[go: up one dir, main page]

WO2024208167A1 - Procédé de traitement d'informations, appareil de traitement d'informations, terminal et dispositif côté réseau - Google Patents

Procédé de traitement d'informations, appareil de traitement d'informations, terminal et dispositif côté réseau Download PDF

Info

Publication number
WO2024208167A1
WO2024208167A1 PCT/CN2024/085422 CN2024085422W WO2024208167A1 WO 2024208167 A1 WO2024208167 A1 WO 2024208167A1 CN 2024085422 W CN2024085422 W CN 2024085422W WO 2024208167 A1 WO2024208167 A1 WO 2024208167A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
target
model
distance
measurement information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/085422
Other languages
English (en)
Chinese (zh)
Inventor
贾承璐
孙鹏
杨昂
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Vivo Mobile Communication Co Ltd
Original Assignee
Vivo Mobile Communication Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Vivo Mobile Communication Co Ltd filed Critical Vivo Mobile Communication Co Ltd
Publication of WO2024208167A1 publication Critical patent/WO2024208167A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to an information processing method, an information processing device, a terminal and a network side device.
  • a machine learning (ML) model can be used to locate the terminal.
  • factors such as the displacement of the terminal and changes in the communication environment in which the terminal is located will reduce the accuracy of the positioning results obtained by the ML positioning model. Therefore, in the related art, the positioning method based on the ML model has the defect of low positioning reliability.
  • the embodiments of the present application provide an information processing method, an information processing device, a terminal and a network-side device, which can monitor the accuracy of the output information of a first model, so as to promptly determine that the first model is invalid when the accuracy of the output information of the first model is low, thereby reducing the risk of low positioning reliability caused by continuing to position the terminal using the invalid first model.
  • an information processing method which is executed by a terminal, and the method includes:
  • the terminal inputs the target measurement information into a first model to obtain first target information output by the first model, where the first target information is related to the location of the terminal;
  • the terminal obtains a supervision result of the first model, wherein the supervision result of the first model is determined based on an association relationship between the first target information and second target information, the second target information is information output by the second model after the target measurement information is input into the second model, and the second target information is related to the location of the terminal.
  • an information processing method which is performed by a first node, and the method includes:
  • the first node receives first information from the terminal, wherein the first information includes target measurement information, or the first information includes first target information and target measurement information, the first target information is information output by the first model after the target measurement information is input into the first model, and the first target information is related to the location of the terminal;
  • the first node inputs the target measurement information into a second model to obtain second target information output by the second model, where the second target information is related to the location of the terminal;
  • the first node sends at least one of the second information and the second target information to the terminal, wherein the supervision result of the first model is determined based on the association relationship between the first target information and the second target information,
  • the second information is related to the supervision result or effectiveness of the first model.
  • an information processing device including:
  • a first processing module configured to input target measurement information into a first model to obtain first target information output by the first model, where the first target information is related to a location of a terminal;
  • An acquisition module is used to obtain the supervision result of the first model, wherein the supervision result of the first model is determined based on the association relationship between the first target information and second target information, the second target information is information output by the second model after the target measurement information is input into the second model, and the second target information is related to the location of the terminal.
  • an information processing device comprising:
  • a first receiving module configured to receive first information from a terminal, wherein the first information includes target measurement information, or the first information includes first target information and target measurement information, the first target information is information output by the first model after the target measurement information is input into a first model, and the first target information is related to a location of the terminal;
  • a second processing module configured to input the target measurement information into a second model to obtain second target information output by the second model, where the second target information is related to the location of the terminal;
  • a first sending module is used to send at least one of the second information and the second target information to the terminal, wherein the supervision result of the first model is determined based on the association relationship between the first target information and the second target information, and the second information is related to the supervision result or validity of the first model.
  • 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 processor is used for the terminal to input target measurement information into a first model to obtain first target information output by the first model, wherein the first target information is related to the location of the terminal; the processor or the communication interface is used to obtain a supervision result of the first model, wherein the supervision result of the first model is determined based on an association relationship between the first target information and second target information, the second target information is information output by the second model after the target measurement information is input into the second model, and the second target information is related to the location of the terminal.
  • a first node comprising a processor and a memory, wherein the memory stores a program or instruction that can be executed on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the second aspect are implemented.
  • a first node comprising a processor and a communication interface, wherein the communication interface is used to receive first information from a terminal, wherein the first information includes target measurement information, or the first information includes first target information and target measurement information, the first target information is information output by the first model after the target measurement information is input into a first model, and the first target information is related to the location of the terminal; the processor is used to input the target measurement information into a second model to obtain second target information output by the second model, and the first target information is related to the location of the terminal; The second target information is related to the location of the terminal; the communication interface is also used to send the second information and at least one of the second target information to the terminal, wherein the supervision result of the first model is determined based on the association relationship between the first target information and the second target information, and the second information is related to the supervision result or validity of the first model.
  • 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 first node, the terminal can be used to execute the steps of the method described in the first aspect, and the first node 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 information processing method as described in the first aspect, or to implement the steps of the information processing method as described in the second aspect.
  • the same target measurement information can be input into the first model and the second model respectively to obtain the first target information output by the first model and the second target information output by the second model.
  • the first model when the first model is valid, there is a certain correlation between the first target information and the second target information.
  • the supervision result of the first model can be determined. For example: if the first target information and the second target information correspond to the same terminal location information, the first model is determined to be valid. If the first target information and the second target information correspond to different terminal location information, the first model is determined to be invalid.
  • FIG1 is a schematic diagram of the structure of a wireless communication system to which an embodiment of the present application can be applied;
  • FIG. 2 is a schematic diagram of a structure of an information processing method provided in an embodiment of the present application
  • FIG3 is a schematic diagram of the architecture of a neural network model
  • Fig. 4 is a schematic diagram of a neuron
  • FIG5a is a flow chart of a model supervision scheme 1 in an embodiment of the present application.
  • FIG5 b is a flow chart of a second model supervision scheme in an embodiment of the present application.
  • FIG5c is a flow chart of model supervision scheme 3 in an embodiment of the present application.
  • FIG5d is a graph of the empirical cumulative distribution (Empirical CDF) function of the third distance in an embodiment of the present application.
  • FIG6 is a flow chart of an information processing method provided in an embodiment of the present application.
  • FIG7 is a schematic diagram of the structure of an information processing device provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of an information processing device provided in an embodiment of the present application.
  • FIG9 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application.
  • FIG10 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application.
  • FIG11 is a schematic diagram of the structure of a network side device provided in an embodiment of the present application.
  • FIG. 12 is a schematic diagram of the structure of another 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 either 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 operations to be performed or request results 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 is a block diagram of a wireless communication system applicable to the embodiments 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 palm computer, a netbook, an ultra-mobile personal computer (Ultra-mobile Personal Computer, UMPC), a mobile Mobile Internet Device (MID), Augmented Reality (AR), Virtual Reality (VR) equipment, robots, wearable devices (Wearable Device), flight vehicles (flight vehicles), vehicle-mounted equipment (VUE), ship-mounted equipment, pedestrian terminals (PUE), smart homes (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), game consoles, personal computers (PC), ATMs or self-service machines and other terminal-side devices.
  • MID mobile Mobile Internet Device
  • AR Augmented Reality
  • VR Virtual Reality
  • VA
  • 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 equipment 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 (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 (WLAN) access point (AS) or a wireless fidelity (WiFi) node, etc.
  • WLAN wireless local area network
  • AS wireless local area network
  • 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
  • the core network device may include but is not limited to at least one of the following: core network node, core network function, location management function (Location Management Function, LMF), mobility management entity (Mobility Management Entity, MME), access mobility management function (Access and Mobility Management Function, AMF), session management function (Session Management Function, SMF), user plane function (User Plane Function, UPF), policy control function (Policy Control Function, PCF), policy and charging rules function unit (Policy and Charging Rules Function, PCRF), edge application service discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data storage (Unified Data Repository, UDR), home user server (Home Subscriber Server, HSS), centralized network configuration (CNC), network storage function (Network Repository Function, NRF), network exposure function (Network Exposure Function, NEF), local NEF (Local NEF, or L-NEF), Binding Support Function (BSF), Application Function (AF), etc.
  • an information processing method provided in an embodiment of the present application, the execution subject of which may be a terminal, and the terminal may be a terminal 11 of various types listed in FIG1 , or other terminals other than the terminal types listed in the embodiment shown in FIG1 , which are not specifically limited here.
  • the information processing method may include the following steps:
  • Step 201 The terminal inputs target measurement information into a first model to obtain first target information output by the first model, where the first target information is related to the location of the terminal.
  • Step 202 The terminal obtains a supervision result of the first model, wherein the supervision result of the first model is determined based on an association relationship between the first target information and second target information, the second target information is information output by the second model after the target measurement information is input into the second model, and the second target information is related to the location of the terminal.
  • the target measurement information may be information obtained by the terminal measuring a reference signal.
  • the reference signal may include at least one of the following:
  • CSI-RS CSI Reference Signal
  • SRS Sounding Reference Signal
  • SSB Synchronization Signal and PBCH block
  • PRS Positioning Reference Signal
  • TRS Tracking Reference Signal
  • PTRS Phase-Tracking Reference Signal
  • the above-mentioned target measurement information may include at least one of time domain channel impulse response (Channel Impulse Response, CIR), delay power spectrum (Power Delay Profile, PDP), channel frequency response, channel energy response, reference signal received power (Reference Signal Received Power, RSRP), reference signal received path power (Reference Signal Received Path Power, RSRPP), reference signal received quality (Reference Signal Received Quality, RSRQ), signal to interference plus noise ratio (Signal to Interference plus Noise Ratio, SINR), and delay Doppler domain channel.
  • CIR Channel Impulse Response
  • PDP Power Delay Profile
  • RSRP Reference Signal Received Power
  • RSRPP reference signal received path power
  • RSRQ Reference Signal Received Quality
  • SINR Signal to interference plus Noise Ratio
  • the above-mentioned measurement information such as channel frequency response, channel energy response, RSRP, RSRPP, RSRQ and SINR can be layer 1 (Layer 1, L1) measurement information, that is, real-time measurement information; or these measurement information can be layer 3 (Layer 3, L3) measurement information, that is, measurement information after smoothing and filtering the real-time measurement information and historical measurement information.
  • the target measurement information includes T TRPs or cell-associated measurement information, where T is an integer greater than or equal to 1.
  • the reference signal used to measure and obtain the target measurement information may be sent by one or at least two TRPs or cells.
  • the target reference signal used to estimate and obtain the target measurement information includes M TRPs or cell-associated reference signals, where M is an integer greater than or equal to 1.
  • the target measurement information may be preset, such as configured on the network side.
  • the first target information is related to the location of the terminal, and the first target information may include the location information of the terminal, or the first target information includes feature information related to the location of the terminal.
  • the location information of the terminal includes at least one of the following:
  • the relative position information of the terminal is the relative position information of the terminal.
  • the absolute position information may include the coordinate position information of the terminal, such as Global Positioning System (GPS) positioning information.
  • GPS Global Positioning System
  • the relative position information may be the position information of the terminal relative to a reference point, such as the position information of the terminal relative to a base station.
  • the target model may be used to output feature information related to the terminal location.
  • the characteristic information related to the location of the terminal may include at least one of the following:
  • LOS Line of Sight
  • TOA path arrival time
  • LOS Line of Sight
  • AOA Angle of Arrival
  • LOS Line of Sight
  • RSTD path reference signal time difference
  • PMI Precoding Matrix Indicator
  • the above-mentioned LOS path can be the actual LOS path between the terminal and the network side device, or it can be the hypothetical LOS path between the terminal and the network side device. That is to say, no matter whether there is a LOS path between the user equipment (User Equipment, UE) and the base station in actual circumstances, the TOA, AOA, AOD, RSTD, etc. of the LOS path between the UE and the base station can be determined based on the embodiments of the present application, and then the UE's location information can be determined accordingly.
  • User Equipment User Equipment
  • the TOA, AOA, AOD, RSTD of the above-mentioned LOS path, as well as beam quality, channel compression indication, PMI, time domain channel information, frequency domain channel information, spatial domain channel information, cell communication quality, cell switching judgment results, etc. are all information closely related to the position of the terminal. That is, when the position of the terminal changes, the TOA, AOA, AOD, RSTD of the above-mentioned LOS path, as well as beam quality, channel compression indication, PMI, time domain channel information, frequency domain channel information, spatial domain channel information, cell communication quality, cell switching judgment results, etc. will also change accordingly. For example: the farther the terminal is from the base station, the worse the beam quality will be, the transmission delay of the reference signal will increase, the cell communication quality will deteriorate, and even trigger cell switching.
  • the second model may be a supervisory model of the first model, used to supervise the accuracy of the first model or to supervise whether the first model is effective.
  • the second model may be a model corresponding to a different space than the first model, such as the first model corresponding to The physical space, the second model corresponds to the latent space, such as the feature space.
  • the second model can be used to map CIR to another latent space, and the relative position relationship d2 of the UE in the latent space is consistent with the relative position relationship d1 of the UE in the physical space.
  • the first model if the first model is valid, the relative positions of the CIRs of the two UEs in the latent space and the physical space should be consistent; if the first model fails, the relative positions of the CIRs of the two UEs in the latent space and the physical space are no longer consistent.
  • the physical space may be understood as the space corresponding to the position coordinates of the UE, such as the position coordinates of the UE may be regarded as a point in the physical space.
  • g is the first model to be supervised, that is, the mapping from CIR to physical space
  • f is the second model, that is, the mapping from CIR to latent space
  • d1 represents the first distance
  • d2 represents the second distance
  • represents the third distance
  • ⁇ represents the third threshold.
  • the second target information is related to the location of the terminal.
  • the second target information may include the location information of the terminal, or the second target information may include distance information between at least two terminal locations, for example, distance information between the locations of the same terminal at two different times, or distance information between the locations of different terminals.
  • the second target information may be relative position information of the terminal, and a reference position of the second target information is different from a reference position of the first target information.
  • the second target information includes characteristic information related to the location of the terminal, or the second target information includes the distance or difference between characteristic information related to the locations of at least two terminals of the terminal.
  • first target information and the second target information are different types of information, such as the first target information is the location information of the terminal, and the second target information is the distance information between the locations of the terminal at different times, or even if the first target information and the second target information correspond to different spaces, such as the first target information is the location information of the terminal in the physical space, and the second target information is the location information of the terminal in the latent space, given that the first target information and the second target information are respectively related to the location of the terminal, there is still a certain correlation between the first target information and the second target information, such as the first distance between the locations of the terminal at different times in the physical space determined based on the first target information, and the second distance between the locations of the terminal at different times in the latent space determined based on the second target information can be equal, or linearly related.
  • the model supervision results such as the accuracy of the first target information output by the first target or whether the first model is effective can be determined.
  • the first target information is usually the terminal position in the physical space
  • the second target information is the terminal position in the latent space
  • the terminal distance is used as an example for illustration, which does not constitute a specific limitation here.
  • first model and the second model in the implementation of the present application can be an artificial intelligence (AI) model or a machine learning (ML) model.
  • AI artificial intelligence
  • ML machine learning
  • the first model and the second model are AI models as an example in the embodiments of the present application.
  • AI modules can be implemented in many ways, 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 neural network includes an input layer, a hidden layer, and an output layer, which can predict possible output results (Y) based on the input and output information (X 1 ⁇ X n ) obtained from the input layer.
  • K the total number of input parameters.
  • 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 f(.). With the model neural network, 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 W and b to minimize the value of the above loss function. The smaller the loss value, the closer our neural network is to the actual situation.
  • the common optimization algorithms are basically based on the error back propagation (BP) algorithm.
  • the basic idea of the error back propagation 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.
  • Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (which means stochastic gradient descent with momentum), adaptive gradient descent (Adaptive gradient descent, Adagrad), adaptive learning rate adjustment (Adadelta), root mean square error speed reduction (root mean square prop, RMSprop), adaptive momentum estimation (Adaptive Moment Estimation, Adam), etc.
  • the above-mentioned second model can be deployed in the terminal, or it may be deployed in the first node, and the first node may be a network-side device, such as an access network device or a core network device, or the first node may be any device with a target model, such as an application server.
  • the first node is usually an access network device, such as a base station, for example, which is not specifically limited here.
  • the above-mentioned second target information needs to input the target measurement information into the second model to obtain, and the supervision result of the above-mentioned first model needs to be determined based on the association relationship between the first target information and the second target information.
  • the terminal may obtain the supervision result of the first model by processing the target measurement information based on its own second model to obtain the second target information, and compare the first target information and the second target information obtained based on the same target measurement information to determine the supervision result of the first model based on the correlation between the two.
  • the terminal may obtain the supervision result of the first model in the following manner: the terminal sends target measurement information to a first node having a second model, and the first node processes the target measurement information using the second model to obtain second target information, and feeds back the second target information to the terminal. Thereafter, the terminal may compare the first target information and the second target information obtained based on the same target measurement information to determine the supervision result of the first model based on the correlation between the two.
  • the terminal may obtain the supervision result of the first model in such a manner that: the terminal sends target measurement information and first target information to a first node having a second model, and the first node processes the target measurement information using the second model to obtain the second target information, and after comparing the first target information and the second target information obtained based on the same target measurement information to determine the supervision result of the first model according to a correlation between the two, the second information is fed back to the terminal to indicate the supervision result of the first model through the second information, such as the second information indicating that the first model is valid or invalid, or indicating that the first model is valid in some scenarios and invalid in other scenarios, etc.
  • the first target information and the second target information in the embodiment of the present application are obtained based on the same target measurement information, that is, the same target measurement information is input into the first model and the second model respectively, then the information output by the first model is the first target information, and the information output by the second model is the second target information.
  • the terminal location information or the feature information related to the location corresponding to the same target measurement information should be the same or can be related. Based on this, the supervision result of the first model can be determined based on the association relationship between the first target information and the second target information, such as whether the first model is valid or invalid.
  • the supervision result of the first model determined based on the association relationship between the first target information and the second target information in the embodiment of the present application may be determined based on the association relationship between the first target information and the second target information corresponding to the same target measurement information.
  • the terminal when the terminal has the first model and the first node has the second model, the terminal obtains the supervision result of the first model, including:
  • the terminal sends first information to the first node, where the first information includes the target measurement information, or the first information includes the first target information and the target measurement information;
  • the terminal receives second target information from the first node and determines the supervision result of the first model based on the association relationship between the first target information and the second target information, or the terminal receives second information from the first node, and the second information is related to the supervision result or validity of the first model.
  • the terminal sends first target information and the target measurement information to the first node, and the first node inputs the target measurement information into a second model to obtain second target information. Then, the first node determines the supervision result of the first model based on the association relationship between the first target information and the second target information, that is, the first node determines the supervision result of the first model and indicates the supervision result or validity-related information of the first model to the terminal through the second information.
  • the terminal sends the target measurement information to the first node, the first node inputs the target measurement information into the second model to obtain second target information, and then the first node sends the second target information to the terminal. Finally, the terminal determines the supervision result of the first model based on the association relationship between the first target information and the second target information, that is, the supervision result of the first model is determined by the terminal.
  • the terminal when the terminal has the first model and the second model, the terminal obtains the supervision result of the first model, including:
  • the terminal inputs the target measurement information into the second model to obtain the second target information output by the second model;
  • the terminal determines a supervision result of the first model according to an association relationship between the first target information and the second target information.
  • the terminal has both the first model and the second model. Therefore, the terminal can use the first model to obtain the first target information, use the second model to obtain the second target information, and compare the first target information and the second target information corresponding to the same target measurement information to determine the supervision result of the first model based on the correlation between the first target information and the second target information.
  • the method further includes:
  • the terminal sends third information to the first node, where the third information is related to the supervision result or validity of the first model.
  • the terminal reports the supervision results or validity-related information of the first model to the first node, so that the first node can decide whether to use the data output by the first model as positioning information.
  • the first node uses other methods (such as non-AI methods) to determine the location information of the terminal; or the first node decides whether to update the first model.
  • the first model fails, the first node trains and updates the first model, and sends the updated first model to the terminal; or, the first node decides whether to deactivate the current first model and activate or select a new first model.
  • the first node deactivates the currently used first model and activates or selects a new first model.
  • the terminal determines the supervision result of the first model according to the association relationship between the first target information and the second target information, including:
  • the terminal obtains a first distance between a group of first target information and obtains a second distance between a group of second target information, wherein the group of first target information and the group of second target information are obtained by processing the same group of target measurement information based on the first model and the second model respectively;
  • the terminal determines a supervision result of the first model according to a difference or correlation between the first distance and the second distance.
  • a set of target measurement information may include at least two pieces of target measurement information.
  • a set of target measurement information includes two pieces of target measurement information.
  • a set of target measurement information may include at least two pieces of target measurement information from the same terminal at different times.
  • the time may include an Orthogonal Frequency Division Multiplex (OFDM) symbol, a subframe, a frame, a nanosecond, a microsecond, a millisecond, a second, a minute, an hour, a day, a month, etc.
  • a set of target measurement information may include target measurement information obtained by the terminal by measuring a reference signal at time 1, and target measurement information obtained by the terminal by measuring a reference signal at time 2.
  • a set of target measurement information may include target measurement information from at least two different terminals.
  • a set of target measurement information may include target measurement information of terminal A and target measurement information of terminal B.
  • a set of target measurement information may include at least two target measurement information from different terminals
  • the terminal needs to obtain the target measurement information of other terminals so as to utilize the target measurement information of the terminal and the target measurement information of other terminals to form a set of target measurement information.
  • the first node when the first node has the second model, different terminals can respectively send their respective target measurement information to the first node, such as a base station, so that the base station uses the target measurement information of different terminals to form a group of target measurement information.
  • the first node in the present application may also be a core network device.
  • the first node in the embodiment of the present application is taken as an example that the base station is an example, which does not constitute a specific limitation here.
  • the terminal may input target measurement information in a set of target measurement information into the first model respectively to obtain a set of first target information respectively output by the first model, and then calculate the first distance between any two first target information in the set of first target information.
  • a set of target measurement information includes target measurement information A of UE 1 and target measurement information B of UE 2
  • the terminal inputs the target measurement information A and the target measurement information B into the first model respectively, obtains the location information of UE 1 and the location information of UE 2 respectively output by the first model, and then calculates the first distance between UE 1 and UE 2 based on this.
  • a set of target measurement information includes CIRs collected by the same terminal in time slots (slot) 1 and slot 2, respectively
  • the terminal inputs the CIR collected in slot 1 and the CIR collected in slot 2 into the first model respectively, obtains the position information of the terminal in slot 1 and the position information of the terminal in slot 2 respectively output by the first model, and then calculates the first distance between the positions of the terminals in slot 1 and slot 2 respectively.
  • the terminal may input a set of target measurement information into the second model in parallel to obtain second target information output by the second model, where the second target information is the second distance.
  • the terminal inputs the target measurement information into the second model to obtain second target information output by the second model, including:
  • the terminal inputs a set of target measurement information into a second model, and obtains second target information output by the second model, wherein the second target information includes:
  • the distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information is a distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information.
  • a set of target measurement information includes target measurement information A of UE 1 and target measurement information B of UE 2
  • the terminal inputs target measurement information A and target measurement information B into the second model in parallel to obtain the second distance between UE 1 and UE 2 output by the second model.
  • a set of target measurement information includes CIRs collected by the same terminal in slot 1 and slot 2 respectively
  • the terminal inputs the CIR collected in slot 1 and the CIR collected in slot 2 into the second model in parallel, and obtains the second distance between the positions of the terminal in slot 1 and slot 2 respectively output by the second model.
  • the terminal may input a set of target measurement information into the second model respectively to obtain a set of second target information respectively output by the second model, and then calculate the second distance between any two second target information in the set of second target information.
  • a parallel Siamese network may be used to train the second model.
  • the twin network includes two positioning models with consistent structures and parameters.
  • the distance between the predicted positions output by the two positioning models is obtained as the output result of the twin network, and the output result of the twin network is compared with the distance in the label to obtain the loss function of the twin network, such as the mean absolute error (MAE).
  • MAE mean absolute error
  • the two target measurement information in the group of target measurement information are respectively input into the first model, and two first target information will be obtained.
  • the distance between the two first target information is the first distance; the two target measurement information in the group of target measurement information are input into the second model in parallel, and the second distance will be obtained.
  • the first distance includes at least one of the following: Euclidean distance, Manhattan distance, and cosine distance;
  • the second distance includes at least one of the following: Euclidean distance, Manhattan distance, and cosine distance.
  • Minkowski distance is defined as: given a sample space X, X is a set of points in an m-dimensional real vector space, where x i ,x j ⁇ X, the Minkowski distance between the two is:
  • d ij the Euclidean distance
  • d ij represents the Chebyshev distance, which takes the maximum absolute value of the difference between the coordinate values, that is,
  • the difference between the first distance and the second distance may include: a difference between the first distance and the second distance, or a spatial distance.
  • the correlation between the first distance and the second distance may include: the similarity between the first distance and the second distance, such as whether the first distance and the second distance are equal, and whether the first distance can be linearly transformed to obtain the second distance.
  • the correlation between the first distance and the second distance may be measured by calculating a correlation coefficient between the first distance and the second distance.
  • X is a set of points in an m-dimensional real vector space, where x i ,x j ⁇ X, the correlation coefficient of samples x i ,x j can be expressed as the following formula:
  • rij represents the correlation coefficient between xi and xj .
  • X is a set of points in an m-dimensional real vector space, where x i ,x j ⁇ X, then the correlation coefficient of samples x i ,x j can be the cosine of the angle between samples x i ,x j , which can be expressed as the following formula:
  • s ij represents the cosine of the angle between xi and xj .
  • the correlation and distance between the first distance and the second distance in the embodiment of the present application are two different concepts, wherein the distance describes the spatial similarity, while the correlation describes the consistency of the change trend.
  • the terminal determines the supervision result of the first model according to the difference or correlation between the first distance and the second distance, which may be: if the first distance is equal to the second distance, or the first distance and the second distance satisfy a preset linear conversion relationship, or the difference between the first distance and the second distance is less than or equal to a certain threshold, or after the first distance and the second distance are converted to the space of the same dimension, the difference between the two is less than or equal to a certain threshold, then the first model can be judged to be valid.
  • the difference or correlation between the first distance and the second distance may be: if the first distance is equal to the second distance, or the first distance and the second distance satisfy a preset linear conversion relationship, or the difference between the first distance and the second distance is less than or equal to a certain threshold, or after the first distance and the second distance are converted to the space of the same dimension, the difference between the two is less than or equal to a certain threshold, then the first model can be judged to be valid.
  • the first model can be judged to be invalid.
  • the terminal determines the supervision result of the first model according to the difference between the first distance and the second distance, including:
  • the terminal determines that the first model is invalid, and the first condition includes at least one of the following:
  • the difference between the first distance and the second distance is greater than a first threshold
  • F groups of first target measurement information wherein a difference between a first distance and a second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is greater than a second threshold
  • the third distance between the first distance distribution and the second distance distribution is greater than or equal to the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the difference between the first distance and the second distance may be a difference value obtained by directly subtracting the first distance from the second distance, or a distance value obtained by performing a distance comparison after transforming the first distance and the second distance into the same spatial coordinates.
  • the first target measurement information represents target measurement information that satisfies the following condition: a difference between a first distance and a second distance corresponding to the same group of target measurement information is greater than a first threshold.
  • the difference value between the first distance and the second distance corresponding to the same set of target measurement information is greater than the first threshold, which can reflect that the output results of the first model and the second model are inconsistent.
  • the first model may be judged to be invalid if the number of times the output results of the first model and the second model are inconsistent reaches a certain threshold F, or the probability of the output results of the first model and the second model being inconsistent reaches a second threshold.
  • the first model and the second model may be used to obtain the first distance and the second distance corresponding to each of all groups of target measurement information or preset R groups of target measurement information measured within the target time range, respectively, and then, based on the third distance between the distribution of the first distance and the distribution of the second distance, it is determined that the first model has failed.
  • the third distance includes at least one of the following:
  • the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution is the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution.
  • line X as shown in FIG5d represents the Empirical CDF function curve of the third distance when the target measurement information is the measurement information measured in the same environment as the first training set;
  • line Y as shown in FIG5d represents the Empirical CDF function curve of the third distance when the target measurement information is the measurement information measured in a different environment from the first training set.
  • first threshold, second threshold, third threshold, F and other thresholds in the embodiment of the present application can be determined by the terminal and reported to the network side device, or the above thresholds can be indicated by the network side device or by protocol agreement, which will not be repeated here.
  • the terminal determines the supervision result of the first model according to the difference between the first distance and the second distance, including:
  • the terminal determines that the first model is valid, and the second condition includes at least one of the following:
  • the difference between the first distance and the second distance is less than or equal to a first threshold
  • the number of groups of first target measurement information is less than or equal to F, wherein the difference between the first distance and the second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is less than or equal to a second threshold
  • the third distance between the first distance distribution and the second distance distribution is less than the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the present embodiment is similar to the previous embodiment in how to determine the failure of the first model, except that: In this implementation manner, it is used to determine whether the first model is valid.
  • the specific explanation can refer to the explanation in the previous implementation manner, which will not be repeated here.
  • the supervision result of the first model includes at least one of the following:
  • the number of groups of target measurement information that meets the first condition such as the number of groups of first target measurement information
  • the proportion of target measurement information that meets the first condition such as the proportion of the first target measurement information in all target measurement information within a specified time range, or the proportion of the first target measurement information in a specified R group of target measurement information;
  • Timestamp information such as timestamp information of obtaining the supervision result of the first model, or timestamp information of target measurement information based on which the supervision result of the first model is obtained.
  • the method further includes at least one of the following:
  • the terminal performs a first preprocessing on the target measurement information, wherein the data input by the first model includes the target measurement information after the first preprocessing;
  • the terminal performs a second preprocessing on the target measurement information, wherein the data input by the second model includes the target measurement information after the second preprocessing.
  • the first preprocessing and the second preprocessing may be the same preprocessing, such as compression, quantization, truncation, filtering, normalization, etc.
  • the first preprocessing and the second preprocessing may reduce the data volume of the target measurement information, such as reducing the number of bits of the target measurement information, or the first preprocessing and the second preprocessing may simplify or change the format of the target measurement information to facilitate input into the first model and the second model.
  • the second pre-processed target measurement information may be sent to the first node, thereby reducing air interface resources consumed by transmitting the target measurement information.
  • the method further includes at least one of the following:
  • the terminal performs a first post-processing on the output information of the first model, wherein the first target information includes information obtained by the first post-processing;
  • the terminal performs second post-processing on the output information of the second model, wherein the second target information includes information obtained by the second post-processing.
  • the first post-processing and the second post-processing may be the same or similar processing, and the information after the first post-processing and the second post-processing are in the same dimension or in the same space, so that it is easy to obtain the correlation relationship between the first target information and the second target information.
  • the first post-processing and the second post-processing are used to convert the position information output by the first model and the position information output by the second model into the global coordinate position. In this way, by comparing whether the two global coordinate positions are consistent, the first Whether the output information of the model is consistent with the output information of the second model.
  • the first post-processing and the second post-processing are used to convert the time domain information output by the first model and the time domain information output by the second model into frequency domain information. In this way, by comparing whether the two frequency domain information are consistent, it can be determined whether the output information of the first model is consistent with the output information of the second model.
  • the same target measurement information can be input into the first model and the second model respectively to obtain the first target information output by the first model and the second target information output by the second model.
  • the first model when the first model is valid, there is a certain correlation between the first target information and the second target information.
  • the supervision result of the first model can be determined. For example: if the first target information and the second target information correspond to the same terminal location information, the first model is determined to be valid. If the first target information and the second target information correspond to different terminal location information, the first model is determined to be invalid.
  • the first node may be a network side device or a server, wherein the network side device may be various types of network side devices 12 listed in Figure 1, such as an access network device or a core network device, or other network side devices other than the types of network side devices listed in the embodiment shown in Figure 1.
  • the first node is usually taken as a base station in the embodiment of the present application for example. This does not constitute a specific limitation.
  • the signal processing method may include the following steps:
  • Step 601 the first section receives first information from a terminal, wherein the first information includes target measurement information, or the first information includes first target information and target measurement information, the first target information is information output by the first model after the target measurement information is input into a first model, and the first target information is related to the location of the terminal.
  • Step 602 The first node inputs the target measurement information into a second model to obtain second target information output by the second model, where the second target information is related to the location of the terminal.
  • Step 603 The first node sends at least one of the second information and the second target information to the terminal, wherein the supervision result of the first model is determined based on the association relationship between the first target information and the second target information, and the second information is related to the supervision result or validity of the first model.
  • the supervision result of the first model is determined by the first node.
  • the method before the first node sends the second information to the terminal, the method further includes:
  • the first node determines the supervision result of the first model according to the association relationship between the first target information and the second target information.
  • the supervision result of the first model is determined by the terminal.
  • the first node interacts with the terminal to jointly implement the model supervision function of the first model.
  • the target measurement information includes at least one of the following:
  • Time domain channel impulse response channel frequency response, delay power spectrum, channel energy response, reference signal received power RSRP, reference signal received path power RSRPP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, delay Doppler domain channel.
  • the first node determines the supervision result of the first model according to the association relationship between the first target information and the second target information, including:
  • the first node obtains a first distance between a group of first target information and obtains a first distance between a group of second target information
  • the first target information and the second target information are obtained by processing the same set of target measurement information based on the first model and the second model respectively;
  • the first node determines the supervision result of the first model according to the difference between the first distance and the second distance, including:
  • the first node determines that the first model fails, and the first condition includes at least one of the following:
  • the difference between the first distance and the second distance is greater than a first threshold
  • F groups of first target measurement information wherein a difference between a first distance and a second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • the third distance between the first distance distribution and the second distance distribution is greater than or equal to the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the first node determines the supervision result of the first model according to the difference between the first distance and the second distance, including:
  • the first node determines that the first model is valid, and the second condition includes at least one of the following:
  • the difference between the first distance and the second distance is less than or equal to a first threshold
  • the number of groups of first target measurement information is less than or equal to F, wherein the difference between the first distance and the second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is less than or equal to a second threshold
  • the third distance between the first distance distribution and the second distance distribution is less than the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the set of target measurement information includes at least one of the following:
  • the first node inputs the target measurement information into the second model to obtain second target information output by the second model, including:
  • the first node inputs a set of target measurement information into the second model, and obtains second target information output by the second model, wherein the second target information includes:
  • the distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information is a distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information.
  • the third distance includes at least one of the following:
  • the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution is the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution.
  • the first distance includes at least one of the following: Euclidean distance, Manhattan distance, and cosine distance;
  • the second distance includes at least one of the following: Euclidean distance, Manhattan distance, and cosine distance.
  • the supervision result of the first model includes at least one of the following:
  • the target measurement information includes T TRPs or cell-associated measurement information, where T is an integer greater than or equal to 1.
  • the target reference signal used to estimate the target measurement information includes M TRPs or cell-associated reference signals, where M is an integer greater than or equal to 1.
  • the first node in the case where the second model is located at the first node, the first node interacts with the terminal to utilize the second model of the first node to monitor the effectiveness of the first model used by the terminal, which can achieve similar beneficial effects as the method embodiment shown in Figure 2. To avoid repetition, it will not be repeated here.
  • the information processing method provided in the embodiment of the present application can be executed by an information processing device.
  • the information processing device provided in the embodiment of the present application is described by taking the information processing device executing the information processing method as an example.
  • the information processing device 700 provided in the embodiment of the present application may be a device in a terminal.
  • the information processing device 700 may include the following modules:
  • a first processing module 701 is used to input target measurement information into a first model to obtain first target information output by the first model, where the first target information is related to the location of the terminal;
  • the acquisition module 702 is used to obtain the supervision result of the first model, wherein the supervision result of the first model is determined based on the association relationship between the first target information and second target information, the second target information is information output by the second model after the target measurement information is input into the second model, and the second target information is related to the location of the terminal.
  • the terminal has the first model and the first node has the second model:
  • the acquisition module 702 includes: a first sending unit, a first receiving unit and a first determining unit;
  • a first sending unit configured to send first information to the first node, where the first information includes the target measurement information, or the first information includes the first target information and the target measurement information;
  • a first receiving unit configured to receive second target information from the first node
  • a first determining unit configured to determine a supervision result of the first model according to an association relationship between the first target information and the second target information
  • the acquisition module 702 includes: the first sending unit and the second receiving unit;
  • the second receiving unit is used to receive second information from the first node, where the second information is related to the supervision result or validity of the first model.
  • the acquiring module 702 includes:
  • a first processing unit configured to input the target measurement information into the second model to obtain the second target information output by the second model
  • the second determining unit is used to determine the supervision result of the first model according to the association relationship between the first target information and the second target information.
  • the information processing device 700 further includes:
  • the second sending module is used to send third information to the first node, where the third information is related to the supervision result or validity of the first model.
  • the target measurement information includes at least one of the following:
  • Time domain channel impulse response channel frequency response, delay power spectrum, channel energy response, reference signal received power RSRP, reference signal received path power RSRPP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, delay Doppler domain channel.
  • the first target information includes at least one of the following:
  • the first determining unit is specifically configured to:
  • a supervision result of the first model is determined according to a difference or correlation between the first distance and the second distance.
  • the first determining unit is specifically configured to:
  • the first condition includes at least one of the following:
  • the difference between the first distance and the second distance is greater than a first threshold
  • F groups of first target measurement information wherein a difference between a first distance and a second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is greater than a second threshold
  • the third distance between the first distance distribution and the second distance distribution is greater than or equal to the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the first determining unit is specifically configured to:
  • the first model is determined to be valid, and the second condition includes at least one of the following:
  • the difference between the first distance and the second distance is less than or equal to a first threshold
  • the number of groups of first target measurement information is less than or equal to F, wherein the difference between the first distance and the second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is less than or equal to a second threshold
  • a third distance between the first distance distribution and the second distance distribution is less than a third threshold value, and the first distance distribution is The distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the set of target measurement information includes at least one of the following:
  • the first processing unit is specifically configured to:
  • a set of target measurement information is input into a second model, and second target information output by the second model is obtained, wherein the second target information includes:
  • the distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information is a distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information.
  • the third distance includes at least one of the following:
  • the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution is the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution.
  • the supervision result of the first model includes at least one of the following:
  • the information processing device 700 further includes at least one of the following:
  • a third processing module configured to perform a first preprocessing on the target measurement information, wherein the data input by the first model includes the target measurement information after the first preprocessing;
  • the fourth processing module is used to perform a second preprocessing on the target measurement information, wherein the data input by the second model includes the target measurement information after the second preprocessing.
  • the information processing device 700 further includes at least one of the following:
  • a fifth processing module configured to perform a first post-processing on the output information of the first model, wherein the first target information includes information obtained through the first post-processing;
  • a sixth processing module is used to perform a second post-processing on the output information of the second model, wherein the second target information includes information obtained by the second post-processing.
  • the target measurement information includes T TRPs or cell-associated measurement information, where T is an integer greater than or equal to 1.
  • the target reference signal used to estimate the target measurement information includes M TRPs or cell-associated reference signals, where M is an integer greater than or equal to 1.
  • the information processing device 700 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 information processing device 700 provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 2 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the information processing device 800 provided in the embodiment of the present application may be a device within a first node.
  • the first node may be a network-side device or other devices other than the network-side device, such as a server.
  • the information processing device 800 may include the following modules:
  • a first receiving module 801 is configured to receive first information from a terminal, wherein the first information includes target measurement information, or the first information includes first target information and target measurement information, the first target information is information output by the first model after the target measurement information is input into a first model, and the first target information is related to the location of the terminal;
  • a second processing module 802 is used to input the target measurement information into a second model to obtain second target information output by the second model, where the second target information is related to the location of the terminal;
  • the first sending module 803 is used to send at least one of the second information and the second target information to the terminal, wherein the supervision result of the first model is determined based on the association relationship between the first target information and the second target information, and the second information is related to the supervision result or validity of the first model.
  • the information processing device 800 further includes:
  • the first determination module is used to determine the supervision result of the first model according to the association relationship between the first target information and the second target information.
  • the information processing device 800 further includes:
  • the second receiving module is used to receive third information from the terminal, where the third information is related to the supervision result or effectiveness of the first model.
  • the target measurement information includes at least one of the following:
  • Time domain channel impulse response channel frequency response, delay power spectrum, channel energy response, reference signal received power RSRP, reference signal received path power RSRPP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, delay Doppler domain channel.
  • the first target information includes at least one of the following:
  • the first determining module is specifically configured to:
  • a supervision result of the first model is determined according to a difference or correlation between the first distance and the second distance.
  • the first determining module is specifically configured to:
  • the first condition includes at least one of the following:
  • the difference between the first distance and the second distance is greater than a first threshold
  • F groups of first target measurement information wherein the difference between the first distance and the second distance corresponding to the same group of first target measurement information is greater than the first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is greater than a second threshold
  • the third distance between the first distance distribution and the second distance distribution is greater than or equal to the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the first determining module is specifically configured to:
  • the first model is determined to be valid, and the second condition includes at least one of the following:
  • the difference between the first distance and the second distance is less than or equal to a first threshold
  • the number of groups of first target measurement information is less than or equal to F, wherein the first distance and the second distance corresponding to the same group of first target measurement information are less than or equal to F.
  • the difference value of the distance is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is less than or equal to a second threshold
  • the third distance between the first distance distribution and the second distance distribution is less than the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the set of target measurement information includes at least one of the following:
  • the second processing module 802 is specifically configured to:
  • the first node inputs a set of target measurement information into the second model, and obtains second target information output by the second model, wherein the second target information includes:
  • the distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information is a distance between positions corresponding to at least two pieces of target measurement information in the set of target measurement information.
  • the third distance includes at least one of the following:
  • the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution is the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution.
  • the supervision result of the first model includes at least one of the following:
  • the target measurement information includes T TRPs or cell-associated measurement information, where T is an integer greater than or equal to 1.
  • the target reference signal used to estimate the target measurement information includes M TRPs or cell-associated reference signals, where M is an integer greater than or equal to 1.
  • the information processing device 800 provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 6 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 900, including a processor 901 and a memory 902, wherein the memory 902 stores a program or instruction that can be run on the processor 901.
  • the communication device 900 is
  • the program or instruction is executed by the processor 901 to implement the various steps of the above-mentioned information transmission method embodiment, and can achieve the same technical effect.
  • the communication device 900 is a network side device
  • the program or instruction is executed by the processor 901 to implement the various steps of the above-mentioned information processing method embodiment, and can achieve the same technical effect. To avoid repetition, it is not 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 10 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 1000 includes but is 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 at least some of the components of a processor 1010.
  • the terminal 1000 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 10 10 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 FIG10 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.
  • the input unit 1004 may include a graphics processing unit (GPU) 10041 and a microphone 10042, and the graphics processor 10041 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 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, etc.
  • the user input unit 1007 includes a touch panel 10071 and at least one of 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, 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 RF unit 1001 can transmit the data to the processor 1010 for processing; in addition, the RF unit 1001 can send uplink data to the network side device.
  • the RF unit 1001 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 1009 can be used to store software programs or instructions and various data.
  • the memory 1009 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 1009 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 (SRAM), or a volatile memory.
  • RAM random access memory
  • SRAM static random access memory
  • SRAM dynamic random access memory
  • volatile memory volatile memory.
  • the memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.
  • the processor 1010 may include one or more processing units; optionally, the processor 1010 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 1010.
  • the processor 1010 is configured to input the target measurement information into a first model to obtain first target information output by the first model, where the first target information is related to the location of the terminal;
  • At least one of the processor 1010 and the radio frequency unit 1001 is used to obtain the supervision result of the first model, wherein the supervision result of the first model is determined based on the association relationship between the first target information and the second target information, the second target information is information output by the second model after the target measurement information is input into the second model, and the second target information is related to the location of the terminal.
  • the obtaining of the supervision result of the first model performed by the processor 1010 and the radio frequency unit 1001 includes:
  • the radio frequency unit 1001 is configured to send first information to the first node, where the first information includes the target measurement information, or the first information includes the first target information and the target measurement information;
  • the radio frequency unit 1001 is also used to receive second target information from the first node, and the processor 1010 is used to determine the supervision result of the first model based on the association relationship between the first target information and the second target information.
  • the radio frequency unit 1001 is also used to receive second information from the first node, and the second information is related to the supervision result or validity of the first model.
  • the obtaining of the supervision result of the first model performed by the processor 1010 and the radio frequency unit 1001 includes:
  • Processor 1010 configured to input the target measurement information into the second model to obtain the second target information output by the second model;
  • Processor 1010 is further used to determine the supervision result of the first model according to the association relationship between the first target information and the second target information.
  • the radio frequency unit 1001 is further used to send third information to the first node, where the third information is related to the supervision result or validity of the first model.
  • the target measurement information includes at least one of the following:
  • Time domain channel impulse response channel frequency response, delay power spectrum, channel energy response, reference signal received power RSRP, reference signal received path power RSRPP, reference signal received quality RSRQ, signal to interference plus noise ratio SINR, delay Doppler domain channel.
  • the first target information includes at least one of the following:
  • the determining, by the processor 1010, a supervision result of the first model according to an association relationship between the first target information and the second target information includes:
  • a supervision result of the first model is determined according to a difference or correlation between the first distance and the second distance.
  • the determining of the supervision result of the first model according to the difference between the first distance and the second distance performed by the processor 1010 includes:
  • the first condition includes at least one of the following:
  • the difference between the first distance and the second distance is greater than a first threshold
  • F groups of first target measurement information wherein a difference between a first distance and a second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is greater than a second threshold
  • the third distance between the first distance distribution and the second distance distribution is greater than or equal to the third threshold value, and the first distance distribution is the distribution of the first distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range; the second distance distribution is the distribution of the second distances corresponding to all groups of target measurement information or the preset R groups of target measurement information measured within the target time range.
  • the determining of the supervision result of the first model according to the difference between the first distance and the second distance performed by the processor 1010 includes:
  • the first model is determined to be valid, and the second condition includes at least one of the following:
  • the number of groups of first target measurement information is less than or equal to F, wherein the difference between the first distance and the second distance corresponding to the same group of first target measurement information is greater than a first threshold, F is an integer greater than or equal to 1, and R is an integer greater than or equal to F;
  • a proportion of the number of groups of the first target measurement information is less than or equal to a second threshold
  • the set of target measurement information includes at least one of the following:
  • the step of inputting the target measurement information into the second model to obtain second target information output by the second model, performed by the processor 1010 includes:
  • a set of target measurement information is input into a second model, and second target information output by the second model is obtained, wherein the second target information includes:
  • the third distance includes at least one of the following:
  • the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution is the maximum vertical distance between the cumulative probability density curves of the first distance distribution and the second distance distribution.
  • the supervision result of the first model includes at least one of the following:
  • processor 1010 is further configured to perform at least one of the following:
  • the target measurement information is subjected to a second preprocessing, wherein the data input by the second model includes the target measurement information after the second preprocessing.
  • processor 1010 is further configured to perform at least one of the following:
  • the output information of the second model is subjected to a second post-processing, wherein the second target information includes information obtained through the second post-processing.
  • the target measurement information includes T TRPs or cell-associated measurement information, where T is an integer greater than or equal to 1.
  • the target reference signal used to estimate the target measurement information includes M TRPs or cell-associated reference signals, where M is an integer greater than or equal to 1.
  • 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 6.
  • the network side device embodiment corresponds to the above network side device method embodiment, and each implementation process and implementation method of the above 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 1100 includes: an antenna 1101, a radio frequency device 1102, a baseband device 1103, a processor 1104 and a memory 1105.
  • the antenna 1101 is connected to the radio frequency device 1102.
  • the radio frequency device 1102 receives information through the antenna 1101 and sends the received information to the baseband device 1103 for processing.
  • the baseband device 1103 processes the information to be sent and sends it to the radio frequency device 1102.
  • the radio frequency device 1102 processes the received information and sends it out through the antenna 1101.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 1103, which includes a baseband processor.
  • the baseband device 1103 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 11, one of which is, for example, a baseband processor, which is connected to the memory 1105 through a bus interface to call the program in the memory 1105 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 1106, which is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the network side device 1100 of the embodiment of the present application further includes: instructions or programs stored in the memory 1105 and executable on the processor 1104, and the processor 1104 calls the instructions or programs in the memory 1105 to execute as shown in FIG. 8
  • the methods executed by each module achieve the same technical effect, so they will not be described here to avoid repetition.
  • the embodiment of the present application also provides a network side device.
  • the network side device 1200 includes: a processor 1201, a network interface 1202, and a memory 1203.
  • the network interface 1202 is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the network side device 1200 of the embodiment of the present application also includes: instructions or programs stored in the memory 1203 and executable on the processor 1201.
  • the processor 1201 calls the instructions or programs in the memory 1203 to execute the method executed by each module as shown in Figure 8 and achieves 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 method embodiment shown in Figure 2 or Figure 6 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 method embodiment shown in Figure 2 or Figure 6, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
  • the embodiments of the present application further provide a computer program/program product, which is stored in a storage medium, and is executed by at least one processor to implement the various processes of the method embodiment shown in Figure 2 or Figure 6, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides a communication system, including: a terminal and a first node, wherein the terminal can be used to execute the steps of the information processing method shown in FIG. 2 , and the first node can be used to execute the steps of the information processing method shown in FIG. 6 .
  • the above embodiment method can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the machine software product is stored in a storage medium (such as ROM, RAM, disk, CD, etc.), including a number of instructions for enabling the terminal or network side device to execute the methods described in each embodiment of the present application.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente demande appartient au domaine technique des communications. Sont divulgués un procédé de traitement d'informations, un appareil de traitement d'informations, un terminal et un dispositif côté réseau. Le procédé de traitement d'informations dans les modes de réalisation de la présente demande comprend les étapes suivantes : un terminal entre des informations de mesure cibles dans un premier modèle, de façon à obtenir des premières informations cibles, qui sont délivrées par le premier modèle, les premières informations cibles étant relatives à la position du terminal ; et le terminal acquiert un résultat de supervision du premier modèle, le résultat de supervision du premier modèle étant déterminé sur la base d'une relation d'association entre les premières informations cibles et des secondes informations cibles, les secondes informations cibles étant des informations qui sont délivrées par un second modèle après l'entrée des informations de mesure cibles dans le second modèle et les secondes informations cibles étant relatives à la position du terminal.
PCT/CN2024/085422 2023-04-07 2024-04-02 Procédé de traitement d'informations, appareil de traitement d'informations, terminal et dispositif côté réseau Pending WO2024208167A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202310371177.1A CN118780013A (zh) 2023-04-07 2023-04-07 信息处理方法、信息处理装置、终端及网络侧设备
CN202310371177.1 2023-04-07

Publications (1)

Publication Number Publication Date
WO2024208167A1 true WO2024208167A1 (fr) 2024-10-10

Family

ID=92971077

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/085422 Pending WO2024208167A1 (fr) 2023-04-07 2024-04-02 Procédé de traitement d'informations, appareil de traitement d'informations, terminal et dispositif côté réseau

Country Status (2)

Country Link
CN (1) CN118780013A (fr)
WO (1) WO2024208167A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112748453A (zh) * 2020-08-13 2021-05-04 腾讯科技（深圳）有限公司 道路侧定位方法、装置、设备及存储介质
WO2021206189A1 (fr) * 2020-04-07 2021-10-14 엘지전자 주식회사 Procédé et dispositif de réception de signal fondés sur l'apprentissage
CN113543305A (zh) * 2020-04-22 2021-10-22 维沃移动通信有限公司 定位方法、通信设备和网络设备
CN114077912A (zh) * 2020-08-14 2022-02-22 华为技术有限公司 数据预测方法以及数据预测装置
CN114521012A (zh) * 2020-11-18 2022-05-20 维沃移动通信有限公司 定位方法、装置、终端设备、基站及位置管理服务器

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021206189A1 (fr) * 2020-04-07 2021-10-14 엘지전자 주식회사 Procédé et dispositif de réception de signal fondés sur l'apprentissage
CN113543305A (zh) * 2020-04-22 2021-10-22 维沃移动通信有限公司 定位方法、通信设备和网络设备
CN112748453A (zh) * 2020-08-13 2021-05-04 腾讯科技（深圳）有限公司 道路侧定位方法、装置、设备及存储介质
CN114077912A (zh) * 2020-08-14 2022-02-22 华为技术有限公司 数据预测方法以及数据预测装置
CN114521012A (zh) * 2020-11-18 2022-05-20 维沃移动通信有限公司 定位方法、装置、终端设备、基站及位置管理服务器

Also Published As

Publication number Publication date
CN118780013A (zh) 2024-10-15

Similar Documents

Publication Publication Date Title
CN116567806B (zh) 基于人工智能ai模型的定位方法及通信设备
WO2023098662A1 (fr) Procédé de positionnement et dispositif de communication
WO2023098661A1 (fr) Procédé de positionnement et dispositif de communication
US20240373257A1 (en) Information interaction method and apparatus, and communication device
US20240323741A1 (en) Measurement method and apparatus, device, and storage medium
US20250227507A1 (en) Ai model processing method and apparatus, and communication device
WO2024008111A1 (fr) Procédé et dispositif d'acquisition de données
WO2023040888A1 (fr) Procédé et appareil de transmission de données
WO2024125525A1 (fr) Procédé de rapport de puissance de calcul ia, terminal et dispositif côté réseau
WO2024083004A1 (fr) Procédé de configuration de modèle d'ia, terminal et dispositif côté réseau
WO2024208167A1 (fr) Procédé de traitement d'informations, appareil de traitement d'informations, terminal et dispositif côté réseau
WO2024067434A1 (fr) Procédé et appareil de configuration de rs, terminal et dispositif côté réseau
WO2024027576A1 (fr) Procédé et appareil de supervision de performance pour modèle de réseau d'ia, et dispositif de communication
WO2023155839A1 (fr) Procédé et appareil d'apprentissage en ligne pour un modèle d'ia, ainsi que dispositif de communication et support de stockage lisible
WO2025108195A1 (fr) Procédé et appareil de détermination de modèle, et dispositif de communication
WO2025036223A1 (fr) Procédé de rapport d'informations, procédé de réception d'informations et dispositif
WO2025092999A1 (fr) Procédé et appareil de supervision de performance de modèle, et dispositif
WO2025140232A1 (fr) Procédé et appareil de rapport de données, dispositif et support
WO2024235140A1 (fr) Procédé et appareil de traitement de positionnement, et dispositif
CN120456087A (zh) 一种定位测量处理方法、装置、通信设备及存储介质
WO2025140432A1 (fr) Procédé et appareil de création de compte-rendu d'informations
WO2025092998A1 (fr) Procédé de transmission d'informations, appareil et dispositif
WO2024093997A1 (fr) Procédé et appareil de détermination d'applicabilité de modèle, et dispositif de communication
WO2025108390A1 (fr) Procédé et appareil de positionnement basé sur un modèle d'ia, dispositif et support de stockage lisible
CN120602970A (zh) 测量处理方法、测量配置方法、装置、设备及可读存储介质

Legal Events

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

Ref document number: 24784251

Country of ref document: EP

Kind code of ref document: A1