In this way, there is no need to indicate the model inputs and outputs of other AI models, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A and model B with the same model input and output to device 2, then device 1 can indicate model A to device 2 and indicate that the model input and output of model A and model B are the same.
  • Device 2 can determine the model input and output of model A based on model B. In this way, device 1 can indicate model A to device 2.
  • model A there is no need to indicate the model input and output of model B, thereby avoiding repeated transmission of the same model input and output, reducing transmission resources, and avoiding waste of transmission resources.
  • the indication information when the indication information indicates a third association relationship, that is, indicating that the model inputs and outputs of multiple AI models are the same, the indication information may further indicate the differences between other model information of the multiple AI models except for the model inputs and outputs. In this way, the model information of the remaining AI models can be determined based on the model information of one of the AI models and the differences between the model information of multiple AI models.
  • the same model structure includes but is not limited to the same number of model layers, and/or the same number of elements included in the model layers, and/or the same type of model layers, and/or the same type of activation functions (if any) in the model layers.
  • the same model structure of multiple AI models may include at least one of the following:
  • the number of layers of neurons is the same;
  • the number of neurons included in each layer is the same;
  • the type of activation function included in each layer of neurons is the same;
  • the type of neurons in each layer (e.g., batch normalization layer, layer normalization layer, convolutional layer, etc.) is the same.
  • Model A and Model B are both models in the model pool, and their functions are positioning. They contain the same number of convolutional layers, but Model A and Model B correspond to different usage scenarios and have different parameters of the convolutional layers, such as different positioning reference signal (PRS) configurations. Then Model A and Model B have the same structure.
  • PRS positioning reference signal
  • the structures of other AI models can be determined based on the model structure of one of the AI models. In this way, there is no need to indicate the model structures of other AI models. That is, when transmitting the model, there is no need to transmit the model structures of other AI models, but only the parameters corresponding to the model structure. This can reduce the transmission resources consumed when indicating the model and avoid waste of transmission resources.
  • device 1 wants to indicate model A to device 2, and model B is pre-configured in device 2.
  • Model A and model B have the same model structure.
  • device 1 indicates model A, it can indicate that the model structures of model A and model B are the same.
  • Device 2 can determine the model structure of model A based on model B. In this way, device 1 does not need to transmit the model structure information of model A, thereby reducing transmission resources and avoiding waste of transmission resources.
  • device 1 sends a model pool to device 2.
  • the model pool contains multiple models. These models have the same model structure but different model parameters. If the model configured in device 2 fails, device 1 can update the failed model by only configuring the parameters.
  • the indication information when the indication information indicates a fourth association relationship, that is, indicating that the model structures of multiple AI models are the same, the indication information may further indicate the difference between other model information of the multiple AI models except the model structure, so that the model information of the remaining AI models can be determined based on the model information of one of the AI models and the difference between the model information of multiple AI models.
  • the fifth association relationship represents that some model structures of multiple AI models are the same.
  • model structures of multiple AI models have both common and different parts.
  • the common partial structures of model A and model B can be:
  • the structure of the first N1 model layers of model A is the same as the structure of the first N1 model layers of model B, and N1 is less than the total number of layers M1 of model A and less than the total number of layers M2 of model B; or,
  • the number of model layers of model A is the same as the number of model layers of model B, but the number of neurons in each model layer of model A is smaller than the number of neurons in each model layer of model B.
  • Model A and Model B may be the same in other cases besides the above three cases, which will not be explained one by one here.
  • model A is a positioning model
  • the input is the channel impulse response of multiple TRPs
  • the output is the UE position.
  • Model B is used to supervise the effectiveness of model A
  • the input is also the channel impulse response of multiple TRPs
  • the output is the confidence that model A is effective under the current input.
  • Only the last layer structure is different between model B and model A.
  • the number of neurons in the last layer of model B is changed from 2 dimensions of model A to 1 dimension, and the activation function is changed from linear to sigmoid.
  • some structures of model A and model B are the same.
  • model A and model B also have the above-mentioned first association relationship and second association relationship.
  • the same partial structures of other AI models can be determined based on the model structure of one of the AI models. In this way, there is no need to indicate the same model structure parts of other AI models, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A to device 2, and model B is pre-configured in device 2.
  • Model A and model B have some identical model structures.
  • device 1 when device 1 indicates model A, it can indicate that model A and model B have some identical model structures.
  • Device 2 can determine the partial model structure of model A based on model B. In this way, device 1 does not need to transmit the model structure information in model A that is identical to that in model B, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information when the indication information indicates the fifth association relationship, that is, indicating that some model structures of multiple AI models are the same, the indication information may further indicate the difference between other model information of the multiple AI models except for the partial model structure, so that the model information of the remaining AI models can be determined based on the model information of one of the AI models and the difference between the model information of multiple AI models.
  • the indication information may also include the differences in the model structures of the multiple AI models.
  • the model structures of the remaining models may be determined based on the model structure of one of the AI models and the same and different parts of the structures of the multiple models indicated by the indication information.
  • the indication information can also indicate the difference between the last (M1-N1) model layers of model A and the last (M2-N1) model layers of model B.
  • the last (M1-N1) model layers of model A each contain 1 neuron
  • the last (M2-N1) model layers of model B each contain 2 neurons.
  • the sixth association relationship represents that some model structures and corresponding parameters of multiple AI models are the same.
  • the sixth association relationship not only indicates that the partial structures of multiple AI models are the same, but also further indicates that the parameters corresponding to the same partial structures are the same.
  • the parameters here can be model parameters corresponding to the model structure.
  • the parameters corresponding to the model structure can be the weights and biases of the model structure.
  • an AI model trained in one scenario can be migrated to another similar scenario.
  • the model structure and parameters of the first N layers of the original model can be kept unchanged, and only the structure and corresponding parameters of the last M layers of the original model are changed.
  • the first N layer structures and corresponding parameters between the original model and the retrained new model are the same.
  • the same partial structures and corresponding parameters of other AI models can be determined based on the model structure and corresponding parameters of one of the AI models. In this way, there is no need to indicate the same model structure parts and corresponding parameters of other AI models, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A to device 2, and model B is pre-configured in device 2. Some model structures and corresponding parameters of model A and model B are the same. Then, when device 1 indicates model A, it can indicate that some model structures and corresponding parameters of model A and model B are the same. Device 2 can determine some model structures and corresponding parameters of model A based on model B. In this way, device 1 does not need to transmit the information of model structure and corresponding parameters in model A that are the same as those in model B, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information when the indication information indicates the sixth association relationship, that is, indicating that some model structures and corresponding parameters of multiple AI models are the same, the indication information may further indicate the difference between other model information of multiple AI models except for the partial model structure and corresponding parameters. In this way, the model information of the remaining AI models can be determined based on the model information of one of the AI models and the difference between the model information of multiple AI models.
  • the indication information may also include the differences in the model structures of the multiple AI models.
  • the model structures of the remaining models may be determined based on the model structure of one of the AI models and the same and different parts of the structures of the multiple models indicated by the indication information.
  • the seventh relationship represents that the model structures and corresponding parameters of multiple AI models are the same.
  • the multiple AI models can be considered to be the same model.
  • the models corresponding to multiple UEs in the same scene may be exactly the same.
  • the structures and corresponding parameters of other AI models can be determined based on the model structure and corresponding parameters of one AI model, so that there is no need to The structure and corresponding parameters of other AI models can be indicated, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A to device 2, and model B is pre-configured in device 2.
  • the model structures and corresponding parameters of model A and model B are the same.
  • device 1 when device 1 indicates model A, it can indicate that the model structures and corresponding parameters of model A and model B are the same.
  • Device 2 can determine the model structure and corresponding parameters of model A based on model B. In this way, device 1 does not need to transmit the information of the model structure and corresponding parameters of model A, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information may further indicate the difference between other model information of the multiple AI models except the model structure and corresponding parameters, so that the model information of the remaining AI models can be determined based on the model information of one of the AI models and the difference between the model information of multiple AI models.
  • An eighth association relationship represents that the first model among the multiple AI models is a sub-model of the second model among the multiple AI models.
  • the first model is a sub-model of the second model, which can be considered as a part of the second model.
  • model A can be the first N3 model layers of model B, and the structures and corresponding parameters of the first N3 model layers are the same.
  • N3 is less than the total number of model layers of model B.
  • model A is its first N+1 layers of encoder part or the last M+1 layers of decoder part, then model A is a submodel of model B.
  • the first model can be determined according to the second model, so there is no need to indicate the first model.
  • some structures and parameters in the second model can be determined according to the first model, so there is no need to indicate the parts of the second model that are the same as the first model, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A to device 2, and model B is pre-configured in device 2, and model A is a sub-model of model B. Then, when device 1 indicates model A, it can indicate that model A is a sub-model of model B. Device 2 can determine model A based on model B. In this way, device 1 does not need to transmit model A, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information may also include the first part in the second model, the structure of the first part is the same as the structure of the first model, and the parameters of the first part are the same as the parameters of the first model. That is to say, when the indication information indicates the eighth association relationship, it may also indicate which part of the second model the first model is, and the part is the first part.
  • the first model is the first N layers of the second model, the encoder, or the last M layers of the second model, the decoder, etc. In this way, the specific structure and specific parameters of the first model can be determined according to the second model, or the structure and corresponding parameters of the part of the second model that is the same as the first model can be determined according to the first model.
  • the indication information may further include a model portion of the second model that is different from the first model, and the remaining portion is the same as the first model.
  • the specific structure and specific parameters of the first model may also be determined based on the second model, or the second model may be determined based on the first model.
  • a ninth association relationship wherein the ninth association relationship represents that the structure of the third model among the multiple AI models is a substructure of the fourth model among the multiple AI models.
  • the third model is a substructure of the fourth model. It can be considered that the third model has the same structure as a part of the fourth model, but the corresponding parameters are different.
  • the structure of model A is the same as the structure of the first N3 model layers of model B, but the parameters of model A are different from the parameters of the first N3 model layers of model B.
  • N3 is less than the total number of model layers of model B.
  • a possible application scenario may be to select a part of the complex model B structure as the structure of model A to reduce the complexity of the model.
  • the structure of the third model can be determined based on the structure of the fourth model, so that there is no need to indicate the structure of the third model.
  • part of the structure in the fourth model can be determined based on the third model, so that there is no need to indicate the part of the fourth model that has the same structure as the third model, thereby reducing the transmission resources consumed during model indication and avoiding waste of transmission resources.
  • device 1 wants to indicate model A to device 2, and model B is pre-configured in device 2.
  • the structure of model A is a substructure of model B.
  • device 1 indicates model A, it can indicate that model A is a substructure of model B.
  • Device 2 determines the structure of model A based on the structure of model B. In this way, device 1 does not need to transmit the structural information of model A, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information can also be used for the second part in the fourth model, the structure of the second part is the same as the structure of the third model, and the parameters of the second part are different from the parameters of the third model. That is to say, when the indication information indicates the ninth association relationship, it can also indicate which part of the fourth model the third model is, and this part is the second part.
  • the third model is the first N layers, encoder of the fourth model, or the last M layers, decoder, etc. of the fourth model, but the corresponding parameters are different. In this way, the specific structure of the third model can be determined according to the fourth model, or the structure of the part of the model in the fourth model that has the same structure as the third model can be determined according to the structure of the third model.
  • the indication information may further include structural parts of the fourth model that are different from the third model, and the remaining structural parts are the same as the third model.
  • the specific structure of the third model may be determined based on the fourth model, or the structure of the fourth model may be determined based on the structure of the third model.
  • the tenth association relationship represents that the output of the fifth model among the multiple models is the input of the sixth model among the multiple models.
  • the fifth model is the input of the sixth model. It can be considered that the fifth model and the sixth model are in a cascade relationship, and the output of the fifth model can be used as the input of the sixth model.
  • the original CSI can be first predicted by model A and then input to model B for compression.
  • model A and model B are Model B is a cascade relationship, and the output of model A is the input of model B.
  • device 1 wants to indicate model C to device 2.
  • Model A and model B are pre-configured in device 2.
  • Model C is a combination of model A and model B, and the model output of model A is the model input of model B.
  • device 1 indicates model C, it can indicate that the model output of model A is the model input of model B.
  • Device 2 can determine model C based on the instruction of device 1 and models A and B. In this way, device 1 does not need to transmit model C, thereby reducing transmission resources and avoiding waste of transmission resources.
  • the indication information may indicate any one or more combinations thereof, which may be specifically determined according to the actual application scenario and is not specifically limited here.
  • the indication information may also indicate the identification information of the AI model when indicating at least one of the above ten association relationships, and the identification information of the AI model may be used to determine multiple AI models with association relationships.
  • the indication information when indicating the identification information of the AI model, may include at least one of the following:
  • a first list wherein the first list includes identification information of multiple AI models arranged in a specified order, and the specified order corresponds to an association order of the association relationship.
  • the specified order may be the order of precedence, top-bottom order, etc. of the identification information of the AI models in the first list.
  • the association order of the association relationship may be the order of precedence in which the AI models have the association relationship.
  • the first list contains identification information of two AI models, namely model A and model B.
  • Model A and model B have a sequence, that is, model A comes first and model B comes later.
  • the association relationship indicated by the indication information is the eighth association relationship mentioned above, in the eighth association relationship, the association order is the first model in front and the second model in the back, it can be determined that model A is a submodel of model B.
  • the indication information when used to indicate at least one of the above-mentioned eighth, ninth and tenth association relationships, the indication information may include a first list, so that the communication device that receives the indication information can easily determine which AI model is a sub-model of other AI models, or which AI model is a sub-structure of other AI models, or which AI model's output is the input of other AI models.
  • the remaining AI models may be models pre-agreed upon by the communicating parties.
  • the indication information includes identification information of model A
  • model B is a model pre-agreed upon by the communicating parties
  • the indication information is used for the above-mentioned first association relationship.
  • the indication information is specifically used to indicate that the model input of model A is the same as the model input of model B. According to the indication information, it can be determined that the model input of model A is the pre-agreed input of model B.
  • the remaining AI models may also be AI models that the communication device that receives the indication information needs to determine or construct.
  • the indication information includes identification information of model A, and the indication information is used to indicate the ninth association relationship.
  • the indication information is specifically used to indicate that there is a ninth association relationship between model B and model A.
  • the communication device can determine or construct model B based on the indication information, and model B is a substructure of model A.
  • the indication information can indicate the identification information of multiple AI models with associated relationships, and at the same time indicate what kind of association the multiple AI models have.
  • the indication information can include the identification information of model A and the identification information of model B.
  • the indication information also indicates that the model outputs of model A and model B are the same, and that some structures of model A and model B are the same.
  • the technical solution provided in the embodiment of the present application can avoid the problem of waste of transmission resources caused by repeated transmission of model features when the AI model is transmitted or the AI model information is indicated.
  • AI models such as model input, model output, structure and parameters, etc.
  • indication information it can potentially indicate the association relationship between the models, thereby providing a more flexible operation method for the joint processing of multiple AI-based applications.
  • the association relationship between multiple AI models can be indicated through the indication information, so that the model information that needs to be indicated can be determined through the association relationship between the AI models, thereby realizing the indication of the AI model.
  • the traditional model indication since it is not necessary to indicate all the model information, it is possible to reduce transmission resources and avoid the waste of transmission resources.
  • the model indication method provided in the embodiment of the present application can be executed by a model indication device.
  • the model indication device provided in the embodiment of the present application is described by taking the execution of the model indication method by the model indication device as an example.
  • Fig. 3 is a schematic diagram of the structure of an AI model indication device according to an embodiment of the present application, which may correspond to a communication device in other embodiments. As shown in Fig. 3, the device 300 includes the following modules.
  • the communication module 301 is used to send or receive indication information, where the indication information is used to indicate the association relationship between multiple artificial intelligence AI models.
  • the association relationship includes at least one of the following:
  • a first association relationship wherein the first association relationship represents that model inputs of the multiple AI models are the same;
  • a second association relationship wherein the second association relationship represents that the model outputs of the multiple AI models are the same;
  • a third association relationship wherein the third association relationship represents that the model inputs and model outputs of the multiple AI models are the same;
  • a fourth association relationship wherein the fourth association relationship represents that the model structures of the multiple AI models are the same;
  • a fifth association relationship wherein the fifth association relationship represents that some model structures of the multiple AI models are the same;
  • a sixth association relationship wherein the sixth association relationship represents that some model structures and corresponding parameters of the multiple AI models are the same;
  • a seventh association relationship wherein the seventh association relationship represents that the model structures and corresponding parameters of the multiple AI models are the same;
  • an eighth association relationship wherein the eighth association relationship represents that the first model among the multiple AI models is a submodel of the second model among the multiple AI models;
  • the ninth association relationship represents that a structure of a third model among the multiple AI models is a substructure of a fourth model among the multiple AI models;
  • the tenth association relationship represents that the output of the fifth model among the multiple AI models is the input of the sixth model among the multiple AI models.
  • model input is the same, including at least one of the following:
  • the model inputs are of the same type
  • the format of the model inputs is the same.
  • model outputs are the same, including at least one of the following:
  • the model outputs are of the same type
  • the model outputs are in the same format.
  • the indication information is also used to indicate the difference in model structures of the multiple AI models.
  • the indication information when the association relationship includes the eighth association relationship, also includes a first part in the second model, a structure of the first part is the same as a model structure of the first model, and parameters of the first part are the same as parameters of the first model.
  • the indication information when the association relationship includes the ninth association relationship, the indication information also includes the second part in the fourth model, the structure of the second part is the same as the model structure of the third model, and the parameters of the second part are different from the parameters of the third model.
  • the indication information includes at least one of the following:
  • a first list wherein the first list includes identification information of the plurality of AI models arranged in a specified order, and the specified order corresponds to an association order of the association relationships;
  • Identification information of some AI models among the multiple AI models, and the remaining AI models have the association relationship with the some AI models
  • the identification information of the multiple AI models and the association relationship between the multiple AI models are described.
  • the process of the method 200 corresponding to the embodiment of the present application can be referred to, and the various units/modules in the device 300 and the above-mentioned other operations and/or functions are respectively for implementing the corresponding processes in the method 200, and can achieve the same or equivalent technical effects. For the sake of brevity, they will not be repeated here.
  • the model indicating device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.
  • the electronic device can be a terminal, or it can be other devices other than a terminal.
  • the terminal can include but is not limited to the types of terminals 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.
  • the AI model indication device provided in the embodiment of the present application can implement each process implemented by the method embodiment of Figure 2 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the present embodiment also provides a communication device 400, including a processor 401 and a memory. 402, the memory 402 stores a program or instruction that can be run on the processor 401.
  • the program or instruction is executed by the processor 401 to implement the various steps of the above-mentioned AI model indication method embodiment, and can achieve the same technical effect.
  • the communication device 400 is a network side device, the program or instruction is executed by the processor 401 to implement the various steps of the above-mentioned AI model indication method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application also provides a communication device, including a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a program or instruction to implement the steps in the method embodiment shown in Figure 2.
  • the communication device embodiment corresponds to the above-mentioned communication device side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the communication device embodiment and can achieve the same technical effect.
  • Figure 5 is a schematic diagram of the hardware structure of a communication device implementing an embodiment of the present application.
  • the communication device 500 includes but is not limited to: a radio frequency unit 501, a network module 502, an audio output unit 503, an input unit 504, a sensor 505, a display unit 506, a user input unit 507, an interface unit 508, a memory 509 and at least some of the components of a processor 510.
  • the communication device 500 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 5 10 through a power management system, so that the power management system can manage charging, discharging, and power consumption.
  • a power source such as a battery
  • the communication device structure shown in FIG5 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.
  • the input unit 504 may include a graphics processing unit (GPU) 5041 and a microphone 5042, and the graphics processor 5041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode.
  • the display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 507 includes a touch panel 5071 and at least one of other input devices 5072.
  • the touch panel 5071 is also called a touch screen.
  • the touch panel 5071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.
  • the radio frequency unit 501 after receiving downlink data from the network side device, can transmit the data to the processor 510 for processing; in addition, the radio frequency unit 501 can send uplink data to the network side device.
  • the radio frequency unit 501 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 509 can be used to store software programs or instructions and various data.
  • the memory 509 can mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area can store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc.
  • the memory 509 can include a volatile memory or a non-volatile memory.
  • the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (ROM), or a programmable read-only memory (PROM).
  • the volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM).
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • DRRAM direct memory bus random access memory
  • the processor 510 may include one or more processing units; optionally, the processor 510 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 510.
  • the radio frequency unit 501 is used to send or receive indication information, and the indication information is used to indicate the association relationship between multiple artificial intelligence AI models.
  • the association relationship between multiple AI models can be indicated through the indication information, so that the model information that needs to be indicated can be determined through the association relationship between the AI models, thereby realizing the indication of the AI model.
  • the traditional model indication since it is not necessary to indicate all the model information, it is possible to reduce transmission resources and avoid the waste of transmission resources.
  • the communication device 500 provided in the embodiment of the present application can also implement the various processes of the embodiment shown in Figure 2 above, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application also provides a communication device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method embodiment shown in Figure 2.
  • the communication device embodiment corresponds to the above communication device method embodiment, and each implementation process and implementation method of the above method embodiment can be applied to the communication device embodiment, and can achieve the same technical effect.
  • the communication device 600 includes: an antenna 61, a radio frequency device 62, a baseband device 63, a processor 64, and a memory 65.
  • the antenna 61 is connected to the radio frequency device 62.
  • the radio frequency device 62 receives information through the antenna 61 and sends the received information to the baseband device 63 for processing.
  • the baseband device 63 processes the information to be sent and sends it to the radio frequency device 62.
  • the radio frequency device 62 processes the received information and sends it out through the antenna 61.
  • the method executed by the communication device in the above embodiment may be implemented in the baseband device 63, which includes a baseband processor.
  • the baseband device 63 may include, for example, at least one baseband board on which a plurality of chips are arranged. As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 65 through a bus interface to call the program in the memory 65 to execute the communication device operations shown in the above method embodiment.
  • the communication device may also include a network interface 66, which is, for example, a Common Public Radio Interface (CPRI).
  • a network interface 66 which is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the communication device 600 of the embodiment of the present application also includes: instructions or programs stored in the memory 65 and executable on the processor 64.
  • the processor 64 calls the instructions or programs in the memory 65 to execute the methods executed by the modules shown in Figure 3 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored.
  • a program or instruction is stored.
  • the various processes of the above-mentioned AI model indication method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the processor is a processor in the communication device described in the above embodiment.
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.
  • the readable storage medium may be a non-transient readable storage medium.
  • An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned AI model indication method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
  • the embodiments of the present application further provide a computer program/program product, which is stored in a storage medium.
  • the computer program/program product is executed by at least one processor to implement the various processes of the above-mentioned AI model indication method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the above-mentioned embodiment method can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the computer software product is stored in a storage medium (such as ROM, RAM, disk, CD, etc.), including several instructions to enable the terminal or network side device to execute the method described in each embodiment of the present application.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente demande appartient au domaine technique des communications. Sont divulgués un procédé d'indication de modèle d'IA et un dispositif de communication. Le procédé d'indication de modèle d'IA dans les modes de réalisation de la présente demande comprend : un dispositif de communication envoie ou reçoit des informations d'indication, les informations d'indication étant utilisées pour indiquer une relation d'association entre une pluralité de modèles d'IA.
PCT/CN2024/076413 2023-02-13 2024-02-06 Procédé d'indication de modèle d'ia et dispositif de communication Ceased WO2024169797A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202310114133.0 2023-02-13
CN202310114133.0A CN118487952A (zh) 2023-02-13 2023-02-13 Ai模型指示方法及通信设备

Publications (1)

Publication Number Publication Date
WO2024169797A1 true WO2024169797A1 (fr) 2024-08-22

Family

ID=92195475

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/076413 Ceased WO2024169797A1 (fr) 2023-02-13 2024-02-06 Procédé d'indication de modèle d'ia et dispositif de communication

Country Status (2)

Country Link
CN (1) CN118487952A (fr)
WO (1) WO2024169797A1 (fr)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111930346A (zh) * 2020-07-15 2020-11-13 北京百度网讯科技有限公司 人工智能信息的处理方法、装置、电子设备和存储介质
US20210248447A1 (en) * 2020-02-12 2021-08-12 Palantir Technologies Inc. System and method for chaining discrete models
CN113377503A (zh) * 2020-03-09 2021-09-10 阿尔法云计算(深圳)有限公司 一种协作式ai的任务调度方法、装置与系统
US11119630B1 (en) * 2018-06-19 2021-09-14 Palantir Technologies Inc. Artificial intelligence assisted evaluations and user interface for same
US20220318640A1 (en) * 2021-03-31 2022-10-06 Fujitsu Limited Automated empathetic reconciliation of decisions of artificial intelligence (ai) models
CN115460100A (zh) * 2021-06-09 2022-12-09 华为技术有限公司 一种应用人工智能的通信方法及通信装置
CN115511085A (zh) * 2022-10-31 2022-12-23 上海浦东发展银行股份有限公司 一种模型数据处理方法、装置、设备及存储介质

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11119630B1 (en) * 2018-06-19 2021-09-14 Palantir Technologies Inc. Artificial intelligence assisted evaluations and user interface for same
US20210248447A1 (en) * 2020-02-12 2021-08-12 Palantir Technologies Inc. System and method for chaining discrete models
CN113377503A (zh) * 2020-03-09 2021-09-10 阿尔法云计算(深圳)有限公司 一种协作式ai的任务调度方法、装置与系统
CN111930346A (zh) * 2020-07-15 2020-11-13 北京百度网讯科技有限公司 人工智能信息的处理方法、装置、电子设备和存储介质
US20220318640A1 (en) * 2021-03-31 2022-10-06 Fujitsu Limited Automated empathetic reconciliation of decisions of artificial intelligence (ai) models
CN115460100A (zh) * 2021-06-09 2022-12-09 华为技术有限公司 一种应用人工智能的通信方法及通信装置
CN115511085A (zh) * 2022-10-31 2022-12-23 上海浦东发展银行股份有限公司 一种模型数据处理方法、装置、设备及存储介质

Also Published As

Publication number Publication date
CN118487952A (zh) 2024-08-13

Similar Documents

Publication Publication Date Title
WO2023066288A1 (fr) Procédé de demande de modèle, procédé de traitement de demande de modèle et dispositif associé
WO2023160547A1 (fr) Procédé de réponse aux informations, procédé d'envoi d'informations, terminal et dispositif côté réseau
WO2023143581A1 (fr) Procédé et appareil de transmission d'information, et terminal et périphérique de reseau
US20240405955A1 (en) Information activation method, terminal, and network side device
US20240330776A1 (en) Client screening method and apparatus, client, and central device
WO2024022316A1 (fr) Procédé d'envoi de signal de référence de liaison descendante, appareil, terminal et dispositif côté réseau
WO2024067281A1 (fr) Procédé et appareil de traitement de modèle d'ia, et dispositif de communication
WO2024012239A1 (fr) Procédé de traitement de configuration de condition, appareil de traitement de configuration de condition et terminal
WO2023174253A1 (fr) Procédé et dispositif de traitement de modèle d'ia
WO2024169797A1 (fr) Procédé d'indication de modèle d'ia et dispositif de communication
WO2023165393A1 (fr) Procédé et dispositif de transmission, terminal, ainsi que support d'enregistrement
WO2023098535A1 (fr) Procédé et appareil d'interaction d'informations, et dispositif de communication
WO2023109759A1 (fr) Procédé et appareil de transmission prach, et terminal
EP4451740A1 (fr) Procédé de détermination de préférence de trajet, terminal et dispositif côté réseau
US20250036978A1 (en) Model fine-tuning method and apparatus, and device
WO2024067878A1 (fr) Procédé et appareil de prédiction de canal, et dispositif de communication
WO2024235043A1 (fr) Procédé et appareil d'acquisition d'informations, et dispositif de communication
CN120238945A (zh) 上报方法、传输方法、装置、终端及网络侧设备
WO2025108288A1 (fr) Procédé et appareil de surveillance de modèle, et dispositif de communication
CN120282160A (zh) Ai单元的激活方法、终端及网络侧设备
WO2024061287A1 (fr) Procédé et appareil de transmission de modèle d'intelligence artificielle (ai), terminal et support
WO2024169794A1 (fr) Procédé de supervision de modèle et dispositif de communication
WO2025140432A1 (fr) Procédé et appareil de création de compte-rendu d'informations
WO2025140604A1 (fr) Procédés et appareils de rapport d'informations, et premiers dispositifs et seconds dispositifs
WO2025157103A1 (fr) Procédés et appareils de traitement de mesure rrm, terminal et dispositif côté réseau

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24756170

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE