Method and apparatus in node for wireless communication

Abstract

Provided in the present application are a method and apparatus in a node for wireless communication. The method comprises: sending at least one piece of 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, and the 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 traditional solutions for positioning a first node on the basis of an AI / ML model, first information reported by the first node comprising a first parameter associated with first soft information helps to perform soft decision positioning of the first node on the basis of the first parameter (e.g., positioning the position of the first node on the basis of the first soft information determined by the first parameter), thereby enhancing positioning accuracy.

Description

Method and apparatus in a node for wireless communication TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, and more particularly, to a method and apparatus in a node for wireless communication. BACKGROUND

[0002] In some scenarios, a first node (e.g., a terminal device) can predict first positioning information based on an artificial intelligence (AI) / machine learning (ML) model, the first positioning information being used to determine a position of the first node. However, the first positioning information generated by the conventional AI / ML model can have errors, which in turn leads to a decline in positioning accuracy.

[0003] SUMMARY

[0004] The present application provides a method and apparatus in a node for wireless communication. The various aspects of the present application are described below.

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

[0006] 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: first positioning information and a first parameter, the first parameter being associated with a first soft information; the first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, and the first soft information is a likelihood value of the first positioning information, the first positioning information being used to determine a position of the first node.

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

[0008] In a fourth aspect, a second node for wireless communication is provided, comprising: a second transceiver configured to receive at least one first information, the first information comprising one or more of: first positioning information and first parameters associated with first soft information; the first parameters; wherein the first positioning information and the first parameters are outputs of a first AI / ML model, and the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine a position of a first node.

[0009] In a fifth aspect, a first node for wireless communication is provided, comprising a transceiver, a memory and a processor, the memory configured to store a program, the processor configured to invoke the program in the memory and control the transceiver to receive or send signals, so that the first node performs the method of the first aspect.

[0010] In a sixth aspect, a second node for wireless communication is provided, comprising a transceiver, a memory and a processor, the memory configured to store a program, the processor configured to invoke the program in the memory and control the transceiver to receive or send signals, so that the second node performs the method of the second aspect.

[0011] In a seventh aspect, the embodiments of the present application provide a communication system, which comprises the first node and / or the second node described above. In another possible design, the system can further comprise other devices interacting with the first node or the second node in the schemes provided by the embodiments of the present application.

[0012] In an eighth aspect, the embodiments of the present application provide a computer-readable storage medium, which stores a computer program. The computer program causes a computer to perform some or all of the steps of the methods in the above aspects.

[0013] In a ninth aspect, the embodiments of the present application provide a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to perform some or all of the steps of the methods in the above aspects. In some implementations, the computer program product can be a software installation package.

[0014] In a tenth aspect, the embodiments of the present application provide a chip. The chip comprises a memory and a processor. The processor can invoke and run a computer program from the memory, so as to implement some or all of the steps described in the methods in the above aspects.

[0015] In the embodiments of the present application, the first node can 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; the 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 AI / ML model-based positioning scheme of the first node, the first information reported by the first node includes the first parameter associated with the first soft information, which is helpful for 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), so as to enhance the positioning accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0016] FIG. 1 is a wireless communication system 100 to which embodiments of the present application are applied.

[0017] FIG. 2 is an example diagram of AI / ML model-based assisted positioning to which embodiments of the present application are applicable.

[0018] FIG. 3 is another example diagram of AI / ML model-based assisted positioning to which embodiments of the present application are applicable.

[0019] FIG. 4 is a schematic diagram of a multi-round-trip time (Multi-RTT) positioning method.

[0020] FIG. 5 is a schematic diagram of a downlink time difference of arrival (DL-TDOA) positioning method.

[0021] FIG. 6 is a schematic diagram of an uplink time difference of arrival (UL-TDOA) positioning method.

[0022] FIG. 7 is a schematic flowchart of a method in a first node for wireless communication according to an embodiment of the present application.

[0023] FIG. 8 is a schematic diagram of an implementation of the first information.

[0024] FIG. 9 is a schematic diagram of another implementation of the first information.

[0025] FIG. 10 is a schematic diagram of yet another implementation of the first information.

[0026] FIG. 11 is a schematic diagram of a method of reconstructing a likelihood function of the first positioning information.

[0027] FIG. 12 is a schematic diagram of another method of reconstructing a likelihood function of the first positioning information.

[0028] FIG. 13 is a schematic diagram of another method of reconstructing a likelihood function of the first positioning information.

[0029] FIG. 14 is an example diagram of a correlation scheme of the first information.

[0030] FIG. 15 is an example diagram of a correlation scheme of another first information.

[0031] FIG. 16 is an example diagram of a scheme in which a correlation scheme of the first information is combined with a correlation scheme of the first request.

[0032] FIG. 17 is a schematic diagram of a structure of a first node for wireless communication provided by an embodiment of the present application.

[0033] FIG. 18 is a schematic diagram of a structure of a second node for wireless communication provided by an embodiment of the present application.

[0034] FIG. 19 is a schematic structural diagram of an apparatus provided by an embodiment of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0036] FIG. 1 is a wireless communication system 100 to which embodiments of the present application are applied. The wireless communication system 100 can include a network device 110 and a terminal device 120. The network device 110 can be a device that communicates with the terminal device 120. The network device 110 can provide communication coverage for a specific geographic area and can communicate with the terminal device 120 located in the coverage area.

[0037] FIG. 1 exemplarily shows one network device and two terminals. Optionally, the wireless communication system 100 can include multiple network devices and each network device can include other numbers of terminal devices within its coverage range, which is not limited in the embodiments of the present application.

[0038] Optionally, the wireless communication system 100 can further include a network controller, a mobile management entity, and other network entities, which are not limited in the embodiments of the present application.

[0039] It should be understood that the technical solutions of the embodiments of the present application can be applied to various communication systems, for example: a 5th generation (5G) system or new radio (NR), a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD), and the like. The technical solutions provided in the present application can also be applied to future communication systems, such as a 6th generation mobile communication system, a satellite communication system, and the like.

[0040] The terminal device in the embodiments of the present application can also be referred to as a user equipment (UE), an access terminal, a user unit, a user station, a mobile station, a mobile station (MS), a mobile terminal (MT), a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent, or a user apparatus. The terminal device in the embodiments of the present application can refer to a device that provides voice and / or data connectivity for a user, and can be used to connect people, things, and machines, such as handheld devices with wireless connection functions, vehicle-mounted devices, and the like. The terminal device in the embodiments of the present application can be a mobile phone, a tablet computer (Pad), a notebook computer, a palm computer, 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, and the like. Optionally, the UE can be used to act as a base station. For example, the UE can act as a scheduling entity, which provides a sidelink signal between UEs in V2X or D2D, and the like. 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.

[0041] The network device in the embodiments of the present application can be a device for communicating with a terminal device, which can also be referred to as an access network device or a radio access network device, such as a network device, which can be a base station. The network device in the embodiments of the present application can refer to a radio access network (RAN) node (or device) that accesses a terminal device to a wireless network. The base station can broadly cover various names in the following or be replaced by the following names, such as: Node B (NodeB), evolved Node B (eNB), next generation Node B (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting 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, baseband 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, modem or chip for being arranged in the foregoing device or apparatus. The base station can also be a mobile switching center and a device that undertakes the function of a base station in device-to-device (D2D), vehicle-to-everything (V2X), machine-to-machine (M2M) communication, network side device in 6G network, device that undertakes the function of a base station in future communication system, etc. The base station can support networks of the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.

[0042] The base station can be fixed or mobile. For example, a helicopter or a drone can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station. In other examples, a helicopter or a drone can be configured to act as a device that communicates with another base station.

[0043] In some deployments, the network device in the embodiments of the present application can refer to a CU or a DU, or the network device includes a CU and a DU. The gNB can also include an AAU.

[0044] The network device and the terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on the water surface; can also be deployed on airplanes, balloons and satellites in the air. The scenarios in which the network device and the terminal device are located are not limited in the embodiments of the present application.

[0045] It should be understood that all or part of the functions of the communication device in the present application can also be implemented by software functions running on hardware, or by virtualized functions instantiated on a platform (such as a cloud platform).

[0046] Positioning technology

[0047] Positioning technology plays an important role in many scenarios of a communication system, and positioning capability is one of the core capabilities of related protocols. The positioning function is added in related versions of the communication protocol, which utilizes the multi-beam characteristics of the multiple-input multiple-output (MIMO) technology to define positioning technologies such as Multi-RTT based on a cell, time difference of arrival (TDOA), angle-of-arrival (AoA) measurement method, and angle-of-departure (AoD) measurement method.

[0048] With large-scale deployment of communication systems, vertical industry scenarios have put forward more and more urgent demands for positioning services. For operators, there is also great expectation for the broad market of future location services, and it is urgent to expand location value-added services to provide higher-precision positioning services (20 cm) for ordinary users and vertical industries. Therefore, the 3rd generation partnership project (3GPP) determines to enhance the following aspects in subsequent versions: higher precision (horizontal / vertical), low latency (physical layer and high-layer end-to-end positioning latency), network and / or device efficiency, high integrity and reliability (for global navigation satellite system (GNSS)).

[0049] To further improve the accuracy, in some scenarios, two promising techniques are considered to be introduced in the positioning of radio access technology (RAT): one is to increase the transmission bandwidth of the positioning reference signal (PRS) based on the bandwidth aggregation of the PRS / sounding reference signal (SRS) in the communication system; the other is to use carrier phase measurement.

[0050] However, one major problem of the above-mentioned PRS or SRS-based positioning techniques is the sharp decline in positioning accuracy in non-line of sight (NLOS) severe scenarios. The traditional timing information reported in the positioning techniques is about the timing information of the first path, but the first path may be an NLOS path, in which case the positioning accuracy will be severely degraded. In the traditional method, the timing information can also be combined with the LOS indication, which indicates whether a propagation path is an LOS path, i.e., the timing information of the LOS path is filtered out using the LOS indication, but this method cannot complete positioning in the NLOS severe case. 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 detected path, and the timing information of the additional detected path is also allowed to be reported in the form of difference relative to the first path. However, the first detected path may be a line-of-sight (LOS) propagation path or an NLOS propagation path. Since the position calculation method relies on the timing information of the LOS path, if the first detected path is an NLOS path, the timing error will be increased, resulting in a decline in positioning accuracy.

[0051] To address the above-mentioned situation, the subsequent version of 3GPP will support an AI / ML model-based positioning accuracy enhancement scheme. Five relevant sub-use cases are identified in the relevant report, among which case 2a and case 3a are both AI / ML model-assisted positioning use cases, i.e., the AI / ML model is used to generate prediction information of positioning measurement at the terminal device (e.g., UE) or the access network device (e.g., gNB) side, and the information is reported to the core network element (e.g., LMF).

[0052] As an example, referring to FIG. 2, in case 2a, the positioning of the terminal device is 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 an 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 (LPP), and the core network element calculates the position according to the timing information in the received measurement report to obtain the position information of the terminal device. In the embodiments of the present application, the timing information can be taken as an example of the first positioning information.

[0053] As an example, referring to FIG. 3, in case 3a, the positioning of the terminal device is 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 an 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 NR positioning protocol a (NRPPa), and the core network element calculates the position according to the timing information in the received measurement report to obtain the position information of the terminal device.

[0054] Taking the three time-based positioning methods as an example, the timing information and the position calculation method in the foregoing are illustrated below. The three time-based positioning methods include multi-RTT, DL-TDOA and UL-TDOA.

[0055] In the multi-RTT positioning method, referring to FIG. 4, the reported timing information is the receive-transport time difference (RX-TX time difference), that is, the terminal device measures the DL-PRS to obtain the receive-transport time difference on the terminal device side, the access network device measures the UL-SRS to obtain the receive-transport time difference on the access network device side, and the core network element is aggregated. The core network element converts the receive-transport time difference into the distance between the terminal device and the access network device, thereby positioning.

[0056] In the DL-TDOA positioning method, referring to FIG. 5, the reported timing information is the reference signal time difference (RSTD), that is, the difference between the arrival times of the DL-PRS transmitted by different transmission and receiving points (TRPs) relative to the reference TRP (for example, the DL-TDOA 3,1 With the DL-TDOA 2,1 ) in FIG. 5). The core network element converts the RSTD into a distance, thereby forming a hyperbola, and the position of the terminal device can be obtained by solving the hyperbola equation.

[0057] In the UL-TDOA positioning method, referring to FIG. 6, the reported timing information is 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 (for example, the UL-TDOA 3,1 With the UL-TDOA 2,1 ) in FIG. 6). The core network element calculates the position of the terminal device using the RTOA provided by each TRP.

[0058] As described above, in some scenarios, a first node (for example, a terminal device) can predict first positioning information based on an 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 in turn leads to a decrease in positioning accuracy.

[0059] Taking the timing information generated by the AI / ML model in case 2a or case 3a in the 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 environmental factors in actual deployment and imperfections of the AI / ML model itself, the generated timing information also has certain errors, which in turn leads to inaccurate positioning. Possible reasons for this problem include the following two points: due to imperfect training data set, lack of high-precision ground truth labels, etc., 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, unavoidable channel noise or interference will affect the received DL-PRS or UL-SRS, which in turn affects the input of the AI / ML model, resulting in the generated timing information deviating from the true value.

[0060] To address the above issues, embodiments of this application provide a method in a node for wireless communication. The first node in the method can 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 first soft information; the 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. Compared with the traditional AI / ML model-based positioning scheme of the first node, the first information reported by the first node includes the first parameter associated with the first soft information, which helps to make a soft decision positioning of the first node based on the first parameter (e.g., positioning the position of the first node based on the first soft information determined based on the first parameter), so as to enhance the positioning accuracy.

[0061] The method in the first node for wireless communication of embodiments of this application is described below in conjunction with FIG. 7. FIG. 7 is a schematic flowchart of the method in the first node for wireless communication of embodiments of this application. As shown in FIG. 7, the method is performed by the first node.

[0062] As an embodiment, the first node can be a network-controlled repeater (NCR).

[0063] As an embodiment, the first node can be a terminal device. For example, the terminal device 120 shown in FIG. 1.

[0064] As an embodiment, the first node can be a relay, such as a relay terminal.

[0065] As an embodiment, the first node can deploy a first AI / ML model to make a prediction of the first positioning information, so the first node can be a terminal device or an access network device. For example, when the first AI / ML model is deployed at the terminal device side, the first node can be a UE; when the first AI / ML model is deployed at the access network device side, the first node can be a base station.

[0066] The method shown in FIG. 7 includes step S710, which is described below.

[0067] In step S710, the first node sends at least one first information. Exemplarily, the first node can send the at least one first information to a second node.

[0068] As an embodiment, the second node can be a network device. For example, the second node is the core network element shown in FIG. 2. For another example, the second node is an LMF.

[0069] Embodiment 1: First information

[0070] In the embodiments of the present application, the protocol through which the first node sends the first information is not limited. In some embodiments, the first node sends the first information through LPP. For example, the first node is a terminal device, and the terminal device sends the first information to the second node through LPP. In other embodiments, the first node sends the first information through NRPPa. For example, the first node is an access network device, and the access network device sends the first information to the second node through NRPPa.

[0071] In some embodiments, the first information includes one or more of the following: the first positioning information and the first parameter; the first parameter.

[0072] In some embodiments, the first parameter is associated with the first soft information, and the first positioning information is the output of the first AI / ML model.

[0073] In the embodiments of the present application, the first AI / ML model is not limited. In some embodiments, the first AI model can include a deep learning model. For example, a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory (LSTM), a generative adversarial network (GAN), a variational autoencoder (VAE), a transformer, etc. In some embodiments, the first ML model can include a model generated through machine learning or device self-learning. For example, deep reinforcement learning (DRL).

[0074] In some embodiments, the first positioning information can comprise one or more of: 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); access network device side transceiving time difference; terminal device side transceiving time difference.

[0075] As an embodiment, the first positioning information can comprise first timing information, which can be understood as information related to time. For example, the first timing information comprises at least one of RSTD, RTOA, TOA, TOF, access network device side transceiving time difference, terminal device side transceiving time difference.

[0076] As an embodiment, the first positioning information can comprise first angle information. For example, the first timing information comprises at least one of AOD and TOA.

[0077] In some embodiments, the first soft information is used to characterize a relationship between the first positioning information and the first input.

[0078] As an embodiment, the first input is input data of the first AI / ML model.

[0079] As an embodiment, the first input comprises measurement values for positioning reference signals.

[0080] As a sub-embodiment of the above-mentioned embodiment, the positioning reference signals can be existing reference signals. For example, the first input comprises transceiving time difference measured on UL-SRS. For another example, the first input comprises transceiving time difference measured on DL-PRS.

[0081] As a sub-embodiment of the above-mentioned embodiment, the positioning reference signals can be newly defined positioning reference signals.

[0082] As an embodiment, the first input comprises processing information of the measurement values for the positioning reference signals. For example, the first input comprises average values calculated from transceiving time difference measured multiple times on DL-PRS.

[0083] As an embodiment, the first input comprises measurement values for downlink signals. For example, the first input comprises channel impulse responses (CIRs) measured on downlink pilot signals.

[0084] As an embodiment, the first input comprises processed information of measurement values for downlink signals. For example, the first input comprises power delay profiles (PDPs) processed from CIRs measured on downlink pilot signals.

[0085] 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.

[0086] In some embodiments, the statistical representation is a likelihood value, i.e., the first soft information is a likelihood value of the first positioning information, or the first soft information can quantify likelihoods of all possible first positioning information.

[0087] As an embodiment, the first soft information is a probability information, which can be understood as that the larger the value of the first soft information is, the larger the probability of the first positioning information corresponding to the first soft information is under the given parameters of the first AI / ML model.

[0088] As an embodiment, the first soft information is a number between 0 and 1.

[0089] As a sub-embodiment of the above embodiment, the first soft information is a fraction between 0 and 1. For example, the first soft information is 0.5.

[0090] As a sub-embodiment of the above embodiment, the first soft information comprises at least one of 0 and 1. For example, the first soft information is 1.

[0091] In some embodiments, the first positioning information is used to determine a position of the first node.

[0092] In some embodiments, the position of the first node comprises a geographical position of the first node, i.e., the position of the first node comprises an absolute position of the first node.

[0093] As a sub-embodiment of the above embodiment, the position of the first node comprises at least one of a longitude and a latitude of the first node. For example, the position of the first node comprises a longitude and a latitude of the first node.

[0094] As an embodiment, the position of the first node comprises a distance between the first node and a reference point. For example, the position of the first node comprises distances between the first node and two reference points, and based on the position of the first node and absolute positions of the two reference points, an absolute position of the first node can be determined.

[0095] As one sub-example of the above example, the reference point can be any device in the wireless communication system. For example, the reference point can be at least one of a terminal device, a relay node, a network device, a TRP.

[0096] As one sub-example of the above example, the reference point can be a reference geographic location. For example, the reference point is a specific latitude and longitude coordinate.

[0097] As one example, the position of the first node includes a relative position between the first node and the reference point. For example, the reference point is a TRP, and the position of the first node includes a relative position between the first node and the TRP.

[0098] In some examples, 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 a DL-PRS transmitted by an access network device, and generates the first positioning information and the first soft information using a 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 an UL-SRS transmitted by a terminal device, and generates the first positioning information and the first soft information using a first AI / ML model deployed on the access network device side.

[0099] In some examples, the first parameter is associated with the first soft information, which can be understood as that the first parameter includes the first soft information. For example, the first parameter includes a likelihood value of the first positioning information.

[0100] In some examples, the first node transmits N first information, the first parameter includes the first soft information, and N is an integer greater than or equal to 1.

[0101] As one example, the above N is configured by a network device. For example, before the first node transmits the first information, the network device transmits configuration information to the first node, and the configuration information includes the first threshold.

[0102] As one example, the above N is a fixed value, and therefore the transmission mode of the first information is also referred to as “fixed number of first information reporting”.

[0103] As one example, the above N is associated with the first node. For example, if the first node is a first terminal device, N takes a value of N1; if the first node is a second terminal device, N takes a value of N2.

[0104] As one example, the above N is also associated with one or more of the following: a number of resource sets configured by a network device; a first AI / ML model. For example, a first AI / ML model deployed on the first node side takes a measurement as input and outputs K first positioning information θ and corresponding first soft information corresponding to the measurement The process of the first MI / ML model generating the first positioning information and the first soft information is shown in FIG. 8. The number of resource sets configured by the network device is M, and the first node needs to perform M times of positioning measurement. The first AI / ML model inputs the M times of measurement in sequence, and generates M*K pairs of the first positioning information and the first soft information in total The first information includes one first positioning information and one first parameter, and therefore, the number of the first information sent by the first node is M*K, and N=M*K.

[0105] In some embodiments, the first parameter includes the first soft information, and the first information carries at least one first soft information. 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 satisfying the first condition.

[0106] In some embodiments, the above-mentioned first condition includes that the first soft information is greater than a first threshold, that is, any first soft information in the at least one first soft information carried by the first information is greater than the first threshold. For example, the first AI / ML model inputs one measurement, and outputs 10 first positioning information θ and the corresponding first soft information If 5 first soft information in the 10 first soft information are greater than the first threshold, the first information carries the 5 first soft information greater than the first threshold.

[0107] As an example, 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.

[0108] As an example, the first positioning information carried by the first information can 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 one measurement, and outputs 10 first positioning information θ and the corresponding first soft information If 5 first soft information in 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. As another example, referring to 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, the first positioning information and the first soft information corresponding to the first soft information above the first threshold in FIG. 9. In this embodiment, the first soft information and the first positioning information included in the first information are both determined based on the first threshold, and therefore, the sending mode of the first information is also called "threshold-based first information reporting".

[0109] In some embodiments, the first parameter is associated with the first soft information, which can be understood as that the first parameter comprises 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 the first function, and the first parameter comprises 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, and then the first soft information can be obtained.

[0110] In some embodiments, the first parameter comprises one or more of the following: a parameter of a likelihood function of at least one first positioning information; a weight of the likelihood function of the at least one first positioning information.

[0111] In the embodiments of the present application, the parameter of the likelihood function of the first positioning information is not limited. For example, the likelihood function of the first positioning information is Gaussian distribution or normal distribution, and the parameter of the likelihood function of the first positioning information is mean and standard deviation (or variance). For another example, the likelihood function of the first positioning information is laplace distribution, and the parameter of the likelihood function of the first positioning information is location parameter and scale parameter.

[0112] As an example, a weight of a likelihood function of at least one first positioning information can be understood as that the likelihood function of the at least one first positioning information occupies a weight in the likelihood function of the at least one first positioning information.

[0113] As an example, the first parameter comprises the parameter of the likelihood function of the at least one first positioning information and the weight of the likelihood function of the at least one first positioning information. Accordingly, based on the parameter of the likelihood function of the at least one first positioning information and the weight of the likelihood function of the at least one first positioning information, the mixed likelihood model of the at least one first positioning information can be determined.

[0114] As an example, the linear combination of the mixed likelihood model of the first positioning information can be fitted to obtain the likelihood function of the first positioning information. For example, the mixed likelihood model of the at least one first positioning information corresponding to M times of quantity is a mixed Gaussian model, and the linear combination of the plurality of Gaussian distribution functions in the mixed Gaussian model can be fitted to obtain the likelihood function of the at least one first positioning information.

[0115] In some embodiments, the first parameter comprises one or more of the following: at least one first mean; at least one first standard deviation; at least one first weight.

[0116] As an example, a first mean and a first standard deviation are parameters of a likelihood function of at least one first positioning information. Accordingly, based on the at least one first mean and the at least one first standard deviation, the likelihood function of the at least one first positioning information can be determined.

[0117] As an embodiment, a first weight is a weight of a likelihood function of a first positioning information.

[0118] As an embodiment, the first information only includes 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 only includes parameters related to the likelihood function of the first positioning information, and therefore the sending mode of such first information is also referred to as "first information reporting based on likelihood model".

[0119] As an embodiment, the likelihood function of the first positioning information is a Gaussian distribution, and the first parameters include one or more of the following: the mean μ i of the i-th Gaussian distribution; the standard deviation σ i of the i-th Gaussian distribution; and the weight α i of the i-th Gaussian distribution, where i is the number of measurements. For example, referring to FIG. 10, the parameter estimator (PE) module of the first AI / ML model deployed at the first node inputs M measurements and outputs M pairs of parameters (α i , μ i , σ i ) of different Gaussian distributions, i.e., M first parameters, where μ i is the mean of the i-th Gaussian distribution, σ i is the standard deviation of the i-th Gaussian distribution, and α i is the weight of the i-th Gaussian distribution. During training, the accuracy of the fitting of the likelihood function is evaluated by the first positioning information generator, thereby assisting the training of the PE. The Gaussian mixture model is obtained by a mixer where is a Gaussian distribution. The sampling module samples M estimated first positioning information according to the Gaussian mixture model. The error between the M estimated first positioning information and the true first positioning information of the M measurements is calculated and fed back to the PE, thereby assisting the training of the PE. 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 parameters, and at this time the number of first information is M.

[0120] In some embodiments, the first node receives first configuration information. Illustratively, the first node receives the first configuration information sent by the second node.

[0121] In some embodiments, the first configuration information is used to configure 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.

[0122] In some embodiments, the measurements for the M resource sets are used to determine the input of the first AI / ML model. For example, referring to FIG. 10, the measurements for the M resource sets are the input of the parameter estimation module of the first AI / ML model.

[0123] As an embodiment, the measurements for the M resource sets are used to determine the input of the first AI / ML model training phase. For example, referring to FIG. 10, during the training process, the measurements for the M resource sets are the input of the PE module of the first AI / ML model, which are used to train the related parameters of the PE module.

[0124] As an embodiment, the measurements for the M resource sets are used to determine the input of the first AI / ML model inference phase. For example, referring to FIG. 10, during the inference process, the measurements for the M resource sets are the input of the PE module of the first AI / ML model, which are used to output the first parameters.

[0125] As an embodiment, the first configuration information is used to configure the number of positioning measurements. For example, the number M of resource sets configured based on the first configuration information can determine that the first node performs positioning measurements M times. Through multiple measurements, the number of samples of the first AI / ML model can be increased, which helps to further enhance the positioning accuracy.

[0126] As an embodiment, the number of positioning measurements can be understood as the number of times the first node performs positioning measurements before generating the first positioning information and / or the first soft information using the first AI / ML model. For example, the first configuration information is used to configure the number of positioning measurements as M, assuming that the first node is a terminal device, the terminal device measures M times of DL-PRS sent by the access network device, and inputs the M times of measurement results to the first AI / ML model to generate the first positioning information and the first soft information.

[0127] As an embodiment, the first configuration information is used to configure the transmission mode of the first information. Accordingly, the first node receiving the first configuration information can determine how to transmit the first information based on the first configuration information.

[0128] As an embodiment, the transmission mode of the first information can be understood as the reporting scheme of the first information.

[0129] As an embodiment, the transmission mode of the first information includes that the first node transmits N first information, the first parameters include the first soft information, and N is an integer greater than or equal to 1.

[0130] As an embodiment, the transmission mode of the first information includes that the first parameters include 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.

[0131] As an embodiment, the sending manner of the first information comprises: the first parameters comprise one or more of: at least one first mean value; at least one first standard deviation; at least one first weight.

[0132] It should be noted that the above three sending manners of the first information have been explained above, and will not be repeated here.

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

[0134] As an embodiment, the first configuration information is used to configure the first threshold value above. For example, the first threshold value is carried in a field of the first configuration information.

[0135] As an embodiment, the first configuration information is received before the first node sends the first information, so as to facilitate the first node to determine the M measurement sets and / or the sending manner of the first information, etc.

[0136] In some embodiments, the second node reconstructs the likelihood function of the first positioning information after receiving the first information sent by the first node.

[0137] 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 parameters comprise first soft information, and the first soft information is a likelihood value of the first positioning information. The likelihood function of the first positioning information sent by the first node is as shown on the left side of FIG. 11, and the likelihood function of the first positioning information reconstructed by interpolation can be obtained as shown on the right side of FIG. 11. For another example, referring to FIG. 12, assuming that the first parameters comprise first soft information, and the first information carries at least one first soft information, any first soft information in the at least one first soft information is greater than the first threshold value, at this time, the likelihood function of the first positioning information sent by the first node refers to the part above the first threshold value on the left side of FIG. 12, and the likelihood function of the first positioning information reconstructed by interpolation can be obtained as shown on the right side of FIG. 12.

[0138] As an embodiment, the second node reconstructs the likelihood function of the first positioning information based on parameters.

[0139] As a sub-embodiment of the above-mentioned embodiment, the above-mentioned parameters can be the first parameters. For example, assuming that the likelihood function of the first positioning information is Gaussian distribution, the first parameters comprise one or more of: the mean value μ i of the i-th Gaussian distribution; the standard deviation σ i of the i-th Gaussian distribution; the weight α i of the i-th Gaussian distribution, that is, the first parameters of the first node are M different Gaussian distributions (α i , μi ,σ i The first node uses the first parameter to linearly combine different Gaussian distributions to obtain a Gaussian mixture model that fits the likelihood function of the first localization information. The likelihood function is then reconstructed, as shown in Figure 13.

[0140] In some embodiments, the second node fuses the first location information.

[0141] As an example, the first location information to be fused is associated with different TRPs. For instance, if the first location information associated with different TRPs is independent of each other, then multiplying the likelihood functions of each first location information yields the joint likelihood function of each first location information, thereby fusing the first location information associated with different TRPs. in It comes from the TRP set. First location information It is the likelihood function of the first location information associated with the i-th TRP.

[0142] In some embodiments, the second node calculates the position of the first node based on the result of fusing the first positioning information.

[0143] It should be noted that the location of the first node has been described above and will not be repeated here.

[0144] As an example, the position of the first node is its coordinates. For instance, assuming the first node is a terminal device, the joint likelihood function of the fused first location information is: in It comes from the TRP set. First location information It is the likelihood function of the first location information associated with the i-th TRP. The second node uses maximum likelihood estimation to solve for the coordinates of the first node. Right now

[0145] To facilitate understanding, the following section uses Figures 14 and 15 as examples to illustrate relevant solutions for the first information.

[0146] Figure 14 illustrates the process using the first node as an example of a terminal device, and includes steps S1410 to S1460.

[0147] Suppose that the first node sends N pieces of first information to the second node, and the second node is a core network element. The first information includes first positioning information and first parameters. The first parameters include first soft information, which is the likelihood value of the first positioning information.

[0148] In step S1410, the core network element sends the first configuration information to the terminal device.

[0149] The core network element sends the first configuration information to the terminal device through LPP, the first configuration information is used to configure the number of times M of positioning measurement of the terminal device, and the sending mode of the first information, the sending mode of the first information includes that the terminal device sends N first information, N=M*K. While sending the first configuration information, the core network element can also provide PRS configuration to the terminal device through LPP.

[0150] In step S1420, the terminal device measures and generates the first information.

[0151] After receiving the first configuration information, the terminal device measures M times of DL-PRS sent by the access network device, and inputs the measurement result into the first AI / ML model to generate M*K first information, one first information includes one first positioning information and one first soft information.

[0152] In step S1430, the terminal device sends at least one first information to the core network element.

[0153] The terminal device sends M*K first information generated in step S1430 to the core network element through LPP.

[0154] In step S1440, the core network element reconstructs the likelihood function of the first positioning information.

[0155] The core network element receives M*K first information sent by the terminal device, obtains the likelihood function of the first positioning information based on the first information, and performs interpolation reconstruction on the likelihood function to obtain the reconstructed likelihood function of the first positioning information.

[0156] In step S1450, the core network element fuses the first positioning information.

[0157] 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, Wherein is the first positioning information from the TRP set is the likelihood function of the first positioning information associated with the i-th TRP.

[0158] In step S1460, the core network element calculates the position of the terminal device.

[0159] 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 That is​

[0160] FIG. 15 takes the first node as an access network device as an example for introduction, and FIG. 15 includes steps S1510 to S1560.

[0161] It is assumed 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 a first parameter, a likelihood function of the first positioning information is a Gaussian distribution, the first parameter includes a mean μ i , a standard deviation σ i of the i-th Gaussian distribution, and a weight α i of the i-th Gaussian distribution.

[0162] In step S1510, the core network element sends first configuration information to the access network device.

[0163] 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 number of times M of positioning measurement performed by the access network device, and the sending mode of the first information, the sending mode of the first information is that the first parameter includes (α i , μ i , σ i ).

[0164] In step S1520, the access network device measures and generates the first information.

[0165] The access network device receives the first configuration information, and can send 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 M times of received UL-SRS, and inputs the measurement result into the first AI / ML model to generate M first information. The first information includes M first information, and the first information includes the first parameter (α i , μ i , σ i ).

[0166] In step S1530, the access network device sends at least one first information to the core network element.

[0167] The access network device sends the M first information generated in S1530 to the core network element through NRPPa.

[0168] In step S1540, the core network element reconstructs the likelihood function of the first positioning information.

[0169] The core network element receives the M first information sent by the access network device, linearly combines different Gaussian distributions by using the first parameter, and obtains a Gaussian mixture model of a fitted likelihood function of the first positioning information is the likelihood function of the reconstructed first positioning information.

[0170] In step S1550, the core network network element fuses the first positioning information.

[0171] The core network network element multiplies the likelihood function of each first positioning information associated with the TRP obtained in step S1540 to obtain a joint likelihood function, i.e. wherein is the first positioning information from the TRP set , and is the likelihood function of the first positioning information associated with the i-th TRP.

[0172] In step S1560, the core network network element calculates the terminal device position.

[0173] The core network network element solves the joint likelihood function obtained in step S1550 by using maximum likelihood estimation to obtain the coordinates of the terminal device i.e.

[0174] To solve the problem that the positioning accuracy may be reduced due to the error of the first positioning information, an embodiment of the present application further provides a method in a first node for wireless communication. The first node sends a first request. The first request carries the capability 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, the network device helps to issue configuration information for the first node to report the first information, thereby improving the positioning accuracy based on the first information.

[0175] Embodiment 2: The first request

[0176] As an embodiment, the first node sends the first request to the second node. For example, the first node is a terminal device, and the second node is a core network network element. The terminal device sends the first request to the core network network element.

[0177] 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, the terminal device sends the first request when the terminal device needs positioning service. Alternatively, if the first node is an access network device, the terminal device can send the first request through the first node when the terminal device needs positioning service.

[0178] 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 the number of 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 information supported by the first node, which takes the value X.

[0179] 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.

[0180] The above describes the related solutions of the first information and the first request respectively through Embodiment 1 and Embodiment 2. In some scenarios, the two solutions can be used independently. In other scenarios, the two solutions can be used in combination. The following describes a solution in which the related solution of the first information and the related solution of the first request are combined.

[0181] In some embodiments, the first request is used to request sending of the first configuration information in the above.

[0182] As an embodiment, the first request is used to request the second node to send the first configuration information, and accordingly, the second node receives the first request and sends the first configuration information to the first node based on the first request.

[0183] As a sub-embodiment of the above embodiment, the second node determines the sending mode of the first information based on the number of at least one first information supported by the first node to send, and then sends the first configuration information. For example, the second node evaluates the capability of the first node according to the number of at least one first information supported by the first node to send, and divides it into three levels of strong, moderate, and weak. For the first node with strong capability, more data can be processed and reported, so 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 carrying at least one first soft information, and any first soft information in the at least one first soft information being greater than the first threshold; and for the first node with weak capability, the sending mode of the first information is configured as the first information including only the first parameter, and the first parameter including one or more of the following: at least one first mean value; at least one first standard deviation; and at least one first weight.

[0184] As a sub-embodiment of the above embodiment, the second node determines the number of times of positioning measurement of 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 times of positioning measurement of the terminal device according to the moving speed of the terminal device. In a rural macro (RMa) scenario, a terminal device supporting high-speed movement (such as a car) is supported. In order to enhance the positioning accuracy, more measurement times are needed. Therefore, the second node has a high measurement times set supporting positioning of the terminal device supporting high-speed movement and a low measurement times set supporting positioning of a terminal device supporting low-speed movement. The second node determines, according to the speed of the terminal device, whether the terminal device is in a high-speed moving state or a low-speed moving state, selects a corresponding measurement number set, and selects a suitable measurement number from the measurement number set, and sends the measurement number to the first node through the first configuration information.

[0185] To facilitate understanding, the following describes a scheme in which the first request and the first information are combined, with reference to FIG. 16. FIG. 16 includes steps S1610 to S1650. It is assumed that the first node is a terminal device, and the second node is a core network element.

[0186] The following describes a case in which the first request carries the capability of the first node to support sending at least one first information.

[0187] In step S1610, the terminal device sends a first request to the core network element.

[0188] The terminal device triggers sending of the first request based on a positioning requirement, and the first request carries the capability of the terminal device to support sending at least one first information.

[0189] In step S1620, the core network element sends first configuration information to the terminal device.

[0190] The core network element receives the first request, evaluates the capability of the terminal device to support sending at least one first information carried by the first request, and appropriately selects a sending mode of the first information, and sends the first configuration information to the terminal device. The first configuration information can be used to configure M measurement sets and the sending mode of the first information.

[0191] In step S1630, the terminal device sends at least one first information to the core network element.

[0192] The terminal device completes M measurements according to the PRS configuration and the first configuration information sent by the core network element, and sends at least one first information to the core network element according to the sending mode of the first information.

[0193] In step S1640, the core network element reconstructs a likelihood function of the first positioning information.

[0194] The core network element receives at least one first information sent by the terminal device, selects a corresponding reconstruction mode of the likelihood function of the first positioning information according to the sending mode of the first information determined in step S1620, and reconstructs the likelihood function of the first positioning information.

[0195] In step S1650, the core network element sends position information of the terminal device to the terminal device.

[0196] The core network element fuses the likelihood function of the reconstructed first positioning information obtained in step S1640 to complete the location calculation of the terminal device and sends the location information (e.g., latitude and longitude) of the terminal device to the terminal device.

[0197] The following section will use the speed at which the first request carries the first node as an example.

[0198] In step S1610, the terminal device sends a first request to the core network element.

[0199] The terminal device triggers the sending of the first request based on the location requirement, and the first request carries the speed of the terminal device.

[0200] In step S1620, the core network element sends the first configuration information to the terminal device.

[0201] The core network element receives the first request and, based on the speed of the terminal device carried in the first request, determines whether the terminal device is moving at high speed or low speed. For terminal devices moving at high speed, the speed is determined from the high measurement set. Choose a suitable number of measurements M h For terminal devices moving at low speeds, from a low set of measurement times Choose a suitable number of measurements M l The core network element sends the first configuration information to the terminal device. This first configuration information can be used to configure M. h Or M l The method of sending a measurement set and the first information.

[0202] In step S1630, the terminal device sends at least one first piece of information to the core network element.

[0203] The terminal device completes the M process based on the PRS configuration and the first configuration information sent by the core network elements. h Or M l The measurement is performed, and at least one piece of first information is sent to the core network element according to the method of sending the first information.

[0204] In step S1640, the core network element reconstructs the likelihood function of the first positioning information.

[0205] The core network element receives at least one first piece of information sent by the terminal device, and selects the reconstruction method of the likelihood function of the first location information according to the transmission method of the first information determined in step S1620, and reconstructs the likelihood function of the first location information.

[0206] In step S1650, the core network element sends the location information of the terminal device to the terminal device.

[0207] The core network 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 (for example, the latitude and longitude) of the terminal device to the terminal device.

[0208] The method embodiments of the present application are described in detail above in combination with FIG. 1 to FIG. 16, and the device embodiments of the present application are described in detail below in combination with FIG. 17 to FIG. 19. It should be understood that the description of the method embodiments corresponds to the description of the device embodiments, and therefore, the parts not described in detail can be referred to the foregoing method embodiments.

[0209] FIG. 17 is a first node for wireless communication provided by an embodiment of the present application. As shown in FIG. 17, the first node 1700 includes a first transceiver 1710.

[0210] The first transceiver 1710 is configured to send at least one first information, the first information including one or more of the following: first positioning information and first parameters associated with first soft information; the first parameters; wherein the first positioning information and the first parameters are outputs of a first AI / ML model, and 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.

[0211] As an embodiment, the first positioning information includes first timing information.

[0212] As an embodiment, the first transceiver 1710 is configured to send at least one first information, including that the first transceiver 1710 is configured to send N first information, and the first parameters include first soft information, and N is an integer greater than or equal to 1.

[0213] As an embodiment, the first parameters include first soft information, and 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 value.

[0214] As an embodiment, the first parameters include one or more of the following: at least one first mean value; at least one first standard deviation; at least one first weight.

[0215] As an embodiment, the first node further includes that the first transceiver 1710 is further configured to receive first configuration information, and the first configuration information is used to configure M resource sets; wherein the measurements of the M resource sets are used to determine the inputs of the first AI / ML model.

[0216] As an embodiment, the first node further includes that the first transceiver 1710 is further configured to send a first request, and the first request is used to request to send the first configuration information.

[0217] As an embodiment, the first request carries one or more of: a capability of the first node to transmit first information; a speed of the first node.

[0218] As an embodiment, the capability of the first node to transmit the at least one first information comprises a capability of the first node to transmit a number of the at least one first information.

[0219] FIG. 18 is a second node for wireless communication, provided by an embodiment of the present application. As shown in FIG. 18, the second node 1200 includes a second transceiver 1810.

[0220] The second transceiver 1810 is configured to receive at least one first information, the first information comprising one or more of: first positioning information and a first parameter associated with first soft information; the first parameter; wherein the first positioning information and the first parameter are outputs of a first AI / ML model, and the first soft information is a likelihood value of the first positioning information, and the first positioning information is used to determine a position of a first node.

[0221] As an embodiment, the first positioning information comprises first timing information.

[0222] As an embodiment, the second transceiver 1810 is configured to receive at least one first information, comprising: the second transceiver 1810 is configured to receive N first information, the first parameter comprises first soft information, and N is an integer greater than or equal to 1.

[0223] As an embodiment, the first parameter comprises first soft information, and the first information carries at least one first soft information, any of the at least one first soft information being greater than a first threshold.

[0224] As an embodiment, the first parameter comprises one or more of: at least one first mean; at least one first standard deviation; at least one first weight.

[0225] As an embodiment, the second node further comprises: the second transceiver 1810 is further configured to transmit first configuration information, the first configuration information being used to configure M resource sets; wherein measurements on the M resource sets are used to determine inputs of the first AI / ML model.

[0226] As an embodiment, the second node further comprises: the second transceiver 1810 is further configured to receive a first request, the first request being used to request transmission of the first configuration information.

[0227] As an embodiment, the first request carries one or more of: a capability of the first node to transmit first information; a speed of the first node.

[0228] As an embodiment, the capability of the first node to transmit the at least one first information comprises a number of the at least one first information that the first node is capable of transmitting.

[0229] In an optional embodiment, the first transceiver 1710 can be a transceiver 1930. The first node 1700 can further include a processor 1910 and a memory 1920, as shown in FIG. 19.

[0230] In an optional embodiment, the second transceiver 1810 can be a transceiver 1930. The second node 1800 can further include a processor 1910 and a memory 1920, as shown in FIG. 19.

[0231] FIG. 19 is a schematic structural diagram of a communication apparatus according to an embodiment of the present application. The dashed line in FIG. 19 indicates that the unit or module is optional. The apparatus 1900 can be used to implement the method described in the foregoing method embodiments. The apparatus 1900 can be a chip, a terminal device, or a network device.

[0232] The apparatus 1900 can include one or more processors 1910. The processor 1910 can support the apparatus 1900 to implement the method described in the foregoing method embodiments. The processor 1910 can be a general purpose processor or a dedicated processor. For example, the processor can be a central processing unit (CPU). Alternatively, the processor can 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, discrete hardware components, etc. The general purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0233] The apparatus 1900 can 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 performs the method described in the foregoing method embodiments. The memory 1920 can be independent of the processor 1910 or integrated in the processor 1910.

[0234] The apparatus 1900 can further include a transceiver 1930. The processor 1910 can communicate with other devices or chips through the transceiver 1930. For example, the processor 1910 can perform data transceiving with other devices or chips through the transceiver 1930.

[0235] The embodiment of the present application further provides a computer readable storage medium for storing a program. The computer readable storage medium can be applied to the terminal or network device provided by the embodiment of the present application, and the program causes the computer to execute the method performed by the terminal or network device in the various embodiments of the present application.

[0236] The embodiment of the present application further 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 by the embodiment of the present application, and the program causes the computer to execute the method performed by the terminal or network device in the various embodiments of the present application.

[0237] The embodiment of the present application further provides a computer program. The computer program can be applied to the terminal or network device provided by the embodiment of the present application, and the computer program causes the computer to execute the method performed by the terminal or network device in the various embodiments of the present application.

[0238] It should be understood that the terms "system" and "network" can be used interchangeably in the present application. In addition, the terms used in the present 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" and the like in the specification and claims of the present application and the drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion.

[0239] In the embodiments of the present application, the "indication" mentioned can be direct indication, or indirect indication, or can be an indication of an associated relationship. For example, A indicates B, which can mean that B can be obtained by A; or A indirectly indicates B, for example, A indicates C, and B can be obtained by C; or A and B have an associated relationship.

[0240] In the embodiments of the present application, the term "corresponding" can mean a direct or indirect corresponding relationship between the two, or can mean an associated relationship between the two, or can mean an indication and being indicated, configuration and being configured, and the like.

[0241] In the embodiments of the present application, the "protocol" can refer to a standard protocol in the communication field, which can include an LTE protocol, an NR protocol, and a related protocol applied to a future communication system, and the present application does not limit this.

[0242] The term "and / or" in the embodiments of the present application is merely an association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects.

[0243] In various embodiments of the present application, the size of the sequence number of the above processes does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0244] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0245] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiments of the present application.

[0246] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0247] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented 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, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions can 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 can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be read by a computer or a data storage device such as a server, data center and the like integrated with one or more available media sets. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, digital video disc (DVD)) or semiconductor media (for example, solid state disk (SSD)) and the like.

[0248] 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 can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method in a first node for wireless communication, characterized by, Comprising: transmitting at least one first information, the first information comprising one or more of: first positioning information and first parameters, the first parameters being associated with first soft information; first parameters; wherein the first positioning information and the first parameters 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 a position of a first node.

2. The method of claim 1, wherein, The first positioning information comprises first timing information.

3. The method of claim 1 or 2, wherein, The transmitting at least one first information comprises: transmitting N of the first information, the first parameters comprising first soft information, N being an integer greater than or equal to 1.

4. The method of claim 1 or 2, wherein, The first parameters comprise first soft information, the first information carrying at least one of the first soft information, any of the at least one first soft information being greater than a first threshold.

5. The method of claim 1 or 2, wherein, The first parameters comprise one or more of: at least one first mean; at least one first standard deviation; at least one first weight.

6. The method of any one of claims 1-5, wherein, The method further comprises: receiving first configuration information, the first configuration information being used to configure M resource sets; wherein measurements for the M resource sets are used to determine inputs of the first AI / ML model.

7. The method of any one of claims 1-6, wherein, The method further comprises: transmitting a first request, the first request being used to request transmission of the first configuration information.

8. The method of claim 7, wherein, The first request carries one or more of: a capability of the first node to transmit first information; a speed of the first node.

9. The method of claim 8, wherein, The capability of the first node to transmit the at least one first information comprises a capability of the first node to transmit a number of the at least one first information.

10. A method in a second node for wireless communication, the method comprising: Comprising: receiving at least one first information, the first information comprising one or more of: first positioning information and first parameters, the first parameters being associated with first soft information; first parameters; wherein the first positioning information and the first parameters 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 a position of a first node.

11. The method of claim 10, wherein, The first positioning information comprises first timing information.

12. The method of claim 10 or 11, wherein, The receiving at least one first information comprises: receiving N of the first information, the first parameters comprising first soft information, N being an integer greater than or equal to 1.

13. The method of claim 10 or 11, wherein, The first parameters comprise first soft information, the first information carrying at least one of the first soft information, any of the at least one first soft information being greater than a first threshold.

14. The method of claim 10 or 11, wherein, The first parameters comprise one or more of: at least one first mean; at least one first standard deviation; at least one first weight.

15. The method of any one of claims 10-14, wherein, The method further comprises: transmitting first configuration information, the first configuration information being used to configure M resource sets; wherein measurements for the M resource sets are used to determine inputs of the first AI / ML model.

16. The method of any one of claims 10-15, wherein, The method further comprises: receiving a first request, the first request being used to request transmission of the first configuration information.

17. The method of claim 16, wherein, The first request carries one or more of: a capability of the first node to transmit first information; a speed of the first node.

18. The method of claim 17, wherein, The capability of the first node to support sending the at least one first information comprises a number of the at least one first information supported by the first node to send.

19. A first node for wireless communication, the first node comprising: Comprises: a first transceiver configured to send at least one first information, the first information comprising one or more of: first positioning information and a first parameter associated with first soft information; a first parameter; wherein 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 a position of the first node.

20. The first node of claim 19, wherein, The first positioning information comprises first timing information.

21. The first node of claim 19 or 20, wherein, The first transceiver is configured to send at least one first information, comprising: the first transceiver is configured to send N pieces of the first information, and the first parameter comprises first soft information, where N is an integer greater than or equal to 1.

22. The first node of claim 19 or 20, wherein, The first parameter comprises first soft information, and the first information carries at least one piece of the first soft information, any piece of the first soft information in the at least one piece of the first soft information being greater than a first threshold.

23. The first node of claim 19 or 20, wherein, The first parameter comprises one or more of: at least one first mean value; at least one first standard deviation; at least one first weight.

24. The first node of any of claims 19-23, wherein, The first node further comprises: The first transceiver is further configured to receive first configuration information, and the first configuration information is used to configure M resource sets; wherein measurements on the M resource sets are used to determine inputs of the first AI / ML model.

25. The first node of any of claims 19-24, wherein, The first node further comprises: The first transceiver is further configured to send a first request, and the first request is used to request sending of the first configuration information.

26. The first node of claim 25, wherein, The first request carries one or more of: a capability of the first node to support sending first information; a speed of the first node.

27. The first node of claim 26, wherein, The capability of the first node to support sending the at least one first information comprises a number of the at least one first information supported by the first node to send.

28. A second node for wireless communication, comprising: Comprises: a second transceiver configured to receive at least one first information, the first information comprising one or more of: first positioning information and a first parameter associated with first soft information; a first parameter; wherein 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 a position of the first node.

29. The second node of claim 28, wherein, The first positioning information comprises first timing information.

30. The second node of claim 28 or 29, wherein, The second transceiver is configured to receive at least one first information, comprising: the second transceiver is configured to receive N pieces of the first information, and the first parameter comprises first soft information, where N is an integer greater than or equal to 1.

31. The second node of claim 28 or 29, wherein, The first parameter comprises first soft information, and the first information carries at least one piece of the first soft information, any piece of the first soft information in the at least one piece of the first soft information being greater than a first threshold.

32. The second node of claim 28 or 29, characterized by The first parameter comprises one or more of: at least one first mean value; at least one first standard deviation; at least one first weight.

33. The second node of any of claims 28-32, wherein, The second node further comprises: The second transceiver is further configured to transmit first configuration information, the first configuration information being used for configuring the M resource sets. The measurement on the M resource sets is used for determining the input of the first AI / ML model.

34. The second node of any of claims 28-33, wherein, The second node further includes: The second transceiver is further configured to receive a first request, the first request being used for requesting to transmit the first configuration information.

35. The second node of claim 34, wherein, The first request carries one or more of: The capability of the first node to transmit the first information; The speed of the first node.

36. The second node of claim 35, wherein, The capability of the first node to transmit the at least one first information includes the number of the at least one first information that the first node supports to transmit.

37. A node for wireless communication, the node comprising: A node comprising a transceiver, a memory and a processor, the memory being configured to store a program, the processor being configured to invoke the program in the memory and control the transceiver to receive or transmit signals, so that the node performs the method in any one of claims 1-9 or 9-18.

38. An apparatus comprising: A device comprising a processor configured to invoke a program from a memory, so that the device performs the method in any one of claims 1-18.

39. A chip, characterized by A chip comprising a processor configured to invoke a program from a memory, so that the device installed with the chip performs the method in any one of claims 1-18.

40. A computer-readable storage medium, comprising: A computer program product having a program stored thereon, the program causing a computer to perform the method in any one of claims 1-18.

41. A computer program product, characterised in that, A computer program product having a program stored thereon, the program causing a computer to perform the method in any one of claims 1-18.

42. A computer program, characterized in that, The computer program product causes a computer to perform the method in any one of claims 1-18.

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