WO2023040886A1 - Procédé et appareil d'acquisition de données - Google Patents

Procédé et appareil d'acquisition de données Download PDF

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Publication number
WO2023040886A1
WO2023040886A1 PCT/CN2022/118709 CN2022118709W WO2023040886A1 WO 2023040886 A1 WO2023040886 A1 WO 2023040886A1 CN 2022118709 W CN2022118709 W CN 2022118709W WO 2023040886 A1 WO2023040886 A1 WO 2023040886A1
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Prior art keywords
information
requirement
error
estimation error
associated parameters
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PCT/CN2022/118709
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English (en)
Chinese (zh)
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杨昂
孙布勒
史斯豪
贾承璐
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维沃移动通信有限公司
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Publication of WO2023040886A1 publication Critical patent/WO2023040886A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W76/00—Connection management

Definitions

  • the present application relates to the field of communication technologies, and in particular to a data collection method and device.
  • the communication device when the communication device collects data, it often only considers how to collect data under the interference of ideal factors, but does not consider how to collect data under the interference of non-ideal factors.
  • Embodiments of the present application provide a data collection method and device, which can improve the performance of a communication system.
  • the embodiment of the present application provides a data collection method, which is executed by the first device, and the method includes:
  • the first information and/or the second information satisfy the first requirement, the first information is selected from the input information, the second Information is selected from the output information.
  • the embodiment of the present application provides a data collection device, which is applied to the first device, and the device includes:
  • a collection module configured to collect input information and/or output information of the artificial intelligence network on the first device side
  • a reporting module configured to send first information and/or second information to a second device, the first information and/or the second information meet the first requirement, and the first information is selected from the input information , the second information is selected from the output information.
  • a first device in a third aspect, includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, and the program or instruction is executed by the processor When executed, the steps of the method described in the first aspect are realized.
  • a first device including a processor and a communication interface, wherein the processor is used to collect input information and/or output information of the artificial intelligence network on the first device side; the communication interface is used to use the The first information and/or the second information are sent to the second device, the first information and/or the second information meet the first requirement, the first information is selected from the input information, and the second information selected from the output message.
  • a readable storage medium is provided, and a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method according to the first aspect are implemented.
  • a sixth aspect provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect .
  • a computer program/program product is provided, the computer program/program product is stored in a non-volatile storage medium, and the program/program product is executed by at least one processor to implement the first aspect The steps of the method.
  • the first device collects the input information and/or output information of the artificial intelligence network; sends the first information and/or the second information to the second device, and the first information and/or the second information
  • the second information meets the first requirement, so that the first information and/or the second information reported to the second device meet the first requirement, which can effectively improve the efficiency and accuracy of data collected by the first device side, and improve the artificial intelligence in the wireless network
  • the performance of the module thereby improving the performance of the communication system.
  • FIG. 1 shows a schematic diagram of a wireless communication system
  • Fig. 2 represents the structural representation of neural network
  • Fig. 3 shows the structural representation of neuron
  • FIG. 4 shows a schematic flow diagram of a data collection method performed by a first device in an embodiment of the present application
  • FIG. 5 shows a schematic structural diagram of a data acquisition device applied to a first device according to an embodiment of the present application
  • FIG. 6 shows a schematic structural diagram of a communication device according to an embodiment of the present application.
  • FIG. 7 shows a schematic diagram of the composition of a terminal according to an embodiment of the present application.
  • first, second and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or described herein and that "first" and “second” distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects.
  • “and/or” in the description and claims means at least one of the connected objects, and the character “/” generally means that the related objects are an "or” relationship.
  • LTE Long Term Evolution
  • LTE-Advanced LTE-Advanced
  • LTE-A Long Term Evolution-Advanced
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • system and “network” in the embodiments of the present application are often used interchangeably, and the described technology can be used for the above-mentioned system and radio technology, and can also be used for other systems and radio technologies.
  • NR New Radio
  • the following description describes the New Radio (NR) system for illustrative purposes, and uses NR terminology in most of the following descriptions, but these techniques can also be applied to applications other than NR system applications, such as the 6th generation (6 th Generation, 6G) communication system.
  • 6G 6th Generation
  • Fig. 1 shows a block diagram of a wireless communication system to which the embodiment of the present application is applicable.
  • the wireless communication system includes a terminal 11 and a network side device 12 .
  • the terminal 11 can also be called a terminal device or a user terminal (User Equipment, UE), and the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital Assistant (Personal Digital Assistant, PDA), handheld computer, netbook, ultra-mobile personal computer (Ultra-Mobile Personal Computer, UMPC), mobile Internet device (Mobile Internet Device, MID), wearable device (Wearable Device) or vehicle-mounted device (Vehicle User Equipment, VUE), pedestrian terminal (Pedestrian User Equipment, PUE) and other terminal-side equipment, wearable devices include: smart watches, bracelets, earphones, glasses, etc.
  • the network side device 12 may be a base station or a core network, where a base station may be called a node B, an evolved node B, an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service Basic Service Set (BSS), Extended Service Set (ESS), Node B, Evolved Node B (eNB), Home Node B, Home Evolved Node B, Wireless Local Area Network (WLAN) Area Network, WLAN) access point, wireless fidelity (Wireless Fidelity, WiFi) node, transmitting and receiving point (Transmitting Receiving Point, TRP) or some other suitable term in the field, as long as the same technical effect is achieved, all
  • a base station may be called a node B, an evolved node B, an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service Basic
  • the core network device may be a location management device, such as , location management functions (LMF, E-SLMC), etc.
  • LMF location management functions
  • E-SLMC location management functions
  • FIG 2 is a schematic diagram of the architecture of the neural network, where the neural network is composed of neurons, and the schematic diagram of the neurons is shown in Figure 3, where a1, a2,...aK are the inputs, and w is the weight (multiplicative coefficient) , b is the bias (additive coefficient), and ⁇ (.) is the activation function.
  • Common activation functions include Sigmoid, tanh, linear rectification function (Rectified Linear Unit, ReLU) and so on.
  • the parameters of the neural network are optimized by an optimization algorithm.
  • An optimization algorithm is an algorithm that minimizes or maximizes an objective function (also called a loss function), and the objective function is often a mathematical combination of model parameters and data. For example, given the data X and its corresponding label Y, construct a neural network model f(.), using the neural network model, the predicted output f(x) can be obtained according to the input x, and the difference between the predicted value and the real value can be calculated The gap between (f(x)-Y), this gap is the loss function.
  • the purpose of the optimization algorithm is to find the appropriate W,b to minimize the value of the above loss function. The smaller the loss value, the closer the model is to the real situation.
  • the current 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 samples are passed in from the input layer, processed layer by layer by each hidden layer, and passed to the output layer. If the actual output of the output layer does not match the expected output, it will enter the error backpropagation stage.
  • Error backpropagation is to transmit the output error layer by layer through the hidden layer to the input layer in some form, and distribute the error to all the units of each layer, so as to obtain the error signal of each layer unit, and this error signal is used as the correction unit Basis for weight.
  • This weight adjustment process of each layer of signal forward propagation and error back propagation is carried out repeatedly.
  • the process of continuously adjusting the weights is also the learning and training process of the network. This process has been carried out until the error of the network output is reduced to an acceptable level, or until the preset number of learning times.
  • optimization algorithms are based on the error or loss obtained by the loss function when the error is backpropagated, and the derivative or partial derivative of the current neuron is calculated, and the learning rate, the previous gradient or derivative or partial derivative are added to obtain the gradient. Pass the gradient to the previous layer.
  • the embodiment of the present application provides a data collection method, as shown in Figure 4, the method includes:
  • Step 101 collecting input information and/or output information of the artificial intelligence network on the first device side;
  • Step 102 Send the first information and/or the second information to the second device, the first information and/or the second information meet the first requirement, the first information is selected from the input information, and the The second information is selected from the output information.
  • the first device collects the input information and/or output information of the artificial intelligence network; sends the first information and/or the second information to the second device, and the first information and/or the second information
  • the second information meets the first requirement, so that the first information and/or the second information reported to the second device meet the first requirement, which can effectively improve the efficiency and accuracy of data collected by the first device side, and improve the artificial intelligence in the wireless network
  • the performance of the module thereby improving the performance of the communication system.
  • the first information can be determined directly from the input information; if the input information does not meet the first requirement, the input information needs to be processed to obtain the first information that meets the first requirement; if the output If the information meets the first requirement, the second information can be determined directly from the output information. If the output information does not meet the first requirement, the output information needs to be processed to obtain the second information that meets the first requirement.
  • the first device and the second device may be the same device or different devices.
  • the sender and receiver of the first information and/or the second information are different modules of the device.
  • the first device may be a terminal
  • the second device may be a terminal or a network side device.
  • the sender of the first information and/or the second information sends the first information and/or the second information to the second device, and the second device can process the second information with a matching AI module or a non-AI module
  • the second information, or the second device directly uses the second information without processing the second information.
  • the acquisition may be performed under the interference of non-ideal factors
  • the second device uses the first information and/or the second information
  • it may add the interference of non-ideal factors to simulate various non-ideal situations.
  • the first information and/or the second information is high SNR data
  • white noise can be added to the high SNR data to simulate a low SNR situation
  • a uniformly changing phase offset is added to the second information to simulate the situation of inaccurate timing.
  • Generative Adversarial Networks GAN
  • GAN Generative Adversarial Networks
  • the method further includes:
  • the second device can learn the first requirement satisfied by the first information and/or the second information, and process the first information and/or the second information quickly and accurately.
  • the collected input information and/or output information may or may not meet the first requirement, and if the collected input information and/or output information does not meet the first requirement, the input information shall be processed to satisfy The first information of the first requirement, and/or, the output information is processed to obtain the second information meeting the first requirement.
  • the method before the step of sending the first information and/or the second information to the second device, the method further includes:
  • the statistical range of statistical features can be configured by the second device, and the statistical range of statistical features can be sent to the first device. In this way, the statistical range can be limited according to the required information, and the first device can avoid obtaining too large a range of data. Statistical Features.
  • the statistical characteristics of the first required associated parameters include at least one of the following:
  • the probability distribution of the associated parameters includes a probability density function (Probability Density Function, PDF), a cumulative distribution function (Cumulative Distribution Function, CDF), a probability mass function (probability mass function, PMF), and may specifically be a timing advance (Timing Probability distribution of Advance, TA), probability distribution of signal-to-noise ratio, etc.;
  • the probability distribution of the error of the associated parameter such as the probability distribution of the TA estimation error, the probability distribution of the estimation error of the signal-to-noise ratio;
  • the mean value of the associated parameters such as the mean value of TA, the mean value of signal-to-noise ratio;
  • the variance of the associated parameters such as the variance of TA, the variance of signal-to-noise ratio;
  • the mean value of the error of the associated parameter such as the mean value of the estimation error of TA, the mean value of the estimation error of the signal-to-noise ratio;
  • the variance of the error of the associated parameter such as the variance of the estimation error of TA, the variance of the estimation error of the signal-to-noise ratio;
  • the value of the cumulative distribution function CDF of the associated parameter at the preset threshold such as the value at CDF 5%, the value at CDF 10%, the value at CDF 50%, the value at CDF 90%, the value at CDF 95% value;
  • the value of the CDF of the error of the associated parameter at the preset threshold such as the value at CDF 5%, the value at CDF 10%, the value at CDF 50%, the value at CDF 90%, the value at CDF 95% value.
  • the foregoing preset threshold may be defined by the protocol, configured by the network side device, or pre-configured.
  • the associated parameters of the first requirement include at least one of the following:
  • Timing advance TA Timing advance
  • Noise power such as white noise, phase noise, etc.
  • Intra-cell interference such as interference from other users in the cell
  • Inter-cell interference such as interference from base stations and/or users in other cells
  • the nonlinear parameters of the signal processing module such as the nonlinear parameters of the power amplifier (Power Amplifer, PA);
  • the moving parameter, the moving parameter of the first device includes the moving speed of the first device and the moving angle of the first device, and the moving speed includes an absolute speed and a relative speed, such as the relative speed of the first device to the base station on the network side, and the relative speed of the first device to the network side base station.
  • the position of the first device includes an absolute position and a relative position
  • the absolute position may be the latitude and longitude of the first device
  • the relative position may be the relative position of the first device to the base station; optionally, if the first information and/or the second
  • the sender of the information is the positioning service module or positioning module of the first device, then the first request needs to include this parameter;
  • Beam quality which can be layer 1 RSRP (L1-RSRP), L1-SINR, L1-RSRP, L1-RSRQ, layer 3 RSRP (L3-RSRP), L3-SINR, L3-RSRP, L3-RSRQ, etc.; where, RSRP (Reference Signal Received Power) is the reference signal received power, SINR (Signal to Interference plus Noise Ratio) is the signal to interference plus noise ratio, RSRQ (Reference Signal Received Quality) is the reference signal received quality, L1 (Layer 1) indicates the layer 1, L3 means layer 3;
  • the first requirement is defined through associated parameters, and the first requirement may include at least one of the following:
  • At least one of the associated parameters is greater than a preset first threshold
  • At least one of the associated parameters is greater than or equal to a preset second threshold
  • At least one of the associated parameters is smaller than a preset third threshold
  • At least one of the associated parameters is less than or equal to a preset fourth threshold.
  • first threshold, second threshold, third threshold, and fourth threshold may be defined by the protocol, configured by the network side device, or pre-configured.
  • the first requirement may be that the mean value of the signal-to-noise ratio is greater than or equal to 30dB, while the mean value of inter-cell interference is less than -5dB, and the TA estimation error is within a certain range, such as the threshold 1 ⁇ 90% of the CDF of the TA estimation error value ⁇ threshold 2.
  • the step of collecting input information and/or output information of the first device-side artificial intelligence network includes:
  • input information and output information that meet the first requirement can be collected, so that the first information and the second information can be directly obtained without processing the input information and output information.
  • the method before sending the first information and/or the second information to the second device, the method further includes:
  • the second information is obtained after processing the collected output information in a preset processing manner.
  • input information and output information that do not meet the first requirement may be collected, and the input information and output information may be processed to obtain first information and second information.
  • the output information satisfies the first requirement
  • the output information meets the first requirement after being processed in a preset processing manner.
  • the input information satisfies the first requirement after being processed in a preset processing manner.
  • the error between the first information and the first reference information is smaller than a preset threshold
  • the error between the second information and the second reference information is smaller than a preset threshold.
  • the protocol defines at least one sample, and the sample includes first reference information and/or second reference information, wherein the first reference information is the label or reference value of the first information, and the second reference information is the label of the second information or reference value.
  • the first device inputs the first reference information into the artificial intelligence network to obtain the second information.
  • the first requirement is that the error between the second information and the second reference information is less than a preset threshold.
  • the error includes any one or a combination of the following:
  • the first reference information and the second reference information can be some reference values defined by the protocol.
  • the first reference information and the second reference information can be obtained by an algorithm close to the theoretical optimum, and the first information and the first The error of the reference signal is within a certain threshold, and the error between the second information that meets the first requirement and the second reference signal is within a certain threshold, wherein the calculation method of the error can be mean square error (Mean Square Error, MSE), normalization Normalized Mean Squared Error (NMSE), cosine similarity, correlation, etc., and at least one of their mathematical relations, such as a*NMSE+b*cosine similarity, that is, using the normalized mean squared error Calculate the first error NMSE, use the cosine similarity to calculate the second error, and use a*first error+b*second error as the final error.
  • MSE mean square error
  • NMSE normalization Normalized Mean Squared Error
  • cosine similarity correlation
  • correlation etc.
  • the artificial intelligence network is located in a channel state information (Channel State Information, CSI) encoding module of the first device, and for the CSI encoding module, the input information may not meet the first requirement.
  • CSI Channel State Information
  • the artificial intelligence network is located in the joint module of the first device, and the joint module includes at least a CSI feedback module.
  • the joint module may be a joint of a CSI feedback module and a CSI reference signal estimation module, then the The input information and/or the output information meet the first requirement; and/or, the input information and/or the output information meet the first requirement after being processed in a preset processing manner.
  • the joint module may be a joint of a precoding module, a CSI feedback module, and a CSI reference signal estimation module, then the input information and/or the output information meet the first requirement; and/or, the input information And/or the output information meets the first requirement after being processed in a preset processing manner.
  • the joint module may be a joint of a scheduling module, a CSI feedback module, and a CSI reference signal estimation module, then the input information and/or the output information meet the first requirement; and/or, the input information and /or the output information meets the first requirement after being processed in a preset processing manner.
  • the joint module may be a joint of a precoding module, a scheduling module, a CSI feedback module, and a CSI reference signal estimation module, then the input information and/or the output information meet the first requirement; and/or, the The input information and/or the output information meet the first requirement after being processed in a preset processing manner.
  • the preset processing method is protocol definition, or, the second device sends to the first device, or the first device sends to the second device, or, the network side device configures , or, the first device reports to the network side device.
  • the input information and the output information include at least one of the following:
  • DMRS Demodulation Reference Signal
  • the associated parameters of the first requirement include at least one of the following:
  • Timing, and/or timing misalignment brings about uniform changes in phase
  • input information may not be considered, and only whether the second information and/or output information meets the first requirement is considered.
  • the input information and the output information include positioning data, and the associated parameters of the first requirement include at least one of the following:
  • Timing, and/or timing misalignment brings about uniform changes in phase
  • the input information and the output information include beam data
  • the first required associated parameters include at least one of the following:
  • Timing, and/or timing misalignment brings about uniform changes in phase
  • the first requirement is a protocol definition, or, the second device sends to the first device, or the second device sends to the first device, or, network-side device configuration, Or, the first device reports to the network side device.
  • the execution subject may be a data collection device, or a module in the data collection device for executing the loading data collection method.
  • the data collection method for loading data executed by the data collection device is taken as an example to illustrate the data collection method provided in the embodiment of the present application.
  • the embodiment of the present application provides a data acquisition device, which is applied to the first device 300. As shown in FIG. 5, the device includes:
  • a collection module 310 configured to collect input information and/or output information of the artificial intelligence network on the first device side;
  • a reporting module 320 configured to send the first information and/or the second information to the second device, the first information and/or the second information meet the first requirement, the first information is selected from the input information, the second information is selected from the output information.
  • the reporting module 320 is further configured to report the first required associated parameters and/or statistical characteristics of the associated parameters.
  • the device also includes:
  • a receiving module configured to receive the statistical range of the statistical feature sent by the second device, where the statistical feature is the statistical feature of the first required association parameter.
  • the statistical characteristics of the first required associated parameters include at least one of the following:
  • the associated parameters of the first requirement include at least one of the following:
  • the first requirement includes at least one of the following:
  • At least one of the associated parameters is greater than a preset first threshold
  • At least one of the associated parameters is greater than or equal to a preset second threshold
  • At least one of the associated parameters is smaller than a preset third threshold
  • At least one of the associated parameters is less than or equal to a preset fourth threshold.
  • the collection module is specifically configured to collect input information that meets the first requirement; and/or
  • the device also includes:
  • a processing module configured to process the collected input information in a preset processing manner to obtain the first information
  • the second information is obtained after processing the collected output information in a preset processing manner.
  • the output information satisfies the first requirement
  • the output information meets the first requirement after being processed in a preset processing manner.
  • the input information satisfies the first requirement after being processed in a preset processing manner.
  • the error between the first information and the first reference information is smaller than a preset threshold
  • the error between the second information and the second reference information is smaller than a preset threshold.
  • the error includes any one or a combination of the following:
  • the artificial intelligence network is located in the channel state information CSI encoding module of the first device, and the input information does not meet the first requirement.
  • the artificial intelligence network is located in a joint module of the first device, and the joint module includes at least a CSI feedback module,
  • the input information and/or the output information meet the first requirement after being processed in a preset processing manner.
  • the preset processing method is protocol definition, or, the second device sends to the first device, or the first device sends to the second device, or, the network side device configures , or, the first device reports to the network side device.
  • the input information and the output information include at least one of the following:
  • the associated parameters of the first requirement include at least one of the following:
  • the input information and the output information include positioning data, and the associated parameters of the first requirement include at least one of the following:
  • the input information and the output information include beam data
  • the first required associated parameters include at least one of the following:
  • the first requirement is a protocol definition, or, the second device sends to the first device, or the second device sends to the first device, or, network-side device configuration, Or, the first device reports to the network side device.
  • the data collection device in the embodiment of the present application may be a device, a device with an operating system or an electronic device, or a component, an integrated circuit, or a chip in a terminal.
  • the apparatus or electronic equipment may be a mobile terminal or a non-mobile terminal.
  • the mobile terminal may include but not limited to the types of terminals 11 listed above, and the non-mobile terminal may be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (Personal Computer, PC), a television ( Television, TV), teller machines or self-service machines, etc., are not specifically limited in this embodiment of the present application.
  • the data acquisition device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 4 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application further provides a communication device 500, including a processor 501, a memory 502, and programs or instructions stored in the memory 502 and operable on the processor 501,
  • a communication device 500 including a processor 501, a memory 502, and programs or instructions stored in the memory 502 and operable on the processor 501
  • the communication device 500 is the first device
  • the program or instruction is executed by the processor 501
  • each process of the above-mentioned embodiment of the data collection method applied to the first device can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a first device.
  • the first device may be a terminal, including a processor and a communication interface, and the processor is used to collect input information and/or output information of the artificial intelligence network on the first device side; the The communication interface is used to send the first information and/or the second information to the second device, the first information and/or the second information meet the first requirement, the first information is selected from the input information, The second information is selected from the output information.
  • This terminal embodiment corresponds to the above-mentioned terminal (ie, first device) side method embodiment, and each implementation process and implementation mode of the above-mentioned method embodiment can be applied to this terminal embodiment, and can achieve the same technical effect.
  • FIG. 7 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 1000 includes but not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010, etc. at least some of the components.
  • the terminal 1000 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 1010 through the power management system, so as to manage charging, discharging, and power consumption through the power management system. Management and other functions.
  • a power supply such as a battery
  • the terminal structure shown in FIG. 7 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.
  • the input unit 1004 may include a graphics processor (Graphics Processing Unit, GPU) 10041 and a microphone 10042, and the graphics processor 10041 is used for the image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 1007 includes a touch panel 10071 and other input devices 10072 .
  • the touch panel 10071 is also called a touch screen.
  • the touch panel 10071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 10072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the radio frequency unit 1001 receives the downlink data from the network side device, and processes it to the processor 1010; in addition, sends the uplink data to the network side device.
  • the radio frequency unit 1001 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
  • the memory 1009 can be used to store software programs or instructions as well as various data.
  • the memory 1009 may mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area may store an operating system, at least one application program or instruction required by a function (such as a sound playback function, an image playback function, etc.) and the like.
  • the memory 1009 may include a high-speed random access memory, and may also include a nonvolatile memory, wherein the nonvolatile memory may be a read-only memory (Read-Only Memory, ROM), a programmable read-only memory (Programmable ROM) , PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) or flash memory.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • PROM erasable programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM electrically erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory for example at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • the processor 1010 may include one or more processing units; optionally, the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application programs or instructions, etc., Modem processors mainly handle wireless communications, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 1010 .
  • the processor 1010 is configured to collect input information and/or output information of the artificial intelligence network on the first device side; send the first information and/or the second information to the second device, and the first information and/or the The second information satisfies the first requirement, the first information is selected from the input information, and the second information is selected from the output information.
  • the processor 1010 is configured to report the first required association parameters and/or statistical features of the association parameters.
  • the processor 1010 is configured to receive the statistical range of the statistical feature sent by the second device.
  • the statistical characteristics include at least one of the following:
  • the associated parameters of the first requirement include at least one of the following:
  • the first requirement includes at least one of the following:
  • At least one of the associated parameters is greater than a preset first threshold
  • At least one of the associated parameters is greater than or equal to a preset second threshold
  • At least one of the associated parameters is smaller than a preset third threshold
  • At least one of the associated parameters is less than or equal to a preset fourth threshold.
  • the processor 1010 is configured to collect input information that meets the first requirement; and/or
  • the processor 1010 is configured to process the collected input information in a preset processing manner to obtain the first information
  • the second information is obtained after processing the collected output information in a preset processing manner.
  • the output information satisfies the first requirement
  • the output information meets the first requirement after being processed in a preset processing manner.
  • the input information satisfies the first requirement after being processed in a preset processing manner.
  • the error between the first information and the first reference information is smaller than a preset threshold
  • the error between the second information and the second reference information is smaller than a preset threshold.
  • the error includes any one or a combination of the following:
  • the artificial intelligence network is located in the channel state information CSI encoding module of the first device, and the input information does not meet the first requirement.
  • the artificial intelligence network is located in a joint module of the first device, and the joint module includes at least a CSI feedback module,
  • the input information and/or the output information meet the first requirement after being processed in a preset processing manner.
  • the preset processing method is protocol definition, or, the second device sends to the first device, or the first device sends to the second device, or, the network side device configures , or, the first device reports to the network side device.
  • the input information and the output information include at least one of the following:
  • the associated parameters of the first requirement include at least one of the following:
  • the input information and the output information include positioning data, and the associated parameters of the first requirement include at least one of the following:
  • the input information and the output information include beam data
  • the first required associated parameters include at least one of the following:
  • the first requirement is a protocol definition, or, the second device sends to the first device, or the second device sends to the first device, or, network-side device configuration, Or, the first device reports to the network side device.
  • the embodiment of the present application also provides a readable storage medium, the readable storage medium may be nonvolatile or volatile, the readable storage medium stores programs or instructions, and the programs or instructions are stored in When executed by the processor, each process of the above data acquisition method embodiment can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.
  • the processor is the processor in the terminal described in the foregoing embodiments.
  • the readable storage medium includes computer readable storage medium, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
  • 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, and the processor is used to run programs or instructions to implement the above-mentioned data collection method embodiment
  • 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 to implement the above-mentioned data collection method embodiment
  • the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase “comprising a " does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.
  • the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
  • the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

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Abstract

La présente demande se rapporte au domaine technique des communications et divulgue un procédé et un appareil d'acquisition de données. Le procédé d'acquisition de données est exécuté par un premier dispositif. Le procédé consiste à : acquérir des informations d'entrée et/ou des informations de sortie d'un réseau d'intelligence artificielle au niveau du premier dispositif ; et envoyer des premières informations et/ou des secondes informations à un second dispositif, les premières informations et/ou les secondes informations satisfaisant une première exigence, les premières informations étant sélectionnées à partir des informations d'entrée et les secondes informations étant sélectionnées à partir des informations de sortie. La solution technique des modes de réalisation de la présente demande peut améliorer les performances d'un système de communication.
PCT/CN2022/118709 2021-09-18 2022-09-14 Procédé et appareil d'acquisition de données WO2023040886A1 (fr)

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Citations (4)

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US20150206542A1 (en) * 2014-01-22 2015-07-23 Comcast Cable Communications, Llc Intelligent Data Delivery
US20200285997A1 (en) * 2019-03-04 2020-09-10 Iocurrents, Inc. Near real-time detection and classification of machine anomalies using machine learning and artificial intelligence
WO2021033827A1 (fr) * 2019-08-22 2021-02-25 주식회사 프로젝트레인보우 Système et procédé d'amélioration de la déficience développementale à l'aide d'un module d'apprentissage profond
CN112651080A (zh) * 2020-12-18 2021-04-13 重庆忽米网络科技有限公司 基于工业ai技术的焊接结构件工艺优化方法及系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150206542A1 (en) * 2014-01-22 2015-07-23 Comcast Cable Communications, Llc Intelligent Data Delivery
US20200285997A1 (en) * 2019-03-04 2020-09-10 Iocurrents, Inc. Near real-time detection and classification of machine anomalies using machine learning and artificial intelligence
WO2021033827A1 (fr) * 2019-08-22 2021-02-25 주식회사 프로젝트레인보우 Système et procédé d'amélioration de la déficience développementale à l'aide d'un module d'apprentissage profond
CN112651080A (zh) * 2020-12-18 2021-04-13 重庆忽米网络科技有限公司 基于工业ai技术的焊接结构件工艺优化方法及系统

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