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WO2025130847A1 - Procédé et appareil de transmission, terminal et dispositif côté réseau - Google Patents

Procédé et appareil de transmission, terminal et dispositif côté réseau Download PDF

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Publication number
WO2025130847A1
WO2025130847A1 PCT/CN2024/139813 CN2024139813W WO2025130847A1 WO 2025130847 A1 WO2025130847 A1 WO 2025130847A1 CN 2024139813 W CN2024139813 W CN 2024139813W WO 2025130847 A1 WO2025130847 A1 WO 2025130847A1
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WO
WIPO (PCT)
Prior art keywords
information
unit
terminal
network side
side device
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.)
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Application number
PCT/CN2024/139813
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English (en)
Chinese (zh)
Inventor
孙布勒
李佳林
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Vivo Mobile Communication Co Ltd
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Vivo Mobile Communication Co Ltd
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Filing date
Publication date
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Publication of WO2025130847A1 publication Critical patent/WO2025130847A1/fr
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
    • H04W8/24—Transfer of terminal data
    • 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 a transmission method, apparatus, terminal and network side equipment.
  • AI models also called AI units
  • model monitoring of AI models is usually based on monitoring indicators or monitoring data reported by terminals, and the network side adjusts the AI model based on the monitoring indicators or monitoring data reported by multiple terminals.
  • the AI model adjusted by the network side in this way is not suitable for all terminals, resulting in poor applicability of the AI model adjustment method.
  • the embodiments of the present application provide a transmission method, apparatus, terminal and network-side equipment, which can solve the problem of poor applicability of the adjustment method of the AI model in the related art.
  • a transmission method which is performed by a terminal, and the method includes:
  • the terminal sends first information to the network side device, where the first information is used to indicate the terminal's adjustment suggestion for the first artificial intelligence AI unit.
  • a transmission method which is performed by a network side device, and the method includes:
  • the network side device receives first information sent by the terminal, where the first information is used to indicate an adjustment suggestion of the terminal to the first AI unit.
  • a transmission device comprising:
  • the first sending module is used to send first information to the network side device, where the first information is used to indicate the adjustment suggestion of the device to the first AI unit.
  • a transmission device comprising:
  • the second receiving module is used to receive first information sent by the terminal, where the first information is used to indicate the terminal's adjustment suggestion for the first AI unit.
  • 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 send first information to a network side device, and the first information is used to indicate the terminal's adjustment suggestions for the first AI unit.
  • 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 comprising a processor and a communication interface, wherein the communication interface is used to receive first information sent by a terminal, and the first information is used to indicate the terminal's adjustment suggestions for the first AI unit.
  • 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 wireless communication system including: a terminal and a network side device, wherein the terminal can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.
  • 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, or to implement 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 program/program product is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.
  • the terminal sends first information to the network side device, and the first information is used to indicate the terminal's adjustment suggestion for the first AI unit.
  • the network side device makes an adjustment to the first AI unit based on the adjustment suggestion reported by the terminal, so that the adjustment to the first AI unit is more in line with the terminal's operating scenario, effectively improving the applicability of the adjustment of the AI unit.
  • FIG1 is a block diagram of a wireless communication system to which an embodiment of the present application can be applied;
  • FIG2 is a flow chart of a transmission method provided in an embodiment of the present application.
  • FIG3 is a flow chart of another transmission method provided in an embodiment of the present application.
  • FIG4 is a flow chart of another transmission method provided in an embodiment of the present application.
  • FIG5 is a structural diagram of a transmission device provided in an embodiment of the present application.
  • FIG6 is a structural diagram of another transmission 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. 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
  • 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 may 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), an aircraft (flight vehicle), a vehicle user equipment (VUE), a shipborne equipment, a pedestrian terminal (Pedestrian User Equipment, PUE), a smart home (a home appliance with wireless communication function, such as a refrigerator, a television, a washing machine or furniture, etc.
  • a smart home a home appliance
  • 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 referred to as 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 may be referred to as a Node B (NB), an evolved Node B (eNB), a next generation Node B (gNB), a New Radio Node B (NR Node B), an access point, a Relay Base Station (RBS), a Serving Base Station (SBS), a Base Transceiver Station (BTS), a radio base station, a radio transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a Home Node B (HNB), a Home Evolved Node B, a Transmission Reception Point (TRP) or other appropriate terms in the field.
  • NB Node B
  • eNB evolved Node B
  • gNB next generation Node B
  • NR Node B New Radio Node B
  • an access point a Relay Base Station
  • SBS Serving Base Station
  • BTS Base Transceiver Station
  • a radio base station a radio transceiver
  • BSS Basic Service Set
  • ESS Extended Service Set
  • HNB Home No
  • AI modules such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application uses neural networks as an example for illustration, but does not limit the specific type of AI modules.
  • the parameters of the neural network are optimized through optimization algorithms.
  • An optimization algorithm is a type of algorithm that can help us minimize or maximize an objective function (sometimes called a 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 the right weights (multiplicative coefficients) and biases (additive coefficients) to minimize the value of the above loss function. The smaller the loss value, the closer our AI model is to the real 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 selected AI algorithms and AI models vary depending on the type of solution.
  • the main way to improve 5G network performance with AI is to enhance or replace existing algorithms or processing modules with algorithms and AI models based on neural networks.
  • algorithms and AI models based on neural networks can achieve better performance than those based on deterministic algorithms.
  • Commonly used neural networks include deep neural networks, convolutional neural networks, and recurrent neural networks. With the help of existing AI tools, neural networks can be built, trained, and verified.
  • CSI prediction For channel state information (CSI) prediction, the historical CSI is input into the AI model, and the AI model analyzes the time domain variation characteristics of the channel and outputs the future CSI.
  • CSI prediction By monitoring and analyzing the system performance, CSI prediction will have a very large performance gain compared to the non-predicted solution. At the same time, the prediction accuracy that can be achieved will be different depending on the predicted future moment.
  • CSI channel state information
  • AI-based CSI prediction can achieve certain gains, it also has certain disadvantages, the most important of which is that AI-based solutions have poor generalization.
  • the performance of the AI model will become very poor, that is, mismatch will occur.
  • the CSI prediction AI model trained with the channel of a terminal moving at a speed of 30km/h will perform poorly in a terminal moving scenario at 60km/h. Therefore, it is necessary to monitor the actual inference performance of CSI prediction and trigger a series of adjustment measures based on the monitoring results.
  • Model monitoring can be implemented based on a variety of indicators, such as monitoring based on input and output data characteristics or distribution, monitoring based on intermediate results of model output (error indicators, accuracy indicators), monitoring based on final performance results, monitoring based on comparison results with other solutions, etc.
  • Statistics are collected on the input or output information of a model being executed (a model being inferred, an activated model), and the distribution information of the input or output information of the model is calculated (such as mean, mean vector, mean matrix, variance, variance vector, variance matrix, covariance, covariance vector, covariance matrix, maximum value, maximum value vector, maximum value matrix, minimum value, minimum value vector, minimum value matrix).
  • the model is trained, there will also be a distribution information range for its applicable input or output.
  • the terminal compares the calculated distribution information with the applicable distribution information range of the model to obtain the monitoring result.
  • the terminal reports the calculated distribution information to the network side, and the network side compares the received distribution information with the applicable distribution information range of the model to obtain the monitoring result.
  • Monitoring of intermediate results (error indicators, precision indicators) calculated based on model output: Compare the output information of the model with its corresponding true value information, and calculate the intermediate results such as the error or precision of the model output.
  • the terminal compares the intermediate result with the preset threshold to obtain the monitoring result.
  • the terminal reports the calculated intermediate result to the network side, and the network side compares the received intermediate result with the preset threshold to obtain the monitoring result.
  • the terminal or network side counts or calculates the current communication system performance, such as throughput, spectrum efficiency, signal-to-noise and interference ratio (SINR), signal-to-noise ratio (SNR), bit error rate, block error rate, packet loss rate, transmission rate (uplink/downlink), peak rate (uplink/downlink), etc.
  • the terminal or network side compares the current communication system performance with the preset threshold to obtain the monitoring result. If the terminal counts or calculates the current communication system performance, the communication system performance can also be reported to the network side.
  • the terminal compares the intermediate results or final performance results obtained based on the model with the intermediate results or final performance results obtained from other solutions to obtain monitoring results.
  • the terminal reports the intermediate results or final performance results obtained based on the model and the intermediate results or final performance results obtained from other solutions to the network side, and the network side compares the intermediate results or final performance results obtained based on the model with the intermediate results or final performance results obtained from other solutions to obtain monitoring results.
  • the monitoring of AI units is usually based on the monitoring indicators or monitoring data reported by the terminals, and the network side adjusts the AI units according to the monitoring indicators or monitoring data reported by multiple terminals.
  • the AI units adjusted by the network side in this way are not applicable to all terminals, resulting in poor applicability of the adjustment method of the AI units.
  • the AI unit described in this application may also be referred to as an AI model, AI structure, etc., or the AI unit may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI unit may be a processing method, algorithm, function, module or unit for a specific data set, or the AI unit may be a processing method, algorithm, function, module or unit running on AI-related hardware such as GPU, NPU, TPU, ASIC, etc., and this application does not make specific restrictions on this.
  • the specific data set includes the input and/or output of the AI unit.
  • the identifier of the AI unit may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, a functionality ID, a physical identifier, a logical identifier, a global identifier, a local identifier, or an identifier of a specific data set associated with the AI unit, or an identifier of a specific scenario, environment, channel characteristic, device related to the AI, or an identifier of a function, feature, capability or module related to the AI, and this application does not make any specific limitations on this.
  • FIG. 2 is a flow chart of a transmission method provided in an embodiment of the present application. As shown in FIG. 2 , the method includes the following steps:
  • Step 201 The terminal sends first information to a network-side device, where the first information is used to indicate an adjustment suggestion of the terminal to the first AI unit.
  • the first AI unit may refer to a specific AI unit.
  • the terminal may determine the first AI unit by an identifier of the AI unit, so that the network side device knows which AI unit the first information is for.
  • the first AI unit is an AI unit running (or used) by the terminal, and the terminal can determine the adjustment suggestions for the first AI unit according to the running status of the first AI unit, such as the model parameters of the first AI unit that need to be adjusted, the monitoring configuration of the first AI unit, etc., or can also include suggestions on whether the first AI unit needs to be adjusted, such as keeping the first AI unit running with the current configuration, switching the first AI unit, etc.
  • the terminal generates first information based on these adjustment suggestions, and sends the first information to the network side device, that is, sends the terminal's adjustment suggestions for the first AI unit to the network side device, thereby helping the network side device to make a decision on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions.
  • the terminal sends first information to the network side device, and the first information is used to indicate the terminal's adjustment suggestion for the first AI unit, and then the terminal can also derive adjustment suggestions based on its own operation of the first AI unit, thereby helping the network side device to make a decision on whether to adjust the first AI unit and how to adjust it based on the adjustment suggestions.
  • the adjustment of the first AI unit by the network side device is made based on the adjustment suggestions reported by the terminal, so that the adjustment of the first AI unit is more in line with the operation scenario of the terminal, and the adjusted first AI unit is more suitable for operation in the terminal, thereby helping to improve the reasoning performance of the first AI unit.
  • the method further includes:
  • the terminal receives configuration information sent by the network side device, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.
  • the network side device before the terminal sends the first information to the network side device, the network side device sends configuration information to the terminal, and the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit. That is, the network side device can use the configuration information to inform the terminal which adjustment suggestions can be reported, so that the terminal can clearly determine which adjustment suggestions need to be collected based on the configuration information, which helps to better generate the first information.
  • the first information or the configuration information includes at least one of the following adjustment suggestions:
  • the second AI unit refers to another AI unit different from the first AI unit
  • the first information or the configuration information may include an identifier for switching to the second AI unit
  • the CSI prediction function may be turned off.
  • the CSI prediction function of the specific scenario may be turned off, but the CSI prediction function of other scenarios may not be adjusted.
  • Adjust the monitoring configuration of the first AI unit including information related to the monitoring window (monitoring duration, number of monitoring samples, etc.), monitoring trigger condition information, monitoring result reporting condition information, etc.;
  • multiple AI units may include the first AI unit
  • the auxiliary information is a part of the input information of the first AI unit and may be of multiple types.
  • the auxiliary information of the first AI unit for implementing CSI prediction may be speed information, location information, perception information, Doppler information, cell identification information, etc.
  • the first information sent by the terminal to the network side device includes at least one adjustment suggestion as above, and then the network side device obtains these adjustment suggestions based on the first information reported by the terminal, thereby helping the network side device to make a decision on whether to adjust the first AI unit and how to adjust it based on the adjustment suggestion.
  • the configuration information sent by the network side device to the terminal may also include at least one adjustment suggestion as above, so that the terminal can clearly know which adjustment suggestions need to be reported based on the configuration information, making the reporting behavior of the terminal more targeted.
  • the first information or the configuration information further includes at least one of the following:
  • each adjustment suggestion in the first information or the configuration information for example, if a certain adjustment suggestion or a related indicator meets a preset threshold condition, then it is determined to report a certain adjustment suggestion;
  • the reporting order of the adjustment suggestions in the first information or the configuration information that is, the order in which the adjustment suggestions are reported, for example, which adjustment suggestions are reported first and which adjustment suggestions are reported later;
  • the dependencies between the adjustment suggestions in the first information or the configuration information for example, which adjustment suggestions need to be reported together, such as adjustment suggestions for canceling the first AI unit and falling back to a non-AI mode need to be reported together, and requesting training for the first AI unit and requesting data collection for training the first AI unit need to be reported together, etc.
  • the reporting of the adjustment suggestion is made more orderly.
  • the method before the terminal sends the first information to the network side device, the method further includes:
  • the terminal receives second information sent by the network side device, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following:
  • monitoring based on monitoring indicators refers to monitoring indicators obtained based on the input and/or output data characteristics or distribution of the AI unit, monitoring of the intermediate results output by the AI unit, monitoring of the final performance results of the AI unit, etc., such as accuracy indicators, error indicators, distribution indicators, scoring indicators, etc. These indicators can be one or a group of numerical values.
  • the monitoring based on raw data refers to the raw data of monitoring indicators obtained by the terminal based on the input and/or output data characteristics or distribution of the AI unit, the intermediate results output by the AI unit, the final performance results of the AI unit, and other schemes, such as the output of the first AI unit, the true value corresponding to the output of the first AI unit, etc.
  • the monitoring based on terminal suggestions is the monitoring in which the terminal reports adjustment suggestions for the first AI unit to the network side device.
  • the network side device sends the second information to the terminal, so that the terminal can clarify which monitoring type to adopt for the first AI unit based on the second information, which helps the terminal to monitor the reasoning performance of the first AI unit, and also helps the terminal to collect relevant data during the operation of the first AI unit to obtain the monitoring data or adjustment suggestions that need to be reported.
  • the terminal sends first information to the network side device, including:
  • the terminal generates first information based on at least one of the second information, the configuration message, and the reasoning performance of the first AI unit;
  • the terminal sends the first information to a network side device.
  • the terminal may generate the first information based on at least one of the second information, the configuration message and the reasoning performance of the first AI unit. For example, the terminal generates the first information based on the configuration information and the reasoning performance of the first AI unit, that is, generates the terminal's adjustment suggestion for the first AI unit; or, when the second information includes terminal-based suggestions, the terminal generates the first information based on the second information, the configuration message and the reasoning performance of the first AI unit, that is, generates the terminal's adjustment suggestion for the first AI unit.
  • the reasoning performance of the first AI unit may include reasoning performance monitoring indicators obtained by the terminal based on the operation of the first AI unit.
  • the terminal can generate the first information based on at least one of the second information, the configuration message and the reasoning performance of the first AI unit, that is, generate the terminal's adjustment suggestion for the first AI unit, thereby clarifying the method of generating the adjustment suggestion.
  • sending of the second information and the configuration information satisfies any one of the following:
  • the second information and the configuration information are carried in the same information and sent.
  • the second information and the configuration information may be two contents or two sub-information in one information.
  • the second information and the configuration information are sent together, which helps to save transmission resources.
  • the second information and the configuration information are carried in the same signaling and sent.
  • the second information and the configuration information are two independent pieces of information, but can be carried in the same signaling and sent, which also helps to save transmission resources;
  • the second information and the configuration information are respectively carried in different signalings and sent, that is, the second information and the configuration information are two independent pieces of information and are respectively carried in different signalings and sent.
  • the method further includes at least one of the following:
  • the terminal sends first capability information to the network side device, where the first capability information is used to indicate a monitoring type of the AI unit supported by the terminal;
  • the terminal sends second capability information to the network side device, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion;
  • the terminal sends third capability information to the network side device, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.
  • the terminal sends the first capability information to the network side device, and uses the first capability information to indicate the monitoring type of the AI unit supported by the terminal, that is, to indicate which type or types of monitoring based on monitoring indicators, monitoring based on raw data, and monitoring based on terminal suggestions the terminal supports, so that the network side device can clearly understand the monitoring type of the AI unit supported by the terminal, which helps the network side device to clarify subsequent behaviors, such as whether to send the configuration information to the terminal.
  • the terminal may send second capability information to the network side device, and use the second capability information to indicate whether the terminal supports the monitoring based on terminal suggestions, so that the network side device can clearly determine whether the terminal supports the monitoring based on terminal suggestions, thereby determining whether the terminal can report adjustment suggestions for the first AI unit, and also helping the network side device decide whether to send the configuration information to the terminal.
  • the terminal may also send third capability information to the network side device, and use the third capability information to indicate the adjustment suggestions that the terminal can make, so that the network side device can know which adjustment suggestions for the first AI unit can be obtained from the terminal, thereby helping the network side device to make adjustment decisions for the first AI unit.
  • the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.
  • the method further includes:
  • the terminal receives third information sent by the network side device, where the third information includes an adjustment decision for the first AI unit.
  • the network side device can make an adjustment decision on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestion, and send the third information to the terminal, and the third information includes the adjustment decision for the first AI unit.
  • the terminal can adjust the first AI unit based on the adjustment decision, so that the adjusted first AI unit is more suitable for operation in the terminal, thereby helping to improve the reasoning performance of the first AI unit.
  • Figure 3 is a flow chart of another transmission method provided by an embodiment of the present application. As shown in Figure 3, the method includes the following steps:
  • Step 301 A network-side device receives first information sent by a terminal, where the first information is used to indicate an adjustment suggestion of the terminal to a first AI unit.
  • the method before the network side device receives the first information sent by the terminal, the method further includes:
  • the network side device sends configuration information to the terminal, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.
  • the first information or the configuration information includes at least one of the following adjustment suggestions:
  • Auxiliary information in the input of the first AI unit is adjusted.
  • the first information or the configuration information further includes at least one of the following:
  • the method before the network side device receives the first information sent by the terminal, the method further includes:
  • the network-side device sends second information to the terminal, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following:
  • sending of the second information and the configuration information satisfies any one of the following:
  • the second information and the configuration information are carried in the same information and sent;
  • the second information and the configuration information are carried in the same signaling and sent;
  • the second information and the configuration information are respectively carried in different signalings and sent.
  • the method further includes at least one of the following:
  • the network side device receives first capability information sent by the terminal, where the first capability information is used to indicate a monitoring type of the AI unit supported by the terminal;
  • the network side device receives second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion;
  • the network side device receives third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.
  • the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.
  • the method further includes:
  • the network side device generates third information according to the first information, and sends the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.
  • the network side device may receive the first information reported by multiple terminals, and generate the third information in combination with the adjustment suggestions in the first information reported by the multiple terminals, that is, generate the adjustment decision for the first AI unit.
  • the network side device can generate the adjustment decision for the first AI unit in combination with the adjustment suggestions reported by the multiple terminals, so that the adjustment decision generated by the network side device integrates the adjustment suggestions reported by the multiple terminals, which makes the adjustment decision for the first AI unit more comprehensive, more helpful to improve the performance of the first AI unit, and also makes the adjustment decision applicable to different terminals.
  • the transmission method applied to the network side device provided in the embodiment of the present application corresponds to the above-mentioned transmission method applied to the terminal side.
  • the specific implementation process and related concepts involved in the embodiment of the present application can refer to the description in the above-mentioned terminal side method embodiment, and this embodiment will not be described in detail.
  • the network side device receives the first information sent by the terminal, and the first information is used to indicate the terminal's adjustment suggestion for the first AI unit, and then the terminal can also make adjustment suggestions based on its own operation of the first AI unit, thereby helping the network side device to make decisions on whether to adjust the first AI unit and how to adjust it based on the adjustment suggestions.
  • the adjustment of the first AI unit by the network side device is made based on the adjustment suggestion reported by the terminal, so that the adjustment of the first AI unit is more in line with the operation scenario of the terminal, and the applicability of the adjustment of the AI unit is effectively improved.
  • FIG. 4 is a flow chart of another transmission method provided in an embodiment of the present application. As shown in FIG. 4, the method includes the following steps:
  • Step 401 The terminal sends first capability information, second capability information and third capability information to a network side device;
  • Step 402 The network side device sends configuration information and second information to the terminal;
  • Step 403 The terminal generates first information based on the configuration information and the second information, where the first information includes an adjustment suggestion of the terminal to the first AI unit;
  • Step 404 The terminal sends first information to the network side device
  • Step 405 The network-side device generates an adjustment decision for the first AI unit according to the first information, and sends third information to the terminal, where the third information includes the adjustment decision.
  • the transmission method provided in the embodiment of the present application can be executed by a transmission device.
  • the transmission device provided in the embodiment of the present application is described by taking the transmission method executed by the transmission device as an example.
  • FIG. 5 is a structural diagram of a transmission device provided in an embodiment of the present application.
  • the transmission device 500 includes:
  • the first sending module 501 is used to send first information to the network side device, where the first information is used to indicate the adjustment suggestion of the device to the first AI unit.
  • the first receiving module is used to receive configuration information sent by the network side device, where the configuration information is used to instruct the device to report adjustment suggestions for the first AI unit.
  • the first information or the configuration information includes at least one of the following adjustment suggestions:
  • Auxiliary information in the input of the first AI unit is adjusted.
  • the first information or the configuration information further includes at least one of the following:
  • the first receiving module is further used for:
  • the first sending module 501 is further used for:
  • the first information is sent to a network side device.
  • sending of the second information and the configuration information satisfies any one of the following:
  • the second information and the configuration information are carried in the same information and sent;
  • the second information and the configuration information are carried in the same signaling and sent;
  • the second information and the configuration information are respectively carried in different signalings and sent.
  • the first sending module 501 is further used for any of the following:
  • the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.
  • the device further comprises:
  • the third receiving module is used to receive third information sent by the network side device, where the third information includes an adjustment decision for the first AI unit.
  • the device e.g., terminal
  • the device can send first information to the network side device, and the first information is used to indicate the adjustment suggestion of the device to the first AI unit, and then the adjustment suggestion can be obtained according to the operation of the first AI unit, so as to help the network side device to make a decision on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestion.
  • the adjustment of the first AI unit by the network side device is made based on the adjustment suggestion reported by the terminal, so that the adjustment of the first AI unit is more in line with the operation scenario of the terminal, and the applicability of the adjustment of the AI unit is effectively improved.
  • the transmission device in the embodiment of the present application may 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 may be a terminal, or may be other devices other than a terminal.
  • the terminal may include but is not limited to the types of terminal 11 listed above, and other devices may be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.
  • the transmission device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 2 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • FIG. 6 is a structural diagram of another transmission device provided in an embodiment of the present application.
  • the transmission device 600 includes:
  • the second receiving module 601 is used to receive first information sent by a terminal, where the first information is used to indicate an adjustment suggestion of the terminal to the first AI unit.
  • the device further comprises:
  • the second sending module is used to send configuration information to the terminal, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.
  • the first information or the configuration information includes at least one of the following adjustment suggestions:
  • Auxiliary information in the input of the first AI unit is adjusted.
  • the first information or the configuration information further includes at least one of the following:
  • the second sending module is further used for:
  • the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following:
  • sending of the second information and the configuration information satisfies any one of the following:
  • the second information and the configuration information are carried in the same information and sent;
  • the second information and the configuration information are carried in the same signaling and sent;
  • the second information and the configuration information are respectively carried in different signalings and sent.
  • the second receiving module 601 is further used for any of the following:
  • the third capability information sent by the terminal is received, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.
  • the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.
  • the device further comprises:
  • a third sending module is used to generate third information according to the first information, and send the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.
  • the device receives the first information sent by the terminal, which helps the device to make a decision on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestion in the first information.
  • the adjustment of the first AI unit by the device is made based on the adjustment suggestion reported by the terminal, so that the adjustment of the first AI unit is more in line with the operation scenario of the terminal, and the applicability of the adjustment of the AI unit is effectively improved.
  • the transmission device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 3 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702, wherein the memory 702 stores a program or instruction that can be run on the processor 701.
  • the communication device 700 is a terminal
  • the program or instruction is executed by the processor 701 to implement the various steps of the above-mentioned transmission method embodiment, and can achieve the same technical effect.
  • the communication device 700 is a network side device
  • the program or instruction is executed by the processor 701 to implement the various steps of the above-mentioned transmission 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 terminal, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps in the method embodiment shown in Figure 2.
  • 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 implementing 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.
  • 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).
  • 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 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 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 810.
  • the radio frequency unit 801 is used to send first information to the network side device, and the first information is used to indicate the terminal's adjustment suggestion for the first AI unit.
  • the terminal sends the first information to the network side device, and the first information is used to indicate the terminal's adjustment suggestion for the first AI unit, and then the terminal can also make adjustment suggestions based on its own operation of the first AI unit, so as to help the network side device make decisions on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestion.
  • the adjustment of the first AI unit by the network side device is made based on the adjustment suggestion reported by the terminal, so that the adjustment of the first AI unit is more in line with the operation scenario of the terminal, and the applicability of the adjustment of the AI unit is effectively improved.
  • the embodiment of the present application also provides a network side device, including a processor and a communication interface, 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 3.
  • the 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 the 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 91, a radio frequency device 92, a baseband device 93, a processor 94 and a memory 95.
  • the antenna 91 is connected to the radio frequency device 92.
  • the radio frequency device 92 receives information through the antenna 91 and sends the received information to the baseband device 93 for processing.
  • the baseband device 93 processes the information to be sent and sends it to the radio frequency device 92.
  • the radio frequency device 92 processes the received information and sends it out through the antenna 91.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 93, which includes a baseband processor.
  • the baseband device 93 may include, for example, at least one baseband board, on which a plurality of chips are arranged, as shown in FIG. 9 , wherein one of the chips is, for example, a baseband processor, which is connected to the memory 95 through a bus interface to call a program in the memory 95 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 96, 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 invention also includes: instructions or programs stored in the memory 95 and executable on the processor 94.
  • the processor 94 calls the instructions or programs in the memory 95 to execute the methods executed by the modules shown in Figure 6 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 transmission method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • 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 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 transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • 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 transmission 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 .... system, including: a terminal and a network side device, the terminal can be used to execute the steps of the transmission method as described above, and the network side device can be used to execute the steps of the transmission method as described above.

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

La présente demande, qui appartient au domaine technique des communications, concerne un procédé et un appareil de transmission, ainsi qu'un terminal et un dispositif côté réseau. Le procédé de transmission dans les modes de réalisation de la présente demande comprend : l'envoi, par un terminal, de premières informations à un dispositif côté réseau, les premières informations étant utilisées pour indiquer une suggestion de réglage du terminal concernant une première unité d'intelligence artificielle (IA).
PCT/CN2024/139813 2023-12-21 2024-12-17 Procédé et appareil de transmission, terminal et dispositif côté réseau Pending WO2025130847A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202311773196.3 2023-12-21
CN202311773196.3A CN120201410A (zh) 2023-12-21 2023-12-21 传输方法、装置、终端及网络侧设备

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WO2025130847A1 true WO2025130847A1 (fr) 2025-06-26

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230066109A1 (en) * 2020-04-21 2023-03-02 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Communication method and related devices
CN116074210A (zh) * 2021-11-01 2023-05-05 维沃移动通信有限公司 信道预测方法、装置、网络侧设备及终端
CN117135650A (zh) * 2022-05-20 2023-11-28 中国移动通信有限公司研究院 人工智能模型配置方法、装置、终端及网络设备
WO2023230969A1 (fr) * 2022-06-01 2023-12-07 北京小米移动软件有限公司 Procédé et appareil de détermination de modèle d'intelligence artificielle, et dispositif de communication et support de stockage

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230066109A1 (en) * 2020-04-21 2023-03-02 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Communication method and related devices
CN116074210A (zh) * 2021-11-01 2023-05-05 维沃移动通信有限公司 信道预测方法、装置、网络侧设备及终端
CN117135650A (zh) * 2022-05-20 2023-11-28 中国移动通信有限公司研究院 人工智能模型配置方法、装置、终端及网络设备
WO2023230969A1 (fr) * 2022-06-01 2023-12-07 北京小米移动软件有限公司 Procédé et appareil de détermination de modèle d'intelligence artificielle, et dispositif de communication et support de stockage

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