WO2024083004A1 - Procédé de configuration de modèle d'ia, terminal et dispositif côté réseau - Google Patents

Procédé de configuration de modèle d'ia, terminal et dispositif côté réseau Download PDF

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Publication number
WO2024083004A1
WO2024083004A1 PCT/CN2023/123880 CN2023123880W WO2024083004A1 WO 2024083004 A1 WO2024083004 A1 WO 2024083004A1 CN 2023123880 W CN2023123880 W CN 2023123880W WO 2024083004 A1 WO2024083004 A1 WO 2024083004A1
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Prior art keywords
model
information
meta
configuration
target
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PCT/CN2023/123880
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English (en)
Chinese (zh)
Inventor
贾承璐
邬华明
孙鹏
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维沃移动通信有限公司
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Publication of WO2024083004A1 publication Critical patent/WO2024083004A1/fr

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    • 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
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to an AI model configuration method, terminal and network side equipment.
  • AI artificial intelligence
  • the embodiments of the present application provide an AI model configuration method, a terminal, and a network-side device, which can solve the problem of poor reliability of model output results.
  • an AI model configuration method comprising:
  • the terminal receives configuration information from a network side device, the configuration information including at least two sets of configuration parameters, the configuration parameters including first information and model meta-information associated with the first information, the first information is used to construct an AI model, and the model meta-information is used to indicate a network environment in which the AI model constructed by the first information runs;
  • the terminal determines, according to the model meta-information, a target AI model to be used in an AI model constructed based on the first information.
  • an AI model configuration method including:
  • the network side device sends configuration information to the terminal, the configuration information includes at least two sets of configuration parameters, the configuration parameters include first information and model meta-information associated with the first information, the first information is used to construct an AI model, and the model meta-information is used to represent the network environment in which the AI model constructed by the first information runs.
  • an AI model configuration device comprising:
  • a receiving module configured to receive configuration information from a network side device, the configuration information including at least two sets of configuration parameters, the configuration parameters including first information and model meta-information associated with the first information, the first information being used to construct an AI model, and the model meta-information being used to indicate a network environment in which the AI model constructed by the first information runs;
  • a determination module is used to determine a target AI model to be used in an AI model constructed based on the first information according to the model meta-information.
  • an AI model configuration device comprising:
  • a sending module is used to send configuration information to a terminal, wherein the configuration information includes at least two sets of configuration parameters, wherein the configuration parameters include first information and model meta-information associated with the first information, wherein the first information is used to construct an AI model, and the model meta-information is used to represent a network environment in which the AI model constructed by the first information runs.
  • a terminal comprising a processor and a memory, wherein the memory stores a program or instruction that can be run 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 terminal comprising a processor and a communication interface, wherein the communication interface is used to receive configuration information from a network side device, the configuration information comprising at least two sets of configuration parameters, the configuration parameters comprising first information and model meta-information associated with the first information, the first information being used to construct an AI model, and the model meta-information being used to represent the network environment in which the AI model constructed by the first information runs; the processor is used to determine, according to the model meta-information, a target AI model to be used in the AI model constructed based on the first information.
  • a network side device which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.
  • a network side device including a processor and a communication interface, wherein the communication interface is used to send configuration information to a terminal, the configuration information includes at least two sets of configuration parameters, the configuration parameters include first information and model meta-information associated with the first information, the first information is used to construct an AI model, and the model meta-information is used to represent the network environment in which the AI model constructed by the first information runs.
  • a communication system comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the AI model configuration method as described in the first aspect, and the network side device can be used to execute the steps of the AI model configuration method as described in the second aspect.
  • 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, or the steps of the method described in the second 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 instructions to implement the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.
  • a computer program/program product is provided, wherein the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.
  • the terminal receives configuration information from the network side device, and the configuration information includes at least two sets of configuration parameters, and the configuration parameters include first information and model meta information associated with the first information, and the model meta information is used to represent the network environment in which the AI model constructed by the first information runs.
  • the terminal can determine whether the current AI model is valid based on the model meta information and the current network environment information, and if the current AI model is invalid, it can further select a target AI model suitable for the current network environment information to perform corresponding operations, thereby avoiding Since the accuracy of AI model output results decreases due to changes in the network environment, the reliability of AI model use is improved.
  • FIG1 is a schematic diagram of a network structure applicable to an embodiment of the present application.
  • FIG2 is a schematic diagram of the structure of neurons of a neural network applicable to an embodiment of the present application.
  • FIG3 is a flow chart of an AI model configuration method provided in an embodiment of the present application.
  • FIG4 is a flow chart of another AI model configuration method provided in an embodiment of the present application.
  • FIG5 is a structural diagram of an AI model configuration device provided in an embodiment of the present application.
  • FIG6 is a structural diagram of another AI model configuration device provided in an embodiment of the present application.
  • FIG7 is a structural diagram of a communication device provided in an embodiment of the present application.
  • FIG8 is a structural diagram of a terminal provided in an embodiment of the present application.
  • FIG. 9 is a structural diagram of a network-side device provided in an embodiment of the present application.
  • first, second, etc. in the specification and claims 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 under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by “first” and “second” are generally of the same type, and the number of objects is not limited.
  • the first object can be one or more.
  • “and/or” in the specification and claims represents at least one of the connected objects, and the character “/" generally represents that the objects associated with each other are in an "or” relationship.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution
  • 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
  • FIG1 shows a block diagram of a wireless communication system applicable to the 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, or a network side device.
  • PDA Personal Digital Assistant
  • PDA netbook
  • ultra-mobile personal computer UMPC
  • MID mobile Internet Device
  • MID augmented reality
  • AR augmented reality
  • VR virtual reality
  • robot wearable device
  • VUE vehicle user equipment
  • pedestrian terminal Pedestrian User Equipment, PUE
  • smart home home equipment with wireless communication function, such as refrigerator, TV, washing machine or furniture, etc.
  • game console personal computer
  • personal computer personal computer, PC
  • wearable device includes: smart watch, smart bracelet, smart headset, smart glasses, smart jewelry (smart bracelet, smart bracelet, smart ring, smart necklace, smart anklet, smart anklet, etc.), smart wristband, smart clothing, etc.
  • the network side device 12 may include an access network device or a core network device, wherein the access network device may also be referred to as a radio access network device, a radio access network (RAN), 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 (WLAN) access point or a wireless fidelity (WiFi) node, etc.
  • WLAN wireless local area network
  • WiFi wireless fidelity
  • the base station may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home B node, a home evolved B node, a transmitting receiving point (TRP) or other appropriate terms in the field, as long as the same technical effect is achieved, the base station is not limited to a specific technical vocabulary, it should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.
  • AI modules such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application takes neural networks as an example for illustration, but does not limit the specific type of AI modules.
  • a neural network is composed of neurons, and a schematic diagram of a neuron is shown in Figure 2.
  • z a 1 *w 1 + ⁇ +a 1 *w 1 + ⁇ +a k *w k +b, a 1 ,a 2 ,...a K are inputs
  • w is the weight (multiplicative coefficient)
  • b is the bias (additive coefficient)
  • ⁇ (.) is the activation function.
  • Common activation functions include the Sigmoid function, the hyperbolic tangent function (tanh), and the Rectified Linear Unit (ReLU).
  • the parameters of the neural network are optimized through optimization algorithms. Optimization algorithms are a type of algorithm that can help us minimize or maximize the objective function (sometimes called loss function).
  • the objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we build a neural network model f(.). With the model, we can get the predicted output f(x) based on the input x, and we can calculate the difference between the predicted value and the true value (f(x)-Y), which is the loss function. Our goal is to find a suitable W,b to minimize the value of the above loss function. The smaller the loss value, the closer our model is to the actual situation.
  • the common optimization algorithms are basically based on the error back propagation (BP) algorithm.
  • BP error back propagation
  • the basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of the signal and the back propagation of the error.
  • the input sample is transmitted from the input layer, processed by each hidden layer layer by layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, it will enter the error back propagation stage.
  • Error back propagation is to propagate the output error layer by layer through the hidden layer to the input layer in some form, and distribute the error to all units in each layer, so as to obtain the error signal of each layer unit, and this error signal is used as the basis for correcting the weights of each unit.
  • This process of adjusting the weights of each layer of the signal forward propagation and error back propagation is repeated.
  • the process of continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until the pre-set number of learning times is reached.
  • the actual network environment is ever-changing.
  • the AI model is configured, there may be a situation where the environment in which the model training data is collected does not match the current environment.
  • the positioning data is collected by the positioning reference unit (Positioning reference unit), and the signal-to-noise ratio (SNR) during collection is relatively high and the network synchronization error is very small.
  • the SNR signal-to-noise ratio
  • the AI model cannot accurately locate the terminal. For this reason, an AI model configuration method of this application is proposed.
  • an embodiment of the present application provides an AI model configuration method.
  • the AI model configuration method includes:
  • Step 301 The terminal receives configuration information from a network-side device, where the configuration information includes at least two sets of configuration parameters, where the configuration parameters include first information and model meta-information associated with the first information, where the first information is used to construct an AI model, and the model meta-information is used to indicate a network environment in which the AI model constructed by the first information runs;
  • Step 302 The terminal determines, according to the model meta-information, a target AI model to be used in an AI model constructed based on the first information.
  • the above-mentioned model meta-information can be understood or replaced by network environment information.
  • the model meta-information can represent the network environment in which the AI model corresponding to the associated model identifier (Identifier, ID) runs during the training phase or actual use.
  • the network environment may include network synchronization error, timing error, and communication link quality.
  • the above-mentioned first information can be understood as the construction parameters for constructing the AI model.
  • the specific content can refer to the relevant I will not go into details about the technology here.
  • each set of configuration parameters corresponds to an AI model
  • the above-mentioned at least two sets of configuration parameters can be carried in one signaling (for example, configured through an information element (IE)), or carried in at least two signalings. That is to say, the network side device can deploy or configure at least two AI models for the terminal through configuration information at the same time or at different times.
  • IE information element
  • determining the target AI model to be used can be understood as determining the activated target AI model.
  • the terminal determines the target AI model, it can activate the target AI model and perform corresponding operations (such as positioning operations) based on the target AI model.
  • the terminal receives configuration information from the network side device, and the configuration information includes at least two sets of configuration parameters, and the configuration parameters include first information and model meta information associated with the first information, and the model meta information is used to represent the network environment in which the AI model constructed by the first information runs.
  • the terminal can determine whether the current AI model is valid based on the model meta information and the current network environment information, and at the same time, if the current AI model is invalid, it can further select a target AI model suitable for the current network environment information to perform corresponding operations, thereby avoiding the reduction in the accuracy of the AI model output results due to changes in the network environment, thereby improving the reliability of the use of the AI model.
  • the terminal determines, according to the model meta-information, a target AI model to be used in an AI model constructed based on the first information, including:
  • the terminal receives second information from the network side device
  • the terminal determines, according to the second information and the model meta-information, the target AI model in the AI model constructed based on the first information;
  • the second information is used to determine the target model meta-information
  • the target model meta-information is the model meta-information associated with the model identifier of the target AI model.
  • the terminal after receiving the above-mentioned second information, can determine the target model meta-information in the model meta-information associated with the first information that matches the second information, thereby determining the target AI model of the AI model constructed by the first information corresponding to the target model meta-information.
  • the second information can be understood as network environment information indicated by the network side device or a signal used by the terminal to determine the network environment information. That is, in some embodiments, the second information includes at least one of the following:
  • first indication information where the first indication information is used to indicate relevant information of the model meta-information, and the relevant information is used to determine at least part of the model meta-information in the target model meta-information;
  • a first reference signal wherein the first reference signal is used to determine at least part of the model meta-information in the target model meta-information.
  • the model meta-information in the above configuration information can be understood as indicating the corresponding network environment during AI model training.
  • the above first indication information is used to indicate the current actual network environment.
  • the corresponding operation is performed based on the target AI model corresponding to the target model meta-information, which can ensure the reliability of the output result of the target AI model.
  • the above-mentioned first indication information may be at least one of a meta-information identifier, a model-associated signal-to-noise ratio, a model-associated signal-to-interference-plus-noise ratio, a model-associated reference signal received power, a model-associated reference signal received quality, a model-associated network synchronization error, and a model-associated timing error.
  • the second information may be the signal-to-noise ratio, that is, the actual signal-to-noise ratio in the current network environment.
  • the terminal may select the model meta-information whose signal-to-noise ratio is closest to the current actual signal-to-noise ratio in the model meta-information as the target model meta-information, thereby using the target AI model suitable for the current signal-to-noise ratio environment to perform related operations, thereby improving the reliability of the output results of the AI model.
  • the network side device may obtain the above-mentioned signal-to-noise ratio based on the measurement of the uplink signal, and then indicate the signal-to-noise ratio to the terminal through the first indication information.
  • the above-mentioned first reference signal can be understood as a downlink reference signal, that is, the terminal can measure the first reference signal to obtain the current actual network environment information, and by comparing the relationship between the measurement result of the first reference signal and the model meta-information, the target model meta-information that best matches the corresponding network environment information during training and the current actual network environment information can be determined.
  • the target AI model corresponding to the model ID associated with the target model meta-information performs corresponding operations to ensure the reliability of the output results of the target AI model.
  • the above-mentioned model meta-information can usually be associated with the model ID, so that the target AI model to be used can be determined based on the model meta-information.
  • the model meta information includes at least one of the following:
  • meta-information identifier wherein the meta-information identifier is associated with third information, wherein the third information includes at least one of a model-associated signal-to-noise ratio, a model-associated signal-to-interference-plus-noise ratio, a model-associated reference signal received power, a model-associated reference signal received quality, a model-associated network synchronization error, and a model-associated timing error;
  • SINR Signal to Interference plus Noise Ratio
  • the above-mentioned meta-information identifier is also associated with the model ID.
  • the terminal can obtain the SNR or SINR value of the first reference signal by measuring the first reference signal.
  • the SNR or SINR value is associated with the channel estimation quality. For example, the larger the SINR, the more accurate the channel estimation.
  • the estimated channel impulse response is used as the input of the AI model, and the positioning accuracy obtained is more accurate.
  • the terminal obtains the RSRP or RSRQ value of the first reference signal by measuring the first reference signal.
  • the RSRP or RSRQ value is associated with the channel estimation quality. For example, the larger the RSRQ, the more accurate the channel estimation.
  • the estimated channel impulse response is used as the input of the AI model, and the obtained positioning accuracy is more accurate.
  • the above-mentioned network synchronization error is used to indicate a network synchronization error range applicable to the AI model. For example, if the network synchronization error is 10ns, it indicates that a network synchronization error range applicable to the corresponding AI model is 0ns to 10ns.
  • the above timing error is used to indicate a timing error range applicable to the AI model. For example, if the timing error is 10ns, it indicates that the timing error range applicable to the corresponding AI model is 0ns to 10ns.
  • the configuration parameters further include at least one of the following:
  • the label error is associated with the reasoning error of the model, and the reasoning error of the model can be represented by a cumulative distribution function (CDF).
  • CDF cumulative distribution function
  • the reasoning error of the model can be a 90% CDF positioning error (or a 90% error, or a 90% position error).
  • the terminal can determine the performance of the model itself based on at least one of the label error and the inference error of the model to further determine whether the AI model is usable. This can further ensure the performance of the AI model used subsequently.
  • the target AI model is used for positioning.
  • determining the target AI model to be used can be understood as determining the AI model used for positioning.
  • the terminal can dynamically select or switch the AI model according to the second indication information and/or the measurement result of the first reference signal, thereby improving the quality of positioning services.
  • e x and e y are Gaussian distributions with mean 0 and standard deviation ⁇ respectively.
  • the above real location label can be called ground truth label.
  • the test data of the indoor factory in the frequency range 1 is shown in Table 1 below.
  • 50% can be understood as 50% CDF position error, which specifically means that in the corresponding cases, when the error is less than 0.35, 0.69, 1.06, 1.70 and 2.71, the proportion of users is 50%.
  • test data of the indoor factory in the frequency range 1 is shown in Tables 2 and 3 below.
  • the positioning error is greater than 10m:
  • the SINR of the CIR of the training set is large and the SINR of the test set is small, the performance degrades sharply; that is, if the CIR noise measured by the device running the model is large, then the positioning performance is difficult to guarantee.
  • test data of the indoor factory in the frequency range 1 is shown in Table 4 below.
  • an embodiment of the present application further provides an AI model configuration method.
  • the AI model configuration method includes:
  • Step 401 The network side device sends configuration information to the terminal, wherein the configuration information includes at least two sets of configuration parameters.
  • the configuration parameters include first information and model meta-information associated with the first information, the first information is used to construct an AI model, and the model meta-information is used to represent a network environment in which the AI model constructed by the first information runs.
  • the method further includes:
  • the network side device sends second information to the terminal, where the second information is used to determine target model meta-information, and the target model meta-information is model meta-information associated with the model identifier of the target AI model.
  • the second information includes at least one of the following:
  • first indication information where the first indication information is used to indicate relevant information of the model meta-information, and the relevant information is used to determine at least part of the model meta-information in the target model meta-information;
  • a first reference signal wherein the first reference signal is used to determine at least part of the model meta-information in the target model meta-information.
  • model meta information includes at least one of the following:
  • meta-information identifier wherein the meta-information identifier is associated with third information, wherein the third information includes at least one of a model-associated signal-to-noise ratio, a model-associated signal-to-interference-plus-noise ratio, a model-associated reference signal received power, a model-associated reference signal received quality, a model-associated network synchronization error, and a model-associated timing error;
  • the signal-to-interference-noise ratio associated with the model is the signal-to-interference-noise ratio associated with the model
  • the reference signal received power associated with the model
  • the configuration parameters also include at least one of the following:
  • the AI model is a model used for positioning.
  • the AI model configuration method provided in the embodiment of the present application can be executed by an AI model configuration device.
  • the AI model configuration device executing the AI model configuration method is taken as an example to illustrate the AI model configuration device provided in the embodiment of the present application.
  • an embodiment of the present application further provides an AI model configuration device.
  • the AI model configuration device 500 includes:
  • a receiving module 501 is used to receive configuration information from a network side device, where the configuration information includes at least two sets of configuration parameters, where the configuration parameters include first information and model meta-information associated with the first information, where the first information is used to construct an AI model, and the model meta-information is used to indicate a network environment in which the AI model constructed by the first information runs;
  • the determination module 502 is used to determine, according to the model meta-information, a target AI model to be used in the AI model constructed based on the first information.
  • the determining module 502 includes:
  • a receiving unit configured to receive second information from a network side device
  • a determining unit configured to determine the target AI model in the AI model constructed based on the first information according to the second information and the model meta-information
  • the second information is used to determine the target model meta-information
  • the target model meta-information is the model meta-information associated with the model identifier of the target AI model.
  • the second information includes at least one of the following:
  • first indication information where the first indication information is used to indicate relevant information of the model meta-information, and the relevant information is used to determine at least part of the model meta-information in the target model meta-information;
  • a first reference signal wherein the first reference signal is used to determine at least part of the model meta-information in the target model meta-information.
  • model meta information includes at least one of the following:
  • meta-information identifier wherein the meta-information identifier is associated with third information, wherein the third information includes at least one of a model-associated signal-to-noise ratio, a model-associated signal-to-interference-plus-noise ratio, a model-associated reference signal received power, a model-associated reference signal received quality, a model-associated network synchronization error, and a model-associated timing error;
  • the signal-to-interference-noise ratio associated with the model is the signal-to-interference-noise ratio associated with the model
  • the reference signal received power associated with the model
  • the configuration parameters also include at least one of the following:
  • the target AI model is used for positioning.
  • the embodiment of the present application further provides an AI model configuration device.
  • the AI model configuration device 600 includes:
  • a sending module 601 is used to send configuration information to a terminal, where the configuration information includes at least two sets of configuration parameters, where the configuration parameters include first information and model meta-information associated with the first information, where the first information is used to construct an AI model, and the model meta-information is used to represent the network environment in which the AI model constructed by the first information runs.
  • the sending module 601 is also used to: send second information to the terminal, the second information is used to determine target model meta-information, and the target model meta-information is model meta-information associated with the model identifier of the target AI model.
  • the second information includes at least one of the following:
  • first indication information where the first indication information is used to indicate relevant information of the model meta-information, and the relevant information is used to determine at least part of the model meta-information in the target model meta-information;
  • a first reference signal wherein the first reference signal is used to determine at least part of the model meta-information in the target model meta-information.
  • model meta information includes at least one of the following:
  • meta-information identifier wherein the meta-information identifier is associated with third information, wherein the third information includes at least one of a model-associated signal-to-noise ratio, a model-associated signal-to-interference-plus-noise ratio, a model-associated reference signal received power, a model-associated reference signal received quality, a model-associated network synchronization error, and a model-associated timing error;
  • the signal-to-interference-noise ratio associated with the model is the signal-to-interference-noise ratio associated with the model
  • the reference signal received power associated with the model
  • the configuration parameters also include at least one of the following:
  • the AI model is a model used for positioning.
  • the AI model configuration 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 terminal 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 configuration device provided in the embodiment of the present application can implement the various processes implemented by the method embodiments of Figures 3 to 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • an embodiment of the present application also provides a communication device 700, including a processor 701 and a memory 702, and the memory 702 stores a program or instruction that can be run on the processor 701.
  • the program or instruction is executed by the processor 701
  • the various steps of the above-mentioned AI model configuration device embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application also provides a terminal, including a processor and a communication interface, the communication interface is used to receive configuration information from a network side device, the configuration information includes at least two sets of configuration parameters, the configuration parameters include first information and model meta-information associated with the first information, the first information is used to build an AI model, and the model meta-information is used to represent the network environment in which the AI model built by the first information runs; the processor is used to determine the target AI model to be used in the AI model built based on the first information according to the model meta-information.
  • This terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the terminal embodiment and can achieve the same technical effect.
  • Figure 8 is a schematic diagram of the hardware structure of a terminal that implements an embodiment of the present application.
  • the terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809 and at least some of the components of a processor 810.
  • the terminal 800 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 810 through a power management system, so as to implement functions such as managing charging, discharging, and power consumption management through the power management system.
  • a power source such as a battery
  • the terminal structure shown in FIG8 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 components differently, which will not be described in detail here.
  • the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 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 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072.
  • the touch panel 8071 is also called a touch screen.
  • the touch panel 8071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 8072 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 801 after receiving downlink data from the network side device, can transmit the data to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network side device.
  • the radio frequency unit 801 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 809 can be used to store software programs or instructions and various data.
  • the memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may 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 809 may include a volatile memory or a non-volatile memory, or the memory 809 may include both volatile and non-volatile memories.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • 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).
  • the memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.
  • the processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the above-mentioned modem processor The processor may not be integrated into the processor 810.
  • the radio frequency unit 801 is used to receive configuration information from a network side device, the configuration information includes at least two sets of configuration parameters, the configuration parameters include first information and model meta information associated with the first information, the first information is used to construct an AI model, and the model meta information is used to indicate a network environment in which the AI model constructed by the first information runs;
  • the processor 810 is used to determine, according to the model meta-information, a target AI model to be used in an AI model constructed based on the first information.
  • the terminal receives configuration information from the network side device, and the configuration information includes at least two sets of configuration parameters, and the configuration parameters include first information and model meta information associated with the first information, and the model meta information is used to represent the network environment in which the AI model constructed by the first information runs.
  • the terminal can determine whether the current AI model is valid based on the model meta information and the current network environment information, and at the same time, if the current AI model is invalid, it can further select a target AI model suitable for the current network environment information to perform corresponding operations, thereby avoiding the reduction in the accuracy of the AI model output results due to changes in the network environment, thereby improving the reliability of the use of the AI model.
  • the embodiment of the present application also provides a network side device, including a processor and a communication interface, the communication interface is used to send configuration information to a terminal, the configuration information includes at least two sets of configuration parameters, the configuration parameters include first information and model meta-information associated with the first information, the first information is used to build an AI model, and the model meta-information is used to represent the network environment in which the AI model built by the first information runs.
  • This network side device embodiment corresponds to the above-mentioned network side device method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this network side device embodiment, and can achieve the same technical effect.
  • the embodiment of the present application also provides a network side device.
  • the network side device 900 includes: an antenna 901, a radio frequency device 902, a baseband device 903, a processor 904 and a memory 905.
  • the antenna 901 is connected to the radio frequency device 902.
  • the radio frequency device 902 receives information through the antenna 901 and sends the received information to the baseband device 903 for processing.
  • the baseband device 903 processes the information to be sent and sends it to the radio frequency device 902.
  • the radio frequency device 902 processes the received information and sends it out through the antenna 901.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 903, which includes a baseband processor.
  • the baseband device 903 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 9, one of which is, for example, a baseband processor, which is connected to the memory 905 through a bus interface to call the program in the memory 905 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 906, which is, for example, a common public radio interface (CPRI).
  • a network interface 906 which is, for example, a common public radio interface (CPRI).
  • CPRI common public radio interface
  • the network side device 900 of the embodiment of the present application also includes: instructions or programs stored in the memory 905 and executable on the processor 904.
  • the processor 904 calls the instructions or programs in the memory 905 to execute the method executed by each module shown in Figure 6 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the present application also provides a readable storage medium, wherein a program or instruction is stored on the readable storage medium.
  • a program or instruction is stored on the readable storage medium.
  • the processor is the processor in the terminal 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 embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, the processor is used to run programs or instructions, implement the various processes of the above-mentioned AI model configuration method embodiment, and can achieve the same technical effect, to avoid repetition, it is not repeated here.
  • the chip mentioned in the embodiment 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, and is executed by at least one processor to implement the various processes of the above-mentioned AI model configuration method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides a communication system, including: a terminal and a network side device, wherein the terminal is used to execute the various processes as shown in Figure 3 and the various method embodiments on the terminal side mentioned above, and the network side device is used to execute the various processes as shown in Figure 4 and the various method embodiments on the network side device side mentioned above, and can achieve the same technical effect. In order to avoid repetition, it will not be repeated here.
  • the technical solution of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.
  • a storage medium such as ROM/RAM, a magnetic disk, or an optical disk
  • a terminal which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente demande se rapporte au domaine technique des communications et divulgue un procédé de configuration de modèle d'IA, ainsi qu'un terminal et un dispositif côté réseau. Le procédé de configuration de modèle d'IA selon les modes de réalisation de la présente demande comprend les étapes suivantes : un terminal reçoit des informations de configuration en provenance d'un dispositif côté réseau, les informations de configuration comprenant au moins deux ensembles de paramètres de configuration, les paramètres de configuration comprenant des premières informations et des méta-informations de modèle associées aux premières informations, les premières informations étant utilisées pour construire un modèle d'IA, et les méta-informations de modèle étant utilisées pour représenter un environnement de réseau dans lequel le modèle d'IA construit en fonction des premières informations s'exécute ; et en fonction des méta-informations de modèle, le terminal détermine un modèle d'IA cible à utiliser à partir des modèles d'IA construits sur la base des premières informations.
PCT/CN2023/123880 2022-10-17 2023-10-11 Procédé de configuration de modèle d'ia, terminal et dispositif côté réseau WO2024083004A1 (fr)

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CN202211267696.5A CN117938689A (zh) 2022-10-17 2022-10-17 Ai模型配置方法、终端及网络侧设备

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022145551A1 (fr) * 2020-12-29 2022-07-07 엘지전자 주식회사 Procédé de transmission ou de réception de signal intelligent, et dispositif associé
CN114826941A (zh) * 2022-04-27 2022-07-29 中国电子科技集团公司第五十四研究所 一种无线通信网络ai模型配置方法
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WO2022145551A1 (fr) * 2020-12-29 2022-07-07 엘지전자 주식회사 Procédé de transmission ou de réception de signal intelligent, et dispositif associé
CN115150847A (zh) * 2021-03-31 2022-10-04 华为技术有限公司 模型处理的方法、通信装置和系统
CN114826941A (zh) * 2022-04-27 2022-07-29 中国电子科技集团公司第五十四研究所 一种无线通信网络ai模型配置方法

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