WO2021213376A1 - 定位方法、通信设备和网络设备 - Google Patents

定位方法、通信设备和网络设备 Download PDF

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Publication number
WO2021213376A1
WO2021213376A1 PCT/CN2021/088366 CN2021088366W WO2021213376A1 WO 2021213376 A1 WO2021213376 A1 WO 2021213376A1 CN 2021088366 W CN2021088366 W CN 2021088366W WO 2021213376 A1 WO2021213376 A1 WO 2021213376A1
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Prior art keywords
information
terminal device
measurement
model
error
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PCT/CN2021/088366
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English (en)
French (fr)
Inventor
庄子荀
王园园
邬华明
司晔
孙鹏
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维沃移动通信有限公司
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Application filed by 维沃移动通信有限公司 filed Critical 维沃移动通信有限公司
Priority to KR1020227040241A priority Critical patent/KR20230002832A/ko
Priority to EP21792944.7A priority patent/EP4142384A4/en
Publication of WO2021213376A1 publication Critical patent/WO2021213376A1/zh
Priority to US17/969,842 priority patent/US20230043111A1/en

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/003Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205Details
    • G01S5/021Calibration, monitoring or correction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009Transmission of position information to remote stations
    • G01S5/0018Transmission from mobile station to base station
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205Details
    • G01S5/0236Assistance data, e.g. base station almanac
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W12/00Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/03Protecting confidentiality, e.g. by encryption
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W12/00Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/04Key management, e.g. using generic bootstrapping architecture [GBA]
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/08Testing, supervising or monitoring using real traffic
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W48/00Access restriction; Network selection; Access point selection
    • H04W48/08Access restriction or access information delivery, e.g. discovery data delivery
    • H04W48/10Access restriction or access information delivery, e.g. discovery data delivery using broadcasted information
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/044Recurrent networks, e.g. Hopfield networks
    • G06N3/0442Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0464Convolutional networks [CNN, ConvNet]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W88/00Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/02Terminal devices

Definitions

  • This application relates to the field of communications, and in particular to a positioning method, communication equipment and network equipment.
  • UE User Equipment
  • IIOT Internet of Things for Industry
  • NLOS non-line of sight
  • One of the technical problems solved by the embodiments of the present application is how to achieve accurate positioning of the UE in the complex multipath and large number of NLOS situations.
  • an embodiment of the present application provides a positioning method applied to a communication device.
  • the method includes: receiving first information, where the first information includes: first machine learning model information, first preprocessing model information, and At least one of the first error model information; according to the first information, information related to the location of the terminal device is determined.
  • an embodiment of the present application provides a communication device, the communication device includes: a receiving module for receiving first information, the first information includes: first machine learning model information, first preprocessing model information And at least one of the first error model information; a positioning module, configured to determine information related to the location of the terminal device according to the first information.
  • an embodiment of the present application provides a terminal device, including: a memory, a processor, and a program or instruction that is stored on the memory and can run on the processor, and the program or instruction is processed by the processor.
  • the steps of the method described in the first aspect are implemented when the device is executed.
  • an embodiment of the present application provides a readable storage medium with a program or instruction stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented .
  • an embodiment of the present application provides a positioning method applied to a network device.
  • the method includes: sending first information to a communication device, where the first information includes: first machine learning model information, first preprocessing At least one of model information and first error model information; wherein the first information is used for the communication device to determine information related to the location of the terminal device.
  • an embodiment of the present application provides a network device, the network device includes: a sending module, configured to send first information to a communication device, the first information includes: first machine learning model information, first preset At least one of the processing model information and the first error model information; wherein the first information is used for the communication device to determine information related to the location of the terminal device.
  • an embodiment of the present application provides a network device, including: a memory, a processor, and a program or instruction that is stored on the memory and can run on the processor, and the program or instruction is processed by the processor.
  • the processor implements the steps of the method described in the first aspect when executed, or the program or instruction implements the steps of the method described in the fifth aspect when the program or instruction is executed by the processor.
  • an embodiment of the present application provides a readable storage medium with a program or instruction stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method described in the fifth aspect are implemented .
  • information related to the location of the terminal device may be determined according to the first information configured in advance to further realize the positioning of the terminal device, where the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • FIG. 1 is one of the schematic flowcharts of a positioning method in an embodiment of the present application
  • FIG. 2 is the second schematic flowchart of a positioning method in an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of a communication device in an embodiment of the present application.
  • FIG. 4 is one of the schematic structural diagrams of a network device in an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a terminal device in an embodiment of the present application.
  • Fig. 6 is the second structural diagram of a network device in an embodiment of the present application.
  • GSM Global System of Mobile Communications
  • CDMA Code Division Multiple Access
  • GSM Wideband Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • GPRS General Packet Radio Service
  • LTE-A Long Term Evolution Advanced
  • NR NR
  • User-side UE can also be called terminal equipment (Mobile Terminal), mobile user equipment, etc., and can communicate with one or more core networks via a radio access network (RAN), and user equipment can be terminal equipment.
  • RAN radio access network
  • user equipment can be terminal equipment.
  • they can be portable, pocket-sized, handheld, computer-built or vehicle-mounted mobile devices, which exchange languages and/or wireless access networks. Or data.
  • Network equipment also called a base station
  • BTS Base Transceiver Station
  • NodeB base station
  • evolutional Node B evolutional Node B
  • LTE Long Term Evolution
  • ENB e-NodeB
  • gNB 5G base station
  • an embodiment of the present application provides a positioning method, which is executed by a communication device.
  • the communication device may be a terminal device or an access network device (such as a base station).
  • the method includes the following process steps.
  • Step 101 Receive first information, where the first information includes: at least one of first machine learning model information, first preprocessing model information, and first error model information.
  • the foregoing first information may be provided by a network device such as a location management function (LMF) entity.
  • LMF location management function
  • the first information may also have other sources, which is not specifically limited here.
  • the foregoing first machine learning model information includes but is not limited to: machine learning or neural network or deep neural network model and parameters of machine learning or neural network or deep neural network model.
  • the above-mentioned machine learning or neural network or deep neural network models include but are not limited to: Convolutional Neural Networks (CNN), such as GoogLeNet, AlexNet, etc.; Recurrent Neural Network (RNN) and long Short-Term Memory (Long Short-Term Memory, LSTM); Recursive Tensor Neural Network RNTN; Generative Adversarial Networks (GAN); Deep Belief Network (DBN); Restricted Boltzmann Machine, RBM) etc.
  • the parameters of the above-mentioned machine learning or neural network or deep neural network model include but are not limited to weight, step size, mean and variance, etc.
  • the foregoing first preprocessing model information includes but is not limited to at least one of the following: filter parameters or models; convolutional layer parameters or models; pooling layer parameters or models; discrete cosine transform (DCT) Transformation parameters or models; wavelet transformation parameters or models; parameters or models of impact channel processing methods; parameters or models of waveform processing methods; parameters or models of signal-related sequence processing methods.
  • the model here can refer to a function model, a network model, a down-sampling model, a graphical model, etc.; the parameters here can refer to a weight, a step size, a mean value, and a variance.
  • the foregoing first error model information includes but is not limited to: at least one of second error model information and third error model information.
  • the second error model information includes, but is not limited to: at least one of position error compensation information, measurement error compensation information, equipment error compensation information, and parameter adjustment information. In this way, based on the second error model information, error compensation can be performed on positioning results, measurement results, errors caused by equipment, or parameter errors.
  • the third error model information includes at least one of machine learning model compensation information, preprocessing model compensation information, position error model compensation information, measurement error model compensation information, equipment error model compensation information, and parameter adjustment model compensation information. In this way, based on the third error model information, error compensation or adjustment can be performed on the aforementioned model or its parameters.
  • the foregoing manner of receiving the first information may include, but is not limited to, at least one of the following.
  • the first information is carried in a positioning assistance data element (Information element, IE). It can be understood that the first information can be obtained from the positioning assistance data information element (ProvideAssistanceData IE). In other words, the network device may send the first information to the communication device in a unicast manner.
  • the first information is carried in a position system information block (posSIB). It can be understood that the first information can be obtained from posSIB. In other words, the network device may send the first information to the communication device in a broadcast manner.
  • posSIB position system information block
  • the above-mentioned posSIB is a cell-specific posSIB or an area-specific posSIB.
  • the first information sent by broadcasting may be broadcast within a cell or within an area.
  • Step 103 Determine information related to the location of the terminal device according to the first information.
  • information related to the location of the terminal device may be determined according to the first information configured in advance to further realize the positioning of the terminal device, where the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • the foregoing step 103 may be specifically executed as the following content: determine the location of the terminal device according to the first information and the first measurement information of the terminal device Related information; wherein the first measurement information is obtained based on signal measurement.
  • the determination may optionally be combined with the first measurement information of the terminal device to further improve the positioning accuracy of the terminal device.
  • the first measurement information of the terminal device may include, but is not limited to: the first measurement information of the terminal device obtained by the terminal device based on signal measurement, and the first measurement information of the terminal device obtained by the access network device based on signal measurement. At least one of the first measurement information.
  • the first measurement information of the terminal device may be obtained by the terminal device based on signal measurement, or may be obtained by the access network device based on signal measurement, which is not specifically limited in the embodiment of the present application.
  • the above-mentioned first measurement information includes but is not limited to at least one of the following: the channel impulse response of the terminal device; the signal waveform of the terminal device; the terminal device The related sequence or waveform of the terminal device; the signal measurement result of the terminal device, the signal measurement result includes but not limited to enhanced cell-ID (Enhanced Cell-ID, E-CID), Observed Time Difference of Arrival (OTDOA) ), New Radio Enhanced Cell-ID (NR-ECID), Multi-Round-Trip Time (Multi-RTT), Downlink Angle of Departure (DL-AOD), Signal measurement results obtained by positioning methods such as Downlink Time Differential (UTDOA), Uplink Angle of Arrival (UL-AOA), and Uplink Time Differential (UTDOA).
  • E-CID enhanced Cell-ID
  • OTDOA Observed Time Difference of Arrival
  • NR-ECID New Radio Enhanced Cell-ID
  • Multi-Round-Trip Time Multi-RTT
  • DL-AOD Downlink Angle of De
  • the above-mentioned information related to the position of the terminal device includes but is not limited to at least one of the following.
  • the positioning result information may be the specific position of the terminal device obtained by performing position calculation according to corresponding measurement information (such as the above-mentioned first measurement information, etc.).
  • the positioning result information of the terminal device may include: terminal location information determined according to the first machine learning model information and the first measurement information .
  • the first machine learning model information may include a machine learning or neural network or deep neural network model for realizing position calculation.
  • the first measurement information can be used as the machine learning or neural network or deep neural network.
  • the input of the network model can output terminal position information after the model is estimated and calculated.
  • the machine learning or neural network or deep neural network model can be pre-trained based on a large amount of training data in a certain area obtained through field signal collection, where the training data includes but is not limited to: channel impulse response, RSRP, The actual location of the terminal, etc.
  • the positioning result information of the terminal device may further include: the positioning result of the terminal device after error compensation is performed according to the first error model information information. In this way, by performing error compensation on the positioning result information, the positioning accuracy can be improved.
  • the terminal location information may be terminal location information determined based on the above-mentioned first machine learning model information and first measurement information, or may be terminal location information determined in other ways, which is not particularly limited herein.
  • Second measurement information determined based on the first measurement information.
  • the information related to the position of the terminal device determined according to the first information or the first information and the first measurement information may not only include the positioning result information of the terminal device, but also may be after further processing the first measurement information.
  • the second measurement information can be used for calculation of the specific location of the terminal device. In other words, after performing certain processing on the first measurement information and then using it for positioning of the terminal device, the size of the first measurement information can be reduced, the reporting overhead is reduced, and the positioning accuracy is improved.
  • the above-mentioned second measurement information includes: the first measurement information that has undergone sparse processing, dimensionality reduction processing, or image processing.
  • corresponding preprocessing model information (such as the aforementioned first preprocessing model information, etc.) can be used to perform preprocessing such as sparse processing, dimensionality reduction processing, or image processing on the first measurement information to obtain the second measurement information.
  • the above-mentioned second measurement information includes but is not limited to at least one of the following: sparse channel impulse response, graphical channel impulse response, channel impulse response represented by multipath, dimensionality reduced graphical channel impulse response, sparse Reduced signal waveforms, down-sampled signal waveforms, imaged signal waveforms, reduced-dimensional imaged channel waveforms, sparsed correlation sequences, down-sampled correlation sequences, multi-path representations of correlation sequences, and sparsed other signal measurements Results, down-sampled other signal measurement results, imaged other signal measurement results, and reduced-dimensional imaged other signal measurement results.
  • the above-mentioned second measurement information includes: the first measurement information after error compensation is performed according to the first error model information.
  • the above-mentioned second measurement information includes: the first measurement information processed by the first preprocessing model information after performing error compensation according to the first error model information.
  • the size of the first measurement information can be reduced, the reporting overhead can be reduced, and the positioning accuracy can be improved.
  • Second machine learning model information determined based on the first machine learning model information.
  • the first machine learning model information may also be optimized.
  • the above-mentioned second machine learning model information includes: the first machine learning model information after error compensation is performed according to the first error model information.
  • Second preprocessing model information determined based on the first preprocessing model information.
  • the first preprocessing model information may also be optimized.
  • the above-mentioned second preprocessing model information includes: the first preprocessing model information after error compensation is performed according to the first error model information.
  • different reporting schemes for the third information can be implemented, where the third information can be used by the network device to determine the location-related information of the terminal device and to determine the position of the terminal device. At least one operation in the update of an information.
  • the communication device may implement active reporting of the third information, that is, the positioning method in the embodiment of the present application, and may also include the following content: reporting the third information to the network device, and the third information is used for supply
  • the network device determines the information related to the location of the terminal device; and/or is used for the network device to update the first information.
  • updating the above-mentioned first information refers to realizing the updating of related models and parameters.
  • the communication device may implement passive reporting of the third information based on the corresponding instruction, that is, the positioning method in the embodiment of the present application may further include the following content: receiving second information, the second information being used to indicate Whether to report the third information to the network device.
  • the foregoing manner of receiving the second information may include, but is not limited to, at least one of the following.
  • the second information is carried in a positioning assistance data cell. It can be understood that the second information can be obtained from the positioning assistance data information element (ProvideAssistanceData IE). In other words, the second information can be sent to the communication device by the network device in a unicast manner.
  • PositionAssistanceData IE the positioning assistance data information element
  • the second information is carried in the position system information block posSIB. It can be understood that the second information can be obtained from the position system information block posSIB. In other words, the second information can be sent to the communication device by the network device in a broadcast manner.
  • the above-mentioned posSIB is a cell-specific posSIB or an area-specific posSIB.
  • the second information sent by broadcasting may be broadcast within a cell or within an area.
  • the type of the position system information block posSIB used to carry at least one of the first information and the second information may define the type of the posSIB according to at least one of the first information and the second information. That is to say, the type of the aforementioned posSIB is defined based on at least one of the first information and the second information.
  • the type of the position system information block posSIB in this embodiment may be a newly defined type, such as [posSibType4-X].
  • the aforementioned third information that needs to be reported may include, but is not limited to, at least one of the following: at least one of the information related to the location of the terminal device; and at least one of the first measurement information.
  • the foregoing process of reporting the third information to the network device may be specifically executed as follows: the third information is carried in the first cell IE and reported to the network equipment.
  • the first IE includes: a location information information element based on a positioning protocol (LTE Positioning Protocol, LPP) or a location information information element based on a new air interface positioning protocol a (NR Positioning Protocol a, NRPPa).
  • LPP LTE Positioning Protocol
  • NR Positioning Protocol a NR Positioning Protocola
  • the above-mentioned location information information element may be the location information information element of various positioning methods, including but not limited to: E-CID, OTDOA, NR-ECID, Multi-RTT, DL-AOD, DL-TDOA, UL- AOA, UL-TDOA and other positioning methods.
  • the following content may be further included: in a case where the first information is encrypted hierarchically, receiving an encryption level corresponding to the first information sent by the network device Key.
  • hierarchical encryption can be implemented based on positioning accuracy requirements, load size, and so on.
  • a group of posSIBs corresponding to the first information with low positioning accuracy and low load is first-level encrypted, and the first-level key is assigned correspondingly;
  • a group of posSIB corresponding to the large first information is subjected to secondary encryption, and the secondary key is assigned accordingly.
  • the technical solution of this application proposes a positioning technology that obtains better positioning performance under complex multipath and large amounts of NLOS, such as machine learning, preprocessing model, error model Positioning technology, etc.
  • the network side may broadcast at least one of error model information, preprocessing model information, and machine learning model information through broadcast or unicast, and the communication device side performs position measurement or calculation based on the above information.
  • the communication device side reports the corresponding compensation parameters, location information or measurement information to the network side, and the network side optimizes the model based on the information reported by the communication device side, and updates the model and related parameters, or implements it based on the corresponding measurement information Positioning of terminal equipment, etc.
  • an embodiment of the present application provides a positioning method, which is executed by a network device.
  • the network device may be a base station or a core network device (such as an LMF entity).
  • the method includes the following process steps.
  • Step 201 Send first information to a communication device, where the first information includes: at least one of first machine learning model information, first preprocessing model information, and first error model information.
  • the first information is used for the communication device to determine information related to the location of the terminal device.
  • the sending of the first information to the communication device may be that the LMF sends the first information to the terminal device in a unicast manner based on the positioning protocol LPP.
  • the information may also be that the LMF sends the first information to the base station in a unicast manner based on the new air interface positioning protocol NRPPa.
  • the sending of the first information to the communication device may be that the base station sends the first information to the terminal device in a broadcast manner through the position system information block posSIB.
  • the communication device may be provided with pre-configured first information for it to determine information related to the location of the terminal device to further realize the positioning of the terminal device, where the first information includes but not It is limited to at least one of the first machine learning model information, the first preprocessing model information, and the first error model information.
  • the first information includes but not It is limited to at least one of the first machine learning model information, the first preprocessing model information, and the first error model information.
  • the foregoing first machine learning model information includes but is not limited to: machine learning or neural network or deep neural network model and parameters of machine learning or neural network or deep neural network model.
  • the above-mentioned machine learning or neural network or deep neural network models include but are not limited to: convolutional neural network CNN, such as googlenet, AlexNet; recurrent neural network RNN and LSTM; recurrent tensor neural network RNTN; generative adversarial network GAN; deep confidence Network DBN; restricted Boltzmann machine RBM, etc.
  • the parameters of the above-mentioned machine learning or neural network or deep neural network model include but are not limited to weight, step size, mean and variance, etc.
  • the aforementioned first preprocessing model information includes but is not limited to at least one of the following: filter parameters or models; convolutional layer parameters or models; pooling layer parameters or models; discrete cosine transform DCT transform parameters or models; wavelet Transformation parameters or models; parameters or models of impact channel processing methods; parameters or models of waveform processing methods; parameters or models of signal-related sequence processing methods.
  • the model here can refer to a function model, a network model, a down-sampling model, a graphical model, etc.; the parameters here can refer to a weight, a step size, a mean value, and a variance.
  • the foregoing first error model information includes but is not limited to: at least one of second error model information and third error model information.
  • the second error model information includes, but is not limited to: at least one of position error compensation information, measurement error compensation information, equipment error compensation information, and parameter adjustment information. In this way, based on the second error model information, error compensation can be performed on positioning results, measurement results, errors caused by equipment, or parameter errors.
  • the third error model information includes at least one of machine learning model compensation information, preprocessing model compensation information, position error model compensation information, measurement error model compensation information, equipment error model compensation information, and parameter adjustment model compensation information. In this way, based on the third error model information, error compensation or adjustment can be performed on the aforementioned model or its parameters.
  • the foregoing manner of sending the first information to the communication device may include, but is not limited to, at least one of the following.
  • the first information is carried in a positioning assistance data cell. It can be understood that the first information may be carried in a positioning assistance data information element (ProvideAssistanceData IE) for transmission. That is, the core network device (such as the LMF) can send the first information to the communication device in a unicast manner.
  • PositionAssistanceData IE positioning assistance data information element
  • the first information is carried in the position system information block posSIB. It can be understood that the first information may be carried in the position system information block posSIB for transmission. That is to say, the first information can be sent to the communication device by the base station in a broadcast manner.
  • the above-mentioned posSIB is a cell-specific posSIB or an area-specific posSIB.
  • the first information sent by broadcasting may be broadcast within a cell or within an area.
  • the above-mentioned first information is specifically used for the communication device to determine the information related to the location of the terminal device according to the first measurement information of the terminal device, where , The first measurement information is measured by the communication device based on the signal.
  • the determination may optionally be combined with the first measurement information of the terminal device to further improve the positioning accuracy of the terminal device.
  • the first measurement information of the terminal device may include, but is not limited to: the first measurement information of the terminal device obtained by the terminal device based on signal measurement, and the first measurement information of the terminal device obtained by the access network device based on signal measurement. At least one of the information.
  • the above-mentioned first measurement information includes but is not limited to at least one of the following.
  • the signal measurement result includes, but is not limited to, based on E-CID, OTDOA , NR-ECID, Multi-RTT, DL-AOD, DL-TDOA, UL-AOA, UL-TDOA and other positioning methods obtained signal measurement results.
  • the above-mentioned information related to the position of the terminal device includes but is not limited to at least one of the following.
  • the positioning result information may be the specific position of the terminal device obtained by performing position calculation according to corresponding measurement information (such as the above-mentioned first measurement information, etc.).
  • the positioning result information of the terminal device may include: terminal location information determined according to the first machine learning model information and the first measurement information .
  • the first machine learning model information may include a machine learning or neural network or deep neural network model for realizing position calculation.
  • the first measurement information can be used as the machine learning or neural network or deep neural network.
  • the input of the network model can output terminal position information after the model is estimated and calculated.
  • the machine learning or neural network or deep neural network model can be pre-trained based on a large amount of training data in a certain area obtained through field signal collection, where the training data includes but is not limited to: channel impulse response, RSRP, The actual location of the terminal, etc.
  • the positioning result information of the terminal device may further include: the positioning result of the terminal device after error compensation is performed according to the first error model information information. In this way, by performing error compensation on the positioning result information, the positioning accuracy can be improved.
  • the terminal location information may be terminal location information determined based on the above-mentioned first machine learning model information and first measurement information, or may be terminal location information determined in other ways, which is not particularly limited herein.
  • Second measurement information determined based on the first measurement information.
  • the information related to the position of the terminal device determined according to the first information or the first information and the first measurement information may not only include the positioning result information of the terminal device, but also may be after further processing the first measurement information.
  • the second measurement information can be used for calculation of the specific location of the terminal device. In other words, after performing certain processing on the first measurement information and then using it for positioning of the terminal device, the size of the first measurement information can be reduced, the reporting overhead is reduced, and the positioning accuracy is improved.
  • the above-mentioned second measurement information includes: the first measurement information that has undergone sparse processing, dimensionality reduction processing, or image processing.
  • corresponding preprocessing model information (such as the aforementioned first preprocessing model information, etc.) can be used to perform preprocessing such as sparse processing, dimensionality reduction processing, or image processing on the first measurement information to obtain the second measurement information.
  • the above-mentioned second measurement information includes but is not limited to at least one of the following: sparse channel impulse response, graphical channel impulse response, channel impulse response represented by multipath, dimensionality reduced graphical channel impulse response, sparse Reduced signal waveforms, down-sampled signal waveforms, imaged signal waveforms, reduced-dimensional imaged channel waveforms, sparsed correlation sequences, down-sampled correlation sequences, multi-path representations of correlation sequences, sparsed other signal measurements Results, down-sampled other signal measurement results, imaged other signal measurement results, and reduced-dimensional imaged other signal measurement results.
  • the above-mentioned second measurement information includes: the first measurement information after error compensation is performed according to the first error model information.
  • the above-mentioned second measurement information includes: the first measurement information processed by the first preprocessing model information after performing error compensation according to the first error model information.
  • the size of the first measurement information can be reduced, the reporting overhead can be reduced, and the positioning accuracy can be improved.
  • Second machine learning model information determined based on the first machine learning model information.
  • the first machine learning model information may also be optimized.
  • the above-mentioned second machine learning model information includes: the first machine learning model information after error compensation is performed according to the first error model information.
  • Second preprocessing model information determined based on the first preprocessing model information.
  • the first preprocessing model information may also be optimized.
  • the above-mentioned second preprocessing model information includes: the first preprocessing model information after error compensation is performed according to the first error model information.
  • the third information can be used by the network device to determine the location-related information of the terminal device and to determine the first information. At least one operation in the update of information.
  • updating the above-mentioned first information refers to realizing the updating of related models and parameters.
  • the foregoing third information may be actively reported by the communication device, that is, the positioning method in the embodiment of the present application may further include the following content: receiving the third information reported by the communication device; according to the third information The information related to the location of the terminal device is determined and/or the first information is updated.
  • the above-mentioned third information may be passively reported by the communication device based on the corresponding indication, that is, the positioning method in this embodiment of the present application may further include the following content: sending the second information to the communication device, The second information is used to indicate whether the communication device reports the third information.
  • the sending of the second information to the communication device may be that the LMF sends the second information to the terminal device in a unicast manner based on the positioning protocol LPP.
  • the information may also be that the LMF sends the second information to the base station in a unicast manner based on the new air interface positioning protocol NRPPa.
  • the sending of the second information to the communication device may be that the base station sends the second information to the terminal device in a broadcast manner through the position system information block posSIB.
  • the foregoing manner of sending the second information to the communication device may include, but is not limited to, at least one of the following.
  • the second information is carried in a positioning assistance data cell. It can be understood that the second information may be carried in a positioning assistance data information element (ProvideAssistanceData IE) for transmission. That is, the network device core network device (such as LMF) can send the second information to the communication device in a unicast manner.
  • PositionAssistanceData IE positioning assistance data information element
  • the second information is carried in the position system information block posSIB. It can be understood that the second information may be carried in the position system information block posSIB for transmission. In other words, the second information can be sent to the communication device by the base station in a broadcast manner.
  • the above-mentioned posSIB is a cell-specific posSIB or an area-specific posSIB.
  • the second information sent by broadcasting may be broadcast within a cell or within an area.
  • the type of the position system information block posSIB used to carry at least one of the first information and the second information may define the type of the posSIB according to at least one of the first information and the second information. That is to say, the type of the aforementioned posSIB is defined based on at least one of the first information and the second information.
  • the type of the position system information block posSIB in this embodiment may be a newly defined type, such as [posSibType4-X].
  • the foregoing third information may include, but is not limited to, at least one of the following: at least one of the information related to the location of the terminal device; and at least one of the first measurement information.
  • the foregoing third information is carried in a first information element IE, where the first IE includes: a location information information element based on the positioning protocol LPP or a location information information element based on the new air interface positioning protocol NRPPa.
  • the above-mentioned location information information element may be the location information information element of various positioning methods, including but not limited to: E-CID, OTDOA, NR-ECID, Multi-RTT, DL-AOD, DL-TDOA, UL- AOA, UL-TDOA and other positioning methods.
  • a key corresponding to the encryption level of the first information is sent to the communication device.
  • hierarchical encryption can be implemented based on positioning accuracy requirements, load size, and so on.
  • a group of posSIBs corresponding to the first information with low positioning accuracy and low load is first-level encrypted, and the first-level key is assigned correspondingly;
  • a group of posSIB corresponding to the large first information is subjected to secondary encryption, and the secondary key is assigned accordingly.
  • the technical solution of this application proposes a positioning technology that obtains better positioning performance under complex multipath and large amounts of NLOS, such as machine learning, preprocessing model, error model Positioning technology, etc.
  • the network side may broadcast at least one of error model information, preprocessing model information, and machine learning model information through broadcast or unicast, and the communication device side performs position measurement or calculation based on the above information.
  • the communication device side reports the corresponding compensation parameters, location information or measurement information to the network side, and the network side optimizes the model based on the information reported by the communication device side, and updates the model and related parameters, or implements it based on the corresponding measurement information Positioning of terminal equipment, etc.
  • an embodiment of the present application provides a communication device 300.
  • the communication device 300 includes a receiving module 301 and a positioning module 303.
  • the receiving module 301 is configured to receive first information, and the first information includes: at least one of the first machine learning model information, the first preprocessing model information, and the first error model information; the positioning module 303 is configured to One piece of information, which determines information related to the location of the terminal device.
  • the above-mentioned positioning module 303 may be specifically configured to: determine information related to the location of the terminal device according to the first information and the first measurement information of the terminal device, and The measurement information is based on the signal measurement.
  • the above-mentioned information related to the location of the terminal device includes at least one of the following: positioning result information of the terminal device; second measurement information determined based on the first measurement information; The second machine model information determined by the first machine learning model information; the second preprocessing model information is determined based on the first preprocessing model information.
  • the positioning result information of the terminal device includes: terminal location information determined according to the first machine learning model information and the first measurement information.
  • the positioning result information of the terminal device includes: positioning result information of the terminal device after error compensation is performed according to the first error model information.
  • the above-mentioned second measurement information includes: the first measurement information that has undergone thinning processing, dimensionality reduction processing, or image processing.
  • the foregoing second measurement information includes: first measurement information after error compensation is performed according to the first error model information.
  • the above-mentioned second measurement information includes: the first measurement information processed by the first preprocessing model information after error compensation is performed according to the first error model information.
  • the above-mentioned second machine learning model information includes: first machine learning model information after error compensation is performed according to the first error model information.
  • the foregoing second preprocessing model information includes: first preprocessing model information after error compensation is performed according to the first error model information.
  • the aforementioned first preprocessing model information includes at least one of the following: filter parameters or models; convolutional layer parameters or models; pooling layer parameters or models; discrete cosine Transform DCT transformation parameters or models; wavelet transformation parameters or models; parameters or models of impact channel processing methods; parameters or models of waveform processing methods; parameters or models of signal-related sequence processing methods.
  • the above-mentioned first error model information includes: at least one of second error model information and third error model information; wherein, the second error model information includes: position error At least one of compensation information, measurement error compensation information, equipment error compensation information, and parameter adjustment information; the third error model information includes: machine learning model compensation information, preprocessing model compensation information, position error model compensation information, and measurement error model compensation At least one of information, equipment error model compensation information, and parameter adjustment model compensation information.
  • the above-mentioned first measurement information includes at least one of the following: the channel impulse response of the terminal device; the signal waveform of the terminal device; the related sequence or waveform of the terminal device; the terminal device Signal measurement results.
  • the communication device 300 of the embodiment of the present application further includes a sending module, which can be used to report third information to the network device, and the third information is used for the network device to determine information related to the location of the terminal device; and/ Or for the network device to update the first information.
  • a sending module which can be used to report third information to the network device, and the third information is used for the network device to determine information related to the location of the terminal device; and/ Or for the network device to update the first information.
  • the above-mentioned receiving module 301 may also be used to: receive second information, which is used to indicate whether to report the third information to the network device.
  • the above-mentioned third information includes at least one of the following: at least one of information related to the location of the terminal device; and at least one of the first measurement information.
  • the aforementioned sending module may be specifically used to: carry the third information in the first information element IE and report it to the network device; where the first IE includes: The location information information element of the location protocol LPP or the location information information element based on the new air interface positioning protocol NRPPa.
  • At least one of the foregoing first information and second information is carried in a positioning assistance data cell.
  • At least one of the foregoing first information and second information is carried in the position system information block posSIB.
  • the type of the aforementioned posSIB is defined based on at least one of the first information and the second information.
  • the above-mentioned posSIB is a cell-specific posSIB or an area-specific posSIB.
  • the above-mentioned receiving module 301 may also be used to: in the case that the first information is hierarchically encrypted, receive the data corresponding to the encryption level of the first information sent by the network device Key.
  • the communication device 300 provided in the embodiment of the present application can implement the aforementioned positioning method performed by the communication device 300, and the relevant explanations about the positioning method are all applicable to the communication device 300, and will not be repeated here.
  • the communication device 300 may be a terminal device or an access network device.
  • information related to the location of the terminal device may be determined according to the first information configured in advance to further realize the positioning of the terminal device, where the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • an embodiment of the present application provides a network device 400.
  • the network device 400 includes a sending module 401, configured to send first information to a communication device.
  • the first information includes: first machine learning model information and first machine learning model information. At least one of preprocessing model information and first error model information; wherein the first information is used for the communication device to determine information related to the location of the terminal device.
  • the foregoing first information is specifically used for the communication device to determine information related to the location of the terminal device according to the first measurement information of the terminal device, where the first measurement information It is measured by the communication device based on the signal.
  • the above-mentioned information related to the location of the terminal device includes at least one of the following: positioning result information of the terminal device; second measurement information determined based on the first measurement information; The second machine model information determined by the first machine learning model information; the second preprocessing model information determined based on the first preprocessing model information.
  • the positioning result information of the terminal device includes: terminal location information determined according to the first machine learning model information and the first measurement information.
  • the positioning result information of the terminal device includes: positioning result information of the terminal device after error compensation is performed according to the first error model information.
  • the above-mentioned second measurement information includes: the first measurement information that has undergone thinning processing, dimensionality reduction processing, or image processing.
  • the foregoing second measurement information includes: first measurement information after error compensation is performed according to the first error model information.
  • the above-mentioned second measurement information includes: the first measurement information processed by the first preprocessing model information after error compensation is performed according to the first error model information.
  • the foregoing second machine learning model information includes: first machine learning model information after error compensation is performed according to the first error model information.
  • the foregoing second preprocessing model information includes: first preprocessing model information after error compensation is performed according to the first error model information.
  • the aforementioned first preprocessing model information includes at least one of the following: filter parameters or models; convolutional layer parameters or models; pooling layer parameters or models; discrete cosine Transform DCT transformation parameters or models; wavelet transformation parameters or models; parameters or models of impact channel processing methods; parameters or models of waveform processing methods; parameters or models of signal-related sequence processing methods.
  • the above-mentioned first error model information includes: at least one of second error model information and third error model information; wherein, the second error model information includes: position error At least one of compensation information, measurement error compensation information, equipment error compensation information, and parameter adjustment information; the third error model information includes: machine learning model compensation information, preprocessing model compensation information, position error model compensation information, and measurement error model compensation At least one of information, equipment error model compensation information, and parameter adjustment model compensation information.
  • the above-mentioned first measurement information includes at least one of the following: the channel impulse response of the terminal device; the signal waveform of the terminal device; the related sequence or waveform of the terminal device; the terminal device Signal measurement results.
  • the network device 400 of the embodiment of the present application further includes: a receiving module and a processing module.
  • the above-mentioned receiving module is used for receiving the third information reported by the communication device; the above-mentioned processing module is used for determining the information related to the location of the terminal device and/or updating the first information according to the third information.
  • the sending module 401 may also be used to send second information to the communication device, where the second information is used to indicate whether the communication device reports the third information.
  • the above-mentioned third information includes at least one of the following: at least one of the information related to the location of the terminal device; and at least one of the first measurement information.
  • the above-mentioned third information is carried in the first cell IE, where the first IE includes: a location information cell based on the positioning protocol LPP or a location based on the new air interface positioning protocol Location information cell of NRPPa.
  • At least one of the first information and the second information is carried in a positioning assistance data cell.
  • At least one of the first information and the second information is carried in the position system information block posSIB.
  • the type of posSIB is defined based on at least one of the first information and the second information.
  • the above-mentioned posSIB is a cell-specific posSIB or an area-specific posSIB.
  • the sending module 401 may also be used to: in the case of performing hierarchical encryption on the first information, the encryption level sent to the communication device corresponds to the encryption level of the first information Key.
  • the network device 400 provided in the embodiment of the present application can implement the aforementioned positioning method performed by the network device 400, and the relevant descriptions about the positioning method are all applicable to the network device 400, and will not be repeated here.
  • the network device 400 may be a core network device.
  • the communication device may be provided with pre-configured first information for it to determine information related to the location of the terminal device to further realize the positioning of the terminal device, where the first information includes but not It is limited to at least one of the first machine learning model information, the first preprocessing model information, and the first error model information.
  • the first information includes but not It is limited to at least one of the first machine learning model information, the first preprocessing model information, and the first error model information.
  • Fig. 5 is a block diagram of a terminal device according to another embodiment of the present application.
  • the terminal device 500 shown in FIG. 5 includes: at least one processor 501, a memory 502, at least one network interface 504, and a user interface 503.
  • the various components in the terminal device 500 are coupled together through the bus system 505.
  • the bus system 505 is used to realize the connection and communication between these components.
  • the bus system 505 also includes a power bus, a control bus, and a status signal bus.
  • various buses are marked as the bus system 505 in FIG. 5.
  • the user interface 503 may include a display, a keyboard, or a pointing device (for example, a mouse, a trackball (trackball), a touch panel, or a touch screen, etc.).
  • a pointing device for example, a mouse, a trackball (trackball), a touch panel, or a touch screen, etc.
  • the memory 502 in the embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), and electrically available Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory.
  • the volatile memory may be a random access memory (Random Access Memory, RAM), which is used as an external cache.
  • RAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • Synchronous DRAM Double Data Rate Synchronous Dynamic Random Access Memory
  • Double Data Rate SDRAM Double Data Rate SDRAM
  • DDRSDRAM Double Data Rate Synchronous Dynamic Random Access Memory
  • Enhanced SDRAM Enhanced SDRAM, ESDRAM
  • Synchronous Link Dynamic Random Access Memory Synchronous Link Dynamic Random Access Memory
  • Synchlink DRAM Synchronous Link Dynamic Random Access Memory
  • SLDRAM Direct Rambus RAM
  • the memory 502 of the system and method described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memory.
  • the memory 502 stores the following elements, executable modules or data structures, or their subsets, or their extended sets: operating system 5021 and application programs 5022.
  • the operating system 5021 includes various system programs or instructions, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks.
  • Application programs or instructions 5022 including various application programs or instructions, such as a media player (Media Player), a browser (Browser), etc., are used to implement various application services.
  • the program or instruction for implementing the method of the embodiment of the present application may be included in the application program or instruction 5022.
  • the terminal device 500 further includes: a program or instruction that is stored in the memory 502 and can be run on the processor 501.
  • a program or instruction that is stored in the memory 502 and can be run on the processor 501.
  • the first information is received, where the first information includes: at least one of the first machine learning model information, the first preprocessing model information, and the first error model information; according to the first information, information related to the location of the terminal device is determined.
  • information related to the location of the terminal device may be determined according to the first information configured in advance to further realize the positioning of the terminal device, where the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • the first information includes but is not limited to the first machine learning model information , At least one of the first preprocessing model information and the first error model information.
  • the method disclosed in the foregoing embodiment of the present application may be applied to the processor 501 or implemented by the processor 501.
  • the processor 501 may be an integrated circuit chip with signal processing capability. In the implementation process, the steps of the foregoing method can be completed by an integrated logic circuit of hardware in the processor 501 or instructions in the form of software.
  • the aforementioned processor 501 may be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (ASIC), a ready-made programmable gate array (Field Programmable Gate Array, FPGA) or other Programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • DSP Digital Signal Processor
  • ASIC application specific integrated circuit
  • FPGA Field Programmable Gate Array
  • the methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor.
  • the software module may be located in a mature readable storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers.
  • the readable storage medium is located in the memory 502, and the processor 501 reads the information in the memory 502, and completes the steps of the foregoing method in combination with its hardware.
  • a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor 501, each step of the above positioning method embodiment is implemented.
  • the embodiments described in the embodiments of the present application may be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof.
  • the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processor (Digital Signal Processing, DSP), digital signal processing equipment (DSP Device, DSPD), programmable Logic device (Programmable Logic Device, PLD), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, and others for performing the functions described in this application Electronic unit or its combination.
  • ASIC application specific integrated circuits
  • DSP Digital Signal Processing
  • DSP Device digital signal processing equipment
  • PLD programmable Logic Device
  • PLD Field-Programmable Gate Array
  • FPGA Field-Programmable Gate Array
  • the technology described in the embodiments of the present application can be implemented by modules (for example, procedures, functions, etc.) that execute the functions described in the embodiments of the present application.
  • the software codes can be stored in the memory and executed by the processor.
  • the memory can be implemented in the processor or external to the processor.
  • the terminal device 500 can implement the various processes implemented by the terminal device in the foregoing embodiments, and in order to avoid repetition, details are not described herein again.
  • FIG. 6 is a structural diagram of a network device applied in an embodiment of the present application.
  • the network device 600 may be a base station or an LMF.
  • the network device 600 is a base station, the method corresponding to claims 1-21 can be implemented
  • the details of the embodiment steps, or the details of the method embodiment steps corresponding to claims 22-42 can be realized, and the same effect can be achieved.
  • the network device 600 is an LMF, the details of the steps of the method embodiment corresponding to claims 22-42 can be realized, and the same effect can be achieved.
  • the network device 600 includes: a processor 601, a transceiver 602, a memory 603, a user interface 604, and a bus interface 605.
  • the network device 600 further includes: Programs or instructions that can be run on the processor 601.
  • the network device 600 is a base station
  • the following steps can be implemented: receiving first information, the first information including: first machine learning model information, first preprocessing model information, and At least one of the first error model information; according to the first information, information related to the location of the terminal device is determined.
  • the information related to the location of the terminal device may be determined according to the pre-configured first information, where the first information includes, but is not limited to, the first machine learning model information, the first preprocessing model information, and the first machine learning model information. At least one of the error model information.
  • the first information includes, but is not limited to, the first machine learning model information, the first preprocessing model information, and the first machine learning model information.
  • At least one of the error model information At least one of the error model information.
  • the network device 600 is a base station or a core network device (such as an LMF)
  • the following steps can be implemented: sending first information to the communication device, where the first information includes: At least one of machine learning model information, first preprocessing model information, and first error model information; wherein the first information is used for the communication device to determine information related to the location of the terminal device.
  • the communication device may be provided with pre-configured first information for it to determine information related to the location of the terminal device to further realize the positioning of the terminal device, where the first information includes but not It is limited to at least one of the first machine learning model information, the first preprocessing model information, and the first error model information.
  • the first information includes but not It is limited to at least one of the first machine learning model information, the first preprocessing model information, and the first error model information.
  • the bus architecture may include any number of interconnected buses and bridges. Specifically, one or more processors represented by the processor 601 and various circuits of the memory represented by the memory 603 are linked together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are all known in the art, and therefore, will not be further described herein.
  • the bus interface 605 provides an interface.
  • the transceiver 602 may be a plurality of elements, including a transmitter and a receiver, and provide a unit for communicating with various other devices on the transmission medium.
  • the user interface 604 may also be an interface capable of externally connecting internally required equipment.
  • the connected equipment includes but not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.
  • the processor 601 is responsible for managing the bus architecture and general processing, and the memory 603 can store data used by the processor 601 when performing operations.
  • the embodiment of the present application also provides a readable storage medium with a program or instruction stored on the readable storage medium.
  • the program or instruction is executed by a processor, the above-mentioned positioning method embodiment applied to a communication device is implemented, and/or an application
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks, or optical disks.
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.
  • a terminal which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

本申请公开了一种定位方法、通信设备和网络设备,属于通信领域。其中,所述定位方法包括:接收第一信息,第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;根据第一信息,确定与终端设备的位置相关的信息。

Description

定位方法、通信设备和网络设备
交叉引用
本发明要求在2020年04月22日提交中国专利局、申请号为202010323445.9、发明名称为“定位方法、通信设备和网络设备”的中国专利申请的优先权,该申请的全部内容通过引用结合在本发明中。
技术领域
本申请涉及通信领域,尤其涉及一种定位方法、通信设备和网络设备。
背景技术
目前,随着对用户设备(User Equipment,UE,也可称之为终端设备)的定位精度需求越来越高,比如工业物联网(Internet of Things for Industry,IIOT)场景中,对定位精度需求非常高。
然而,当智能工厂环境比较复杂时,会存在大量的多径情况以及非视距(Non-Line of Sight,NLOS)概率较高的情况,比如当工厂内部的杂物密度较高时。而当存在大量NLOS情况时,许多传统的基于时间或角度的定位方式的定位精度将受到很大的影响,从而在时间、角度等信息测量不准确的情况下,定位性能将大大下降。
因此,发明人发现现有技术中至少存在如下问题:在高定位精度的需求下,需要一种能够在复杂的多径和大量NLOS情况下实现准确定位的方案。
发明内容
本申请实施例解决的技术问题之一为如何在复杂的多径和大量NLOS情况下实现对UE的准确定位。
第一方面,本申请实施例提供一种定位方法,应用于通信设备,所述方 法包括:接收第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;根据所述第一信息,确定与终端设备的位置相关的信息。
第二方面,本申请实施例提供一种通信设备,所述通信设备包括:接收模块,用于接收第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;定位模块,用于根据所述第一信息,确定与终端设备的位置相关的信息。
第三方面,本申请实施例提供一种终端设备,包括:存储器、处理器及存储在所述存储器上并可在所述处理器上运行的程序或指令,所述程序或指令被所述处理器执行时实现如第一方面所述的方法的步骤。
第四方面,本申请实施例提供一种可读存储介质,所述可读存储介质上存储有程序或指令,所述程序或指令被处理器执行时实现如第一方面所述的方法的步骤。
第五方面,本申请实施例提供一种定位方法,应用于网络设备,所述方法包括:向通信设备发送第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;其中,所述第一信息用于供所述通信设备确定与终端设备的位置相关的信息。
第六方面,本申请实施例提供一种网络设备,所述网络设备包括:发送模块,用于向通信设备发送第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;其中,所述第一信息用于供所述通信设备确定与终端设备的位置相关的信息。
第七方面,本申请实施例提供一种网络设备,包括:存储器、处理器及存储在所述存储器上并可在所述处理器上运行的程序或指令,所述程序或指令被所述处理器执行时实现如第一方面所述的方法的步骤,或者所述程序或指令被所述处理器执行时实现如第五方面所述的方法的步骤。
第八方面,本申请实施例提供一种可读存储介质,所述可读存储介质上 存储有程序或指令,所述程序或指令被处理器执行时实现如第五方面所述的方法的步骤。
在本申请实施例中,可以根据预先配置的第一信息确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
附图说明
此处所说明的附图用来提供对本申请的进一步理解,构成本申请的一部分,本申请的示意性实施例及其说明用于解释本申请,并不构成对本申请的不当限定。在附图中:
图1是本申请实施例中一种定位方法的流程示意图之一;
图2是本申请实施例中一种定位方法的流程示意图之二;
图3是本申请实施例中一种通信设备的结构示意图;
图4是本申请实施例中一种网络设备的结构示意图之一;
图5是本申请实施例中一种终端设备的结构示意图;
图6是本申请实施例中一种网络设备的结构示意图之二。
具体实施方式
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类 似的对象,而不用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外,说明书以及权利要求中“和/或”表示所连接对象的至少其中之一,字符“/”,一般表示前后关联对象是一种“或”的关系。
本申请的技术方案,可以应用于各种通信系统,例如:全球移动通讯系统(Global System of Mobile communication,GSM),码分多址(Code Division Multiple Access,CDMA)系统,宽带码分多址(Wideband Code Division Multiple Access,WCDMA),通用分组无线业务(General Packet Radio Service,GPRS),长期演进/增强长期演进(Long Term EvolutionAdvanced,LTE-A),NR等。
用户端UE也可称之为终端设备(Mobile Terminal)、移动用户设备等,可以经无线接入网(Radio Access Network,RAN)与一个或多个核心网进行通信,用户设备可以是终端设备,如移动电话(或称为“蜂窝”电话)和具有终端设备的计算机,例如,可以是便携式、袖珍式、手持式、计算机内置的或者车载的移动装置,它们与无线接入网交换语言和/或数据。
网络设备,也可称之为基站,可以是GSM或CDMA中的基站(Base Transceiver Station,BTS),也可以是WCDMA中的基站(NodeB),还可以是LTE中的演进型基站(evolutional Node B,eNB或e-NodeB)及5G基站(gNB)。
以下结合附图,详细说明本申请各实施例提供的技术方案。
参见图1所示,本申请实施例提供一种定位方法,由通信设备执行,可选的,该通信设备可以为终端设备也可以为接入网设备(比如基站)。其中,所述方法包括以下流程步骤。
步骤101:接收第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。
可选的,上述第一信息可以由定位管理功能(Location Management  Function,LMF)实体等网络设备提供。当然,该第一信息也可以有其他来源,在此不作具体限定。
可选的,上述第一机器学习模型信息包括但不限于:机器学习或神经网络或深度神经网络模型以及机器学习或神经网络或深度神经网络模型的参数。
其中,上述机器学习或神经网络或深度神经网络模型包括但不限于:卷积神经网络(Convolutional Neural Networks,CNN),如GoogLeNet、AlexNet等;递归或循环神经网络(Recurrent Neural Network,RNN)及长短期记忆网络(Long Short-Term Memory,LSTM);递归张量神经网络RNTN;生成对抗网络(Generative Adversarial Networks,GAN);深度置信网络(DeepBeliefNetwork,DBN);受限玻尔兹曼机(Restricted Boltzmann Machine,RBM)等。上述机器学习或神经网络或深度神经网络模型的参数,包括但不限于权值,步长,均值和方差等。
可选的,上述第一预处理模型信息包括但不限于以下至少之一:滤波器参数或模型;卷积层参数或模型;池化层参数或模型;离散余弦变换(Discrete Cosine Transform,DCT)变换参数或模型;小波变换参数或模型;冲击信道处理方法的参数或模型;波形处理方法的参数或模型;信号相关序列处理方法的参数或模型。其中,这里的模型可以指函数模型、网络模型、降采样模型、图像化模型等;这里的参数可以指权值、步长、均值和方差等。
可选的,上述第一误差模型信息包括但不限于:第二误差模型信息和第三误差模型信息中的至少一个。
其中,所述第二误差模型信息包括但不限于:位置误差补偿信息、测量误差补偿信息、设备误差补偿信息和参数调整信息中的至少一个。如此,基于该第二误差模型信息可以对定位结果、测量结果、设备带来的误差或参数误差进行误差补偿。
所述第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿 信息和参数调整模型补偿信息中的至少一个。如此,基于该第三误差模型信息可以对上述的模型或其参数进行误差补偿或调整。
可选的,上述接收第一信息的方式可以包括但不限于以下至少一种。
(1)所述第一信息携带在定位辅助数据信元(Information element,IE)中。可以理解,可以从定位辅助数据信元(ProvideAssistanceData IE)中获取该第一信息。也就是说,可以由网络设备通过单播的方式将该第一信息发送至通信设备。
(2)所述第一信息携带在位置系统信息块(posSIB)中。可以理解,可以从posSIB中获取该第一信息。也就是说,可以由网络设备通过广播的方式将该第一信息发送至通信设备。
其中,上述posSIB为小区专用(cell specific)posSIB或区域专用(area specific)posSIB。也就是说,通过广播的方式发送的第一信息,可以是在一个小区范围内广播,也可以是在一片区域范围内广播。
步骤103:根据所述第一信息,确定与终端设备的位置相关的信息。
在本申请实施例中,可以根据预先配置的第一信息确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
可选的,在本申请实施例的定位方法中,上述步骤103,可以具体执行为如下内容:根据所述第一信息和所述终端设备的第一测量信息,确定所述与终端设备的位置相关的信息;其中,所述第一测量信息基于信号测量得到。
可以理解,当基于上述第一信息确定与终端设备的位置相关的信息时,可选的可以结合终端设备的第一测量信息进行确定,以进一步提高对终端设备的定位精度。其中,所述终端设备的第一测量信息可以包括但不限于:由 终端设备基于信号测量得到的所述终端设备的第一测量信息和由接入网设备基于信号测量得到的所述终端设备的第一测量信息中的至少一个。所述终端设备的第一测量信息可以是由终端设备基于信号测量得到的,也可以是由接入网设备基于信号测量得到的,本申请实施例对此不作具体限定。
可选的,在本申请实施例的定位方法中,上述第一测量信息包括但不限于以下中的至少一个:所述终端设备的信道冲击响应;所述终端设备的信号波形;所述终端设备的相关序列或波形;所述终端设备的信号测量结果,该信号测量结果包括但不限于基于增强型小区标识(Enhanced Cell-ID,E-CID)、观察到达时间差(Observed Time Difference of Arrival,OTDOA)、新空口增强型小区标识(New RadioEnhanced Cell-ID,NR-ECID)、多重往复时间(Multi-Round-Trip Time,Multi-RTT)、下行到达角度(Downlink Angle of Departure,DL-AOD)、下行到达时间差(Downlink Time Different Of Arrival,UTDOA)、上行到达角度(Uplink Angle of Arrival,UL-AOA)、上行到达时间差(Uplink-time Different Of Arrival,UTDOA)等定位方法得到的信号测量结果。
可选的,在本申请实施例的定位方法中,上述与终端设备的位置相关的信息包括但不限于以下至少之一。
(1)终端设备的定位结果信息。该定位结果信息可以为根据相应的测量信息(比如上述第一测量信息等)进行位置计算得到的终端设备的具体位置。
在一个示例中,若上述第一信息包括第一机器学习模型信息,则上述终端设备的定位结果信息可以包括:根据所述第一机器学习模型信息和所述第一测量信息确定的终端位置信息。
可以理解,基于上述第一机器学习模型信息和第一测量信息可以进行相应的位置计算,准确地得到终端设备的具体位置。举例来说,该第一机器学习模型信息可以包括用于实现位置计算的机器学习或神经网络或深度神经网络模型,如此,则可以将该第一测量信息作为该机器学习或神经网络或深度 神经网络模型的输入,经模型预估计算后即可输出终端位置信息。可选的,该机器学习或神经网络或深度神经网络模型可以基于经过实地的信号采集得到某一区域内大量的训练数据预先训练得到,其中,训练数据包括但不限于:信道冲击响应、RSRP、终端实际位置等。
在另一个示例中,若上述第一信息包括第一误差模型信息,则上述终端设备的定位结果信息还可以包括:根据所述第一误差模型信息进行误差补偿后的所述终端设备的定位结果信息。如此,通过对定位结果信息进行误差补偿,可以提高定位精度。
可选的,在该示例中,根据第一误差模型信息对终端位置信息进行误差补偿,得到终端设备的定位结果信息。其中,所述终端位置信息可以是基于上述第一机器学习模型信息和第一测量信息确定的终端位置信息,也可以是通过其他方式确定的终端位置信息,在此不作特别限定。
(2)基于所述第一测量信息确定的第二测量信息。
可以理解,根据第一信息或者根据第一信息和第一测量信息确定的与终端设备的位置相关的信息除了可以包括终端设备的定位结果信息外,还可以为对第一测量信息进行进一步处理后的第二测量信息。其中,该第二测量信息可以用于终端设备的具体位置的计算。也就是说,在对第一测量信息进行一定的处理后再用于终端设备的定位,可以压缩第一测量信息的大小,降低上报开销,并提高定位精度。
在一个示例中,上述第二测量信息包括:经稀疏化处理、降维化处理或者图像化处理后的所述第一测量信息。可选的,可以使用相应的预处理模型信息(比如上述第一预处理模型信息等)对第一测量信息进行稀疏化处理、降维化处理或者图像化处理等预处理得到该第二测量信息。
可选的,上述第二测量信息包括但不限于以下至少之一:稀疏化的信道冲击响应、图像化的信道冲击响应、多径表示的信道冲击响应、降维的图形化信道冲击响应、稀疏化的信号波形、降采样的信号波形、图像化的信号波 形、降维的图像化信道波形、稀疏化的相关序列、降采样的相关序列、多径表示的相关序列、稀疏化的其他信号测量结果、降采样的其他信号测量结果、图像化的其他信号测量结果和降维的图像化其他信号测量结果。
在另一个示例中,上述第二测量信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一测量信息。
可以理解,通过对第一测量信息预先进行误差补偿后再进行对终端设备的位置计算,可以提高定位精度。
在又一个示例中,上述第二测量信息包括:根据第一误差模型信息进行误差补偿后,再经第一预处理模型信息处理后的第一测量信息。
可以理解,通过对第一测量信息预先进行误差补偿并经预处理模型处理后再进行对终端设备的位置计算,可以压缩第一测量信息的大小,降低上报开销,并提高定位精度。
(3)基于所述第一机器学习模型信息确定的第二机器学习模型信息。
可以理解,在上述第一信息包括第一机器学习模型信息时,为了提高对终端设备的定位精度,还可以对该第一机器学习模型信息进行优化。
在一个示例中,上述第二机器学习模型信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一机器学习模型信息。
(4)基于所述第一预处理模型信息确定的第二预处理模型信息。
可以理解,在上述第一信息包括第一预处理模型信息时,为了提高对终端设备的定位精度,还可以对该第一预处理模型信息进行优化。
在一个示例中,上述第二预处理模型信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一预处理模型信息。
可选的,在本申请实施例的定位方法中,可以实现不同的对第三信息的上报方案,其中,该第三信息可以供网络设备实现对终端设备的位置相关的信息的确定和对第一信息的更新中的至少一个操作。
在一个示例中,通信设备可以实现对第三信息的主动上报,即本申请实 施例的定位方法,还可以包括如下内容:向所述网络设备上报第三信息,所述第三信息用于供所述网络设备确定所述与终端设备的位置相关的信息;和/或,用于供所述网络设备更新所述第一信息。
其中,更新上述第一信息即指实现相关模型及参数的更新。
在另一个示例中,通信设备可以基于相应的指示实现对第三信息的被动上报,即本申请实施例的定位方法,还可以包括如下内容:接收第二信息,所述第二信息用于指示是否向所述网络设备上报第三信息。
可选的,上述接收第二信息的方式可以包括但不限于以下至少一项。
(1)所述第二信息携带在定位辅助数据信元中。可以理解,可以从定位辅助数据信元(ProvideAssistanceData IE)中获取该第二信息。也就是说,可以由网络设备通过单播的方式将该第二信息发送至通信设备。
(2)所述第二信息携带在位置系统信息块posSIB中。可以理解,可以从位置系统信息块posSIB中获取该第二信息。也就是说,可以由网络设备通过广播的方式将该第二信息发送至通信设备。
其中,上述posSIB为小区专用(cell specific)posSIB或区域专用(area specific)posSIB。也就是说,通过广播的方式发送的第二信息,可以是在一个小区范围内广播,也可以是在一片区域范围内广播。
可选的,上述用于携带第一信息和第二信息中的至少一个的位置系统信息块posSIB的类型可以根据第一信息和第二信息中的至少一个定义posSIB的类型。也就是说,上述posSIB的类型基于所述第一信息和所述第二信息中的至少一个定义。
可选的,该实施例中的位置系统信息块posSIB的类型可以为新定义的类型,如[posSibType4-X]。
进一步可选的,上述需要上报的第三信息可以包括但不限于以下至少之一:所述与终端设备的位置相关的信息中的至少一个;所述第一测量信息中的至少一个。
进一步可选的,在需要上报第三信息时,上述向网络设备上报第三信息的过程可以具体执行为如下内容:将所述第三信息携带在第一信元IE中,上报至所述网络设备。
其中,所述第一IE包括:基于定位协议(LTE Positioning Protocol,LPP)的位置信息信元或基于新空口定位协议a(NR Positioning Protocol a,NRPPa)的位置信息信元。
可选的,上述位置信息信元可以是各种定位方法的位置信息信元,包括但不限于:E-CID、OTDOA、NR-ECID、Multi-RTT、DL-AOD、DL-TDOA、UL-AOA、UL-TDOA等定位方法。
可选的,在本申请实施例的定位方法中,还可以包括以下内容:在所述第一信息被分级加密的情况下,接收所述网络设备发送的与所述第一信息的加密等级对应的密钥。
可以理解,通过对第一信息进行分级加密并分配相应的密钥,可以提高信息传输的可靠性和安全性,避免信息泄露,使得接收双方理解一致。可选的,可以基于定位精度要求、负荷大小等实现分级加密。在一个示例中,对可实现定位精度较低、负荷较小的第一信息对应的一组posSIB进行一级加密,相应的分配一级密钥;而对于对可实现定位精度较高、负荷较大的第一信息对应的一组posSIB进行二级加密,相应的分配二级密钥。
由上可知,在高定位精度的需求下,本申请技术方案提出一种在复杂的多径和大量NLOS情况下获得较好的定位性能的定位技术,如基于机器学习、预处理模型、误差模型的定位技术等。具体而言,网络侧可以通过广播或者单播方式播发误差模型信息、预处理模型信息和机器学习模型信息中的至少之一,通信设备侧根据上述信息进行位置测量或计算。可选的,通信设备侧上报相应的补偿参数、位置信息或者测量信息等给网络侧,网络侧通过通信设备侧上报的信息进行模型优化,并更新模型及相关参数,或者基于相应的测量信息实现对终端设备的定位等。
参见图2所示,本申请实施例提供一种定位方法,由网络设备执行,可选的,该网络设备可以为基站,也可以为核心网设备(比如LMF实体)。其中,所述方法包括以下流程步骤。
步骤201:向通信设备发送第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。
其中,所述第一信息用于供所述通信设备确定与终端设备的位置相关的信息。
可选地,在所述网络设备为核心网设备(如LMF实体)的情况下,所述向通信设备发送第一信息,可以是LMF基于定位协议LPP通过单播的方式给终端设备发送第一信息,也可以是LMF基于新空口定位协议NRPPa通过单播的方式给基站发送第一信息。
在所述网络设备为基站的情况下,所述向通信设备发送第一信息,可以是基站通过位置系统信息块posSIB将第一信息通过广播的方式发送给终端设备。
在本申请实施例中,可以通过为通信设备提供预先配置的第一信息,供其确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
可选的,上述第一机器学习模型信息包括但不限于:机器学习或神经网络或深度神经网络模型以及机器学习或神经网络或深度神经网络模型的参数。
其中,上述机器学习或神经网络或深度神经网络模型包括但不限于:卷积神经网络CNN,如googlenet,AlexNet;递归神经网络RNN及LSTM;递归张量神经网络RNTN;生成对抗网络GAN;深度置信网络DBN;受限玻尔兹曼机RBM等。上述机器学习或神经网络或深度神经网络模型的参数, 包括但不限于权值,步长,均值和方差等。
可选的,上述第一预处理模型信息包括但不限于以下至少之一:滤波器参数或模型;卷积层参数或模型;池化层参数或模型;离散余弦变换DCT变换参数或模型;小波变换参数或模型;冲击信道处理方法的参数或模型;波形处理方法的参数或模型;信号相关序列处理方法的参数或模型。其中,这里的模型可以指函数模型、网络模型、降采样模型、图像化模型等;这里的参数可以指权值、步长、均值和方差等。
可选的,上述第一误差模型信息包括但不限于:第二误差模型信息和第三误差模型信息中的至少一个。
其中,所述第二误差模型信息包括但不限于:位置误差补偿信息、测量误差补偿信息、设备误差补偿信息和参数调整信息中的至少一个。如此,基于该第二误差模型信息可以对定位结果、测量结果、设备带来的误差或参数误差进行误差补偿。
所述第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿信息和参数调整模型补偿信息中的至少一个。如此,基于该第三误差模型信息可以对上述模型或其参数进行误差补偿或调整。
可选的,上述向通信设备发送第一信息的方式可以包括但不限于以下至少一项。
(1)所述第一信息携带在定位辅助数据信元中。可以理解,该第一信息可以携带在定位辅助数据信元(ProvideAssistanceData IE)中进行发送。也就是说,可以由核心网设备(如LMF)通过单播的方式将该第一信息发送至通信设备。
(2)所述第一信息携带在位置系统信息块posSIB中。可以理解,该第一信息可以携带在位置系统信息块posSIB中进行发送。也就是说,可以由基站通过广播的方式将该第一信息发送至通信设备。
其中,上述posSIB为小区专用(cell specific)posSIB或区域专用(area specific)posSIB。也就是说,通过广播的方式发送的第一信息,可以是在一个小区范围内广播,也可以是在一片区域范围内广播。
可选的,在本申请实施例的定位方法中,上述第一信息具体用于供所述通信设备根据所述终端设备的第一测量信息,确定所述与终端设备的位置相关的信息,其中,所述第一测量信息由所述通信设备基于信号进测量得到。
可以理解,当基于上述第一信息确定与终端设备的位置相关的信息时,可选的可以结合终端设备的第一测量信息进行确定,以进一步提高对终端设备的定位精度。其中,该终端设备的第一测量信息可以包括但不限于:由终端设备基于信号测量得到的该终端设备的第一测量信息和由接入网设备基于信号测量得到的该终端设备的第一测量信息中的至少一个。
可选的,在本申请实施例的定位方法中,上述第一测量信息包括但不限于以下中的至少一个。
所述终端设备的信道冲击响应;所述终端设备的信号波形;所述终端设备的相关序列或波形;所述终端设备的信号测量结果,该信号测量结果包括但不限于基于E-CID、OTDOA、NR-ECID、Multi-RTT、DL-AOD、DL-TDOA、UL-AOA、UL-TDOA等定位方法得到的信号测量结果。
可选的,在本申请实施例的定位方法中,上述与终端设备的位置相关的信息包括但不限于以下至少之一。
(1)终端设备的定位结果信息。该定位结果信息可以为根据相应的测量信息(比如上述第一测量信息等)进行位置计算得到的终端设备的具体位置。
在一个示例中,若上述第一信息包括第一机器学习模型信息,则上述终端设备的定位结果信息可以包括:根据所述第一机器学习模型信息和所述第一测量信息确定的终端位置信息。
可以理解,基于上述第一机器学习模型信息和第一测量信息可以进行相应的位置计算,准确地得到终端设备的具体位置。举例来说,该第一机器学 习模型信息可以包括用于实现位置计算的机器学习或神经网络或深度神经网络模型,如此,则可以将该第一测量信息作为该机器学习或神经网络或深度神经网络模型的输入,经模型预估计算后即可输出终端位置信息。可选的,该机器学习或神经网络或深度神经网络模型可以基于经过实地的信号采集得到某一区域内大量的训练数据预先训练得到,其中,训练数据包括但不限于:信道冲击响应、RSRP、终端实际位置等。
在另一个示例中,若上述第一信息包括第一误差模型信息,则上述终端设备的定位结果信息还可以包括:根据所述第一误差模型信息进行误差补偿后的所述终端设备的定位结果信息。如此,通过对定位结果信息进行误差补偿,可以提高定位精度。
可选的,在该示例中,根据第一误差模型信息对终端位置信息进行误差补偿,得到终端设备的定位结果信息。其中,所述终端位置信息可以是基于上述第一机器学习模型信息和第一测量信息确定的终端位置信息,也可以是通过其他方式确定的终端位置信息,在此不作特别限定。
(2)基于所述第一测量信息确定的第二测量信息。
可以理解,根据第一信息或者根据第一信息和第一测量信息确定的与终端设备的位置相关的信息除了可以包括终端设备的定位结果信息外,还可以为对第一测量信息进行进一步处理后的第二测量信息。其中,该第二测量信息可以用于终端设备的具体位置的计算。也就是说,在对第一测量信息进行一定的处理后再用于终端设备的定位,可以压缩第一测量信息的大小,降低上报开销,并提高定位精度。
在一个示例中,上述第二测量信息包括:经稀疏化处理、降维化处理或者图像化处理后的所述第一测量信息。可选的,可以使用相应的预处理模型信息(比如上述第一预处理模型信息等)对第一测量信息进行稀疏化处理、降维化处理或者图像化处理等预处理得到该第二测量信息。
可选的,上述第二测量信息包括但不限于以下至少之一:稀疏化的信道 冲击响应、图像化的信道冲击响应、多径表示的信道冲击响应、降维的图形化信道冲击响应、稀疏化的信号波形、降采样的信号波形、图像化的信号波形、降维的图像化信道波形、稀疏化的相关序列、降采样的相关序列、多径表示的相关序列、稀疏化的其他信号测量结果、降采样的其他信号测量结果、图像化的其他信号测量结果和降维的图像化其他信号测量结果。
在另一个示例中,上述第二测量信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一测量信息。
可以理解,通过对第一测量信息预先进行误差补偿后再进行对终端设备的位置计算,可以提高定位精度。
在又一个示例中,上述第二测量信息包括:根据第一误差模型信息进行误差补偿后,再经第一预处理模型信息处理的第一测量信息。
可以理解,通过对第一测量信息预先进行误差补偿并经预处理模型处理后再进行对终端设备的位置计算,可以压缩第一测量信息的大小,降低上报开销,并提高定位精度。
(3)基于所述第一机器学习模型信息确定的第二机器学习模型信息。
可以理解,在上述第一信息包括第一机器学习模型信息时,为了提高对终端设备的定位精度,还可以对该第一机器学习模型信息进行优化。
在一个示例中,上述第二机器学习模型信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一机器学习模型信息。
(4)基于所述第一预处理模型信息确定的第二预处理模型信息。
可以理解,在上述第一信息包括第一预处理模型信息时,为了提高对终端设备的定位精度,还可以对该第一预处理模型信息进行优化。
在一个示例中,上述第二预处理模型信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一预处理模型信息。
可选的,在本申请实施例的定位方法中,可以实现不同的获取第三信息的方案,其中,该第三信息可以供网络设备实现对终端设备的位置相关的信 息的确定和对第一信息的更新中的至少一个操作。
其中,更新上述第一信息即指实现相关模型及参数的更新。
在一个示例中,上述第三信息可以是由通信设备主动上报的,即本申请实施例的定位方法,还可以包括如下内容:接收所述通信设备上报的第三信息;根据所述第三信息确定所述与终端设备的位置相关的信息和/或更新所述第一信息。
在另一个示例中,上述第三信息可以是由通信设备基于相应的指示被动上报的,即本申请实施例的定位方法,还可以包括如下内容:向所述通信设备发送第二信息,所述第二信息用于指示所述通信设备是否上报第三信息。
可选地,在所述网络设备为核心网设备(如LMF实体)的情况下,所述向通信设备发送第二信息,可以是LMF基于定位协议LPP通过单播的方式给终端设备发送第二信息,也可以是LMF基于新空口定位协议NRPPa通过单播的方式给基站发送第二信息。
在所述网络设备为基站的情况下,所述向通信设备发送第二信息,可以是基站通过位置系统信息块posSIB将第二信息通过广播的方式发送给终端设备。
可选的,上述向通信设备发送第二信息的方式可以包括但不限于以下至少一项。
(1)所述第二信息携带在定位辅助数据信元中。可以理解,该第二信息可以携带在定位辅助数据信元(ProvideAssistanceData IE)中进行发送。也就是说,可以由网络设备核心网设备(如LMF)通过单播的方式将该第二信息发送至通信设备。
(2)所述第二信息携带在位置系统信息块posSIB中。可以理解,该第二信息可以携带在位置系统信息块posSIB中进行发送。也就是说,可以由基站通过广播的方式将该第二信息发送至通信设备。
其中,上述posSIB为小区专用(cell specific)posSIB或区域专用(area  specific)posSIB。也就是说,通过广播的方式发送的第二信息,可以是在一个小区范围内广播,也可以是在一片区域范围内广播。
可选的,上述用于携带第一信息和第二信息中的至少一个的位置系统信息块posSIB的类型可以根据第一信息和第二信息中的至少一个定义posSIB的类型。也就是说,上述posSIB的类型基于所述第一信息和所述第二信息中的至少一个定义。
可选的,该实施例中的位置系统信息块posSIB的类型可以为新定义的类型,如[posSibType4-X]。
进一步可选的,上述第三信息可以包括但不限于以下至少之一:所述与终端设备的位置相关的信息中的至少一个;所述第一测量信息中的至少一个。
进一步可选的,上述第三信息携带在第一信元IE中,其中,所述第一IE包括:基于定位协议LPP的位置信息信元或基于新空口定位协议NRPPa的位置信息信元。
可选的,上述位置信息信元可以是各种定位方法的位置信息信元,包括但不限于:E-CID、OTDOA、NR-ECID、Multi-RTT、DL-AOD、DL-TDOA、UL-AOA、UL-TDOA等定位方法。
在对所述第一信息进行分级加密的情况下,向所述通信设备发送的与所述第一信息的加密等级对应的密钥。
可以理解,通过对第一信息进行分级加密并分配相应的密钥,可以提高信息传输的可靠性和安全性,避免信息泄露,使得接收双方理解一致。可选的,可以基于定位精度要求、负荷大小等实现分级加密。在一个示例中,对可实现定位精度较低、负荷较小的第一信息对应的一组posSIB进行一级加密,相应的分配一级密钥;而对于对可实现定位精度较高、负荷较大的第一信息对应的一组posSIB进行二级加密,相应的分配二级密钥。
由上可知,在高定位精度的需求下,本申请技术方案提出一种在复杂的多径和大量NLOS情况下获得较好的定位性能的定位技术,如基于机器学习、 预处理模型、误差模型的定位技术等。具体而言,网络侧可以通过广播或者单播方式播发误差模型信息、预处理模型信息和机器学习模型信息中的至少之一,通信设备侧根据上述信息进行位置测量或计算。可选的,通信设备侧上报相应的补偿参数、位置信息或者测量信息等给网络侧,网络侧通过通信设备侧上报的信息进行模型优化,并更新模型及相关参数,或者基于相应的测量信息实现对终端设备的定位等。
参见图3所示,本申请实施例提供一种通信设备300,该通信设备300包括:接收模块301和定位模块303。
其中,接收模块301,用于接收第一信息,第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;定位模块303,用于根据第一信息,确定与终端设备的位置相关的信息。
可选的,在本申请实施例的通信设备300中,上述定位模块303,可以具体用于:根据第一信息和终端设备的第一测量信息,确定与终端设备的位置相关的信息,第一测量信息基于信号测量得到。
可选的,在本申请实施例的通信设备300中,上述与终端设备的位置相关的信息包括以下至少之一:终端设备的定位结果信息;基于第一测量信息确定的第二测量信息;基于第一机器学习模型信息确定的第二机器模型信息;基于第一预处理模型信息确定第二预处理模型信息。
可选的,在本申请实施例的通信设备300中,上述终端设备的定位结果信息包括:根据第一机器学习模型信息和第一测量信息确定的终端位置信息。
可选的,在本申请实施例的通信设备300中,上述终端设备的定位结果信息包括:根据第一误差模型信息进行误差补偿后的终端设备的定位结果信息。
可选的,在本申请实施例的通信设备300中,上述第二测量信息包括:经稀疏化处理、降维化处理或者图像化处理后的第一测量信息。
可选的,在本申请实施例的通信设备300中,上述第二测量信息包括: 根据第一误差模型信息进行误差补偿后的第一测量信息。
可选的,在本申请实施例的通信设备300中,上述第二测量信息包括:根据第一误差模型信息进行误差补偿后,再经第一预处理模型信息处理后的第一测量信息。
可选的,在本申请实施例的通信设备300中,上述第二机器学习模型信息包括:根据第一误差模型信息进行误差补偿后的第一机器学习模型信息。
可选的,在本申请实施例的通信设备300中,上述第二预处理模型信息包括:根据第一误差模型信息进行误差补偿后的第一预处理模型信息。
可选的,在本申请实施例的通信设备300中,上述第一预处理模型信息包括以下至少之一:滤波器参数或模型;卷积层参数或模型;池化层参数或模型;离散余弦变换DCT变换参数或模型;小波变换参数或模型;冲击信道处理方法的参数或模型;波形处理方法的参数或模型;信号相关序列处理方法的参数或模型。
可选的,在本申请实施例的通信设备300中,上述第一误差模型信息包括:第二误差模型信息和第三误差模型信息中的至少一个;其中,第二误差模型信息包括:位置误差补偿信息、测量误差补偿信息、设备误差补偿信息和参数调整信息中的至少一个;第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿信息和参数调整模型补偿信息中的至少一个。
可选的,在本申请实施例的通信设备300中,上述第一测量信息包括以下中的至少一个:终端设备的信道冲击响应;终端设备的信号波形;终端设备的相关序列或波形;终端设备的信号测量结果。
可选的,本申请实施例的通信设备300,还包括发送模块,可以用于:向网络设备上报第三信息,第三信息用于供网络设备确定与终端设备的位置相关的信息;和/或用于供所述网络设备更新第一信息。
可选的,在本申请实施例的通信设备300中,上述接收模块301,还可 以用于:接收第二信息,第二信息用于指示是否向网络设备上报第三信息。
可选的,在本申请实施例的通信设备300中,上述第三信息包括以下中的至少一个:与终端设备的位置相关的信息中的至少一个;第一测量信息中的至少一个。
可选的,在本申请实施例的通信设备300中,上述发送模块,可以具体用于:将第三信息携带在第一信元IE中,上报至网络设备;其中,第一IE包括:基于定位协议LPP的位置信息信元或基于新空口定位协议NRPPa的位置信息信元。
可选的,在本申请实施例的通信设备300中,上述第一信息和第二信息中的至少一个携带在定位辅助数据信元中。
可选的,在本申请实施例的通信设备300中,上述第一信息和第二信息中的至少一个携带在位置系统信息块posSIB中。
可选的,在本申请实施例的通信设备300中,上述posSIB的类型基于第一信息和第二信息中的至少一个定义。
可选的,在本申请实施例的通信设备300中,上述posSIB为小区专用posSIB或区域专用posSIB。
可选的,在本申请实施例的通信设备300中,上述接收模块301,还可以用于:在第一信息被分级加密的情况下,接收网络设备发送的与第一信息的加密等级对应的密钥。
能够理解,本申请实施例提供的通信设备300,能够实现前述由通信设备300执行的定位方法,关于定位方法的相关阐述均适用于通信设备300,此处不再赘述。其中,该通信设备300可以为终端设备或接入网设备。
在本申请实施例中,可以根据预先配置的第一信息确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模 型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
参见图4所示,本申请实施例提供一种网络设备400,该网络设备400包括:发送模块401,用于向通信设备发送第一信息,第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;其中,第一信息用于供通信设备确定与终端设备的位置相关的信息。
可选的,在本申请实施例的网络设备400中,上述第一信息具体用于供通信设备根据终端设备的第一测量信息,确定与终端设备的位置相关的信息,其中,第一测量信息由通信设备基于信号进测量得到。
可选的,在本申请实施例的网络设备400中,上述与终端设备的位置相关的信息包括以下至少之一:终端设备的定位结果信息;基于第一测量信息确定的第二测量信息;基于第一机器学习模型信息确定的第二机器模型信息;基于第一预处理模型信息确定的第二预处理模型信息。
可选的,在本申请实施例的网络设备400中,上述终端设备的定位结果信息包括:根据第一机器学习模型信息和第一测量信息确定的终端位置信息。
可选的,在本申请实施例的网络设备400中,上述终端设备的定位结果信息包括:根据第一误差模型信息进行误差补偿后的终端设备的定位结果信息。
可选的,在本申请实施例的网络设备400中,上述第二测量信息包括:经稀疏化处理、降维化处理或者图像化处理后的第一测量信息。
可选的,在本申请实施例的网络设备400中,上述第二测量信息包括:根据第一误差模型信息进行误差补偿后的第一测量信息。
可选的,在本申请实施例的网络设备400中,上述第二测量信息包括:根据第一误差模型信息进行误差补偿后,再经第一预处理模型信息处理后的第一测量信息。
可选的,在本申请实施例的网络设备400中,上述第二机器学习模型信 息包括:根据第一误差模型信息进行误差补偿后的第一机器学习模型信息。
可选的,在本申请实施例的网络设备400中,上述第二预处理模型信息包括:根据第一误差模型信息进行误差补偿后的第一预处理模型信息。
可选的,在本申请实施例的网络设备400中,上述第一预处理模型信息包括以下至少之一:滤波器参数或模型;卷积层参数或模型;池化层参数或模型;离散余弦变换DCT变换参数或模型;小波变换参数或模型;冲击信道处理方法的参数或模型;波形处理方法的参数或模型;信号相关序列处理方法的参数或模型。
可选的,在本申请实施例的网络设备400中,上述第一误差模型信息包括:第二误差模型信息和第三误差模型信息中的至少一个;其中,第二误差模型信息包括:位置误差补偿信息、测量误差补偿信息、设备误差补偿信息和参数调整信息中的至少一个;第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿信息和参数调整模型补偿信息中的至少一个。
可选的,在本申请实施例的网络设备400中,上述第一测量信息包括以下中的至少一个:终端设备的信道冲击响应;终端设备的信号波形;终端设备的相关序列或波形;终端设备的信号测量结果。
可选的,本申请实施例的网络设备400,还包括:接收模块和处理模块。
其中,上述接收模块用于接收通信设备上报的第三信息;上述处理模块用于根据第三信息确定与终端设备的位置相关的信息和/或更新第一信息。
可选的,在本申请实施例的网络设备400中,上述发送模块401,还可以用于:向通信设备发送第二信息,第二信息用于指示通信设备是否上报第三信息。
可选的,在本申请实施例的网络设备400中,上述第三信息包括以下中的至少一个:与终端设备的位置相关的信息中的至少一个;第一测量信息中的至少一个。
可选的,在本申请实施例的网络设备400中,上述第三信息携带在第一信元IE中,其中,第一IE包括:基于定位协议LPP的位置信息信元或基于新空口定位协议NRPPa的位置信息信元。
可选的,在本申请实施例的网络设备400中,第一信息和第二信息中的至少一个携带在定位辅助数据信元中。
可选的,在本申请实施例的网络设备400中,第一信息和第二信息中的至少一个携带在位置系统信息块posSIB中。
可选的,在本申请实施例的网络设备400中,posSIB的类型基于第一信息和第二信息中的至少一个定义。
可选的,在本申请实施例的通信设备300中,上述posSIB为小区专用posSIB或区域专用posSIB。
可选的,在本申请实施例的网络设备400中,上述发送模块401,还可以用于:在对第一信息进行分级加密的情况下,向通信设备发送的与第一信息的加密等级对应的密钥。
能够理解,本申请实施例提供的网络设备400,能够实现前述由网络设备400执行的定位方法,关于定位方法的相关阐述均适用于网络设备400,此处不再赘述。其中,该网络设备400可以为核心网设备。
在本申请实施例中,可以通过为通信设备提供预先配置的第一信息,供其确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
图5是本申请另一个实施例的终端设备的框图。图5所示的终端设备500包括:至少一个处理器501、存储器502、至少一个网络接口504和用户接口503。终端设备500中的各个组件通过总线系统505耦合在一起。可理解,总 线系统505用于实现这些组件之间的连接通信。总线系统505除包括数据总线之外,还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见,在图5中将各种总线都标为总线系统505。
其中,用户接口503可以包括显示器、键盘或者点击设备(例如,鼠标,轨迹球(trackball)、触感板或者触摸屏等。
可以理解,本申请实施例中的存储器502可以是易失性存储器或非易失性存储器,或可包括易失性和非易失性存储器两者。其中,非易失性存储器可以是只读存储器(Read-Only Memory,ROM)、可编程只读存储器(Programmable ROM,PROM)、可擦除可编程只读存储器(Erasable PROM,EPROM)、电可擦除可编程只读存储器(Electrically EPROM,EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory,RAM),其用作外部高速缓存。通过示例性但不是限制性说明,许多形式的RAM可用,例如静态随机存取存储器(Static RAM,SRAM)、动态随机存取存储器(Dynamic RAM,DRAM)、同步动态随机存取存储器(Synchronous DRAM,SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM,DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM,ESDRAM)、同步连接动态随机存取存储器(Synchlink DRAM,SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM,DRRAM)。本申请实施例描述的系统和方法的存储器502旨在包括但不限于这些和任意其它适合类型的存储器。
在一些实施方式中,存储器502存储了如下的元素,可执行模块或者数据结构,或者他们的子集,或者他们的扩展集:操作系统5021和应用程序5022。
其中,操作系统5021,包含各种系统程序或指令,例如框架层、核心库层、驱动层等,用于实现各种基础业务以及处理基于硬件的任务。应用程序或指令5022,包含各种应用程序或指令,例如媒体播放器(Media Player)、浏 览器(Browser)等,用于实现各种应用业务。实现本申请实施例方法的程序或指令可以包含在应用程序或指令5022中。
在本申请实施例中,终端设备500还包括:存储在存储器上502并可在处理器501上运行的程序或指令,该程序或指令被处理器501执行时实现如下步骤:
接收第一信息,第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;根据第一信息,确定与终端设备的位置相关的信息。
在本申请实施例中,可以根据预先配置的第一信息确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
上述本申请实施例揭示的方法可以应用于处理器501中,或者由处理器501实现。处理器501可能是一种集成电路芯片,具有信号的处理能力。在实现过程中,上述方法的各步骤可以通过处理器501中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器501可以是通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现成可编程门阵列(Field Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成,或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器,闪存、只读存储器,可编程只读存储器或者电可擦写可编程 存储器、寄存器等本领域成熟的可读存储介质中。该可读存储介质位于存储器502,处理器501读取存储器502中的信息,结合其硬件完成上述方法的步骤。具体地,该可读存储介质上存储有程序或指令,所述程序或指令被处理器501执行时实现如上述定位方法实施例的各步骤。
可以理解的是,本申请实施例描述的这些实施例可以用硬件、软件、固件、中间件、微码或其组合来实现。对于硬件实现,处理单元可以实现在一个或多个专用集成电路(Application Specific Integrated Circuits,ASIC)、数字信号处理器(Digital Signal Processing,DSP)、数字信号处理设备(DSP Device,DSPD)、可编程逻辑设备(Programmable Logic Device,PLD)、现场可编程门阵列(Field-Programmable Gate Array,FPGA)、通用处理器、控制器、微控制器、微处理器、用于执行本申请所述功能的其它电子单元或其组合中。
对于软件实现,可通过执行本申请实施例所述功能的模块(例如过程、函数等)来实现本申请实施例所述的技术。软件代码可存储在存储器中并通过处理器执行。存储器可以在处理器中或在处理器外部实现。
终端设备500能够实现前述实施例中终端设备实现的各个过程,为避免重复,这里不再赘述。
请参阅图6,图6是本申请实施例应用的网络设备的结构图,网络设备600可以为基站或者LMF,在网络设备600为基站的情况下,能够实现权利要求1-21所对应的方法实施例步骤的细节,或者能够实现权利要求22-42所对应的方法实施例步骤的细节,并达到相同的效果。在网络设备600为LMF的情况下,能够实现权利要求22-42所对应的方法实施例步骤的细节,并达到相同的效果。如图6所示,网络设备600包括:处理器601、收发机602、存储器603、用户接口604和总线接口605,其中:在本申请实施例中,网络设备600还包括:存储在存储器603上并可在处理器601上运行的程序或指令。
在网络设备600为基站的情况下,上述程序或指令被处理器601执行时, 能够实现如下步骤:接收第一信息,第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;根据第一信息,确定与终端设备的位置相关的信息。
在本申请实施例中,可以根据预先配置的第一信息确定与终端设备的位置相关的信息,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学习模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
或者,在网络设备600为基站或核心网设备(如LMF)的情况下,上述程序或指令被处理器601执行时,能够实现如下步骤:向通信设备发送第一信息,第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;其中,第一信息用于供通信设备确定与终端设备的位置相关的信息。
在本申请实施例中,可以通过为通信设备提供预先配置的第一信息,供其确定与终端设备的位置相关的信息,以进一步实现对终端设备的定位,其中,该第一信息包括但不限于第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个。如此,通过提供一种基于机器学模型、预处理模型和误差模型等训练模型中的至少一个的定位方案,可以有效地解决多径问题和NLOS问题,从而提高定位精度。
在图6中,总线架构可以包括任意数量的互联的总线和桥,具体由处理器601代表的一个或多个处理器和存储器603代表的存储器的各种电路链接在一起。总线架构还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路链接在一起,这些都是本领域所公知的,因此,本文不再对其进行进一步描述。总线接口605提供接口。收发机602可以是多个元件,即包括发送机和接收机,提供用于在传输介质上与各种其他装置通信的单元。针对不同的用户设备,用户接口604还可以是能够外接内接需要设备的接口, 连接的设备包括但不限于小键盘、显示器、扬声器、麦克风、操纵杆等。
处理器601负责管理总线架构和通常的处理,存储器603可以存储处理器601在执行操作时所使用的数据。
本申请实施例还提供一种可读存储介质,该可读存储介质上存储有程序或指令,该程序或指令被处理器执行时实现上述应用于通信设备的定位方法实施例,和/或应用于网络设备的定位方法实施例的各个过程,且能达到相同的技术效果,为避免重复,这里不再赘述。其中,所述可读存储介质,包括计算机可读存储介质,如计算机只读存储器(Read-Only Memory,ROM)、随机存取存储器(Random Access Memory,RAM)、磁碟或者光盘等。
需要说明的是,在本文中,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外,需要指出的是,本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能,还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能,例如,可以按不同于所描述的次序来执行所描述的方法,并且还可以添加、省去、或组合各种步骤。另外,参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述,本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现,当然也可以通过硬件,但很多情况下前者是更佳的实施方式。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中,包括若干指令用以使得一台终端(可以是手机,计算机,服务器,空调器,或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述,但是本申请并不局限于上述的具体实施方式,上述的具体实施方式仅仅是示意性的,而不是限制性的,本领域的普通技术人员在本申请的启示下,在不脱离本申请宗旨和权利要求所保护的范围情况下,还可做出很多形式,均属于本申请的保护之内。

Claims (58)

  1. 一种定位方法,应用于通信设备,所述方法包括:
    接收第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;
    根据所述第一信息,确定与终端设备的位置相关的信息。
  2. 根据权利要求1所述的方法,其中,所述根据所述第一信息,确定与终端设备的位置相关的信息,包括:
    根据所述第一信息和所述终端设备的第一测量信息,确定所述与终端设备的位置相关的信息;其中,所述第一测量信息基于信号测量得到。
  3. 根据权利要求2所述的方法,其中,所述与终端设备的位置相关的信息包括以下至少之一:
    所述终端设备的定位结果信息;
    基于所述第一测量信息确定的第二测量信息;
    基于所述第一机器学习模型信息确定的第二机器模型信息;
    基于所述第一预处理模型信息确定的第二预处理模型信息。
  4. 根据权利要求3所述的方法,其中,所述终端设备的定位结果信息包括:根据所述第一机器学习模型信息和所述第一测量信息确定的终端位置信息。
  5. 根据权利要求3所述的方法,其中,所述终端设备的定位结果信息包括:根据所述第一误差模型信息进行误差补偿后的所述终端设备的定位结果信息。
  6. 根据权利要求3所述的方法,其中,所述第二测量信息包括:经稀疏化处理、降维化处理或者图像化处理后的所述第一测量信息。
  7. 根据权利要求3所述的方法,其中,所述第二测量信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一测量信息。
  8. 根据权利要求3所述的方法,其中,所述第二机器学习模型信息包括: 根据所述第一误差模型信息进行误差补偿后的所述第一机器学习模型信息。
  9. 根据权利要求3所述的方法,其中,所述第二预处理模型信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一预处理模型信息。
  10. 根据权利要求1所述的方法,其中,所述第一预处理模型信息包括以下至少之一:
    滤波器参数或模型;
    卷积层参数或模型;
    池化层参数或模型;
    离散余弦变换DCT变换参数或模型;
    小波变换参数或模型;
    冲击信道处理方法的参数或模型;
    波形处理方法的参数或模型;
    信号相关序列处理方法的参数或模型。
  11. 根据权利要求1所述的方法,其中,所述第一误差模型信息包括:第二误差模型信息和第三误差模型信息中的至少一个;
    其中,所述第二误差模型信息包括:位置误差补偿信息、测量误差补偿信息、设备误差补偿信息和参数调整信息中的至少一个;
    所述第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿信息和参数调整模型补偿信息中的至少一个。
  12. 根据权利要求2所述的方法,其中,所述第一测量信息包括以下中的至少一个:
    所述终端设备的信道冲击响应;
    所述终端设备的信号波形;
    所述终端设备的相关序列或波形;
    所述终端设备的信号测量结果。
  13. 根据权利要求2所述的方法,其中,所述方法还包括:
    向网络设备上报第三信息,
    所述第三信息用于供所述网络设备确定所述与终端设备的位置相关的信息;
    和/或,用于供所述网络设备更新所述第一信息。
  14. 根据权利要求2所述的方法,其中,所述方法还包括:
    接收第二信息,所述第二信息用于指示是否向网络设备上报第三信息。
  15. 根据权利要求13或14所述的方法,其中,所述第三信息包括以下中的至少一个:
    所述与终端设备的位置相关的信息中的至少一个;
    所述第一测量信息中的至少一个。
  16. 根据权利要求13或14所述的方法,其中,所述向网络设备上报第三信息,包括:
    将所述第三信息携带在第一信元IE中,上报至所述网络设备;
    其中,所述第一IE包括:基于定位协议LPP的位置信息信元或基于新空口定位协议NRPPa的位置信息信元。
  17. 根据权利要求14所述的方法,其中,所述第一信息和第二信息中的至少一个携带在定位辅助数据信元中。
  18. 根据权利要求14所述的方法,其中,所述第一信息和所述第二信息中的至少一个携带在位置系统信息块posSIB中。
  19. 根据权利要求18所述的方法,其中,所述posSIB的类型基于所述第一信息和所述第二信息中的至少一个定义。
  20. 根据权利要求18所述的方法,其中,所述posSIB为小区专用posSIB或区域专用posSIB。
  21. 根据权利要求1所述的方法,其中,所述方法还包括:
    在所述第一信息被分级加密的情况下,接收网络设备发送的与所述第一 信息的加密等级对应的密钥。
  22. 一种定位方法,应用于网络设备,所述方法包括:
    向通信设备发送第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;
    其中,所述第一信息用于供所述通信设备确定与终端设备的位置相关的信息。
  23. 根据权利要求22所述的方法,其中,所述第一信息具体用于供所述通信设备根据所述终端设备的第一测量信息,确定所述与终端设备的位置相关的信息,其中,所述第一测量信息由所述通信设备基于信号测量得到。
  24. 根据权利要求23所述的方法,其中,所述与终端设备的位置相关的信息包括以下至少之一:
    所述终端设备的定位结果信息;
    基于所述第一测量信息确定的第二测量信息;
    基于所述第一机器学习模型信息确定的第二机器模型信息;
    基于所述第一预处理模型信息确定的第二预处理模型信息。
  25. 根据权利要求24所述的方法,其中,所述终端设备的定位结果信息包括:根据所述第一机器学习模型信息和所述第一测量信息确定的终端位置信息。
  26. 根据权利要求24所述的方法,其中,所述终端设备的定位结果信息包括:根据所述第一误差模型信息进行误差补偿后的所述终端设备的定位结果信息。
  27. 根据权利要求24所述的方法,其中,所述第二测量信息包括:经稀疏化处理、降维化处理或者图像化处理后的所述第一测量信息。
  28. 根据权利要求24所述的方法,其中,所述第二测量信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一测量信息。
  29. 根据权利要求24所述的方法,其中,所述第二机器学习模型信息包 括:根据所述第一误差模型信息进行误差补偿后的所述第一机器学习模型信息。
  30. 根据权利要求24所述的方法,其中,所述第二预处理模型信息包括:根据所述第一误差模型信息进行误差补偿后的所述第一预处理模型信息。
  31. 根据权利要求22所述的方法,其中,所述第一预处理模型信息包括以下至少之一:
    滤波器参数或模型;
    卷积层参数或模型;
    池化层参数或模型;
    离散余弦变换DCT变换参数或模型;
    小波变换参数或模型;
    冲击信道处理方法的参数或模型;
    波形处理方法的参数或模型;
    信号相关序列处理方法的参数或模型。
  32. 根据权利要求22所述的方法,其中,所述第一误差模型信息包括:第二误差模型信息和第三误差模型信息中的至少一个;
    其中,所述第二误差模型信息包括:位置误差补偿信息、测量误差补偿信息、设备误差补偿信息和参数调整信息中的至少一个;
    所述第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿信息和参数调整模型补偿信息中的至少一个。
  33. 根据权利要求23所述的方法,其中,所述第一测量信息包括以下中的至少一个:
    所述终端设备的信道冲击响应;
    所述终端设备的信号波形;
    所述终端设备的相关序列或波形;
    所述终端设备的信号测量结果。
  34. 根据权利要求23所述的方法,其中,所述方法还包括:
    接收所述通信设备上报的第三信息;
    根据所述第三信息确定所述与终端设备的位置相关的信息和/或更新所述第一信息。
  35. 根据权利要求23所述的方法,其中,所述方法还包括:
    向所述通信设备发送第二信息,所述第二信息用于指示所述通信设备是否上报第三信息。
  36. 根据权利要求34或35所述的方法,其中,所述第三信息包括以下中的至少一个:
    所述与终端设备的位置相关的信息中的至少一个;
    所述第一测量信息中的至少一个。
  37. 根据权利要求34或35所述的方法,其中,所述第三信息携带在第一信元IE中,其中,所述第一IE包括:基于定位协议LPP的位置信息信元或基于新空口定位协议NRPPa的位置信息信元。
  38. 根据权利要求35所述的方法,其中,所述第一信息和所述第二信息中的至少一个携带在定位辅助数据信元中。
  39. 根据权利要求35所述的方法,其中,所述第一信息和所述第二信息中的至少一个携带在位置系统信息块posSIB中。
  40. 根据权利要求39所述的方法,其中,所述posSIB的类型基于所述第一信息和所述第二信息中的至少一个定义。
  41. 根据权利要求39所述的方法,其中,所述posSIB为小区专用posSIB或区域专用posSIB。
  42. 根据权利要求22所述的方法,其中,所述方法还包括:
    在对所述第一信息进行分级加密的情况下,向所述通信设备发送的与所述第一信息的加密等级对应的密钥。
  43. 一种通信设备,包括:
    接收模块,用于接收第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;
    定位模块,用于根据所述第一信息,确定与终端设备的位置相关的信息。
  44. 根据权利要求43所述的通信设备,其中,所述定位模块用于根据所述第一信息和所述终端设备的第一测量信息,确定所述与终端设备的位置相关的信息;所述第一测量信息基于信号测量得到。
  45. 根据权利要求44所述的通信设备,其中,所述与终端设备的位置相关的信息包括以下至少之一:
    所述终端设备的定位结果信息;
    基于所述第一测量信息确定的第二测量信息;
    基于所述第一机器学习模型信息确定的第二机器模型信息;
    基于所述第一预处理模型信息确定的第二预处理模型信息。
  46. 根据权利要求43所述的通信设备,其中,所述第一预处理模型信息包括以下至少之一:
    滤波器参数或模型;
    卷积层参数或模型;
    池化层参数或模型;
    离散余弦变换DCT变换参数或模型;
    小波变换参数或模型;
    冲击信道处理通信设备的参数或模型;
    波形处理通信设备的参数或模型;
    信号相关序列处理通信设备的参数或模型。
  47. 根据权利要求43所述的通信设备,其中,所述第一误差模型信息包括:第二误差模型信息和第三误差模型信息中的至少一个;
    其中,所述第二误差模型信息包括:位置误差补偿信息、测量误差补偿 信息、设备误差补偿信息和参数调整信息中的至少一个;
    所述第三误差模型信息包括:机器学习模型补偿信息、预处理模型补偿信息、位置误差模型补偿信息、测量误差模型补偿信息、设备误差模型补偿信息和参数调整模型补偿信息中的至少一个。
  48. 根据权利要求44所述的通信设备,其中,所述第一测量信息包括以下中的至少一个:
    所述终端设备的信道冲击响应;
    所述终端设备的信号波形;
    所述终端设备的相关序列或波形;
    所述终端设备的信号测量结果。
  49. 根据权利要求44所述的通信设备,其中,所述通信设备还包括:
    发送模块,用于向网络设备上报第三信息,
    所述第三信息用于供所述网络设备确定所述与终端设备的位置相关的信息;
    和/或,用于供所述网络设备更新所述第一信息。
  50. 根据权利要求44所述的通信设备,其中,所述接收模块,还用于接收第二信息,所述第二信息用于指示是否向网络设备上报第三信息。
  51. 根据权利要求43所述的通信设备,其中,所述接收模块,还用于在所述第一信息被分级加密的情况下,接收网络设备发送的与所述第一信息的加密等级对应的密钥。
  52. 一种网络设备,包括:
    发送模块,用于向通信设备发送第一信息,所述第一信息包括:第一机器学习模型信息、第一预处理模型信息和第一误差模型信息中的至少一个;
    其中,所述第一信息用于供所述通信设备确定与终端设备的位置相关的信息。
  53. 一种终端设备,包括:存储器、处理器及存储在所述存储器上并可在所述处理器上运行的程序或指令,所述程序或指令被所述处理器执行时实现如权利要求1至21中任一项所述的方法的步骤。
  54. 一种网络设备,包括:存储器、处理器及存储在所述存储器上并可在所述处理器上运行的程序或指令,所述程序或指令被所述处理器执行时实现如权利要求1至21中任一项所述的方法的步骤,或者所述程序或指令被所述处理器执行时实现如权利要求22至42中任一项所述的方法的步骤。
  55. 一种可读存储介质,所述可读存储介质上存储有程序或指令或指令,所述程序或指令被处理器执行时实现如权利要求1至21中任一项所述的方法的步骤,或者所述程序或指令被处理器执行时实现如权利要求22至42中任一项所述的方法的步骤。
  56. 一种终端设备,所述通信设备被配置为用于执行如权利要求1至21中任一项所述的方法的步骤。
  57. 一种网络设备,所述网络设备被配置为用于执行如权利要求1至21中任一项所述的方法的步骤,或者如权利要求22至42中任一项所述的方法的步骤。
  58. 一种计算机程序产品,所述计算机程序产品被至少一个处理器执行时实现如权利要求1至21中任一项所述的方法的步骤,或者如权利要求22至42中任一项所述的方法的步骤。
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