Method and apparatus in a node for wireless communication

Abstract

The invention provides a method and an apparatus in a node for wireless communication. The method comprises the steps that at least one piece of first information is sent, the first information comprises one or more of first positioning information and a first parameter, and the first parameter is associated with first soft information; a first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used for determining the position of a first node. Compared with a traditional scheme of positioning the first node based on an AI / ML model, the first information reported by the first node comprises the first parameter associated with the first soft information, so that soft decision positioning of the first node based on the first parameter (for example, positioning the position of the first node based on the first soft information determined based on the first parameter) is facilitated, and the positioning precision is enhanced.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and more specifically, to a method and device in a node for wireless communication. Background Art

[0002] In some scenarios, the first node (e.g., terminal device) can predict first positioning information based on artificial intelligence (AI) / machine learning (ML) model, and the first positioning information is used to determine the position of the first node. However, the first positioning information generated by the traditional AI / ML model may have errors, which may lead to a decrease in positioning accuracy. Summary of the invention

[0003] The present application provides a method and device in a node for wireless communication. The following introduces various aspects of the present application.

[0004] In a first aspect, a method in a first node for wireless communication is provided, comprising: sending at least one first information, the first information comprising one or more of the following: first positioning information and a first parameter, the first parameter being associated with first soft information; a first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

[0005] In a second aspect, a method in a second node for wireless communication is provided, comprising: receiving at least one first information, the first information comprising one or more of the following: first positioning information and a first parameter, the first parameter being associated with first soft information; first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

[0006] According to a third aspect, a first node for wireless communication is provided, comprising: a first transceiver, for sending at least one first information, the first information comprising one or more of the following: first positioning information and a first parameter, the first parameter being associated with first soft information; a first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

[0007] In a fourth aspect, a second node for wireless communication is provided, comprising: a second transceiver for receiving at least one first information, the first information comprising one or more of the following: first positioning information and a first parameter, the first parameter being associated with first soft information; first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

[0008] In a fifth aspect, a first node used for wireless communication is provided, comprising a transceiver, a memory and a processor, wherein the memory is used to store programs, the processor is used to call the programs in the memory and control the transceiver to receive or send signals so that the first node executes the method described in the first aspect.

[0009] In a sixth aspect, a second node used for wireless communication is provided, comprising a transceiver, a memory and a processor, wherein the memory is used to store programs, the processor is used to call the programs in the memory and control the transceiver to receive or send signals so that the second node executes the method described in the second aspect.

[0010] In a seventh aspect, an embodiment of the present application provides a communication system, which includes the first node and / or the second node described above. In another possible design, the system may also include other devices that interact with the first node or the second node in the solution provided in the embodiment of the present application.

[0011] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program enables a computer to execute part or all of the steps in the methods of the above aspects.

[0012] In a ninth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps in the above-mentioned various aspects of the method. In some implementations, the computer program product can be a software installation package.

[0013] In the tenth aspect, an embodiment of the present application provides a chip, which includes a memory and a processor. The processor can call and run a computer program from the memory to implement some or all of the steps described in the methods of the above aspects.

[0014] In an embodiment of the present application, the first node may send at least one first information, the first information including one or more of the following: first positioning information and a first parameter associated with the first soft information; first parameter; wherein the first positioning information and the first parameter are outputs of the first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node. Compared with the traditional solution of positioning the first node based on the AI / ML model, the first information reported by the first node includes the first parameter associated with the first soft information, which helps to perform soft decision positioning of the first node based on the first parameter (for example, positioning the position of the first node based on the first soft information determined by the first parameter) to enhance positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a wireless communication system 100 applied in an embodiment of the present application.

[0016] Figure 2 This is an example diagram of AI / ML model-assisted positioning applicable to the embodiments of the present application.

[0017] Figure 3 This is another example diagram of AI / ML model-assisted positioning applicable to the embodiments of the present application.

[0018] Figure 4 It is a schematic diagram of the multi-round-trip time (Multi-RTT) positioning method.

[0019] Figure 5 It is a schematic diagram of the downlink time difference of arrival (DL-TDOA) positioning method.

[0020] Figure 6 It is a schematic diagram of the uplink time difference of arrival (UL-TDOA) positioning method.

[0021] Figure 7 It is a schematic flowchart of a method in a first node for wireless communication provided in an embodiment of the present application.

[0022] Figure 8 This is a schematic diagram of an implementation method of the first information.

[0023] Fig. 9 It is a schematic diagram of another implementation method of the first information.

[0024] Fig.10 It is a schematic diagram of another implementation method of the first information.

[0025] Fig.11 A schematic diagram of a method for reconstructing a likelihood function of first positioning information.

[0026] Fig.12 It is a schematic diagram of another method for reconstructing the likelihood function of the first positioning information.

[0027] Fig.13 is a schematic diagram of yet another method for reconstructing a likelihood function of first positioning information.

[0028] Fig.14 is an example diagram of a related scheme of the first information.

[0029] Fig.15 is an example diagram of another related scheme of the first information.

[0030] Fig.16 It is an example diagram of a solution in which a relevant solution of the first information and a relevant solution of the first request are combined with each other.

[0031] Fig.17 It is a structural diagram of a first node for wireless communication provided in an embodiment of the present application.

[0032] Fig.18 It is a structural diagram of a second node for wireless communication provided in an embodiment of the present application.

[0033] Fig.19 It is a schematic structural diagram of the device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0035] Figure 1 The wireless communication system 100 used in the embodiment of the present application may include a network device 110 and a terminal device 120. The network device 110 may be a device that communicates with the terminal device 120. The network device 110 may provide communication coverage for a specific geographical area and may communicate with the terminal device 120 located in the coverage area.

[0036] Figure 1 One network device and two terminals are shown exemplarily. Optionally, the wireless communication system 100 may include multiple network devices and each network device may include another number of terminal devices within its coverage area, which is not limited in the embodiments of the present application.

[0037] Optionally, the wireless communication system 100 may also include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiments of the present application.

[0038] It should be understood that the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: the fifth generation (5th generation, 5G) system or new radio (new radio, NR), long term evolution (long term evolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) system, LTE time division duplex (time division duplex, TDD), etc. The technical solutions provided by the present application can also be applied to future communication systems, such as the sixth generation mobile communication system, satellite communication system, and so on.

[0039] The terminal device in the embodiment of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device. The terminal device in the embodiment of the present application may be a device that provides voice and / or data connectivity to a user, and may be used to connect people, objects and machines, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. The terminal device in the embodiment of the present application can be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc. Optionally, the UE can be used to act as a base station. For example, the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc. For example, a cellular phone and a car communicate with each other using a sidelink signal. The cellular phone and the smart home device communicate with each other without relaying the communication signal through the base station.

[0040] The network device in the embodiment of the present application may be a device for communicating with a terminal device, and the network device may also be referred to as an access network device or a wireless access network device, such as a base station. The network device in the embodiment of the present application may refer to a wireless access network (RAN) node (or device) that connects a terminal device to a wireless network. Base station can broadly cover various names as follows, or replace with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmission point (TP), master station MeNB, auxiliary station SeNB, multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, base band unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station can also refer to a communication module, a modem or a chip used to be arranged in the aforementioned device or apparatus. The base station can also be a mobile switching center and a device-to-device D2D, vehicle-to-everything (V2X), machine-to-machine (M2M) communication device that performs the base station function, a network side device in a 6G network, and a device that performs the base station function in a future communication system. The base station can support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form used by the network device.

[0041] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0042] In some deployments, the network device in the embodiments of the present application may refer to a CU or a DU, or the network device includes a CU and a DU. The gNB may also include an AAU.

[0043] The network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of the present application do not limit the scenarios in which the network equipment and terminal equipment are located.

[0044] It should be understood that all or part of the functions of the communication device in the present application may also be implemented by software functions running on hardware, or by virtualization functions instantiated on a platform (eg, a cloud platform).

[0045] Positioning Technology

[0046] Positioning applications play an important role in many scenarios of communication systems, and positioning capability is one of the core capabilities of related protocols. The positioning function has been added to the relevant versions of the communication protocol, which uses the multi-beam characteristics of the multiple-input multiple-output (MIMO) technology to define positioning technologies such as Multi-RTT based on cellular cells, time difference of arrival (TDOA), angle-of-arrival (AoA) measurement method, and angle-of-departure (AoD) measurement method.

[0047] With the large-scale deployment of communication systems, vertical industry scenarios have put forward increasingly urgent demands for positioning services. For operators, they are also full of expectations for the vast market of location services in the future. There is an urgent need to expand location value-added services and provide higher-precision positioning services (20cm) for ordinary users and vertical industries. Therefore, the 3rd Generation Partnership Project (3GPP) has determined that subsequent versions will be enhanced in the following aspects: higher accuracy (horizontal / vertical), low latency (physical layer and high-layer end-to-end positioning latency), network and / or equipment efficiency, high integrity and reliability (for global navigation satellite system (GNSS)).

[0048] In order to further improve the accuracy, in some scenarios, two promising technologies are considered to be introduced in the positioning of radio access technology (RAT): one is to utilize the abundant spectrum in the communication system to increase the transmit and receive bandwidth of the positioning reference signal based on the bandwidth aggregation of the positioning reference signal (PRS) / sounding reference signal (SRS); the other is to use carrier phase measurement.

[0049] However, a major problem with the above-mentioned PRS or SRS-based positioning technology is the sharp drop in positioning accuracy in severe non-line ofsight (NLOS) scenarios. The traditional timing information reported in this positioning technology is the timing information about the first path, but the first path may be an NLOS path, in which case the positioning accuracy will be severely reduced. In the traditional method, the timing information can also be used in combination with the LOS indication, which indicates whether a propagation path is an LOS path, that is, the timing information of the LOS path is screened out using the LOS indication, but this method cannot complete positioning in the case of severe NLOS. In the time-based positioning method, the measurement report received by the location management function (LMF) contains at least the timing information of the first detection path, and also allows the reporting of the timing information of additional detection paths, where the timing information of the additional paths is reported in a differential form relative to the first path. However, the first detection path may be a line-of-sight (LOS) propagation path or an NLOS propagation path. Since the location calculation method relies on the timing information of the LOS path, if the first detected path is an NLOS path, the timing error will increase, resulting in a decrease in positioning accuracy.

[0050] In view of the above situation, the subsequent versions of 3GPP will support the positioning accuracy enhancement solution based on AI / ML models. Five relevant sub-use cases are identified in the relevant report, among which Case 2a and Case 3a are both use cases based on AI / ML model-assisted positioning, that is, using AI / ML models on the terminal device (e.g., UE) or access network device (e.g., gNB) side to generate prediction information of positioning measurement and report the information to the core network element (e.g., LMF).

[0051] As an example, see Figure 2In case 2a, the terminal device is positioned based on the downlink positioning reference signal. The terminal device measures the downlink positioning reference signal (DL-PRS) sent by the access network device, and uses the AI / ML model to predict the timing information of the positioning measurement, and then reports the measurement report containing the timing information to the core network element through the LTE positioning protocol (LTE positioning protocol, LPP). The core network element calculates the position based on the timing information in the received measurement report to obtain the location information of the terminal device. In an embodiment of the present application, the timing information can be used as an example of the first positioning information.

[0052] As an example, see Figure 3 In case 3a, the terminal device is positioned based on the uplink positioning reference signal. The access network device measures the uplink sounding reference signal (UL-SRS) sent by the terminal device, and uses the AI / ML model to predict the timing information of the positioning measurement. Then, the measurement report containing the timing information is reported to the core network element through the NR positioning protocol a (NR positioning protocol a, NRPPa). The core network element calculates the position based on the timing information in the received measurement report to obtain the location information of the terminal device.

[0053] Taking three time-based positioning methods as examples, the following examples illustrate the timing information and location calculation methods mentioned above. The three time-based positioning methods include multi-RTT, DL-TDOA and UL-TDOA.

[0054] In the multi-RTT positioning method, see Figure 4 The timing information reported refers to the receive-transport time difference (RX-TX time difference), that is, the terminal device measures DL-PRS to obtain the receive-transport time difference on the terminal device side, and the access network device measures UL-SRS to obtain the receive-transport time difference on the access network device side, which is summarized to the core network element. The core network element converts the receive-transport time difference into the distance between the terminal device and the access network device for positioning.

[0055] In the DL-TDOA positioning method, see Figure 5The reported timing information refers to the reference signal time difference (RSTD), which is the difference in arrival time of the DL-PRS transmitted at different transmission and receiving points (TRP) relative to the reference TRP measured by the terminal device (e.g. Figure 5 DL-TDOA 3,1 With DL-TDOA 2,1 The core network element converts RSTD into distance to form a hyperbola. By solving the hyperbola equation, the location of the terminal device can be obtained.

[0056] In the UL-TDOA positioning method, see Figure 6 The reported timing information refers to the reference time of arrival (RTOA), that is, the relative time difference between the arrival time of the UL-SRS and the reference time of the TRP itself (e.g. Figure 6 UL-TDOA 3,1 With UL-TDOA 2,1 ). The core network element uses the RTOA provided by each TRP to calculate the location of the terminal device.

[0057] As described above, in some scenarios, the first node (e.g., terminal device) can predict first positioning information based on the AI / ML model, and the first positioning information is used to determine the position of the first node. However, the first positioning information generated by the traditional AI / ML model may have errors, which may lead to a decrease in positioning accuracy.

[0058] Taking the timing information generated by the AI / ML model in Case 2a or Case 3a above as an example, the timing information is the timing information of the direct path between the terminal device and the access network device, which can be regarded as the LOS path timing information calibrated by the AI / ML model. The timing information generated by the AI / ML model can be regarded as the LOS path timing information calibrated by the AI / ML, without considering the actual LOS or NLOS state. However, due to the environmental factors of the actual deployment and the imperfection of the AI / ML model itself, the generated timing information also has certain errors, which leads to inaccurate positioning. Possible causes of this problem include the following two points: due to the imperfect training data set and the scarcity of high-precision ground truth labels, it is difficult to perfectly train the AI / ML model, resulting in errors in the timing information generated by the AI / ML model; in actual deployment, inevitable channel noise or interference will affect the received DL-PRS or UL-SRS, thereby affecting the input of the AI / ML model, causing the generated timing information to deviate from the true value.

[0059] In response to the above problems, an embodiment of the present application provides a method in a node for wireless communication. The first node in the method may send at least one first information, and the first information includes one or more of the following: first positioning information and a first parameter associated with the first soft information; first parameter. Among them, the first positioning information and the first parameter are the outputs of the first AI / ML model, the first soft information is the likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node. Compared with the traditional solution of locating the first node based on the AI / ML model, the first information reported by the first node includes the first parameter associated with the first soft information, which helps to perform soft decision positioning of the first node based on the first parameter (for example, locating the position of the first node based on the first soft information determined by the first parameter) to enhance positioning accuracy.

[0060] Combined with the following Figure 7 A method in a first node for wireless communication according to an embodiment of the present application is introduced. Figure 7 is a schematic flow chart of a method in a first node for wireless communication according to an embodiment of the present application. Figure 7 As shown, the method is executed by the first node.

[0061] As an embodiment, the first node may be a network-controlled repeater (NCR).

[0062] As an embodiment, the first node may be a terminal device. Figure 1 The terminal device 120 is shown.

[0063] As an embodiment, the first node may be a relay, such as a relay terminal.

[0064] As an embodiment, the first node may deploy the first AI / ML model to predict the first positioning information, so the first node may be a terminal device or an access network device. For example, when the first AI / ML model is deployed on the terminal device side, the first node may be a UE; when the first AI / ML model is deployed on the access network device side, the first node may be a base station.

[0065] Figure 7 The method shown includes step S710, which is described below.

[0066] In step S710, the first node sends at least one first information. Exemplarily, the first node may send at least one first information to the second node.

[0067] As an embodiment, the second node may be a network device. Figure 2 The core network element shown. For another example, the second node is LMF.

[0068] Example 1: First Information

[0069] In the embodiments of the present application, the protocol for the first node to send the first information is not limited. In some embodiments, the first node sends the first information via LPP. For example, the first node is a terminal device, and the terminal device sends the first information to the second node via LPP. In other embodiments, the first node sends the first information via NRPPa. For example, the first node is an access network device, and the access network device sends the first information to the second node via NRPPa.

[0070] In some embodiments, the first information includes one or more of the following: first positioning information and first parameters; first parameters.

[0071] In some embodiments, the first parameter is associated with the first soft information, and the first positioning information and the first soft information are outputs of the first AI / ML model.

[0072] In the embodiments of the present application, the first AI / ML model is not limited. In some embodiments, the first AI model may include a deep learning model. For example, deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), generative adversarial networks (GAN), variational autoencoder (VAE), transformer, etc. In some embodiments, the first ML model may include a model generated by machine learning or device self-learning. For example, deep reinforcement learning (DRL).

[0073] In some embodiments, the first positioning information may include one or more of the following: DL-TDOA; UL-TDOA; RSTD; RTOA; AOA; AOD; time of arrival (TOA); reference signal received power (RSRP); reference signal received path power (RSRPP); channel impulse response (CIR); observation arrival time difference (OATD); time of flight (TOF); time difference between sending and receiving on the access network device side; time difference between sending and receiving on the terminal device side.

[0074] As an embodiment, the first positioning information may include first timing information, which can be understood as information related to time. For example, the first timing information includes at least one of RSTD, RTOA, TOA, TOF, the time difference between sending and receiving on the access network device side, and the time difference between sending and receiving on the terminal device side.

[0075] As an embodiment, the first positioning information may include first angle information. For example, the first timing information includes at least one of AOD and TOA.

[0076] In some embodiments, the first soft information is used to characterize the relationship between the first positioning information and the first input.

[0077] As an embodiment, the first input is input data of a first AI / ML model.

[0078] As an embodiment, the first input includes a measurement value for a positioning reference signal.

[0079] As a sub-embodiment of the above embodiment, the positioning reference signal may be an existing reference signal. For example, the first input includes the time difference between transmitting and receiving obtained by measuring the UL-SRS. For another example, the first input includes the time difference between transmitting and receiving obtained by measuring the DL-PRS.

[0080] As a sub-embodiment of the above embodiment, the positioning reference signal may be a newly defined positioning reference signal.

[0081] As an embodiment, the first input includes processing information of a measurement value of a positioning reference signal. For example, the first input includes an average value obtained by calculating a transmit / receive time difference obtained by performing multiple measurements on a DL-PRS.

[0082] As an embodiment, the first input includes a measurement value for a downlink signal. For example, the first input includes a channel impulse response (CIR) obtained by measuring a downlink pilot signal.

[0083] As an embodiment, the first input includes processing information of a measurement value of a downlink signal. For example, the first input includes a power delay profile (PDP) obtained by processing a measurement value CIR of a downlink pilot signal.

[0084] In some embodiments, the first soft information is used to indicate a statistical representation of a relationship between the first positioning information and the first input.

[0085] In some embodiments, the above statistical representation is a likelihood value, that is, the first soft information is a likelihood value of the first positioning information, or in other words, the first soft information can quantify the likelihood of all possible first positioning information.

[0086] As an embodiment, the first soft information is a probability information. It can be understood that the larger the value of the first soft information is, the greater the probability of the first positioning information corresponding to the first soft information occurring under the given parameters of the first AI / ML model.

[0087] As an embodiment, the first soft information is a number between 0 and 1.

[0088] As a sub-embodiment of the above embodiment, the first soft information is a decimal between 0 and 1. For example, the first soft information is 0.5.

[0089] As a sub-embodiment of the above embodiment, the first soft information includes at least one of 0 and 1. For example, the first soft information is 1.

[0090] In some embodiments, the first positioning information is used to determine the position of the first node.

[0091] In some embodiments, the location of the first node includes the geographic location of the first node, that is, the location of the first node includes the absolute location of the first node.

[0092] As a sub-embodiment of the above embodiment, the position of the first node includes at least one of the longitude and latitude of the first node. For example, the position of the first node includes the longitude and latitude of the first node.

[0093] As an embodiment, the position of the first node includes the distance between the first node and the reference point. For example, the position of the first node includes the distance between the first node and two reference points, and the absolute position of the first node can be determined based on the position of the first node and the absolute positions of the two reference points.

[0094] As a sub-embodiment of the above embodiment, the reference point may be any device in the wireless communication system. For example, the reference point may be at least one of a terminal device, a relay node, a network device, and a TRP.

[0095] As a sub-embodiment of the above embodiment, the reference point may be a reference geographical location, for example, a specific latitude and longitude coordinate.

[0096] As an embodiment, the position of the first node includes the relative position between the first node and the reference point. For example, the reference point is TRP, and the position of the first node includes the relative position between the first node and TRP.

[0097] In some embodiments, the first positioning information and the first soft information are output by the first node. For example, the first node is a terminal device, the terminal device measures the DL-PRS sent by the access network device, and generates the first positioning information and the first soft information using the first AI / ML model deployed on the terminal device side. For another example, the first node is an access network device, the access network device measures the UL-SRS sent by the terminal device, and generates the first positioning information and the first soft information using the first AI / ML model deployed on the access network device side.

[0098] In some embodiments, the first parameter is associated with the first soft information, which can be understood as the first parameter including the first soft information. For example, the first parameter includes the likelihood value of the first positioning information.

[0099] In some embodiments, the first node sends N first information, the first parameter includes first soft information, and N is an integer greater than or equal to 1.

[0100] As an embodiment, the above N is configured by the network device. For example, before the first node sends the first information, the network device sends configuration information to the first node, and the configuration information includes the first threshold.

[0101] As an embodiment, the above-mentioned N is a fixed value, so the method of sending the first information is also called "reporting a fixed number of first information".

[0102] As an embodiment, the above N is associated with the first node. For example, if the first node is a first terminal device, the value of N is N1; if the first node is a second terminal device, the value of N is N2.

[0103] As an embodiment, the above N is also associated with one or more of the following: the number of resource sets configured by the network device; the first AI / ML model. For example, the first AI / ML model deployed on the first node side inputs a measurement, and outputs K first positioning information θ corresponding to the measurement and its corresponding first soft information The process of generating the first positioning information and the first soft information by the first MI / ML model is shown in Figure 8 As shown. The number of resource sets configured by the network device is M, then the first node needs to perform M positioning measurements. The first AI / ML model inputs M measurements in sequence, generating a total of M*K first positioning information and first soft information pairs. The first information includes a first positioning information and a first parameter. Therefore, the number of first information sent by the first node is M*K, so N=M*K.

[0104] In some embodiments, the first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information satisfies the first condition. It can be understood that if the first soft information satisfies the first condition, the first node sends the first soft information that satisfies the first condition.

[0105] In some embodiments, the first condition includes that the first soft information is greater than a first threshold, that is, any first soft information of at least one first soft information that the first information can carry is greater than the first threshold. For example, the first AI / ML model inputs a measurement and outputs 10 first positioning information θ corresponding to the measurement and their corresponding first soft information If 5 first soft information among 10 first soft information are greater than the first threshold, the first information carries 5 first soft information greater than the first threshold.

[0106] As an embodiment, the first threshold is configured by the network device. For example, before the first node sends the first information, the network device sends configuration information to the first node, and the configuration information includes the first threshold.

[0107] As an embodiment, the first positioning information carried by the first information may be the first positioning information whose corresponding first soft information is greater than the first threshold. For example, the first information includes the first positioning information and the first parameter, and the first parameter includes the first soft information. The first AI / ML model inputs a measurement and outputs 10 first positioning information θ corresponding to the measurement and their corresponding first soft information If 5 of the 10 first soft information are greater than the first threshold, the first positioning information carried by the first information is the first positioning information corresponding to the 5 first soft information greater than the first threshold. Fig. 9 , the first soft information is the likelihood value of the first positioning information, and the first information sent by the first node carries the first positioning information and the first soft information when the first soft information is greater than the first threshold, that is, Fig. 9In this embodiment, the first soft information and the first positioning information included in the first information are both determined based on the first threshold, so the sending method of the first information is also called "threshold-based first information reporting".

[0108] In some embodiments, the first parameter is associated with the first soft information, which can be understood as the first parameter including information associated with the first soft information. For example, the first soft information is a likelihood value of the first positioning information, the likelihood function of the first positioning information is a first function, the first parameter includes a parameter that can indicate the first function, and the likelihood function of the first positioning information can be obtained based on the first parameter, thereby obtaining the first soft information.

[0109] In some embodiments, the first parameter includes one or more of the following: a parameter of a likelihood function of at least one first positioning information; a weight of a likelihood function of at least one first positioning information.

[0110] In the embodiment of the present application, the parameters of the likelihood function of the first positioning information are not limited. For example, the likelihood function of the first positioning information is a Gaussian distribution or a normal distribution, and the parameters of the likelihood function of the first positioning information are the mean and the standard deviation (or variance). For another example, the likelihood function of the first positioning information is a Laplace distribution, and the parameters of the likelihood function of the first positioning information are the location parameter and the scale parameter.

[0111] As an embodiment, the weight of a likelihood function of first positioning information may be understood as the weight of the likelihood function of the first positioning information in the likelihood function of at least one first positioning information.

[0112] As an embodiment, the first parameter includes a parameter of a likelihood function of at least one first positioning information and a weight of a likelihood function of at least one first positioning information. Accordingly, based on the parameter of the likelihood function of at least one first positioning information and the weight of the likelihood function of at least one first positioning information, a mixed likelihood model of at least one first positioning information can be determined.

[0113] As an embodiment, a linear combination is performed on the mixed likelihood model of the first positioning information to fit the likelihood function of the first positioning information. For example, the mixed likelihood model of at least one first positioning information corresponding to the M-order quantity is a mixed Gaussian model, and a linear combination is performed on multiple Gaussian distribution functions in the mixed Gaussian model to fit the likelihood function of at least one first positioning information.

[0114] In some embodiments, the first parameter includes one or more of: at least one first mean; at least one first standard deviation; at least one first weight.

[0115] As an embodiment, a first mean and a first standard deviation are parameters of a likelihood function of the first positioning information. Accordingly, based on at least one first mean and at least one first standard deviation, a likelihood function of at least one first positioning information can be determined.

[0116] As an embodiment, a first weight is a weight of a likelihood function of the first positioning information.

[0117] As an embodiment, the first information includes only the first parameters, and the first parameters include at least one first mean, at least one first standard deviation, and at least one first weight. The first information includes only parameters related to the likelihood function of the first positioning information, so this method of sending the first information is also called "first information reporting based on likelihood model".

[0118] As an embodiment, the likelihood function of the first positioning information is a Gaussian distribution, and the first parameter includes one or more of the following: the mean μ of the i-th Gaussian distribution i ; Standard deviation σ of the i-th Gaussian distribution i ; Weight α of the i-th Gaussian distribution i , where i is the number of the measurement. For example, see Fig.10 , the parameter estimator (PE) module of the first AI / ML model deployed on the first node side inputs M measurements and outputs M different Gaussian distributed parameter pairs (α i ,μ i ,σ i ), which is M first parameters, where μ i is the mean of the ith Gaussian distribution, σ i is the standard deviation of the ith Gaussian distribution, α i is the weight of the ith Gaussian distribution. During training, the first positioning information generator is used to evaluate the accuracy of the likelihood function fitting, thereby assisting the training of PE. Among them, the Gaussian mixture model is obtained by using a mixer in f(·) is a Gaussian distribution. The sampling module obtains M estimated first positioning information according to the Gaussian mixture model sampling. The error between the M estimated first positioning information and the real first positioning information measured M times is calculated and fed back to PE, thereby assisting PE training. During inference, only the PE module is used to generate the mean, standard deviation and weight of the M Gaussian distributions. The first information sent by the first node only includes the first parameter, and the number of first information is M.

[0119] In some embodiments, the first node receives the first configuration information. Exemplarily, the first node receives the first configuration information sent by the second node.

[0120] In some embodiments, the first configuration information is used to configure the M resource sets. Accordingly, the first node receives the first configuration information and can measure the M resource sets based on the first configuration information.

[0121] In some embodiments, measurements for the M resource sets are used to determine the input of the first AI / ML model. Fig.10 , the measurements for the M resource sets are input to the parameter estimation module of the first AI / ML model.

[0122] As an embodiment, measurements of M resource sets are used to determine the input of the first AI / ML model training phase. Fig.10 During the training process, the measurements of the M resource sets are used as inputs to the PE module of the first AI / ML model and are used to train the relevant parameters of the PE module.

[0123] As an example, measurements of the M resource sets are used to determine the input to the first AI / ML model inference phase. Fig.10 During the inference process, the measurements of the M resource sets are used as input to the PE module of the first AI / ML model to output the first parameters.

[0124] As an embodiment, the first configuration information is used to configure the number of positioning measurements. For example, based on the number M of resource sets configured by the first configuration information, it can be determined that the number of positioning measurements performed by the first node is M. Through multiple measurements, the number of samples of the first AI / ML model can be increased, which helps to further enhance the positioning accuracy.

[0125] As an embodiment, the number of positioning measurements can be understood as the number of positioning measurements performed by the first node before the first positioning information and / or the first soft information are generated using the first AI / ML model. For example, the first configuration information is used to configure the number of positioning measurements to be M. Assuming that the first node is a terminal device, the terminal device measures the DL-PRS sent by the access network device M times, and inputs the M measurement results into the first AI / ML model to generate the first positioning information and the first soft information.

[0126] As an embodiment, the first configuration information is used to configure a sending method of the first information. Accordingly, after receiving the first configuration information, the first node can determine how to send the first information based on the first configuration information.

[0127] As an embodiment, the method of sending the first information can be understood as a reporting scheme of the first information.

[0128] As an embodiment, the method for sending the first information includes: the first node sends N first information, the first parameter includes first soft information, and N is an integer greater than or equal to 1.

[0129] As an embodiment, the sending method of the first information includes: the first parameter includes the first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

[0130] As an embodiment, the method for sending the first information includes: the first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; at least one first weight.

[0131] It should be noted that the above three methods of sending the first information have been explained above and will not be repeated here.

[0132] As an embodiment, the first configuration information is used to configure the above N. For example, N is carried in a field of the first configuration information.

[0133] As an embodiment, the first configuration information is used to configure the first threshold mentioned above. For example, the first threshold is carried in a field of the first configuration information.

[0134] As an embodiment, the first configuration information is received before the first node sends the first information, so that the first node can determine M measurement sets and / or a sending method of the first information, etc.

[0135] In some embodiments, after receiving the first information sent by the first node, the second node reconstructs the likelihood function of the first positioning information.

[0136] As an embodiment, the second node reconstructs the likelihood function of the first positioning information by interpolation. For example, assuming that the first node sends N first information, the first parameter includes first soft information, and the first soft information is the likelihood value of the first positioning information. The likelihood function of the first positioning information sent by the first node is as follows: Fig.11 On the left side of , the likelihood function of the first positioning information can be reconstructed by interpolation to obtain Fig.11 For another example, see Fig.12 , assuming that the first parameter includes the first soft information, the first information carries at least one first soft information, and any of the at least one first soft information is greater than the first threshold, then the likelihood function of the first positioning information sent by the first node is Fig.12 The part above the first threshold on the left side of , by reconstructing the likelihood function of the first positioning information by interpolation, can be obtained Fig.12 on the right side.

[0137] In one embodiment, the second node reconstructs a likelihood function of the first positioning information based on parameters.

[0138] As a sub-embodiment of the above embodiment, the above parameter may be a first parameter. For example, assuming that the likelihood function of the first positioning information is a Gaussian distribution, the first parameter includes one or more of the following: the mean μ of the i-th Gaussian distribution i ; Standard deviation σ of the i-th Gaussian distribution i ; Weight α of the i-th Gaussian distribution i , that is, the first parameter of the first node is M different Gaussian distributions (α i ,μ i ,σ i ), the second node uses the first parameter to linearly combine different Gaussian distributions to obtain a Gaussian mixture model of the likelihood function of the fitted first positioning information So as to reconstruct the likelihood function, see Fig.13 shown.

[0139] In some embodiments, the second node fuses the first positioning information.

[0140] As an embodiment, the first positioning information to be fused is associated with different TRPs. For example, the first positioning information associated with different TRPs are independent of each other, so the joint likelihood function of each first positioning information can be obtained by multiplying the likelihood function of each first positioning information, thereby fusing the first positioning information associated with different TRPs, that is, in It comes from the TRP collection The first positioning information, is the likelihood function of the first positioning information associated with the i-th TRP.

[0141] In some embodiments, based on a result obtained by fusing the first positioning information, the second node calculates the position of the first node.

[0142] It should be noted that the position of the first node has been described above and will not be repeated here.

[0143] As an embodiment, the position of the first node is the coordinates of the first node. For example, assuming that the first node is a terminal device, the joint likelihood function of the fused first positioning information is in It comes from the TRP collection The first positioning information, is the likelihood function of the first positioning information associated with the i-th TRP, and the second node uses the maximum likelihood estimation to solve the coordinates of the first node Right now

[0144] For ease of understanding, the following Fig.14 and Fig.15 The following are examples to introduce the relevant solutions of First Information.

[0145] Fig.14 Take the first node as a terminal device as an example for introduction. Fig.14 Includes steps S1410 to S1460.

[0146] Assume that the first node sends N first information to the second node, the second node is a core network element, the first information includes first positioning information and a first parameter, the first parameter includes first soft information, and the first soft information is a likelihood value of the first positioning information.

[0147] In step S1410, the core network element sends first configuration information to the terminal device.

[0148] The core network element sends first configuration information to the terminal device through LPP, the first configuration information is used to configure the terminal device to perform positioning measurement for a number of times M, and a sending method of the first information, the sending method of the first information includes the terminal device sending N first information, N = M * K. While sending the first configuration information, the core network element may also provide PRS configuration to the terminal device through LPP.

[0149] In step S1420, the terminal device measures and generates first information.

[0150] After receiving the first configuration information, the terminal device measures the DL-PRS sent by the access network device M times, and inputs the measurement results into the first AI / ML model to generate M*K first information, where one first information includes a first positioning information and a first soft information.

[0151] In step S1430, the terminal device sends at least one first information to the core network element.

[0152] The terminal device sends the M*K first information generated in step S1430 to the core network element via LPP.

[0153] In step S1440, the core network element reconstructs the likelihood function of the first positioning information.

[0154] The core network element receives M*K first information sent by the terminal device, obtains a likelihood function of the first positioning information based on the first information, and interpolates and reconstructs the likelihood function to obtain a reconstructed likelihood function of the first positioning information.

[0155] In step S1450, the core network element integrates the first positioning information.

[0156] The core network element multiplies the likelihood function of each first positioning information associated with the TRP obtained in step S1440 to obtain a joint likelihood function, that is, in It comes from the TRP collection The first positioning information, is the likelihood function of the first positioning information associated with the i-th TRP.

[0157] In step S1460, the core network element calculates the location of the terminal device.

[0158] The core network element uses maximum likelihood estimation to solve the joint likelihood function obtained in step S1450 to obtain the coordinates of the terminal device. Right now

[0159] Fig.15 Take the first node as an access network device as an example for introduction. Fig.15 Includes steps S1510 to S1560.

[0160] Assume that the first node sends at least one first information to the second node, the second node is a core network element, the first information only includes the first parameter, the likelihood function of the first positioning information is a Gaussian distribution, and the first parameter includes the mean μ of the i-th Gaussian distribution i , the standard deviation σ of the i-th Gaussian distribution i and the weight α of the i-th Gaussian distribution i .

[0161] In step S1510, the core network element sends first configuration information to the access network device.

[0162] The core network element sends the first configuration information to the access network device through NRPPa, the first configuration information is used to configure the access network device to perform positioning measurement a number of times M, and the sending method of the first information, the sending method of the first information is the first parameter including (α i ,μ i ,σ i ).

[0163] In step S1520, the access network device measures and generates first information.

[0164] The access network device receives the first configuration information and can send the SRS configuration to the terminal device through an RRC message. After receiving the UL-SRS sent by the terminal device, the access network device measures the received UL-SRS M times and inputs the measurement results into the first AI / ML model to generate M first information. The first information includes M first information, and the first information includes a first parameter (α i ,μ i,σ i ).

[0165] In step S1530, the access network device sends at least one first information to the core network element.

[0166] The access network device sends the M first information generated by S1530 to the core network element through NRPPa.

[0167] In step S1540, the core network element reconstructs the likelihood function of the first positioning information.

[0168] The core network element receives the M first information sent by the access network device, and uses the first parameter to linearly combine different Gaussian distributions to obtain a Gaussian mixture model of the likelihood function of the fitted first positioning information. That is the likelihood function of the reconstructed first positioning information.

[0169] In step S1550, the core network element integrates the first positioning information.

[0170] The core network element multiplies the likelihood function of each first positioning information associated with TRP obtained in step S1540 to obtain a joint likelihood function, that is, in It comes from the TRP collection The first positioning information, is the likelihood function of the first positioning information associated with the i-th TRP.

[0171] In step S1560, the core network element calculates the location of the terminal device.

[0172] The core network element uses maximum likelihood estimation to solve the joint likelihood function obtained in step S1550 to obtain the coordinates of the terminal device. Right now

[0173] In order to solve the problem that the first positioning information may have errors and cause the positioning accuracy to decrease, the embodiment of the present application also provides a method in a first node for wireless communication, the first node sends a first request, and the first request carries the ability of the first node to support sending at least one first information and / or the speed of the first node. Based on the first request, it is helpful for the network device to send configuration information applicable to the first node for reporting the first information, thereby improving the accuracy of positioning based on the first information.

[0174] Example 2: First Request

[0175] As an embodiment, the first node sends a first request to the second node. For example, the first node is a terminal device, the second node is a core network element, and the terminal device sends the first request to the core network element.

[0176] As an embodiment, the first request is triggered based on the positioning requirement of the terminal device, that is, if the first node is a terminal device, when the terminal device needs positioning service, the terminal device sends the first request, or if the first node is an access network device, when the terminal device needs positioning service, the first request can be sent through the first node.

[0177] In some embodiments, the capability of the first node to support sending the first information includes the capability of the first node to support sending at least one first information includes the number of the first node to support sending at least one first information. For example, the first node supports sending X first information, and the first request carries the number of the first node to support sending the first information, which is X.

[0178] As an embodiment, the speed of the first node can be understood as the current moving speed of the first node. For example, the first node is a terminal device, and the speed of the first node is the current moving speed of the terminal device.

[0179] The above describes the related schemes of the first information and the first request respectively through Example 1 and Example 2. In some scenarios, the two schemes can be used independently. In other scenarios, the two schemes can be used in combination with each other. The following describes a scheme in which the related scheme of the first information is combined with the related scheme of the first request.

[0180] In some embodiments, the first request is used to request sending the first configuration information mentioned above.

[0181] As an embodiment, the first request is used to request the second node to send the first configuration information. Accordingly, the second node receives the first request and sends the first configuration information to the first node based on the first request.

[0182] As a sub-embodiment of the above embodiment, the second node determines the sending mode of the first information based on the number of the first node supported by the first request to send at least one first information, and then sends the first configuration information. For example, the second node evaluates the capability of the first node according to the number of the first node supported to send at least one first information, and divides it into three levels: strong, moderate, and weak. For the first node with strong capability, more data can be processed and reported, then the second node configures the sending mode of the first information as the first node sending N first information, and the first parameter includes the first soft information; for the first node with moderate capability, the sending mode of the first information is configured as the first parameter including the first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than the first threshold; for the first node with weak capability, the sending mode of the first information is configured as the first information only including the first parameter, and the first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; at least one first weight.

[0183] As a sub-embodiment of the above embodiment, the second node determines the number of positioning measurements performed by the first node based on the speed of the first node carried by the first request, and then sends the first configuration information. For example, the first node is a terminal device, and the second node determines the number of positioning measurements performed by the terminal device according to the moving speed of the terminal device. In the rural macro (RMa) scenario, high-speed moving terminal devices (such as cars) are supported. In order to enhance their positioning accuracy, more measurements are required. Therefore, the second node has a high measurement number set to support the positioning of high-speed mobile terminal devices. and low measurement times set to support positioning of low-speed mobile terminal devices The second node determines whether the terminal device is moving at a high speed or a low speed according to the speed of the terminal device, selects a corresponding measurement number set, selects an appropriate measurement number from the measurement number set, and sends the measurement number to the first node through the first configuration information.

[0184] For ease of understanding, the following Fig.16 An example is given of combining the first request with the related schemes of the first information. Fig.16 The method includes steps S1610 to S1650. Assume that the first node is a terminal device and the second node is a core network element.

[0185] The following description will be made by taking the first request bearing the capability of the first node supporting the sending of at least one first information as an example.

[0186] In step S1610, the terminal device sends a first request to a core network element.

[0187] The terminal device triggers the sending of a first request based on a positioning requirement, and the first request carries a capability of the terminal device to support sending at least one first information.

[0188] In step S1620, the core network element sends first configuration information to the terminal device.

[0189] The core network element receives the first request, evaluates the ability of the terminal device carried in the first request to support sending at least one first information, appropriately selects a method for sending the first information, and sends first configuration information to the terminal device. The first configuration information can be used to configure M measurement sets and a method for sending the first information.

[0190] In step S1630, the terminal device sends at least one first information to the core network element.

[0191] The terminal device completes M measurements according to the PRS configuration and the first configuration information sent by the core network network element, and sends at least one first information to the core network network element according to the sending method of the first information.

[0192] In step S1640, the core network element reconstructs the likelihood function of the first positioning information.

[0193] The core network element receives at least one first information sent by the terminal device, selects a reconstruction method of the likelihood function of the corresponding first positioning information according to the sending method of the first information determined in step S1620, and reconstructs the likelihood function of the first positioning information.

[0194] In step S1650, the core network element sends the location information of the terminal device to the terminal device.

[0195] The core network element fuses the likelihood function of the reconstructed first positioning information obtained in step S1640, completes the position calculation of the terminal device, and sends the position information of the terminal device (for example, longitude and latitude) to the terminal device.

[0196] The following description takes the speed of the first request carrying the first node as an example.

[0197] In step S1610, the terminal device sends a first request to a core network element.

[0198] The terminal device triggers the sending of the first request based on the positioning requirement, and the first request carries the speed of the terminal device.

[0199] In step S1620, the core network element sends first configuration information to the terminal device.

[0200] The core network element receives the first request and determines whether the terminal device is in a high-speed moving state or a low-speed moving state according to the speed of the terminal device carried in the first request. Choose an appropriate number of measurements M h ; For low-speed mobile terminal equipment, from the low measurement number set Choose an appropriate number of measurements M l The core network element sends first configuration information to the terminal device, and the first configuration information can be used to configure M h or M l A measurement set and a method for sending the first information.

[0201] In step S1630, the terminal device sends at least one first information to the core network element.

[0202] The terminal device completes M according to the PRS configuration and the first configuration information sent by the core network element. h or M l The measurement is performed a second time, and at least one first information is sent to the core network element according to the sending method of the first information.

[0203] In step S1640, the core network element reconstructs the likelihood function of the first positioning information.

[0204] The core network element receives at least one first information sent by the terminal device, selects a reconstruction method of the likelihood function of the corresponding first positioning information according to the sending method of the first information determined in step S1620, and reconstructs the likelihood function of the first positioning information.

[0205] In step S1650, the core network element sends the location information of the terminal device to the terminal device.

[0206] The core network element fuses the likelihood function of the reconstructed first positioning information obtained in step S1640, completes the position calculation of the terminal device, and sends the position information of the terminal device (for example, longitude and latitude) to the terminal device.

[0207] Combination of the above Figures 1 to 16 , describes the method embodiment of the present application in detail, and the following is combined with Figures 17 to 19 , describes the device embodiment of the present application in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0208] Fig.17 A first node for wireless communication provided in an embodiment of the present application. Fig.17 As shown, the first node 1700 includes a first transceiver 1710 .

[0209] The first transceiver 1710 is used to send at least one first information, wherein the first information includes one or more of the following: first positioning information and a first parameter, wherein the first parameter is associated with first soft information; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of a first node.

[0210] As an embodiment, the first positioning information includes first timing information.

[0211] As an embodiment, the first transceiver 1710 is used to send at least one first information, including: the first transceiver 1710 is used to send N first information, the first parameter includes first soft information, and N is an integer greater than or equal to 1.

[0212] As an embodiment, the first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

[0213] As an embodiment, the first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; at least one first weight.

[0214] As an embodiment, the first node also includes: the first transceiver 1710 is also used to receive first configuration information, and the first configuration information is used to configure M resource sets; wherein measurements of the M resource sets are used to determine the input of the first AI / ML model.

[0215] As an embodiment, the first node also includes: the first transceiver 1710 is also used to send a first request, and the first request is used to request to send the first configuration information.

[0216] As an embodiment, the first request carries one or more of the following: the capability of the first node to support sending the first information; and the speed of the first node.

[0217] As an embodiment, the capability of the first node to support sending of the at least one first information includes the quantity of the at least one first information supported by the first node to send.

[0218] Fig.18 A second node for wireless communication provided in an embodiment of the present application. Fig.18 As shown, the second node 1200 includes a second transceiver 1810 .

[0219] The second transceiver 1810 is used to receive at least one first information, wherein the first information includes one or more of the following: first positioning information and a first parameter, wherein the first parameter is associated with first soft information; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

[0220] As an embodiment, the first positioning information includes first timing information.

[0221] As an embodiment, the second transceiver 1810 is used to receive at least one first information, including: the second transceiver 1810 is used to receive N first information, the first parameter includes first soft information, and N is an integer greater than or equal to 1.

[0222] As an embodiment, the first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

[0223] As an embodiment, the first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; at least one first weight.

[0224] As an embodiment, the second node also includes: the second transceiver 1810 is also used to send first configuration information, and the first configuration information is used to configure M resource sets; wherein measurements of the M resource sets are used to determine the input of the first AI / ML model.

[0225] As an embodiment, the second node also includes: the second transceiver 1810 is also used to receive a first request, and the first request is used to request to send the first configuration information.

[0226] As an embodiment, the first request carries one or more of the following: the capability of the first node to support sending the first information; and the speed of the first node.

[0227] As an embodiment, the capability of the first node to support sending of the at least one first information includes the quantity of the at least one first information supported by the first node to send.

[0228] In an optional embodiment, the first transceiver 1710 may be a transceiver 1930. The first node 1700 may also include a processor 1910 and a memory 1920. Fig.19 shown.

[0229] In an optional embodiment, the second transceiver 1810 may be a transceiver 1930. The second node 1800 may also include a processor 1910 and a memory 1920. Fig.19 shown.

[0230] Fig.19 It is a schematic structural diagram of a communication device according to an embodiment of the present application. Fig.19 The dotted line in the figure indicates that the unit or module is optional. The device 1900 can be used to implement the method described in the above method embodiment. The device 1900 can be a chip, a terminal device or a network device.

[0231] The device 1900 may include one or more processors 1910. The processor 1910 may support the device 1900 to implement the method described in the foregoing method embodiment. The processor 1910 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0232] The apparatus 1900 may further include one or more memories 1920. The memory 1920 stores a program, which can be executed by the processor 1910, so that the processor 1910 executes the method described in the above method embodiment. The memory 1920 may be independent of the processor 1910 or integrated in the processor 1910.

[0233] The apparatus 1900 may further include a transceiver 1930. The processor 1910 may communicate with other devices or chips through the transceiver 1930. For example, the processor 1910 may transmit and receive data with other devices or chips through the transceiver 1930.

[0234] The present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to a terminal or network device provided in the present application, and the program enables a computer to execute the method performed by the terminal or network device in each embodiment of the present application.

[0235] The embodiment of the present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the terminal or network device provided in the embodiment of the present application, and the program enables the computer to execute the method performed by the terminal or network device in each embodiment of the present application.

[0236] The embodiment of the present application also provides a computer program. The computer program can be applied to the terminal or network device provided in the embodiment of the present application, and the computer program enables a computer to execute the method executed by the terminal or network device in each embodiment of the present application.

[0237] It should be understood that the terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions.

[0238] In the embodiments of the present application, the "indication" mentioned can be a direct indication, an indirect indication, or an indication of an association relationship. For example, A indicates B, which can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association relationship between A and B.

[0239] In the embodiments of the present application, the term "corresponding" may indicate that there is a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship of indication and being indicated, configuration and being configured, etc.

[0240] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communication field, for example, it may include an LTE protocol, an NR protocol, and related protocols used in future communication systems, and the present application does not limit this.

[0241] In the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0242] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0243] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0244] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0245] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0246] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0247] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method in a first node for wireless communication, characterized in that include: Send at least one first information, where the first information includes one or more of the following: first positioning information and a first parameter, wherein the first parameter is associated with the first soft information; The first parameter; The first positioning information and the first parameter are outputs of a first artificial intelligence AI / machine learning ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

2. The method according to claim 1, characterized in that The first positioning information includes first timing information.

3. The method according to claim 1 or 2, characterized in that The sending of at least one first information comprises: N first information are sent, where the first parameter includes first soft information, and N is an integer greater than or equal to 1.

4. The method according to claim 1 or 2, characterized in that: The first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

5. The method according to claim 1 or 2, characterized in that: The first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; At least one first weight.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Receiving first configuration information, where the first configuration information is used to configure M resource sets; The measurements of the M resource sets are used to determine the input of the first AI / ML model.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Send a first request, where the first request is used to request to send the first configuration information.

8. The method according to claim 7, characterized in that The first request carries one or more of the following: The first node supports a capability of sending first information; The speed of the first node.

9. The method according to claim 8, characterized in that The capability of the first node supporting sending the at least one first information includes the number of the at least one first information supported by the first node.

10. A method in a second node for wireless communication, characterized in that: include: Receive at least one first information, where the first information includes one or more of the following: first positioning information and a first parameter, wherein the first parameter is associated with the first soft information; The first parameter; The first positioning information and the first parameter are outputs of a first artificial intelligence AI / machine learning ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

11. The method according to claim 10, characterized in that The first positioning information includes first timing information.

12. The method according to claim 10 or 11, characterized in that The receiving at least one first information comprises: N first information are received, where the first parameter includes first soft information, and N is an integer greater than or equal to 1.

13. The method according to claim 10 or 11, characterized in that: The first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

14. The method according to claim 10 or 11, characterized in that: The first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; At least one first weight.

15. The method according to any one of claims 10 to 14, characterized in that The method further comprises: Sending first configuration information, where the first configuration information is used to configure M resource sets; The measurements of the M resource sets are used to determine the input of the first AI / ML model.

16. The method according to any one of claims 10 to 15, characterized in that The method further comprises: A first request is received, where the first request is used to request sending the first configuration information.

17. The method according to claim 16, characterized in that The first request carries one or more of the following: The first node supports a capability of sending first information; The speed of the first node.

18. The method according to claim 17, characterized in that The capability of the first node supporting sending the at least one first information includes the number of the at least one first information supported by the first node.

19. A first node for wireless communication, characterized in that: include: The first transceiver is configured to send at least one first information, where the first information includes one or more of the following: first positioning information and a first parameter, wherein the first parameter is associated with the first soft information; The first parameter; The first positioning information and the first parameter are outputs of a first artificial intelligence AI / machine learning ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

20. The first node according to claim 19, characterized in that The first positioning information includes first timing information.

21. The first node according to claim 19 or 20, characterized in that: The first transceiver is used to send at least one first information, including: The first transceiver is used to send N first information, the first parameter includes first soft information, and N is an integer greater than or equal to 1.

22. The first node according to claim 19 or 20, characterized in that: The first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

23. The first node according to claim 19 or 20, characterized in that: The first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; At least one first weight.

24. The first node according to any one of claims 19 to 23, characterized in that: The first node also includes: The first transceiver is further configured to receive first configuration information, where the first configuration information is used to configure M resource sets; The measurements of the M resource sets are used to determine the input of the first AI / ML model.

25. The first node according to any one of claims 19 to 24, characterized in that: The first node also includes: The first transceiver is further used to send a first request, where the first request is used to request to send the first configuration information.

26. The first node according to claim 25, characterized in that The first request carries one or more of the following: The first node supports a capability of sending first information; The speed of the first node.

27. The first node according to claim 26, characterized in that The capability of the first node supporting sending the at least one first information includes the number of the at least one first information supported by the first node.

28. A second node for wireless communication, characterized in that: include: The second transceiver is configured to receive at least one first information, where the first information includes one or more of the following: first positioning information and a first parameter, wherein the first parameter is associated with the first soft information; The first parameter; The first positioning information and the first parameter are outputs of a first artificial intelligence AI / machine learning ML model, the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine the position of the first node.

29. The second node according to claim 28, characterized in that The first positioning information includes first timing information.

30. The second node according to claim 28 or 29, characterized in that: The second transceiver is used to receive at least one first information, including: The second transceiver is used to receive N first information, where the first parameter includes first soft information, and N is an integer greater than or equal to 1.

31. The second node according to claim 28 or 29, characterized in that: The first parameter includes first soft information, the first information carries at least one first soft information, and any first soft information in the at least one first soft information is greater than a first threshold.

32. The second node according to claim 28 or 29, characterized in that: The first parameter includes one or more of the following: at least one first mean; at least one first standard deviation; At least one first weight.

33. The second node according to any one of claims 28 to 32, characterized in that: The second node also includes: The second transceiver is further used to send first configuration information, where the first configuration information is used to configure M resource sets; The measurements of the M resource sets are used to determine the input of the first AI / ML model.

34. The second node according to any one of claims 28 to 33, characterized in that: The second node also includes: The second transceiver is further used to receive a first request, where the first request is used to request to send the first configuration information.

35. The second node according to claim 34, characterized in that The first request carries one or more of the following: The first node supports a capability of sending first information; The speed of the first node.

36. The second node according to claim 35, characterized in that The capability of the first node supporting sending the at least one first information includes the number of the at least one first information supported by the first node.

37. A node used for wireless communication, characterized in that: It includes a transceiver, a memory and a processor, wherein the memory is used to store programs, and the processor is used to call the programs in the memory and control the transceiver to receive or send signals so that the node executes the method as described in any one of claims 1-9 or 9-18.

38. A device, characterized in that The device comprises a processor, configured to call a program from a memory so as to enable the device to execute the method according to any one of claims 1 to 18.

39. A chip, characterized in that: It comprises a processor, which is used to call a program from a memory, so that a device equipped with the chip executes the method according to any one of claims 1 to 18.

40. A computer-readable storage medium, characterized in that A program is stored thereon, the program causing a computer to execute the method according to any one of claims 1 to 18.

41. A computer program product, characterized in that The method comprises a program which causes a computer to execute the method according to any one of claims 1 to 18.

42. A computer program, characterized in that The computer program enables a computer to execute the method according to any one of claims 1 to 18.

Images