Positioning model method and apparatus using relative time input

Abstract

A positioning model method and apparatus using a relative time input are described wherein the method comprises: measuring, by a processor of an apparatus, a first channel delay profile having a first path timing from a first reference signal associated with a first network node; adjusting, by the processor, the first path timing based on a time difference associated with a reference network node; and generating, by the processor, a model output based on the first channel delay profile with the adjusted first path timing as a model input, or reporting, by the processor, the first channel delay profile with the adjusted first path timing to a network.

Description

Technical Field

[0001] The present invention relates generally to mobile communications and, more particularly, to artificial intelligence / machine learning (AI / ML) positioning models using relative time inputs relative to devices in mobile communications. Background Art

[0002] Unless otherwise indicated, the approaches described in this section are not prior art to the listed claims and are not admitted to be prior art by inclusion in this section.

[0003] In the fifth generation (5G) new radio (NR) mobile communication, artificial intelligence (AI) / machine learning (ML) solutions are introduced to locate devices. In particular, AI / ML-based positioning can involve training data collection, model training, model reasoning, and model performance monitoring. By using a trained AI / ML positioning model with some model input data, the location of the user equipment (UE) can be properly estimated.

[0004] However, model input data can directly affect positioning performance / accuracy and signaling overhead in model lifecycle management. Therefore, the format of model input data can be a key topic in AI / ML positioning. Therefore, it is necessary to provide model input data in a proper format for AI / ML positioning. Summary of the invention

[0005] The following summary of the invention is illustrative only and is not intended to be limiting in any way. That is, the following summary of the invention is provided to introduce the concepts, key points, benefits and beneficial effects of the novel and non-obvious technologies described herein. Selected embodiments are further described in the detailed description below. Therefore, the following summary of the invention is not intended to identify the essential features of the claimed subject matter, nor is it intended to be used to determine the scope of the claimed subject matter.

[0006] One object of the present invention is to propose a solution or scheme for using relative time as a model input for an Artificial Intelligence / Machine Learning (AI / ML) positioning model for devices in mobile communications.

[0007] In one aspect, a method may involve an apparatus measuring a first channel delay profile having a first path timing based on a first reference signal associated with a first network node. The method may also involve the apparatus adjusting the first path timing based on a time difference associated with a reference network node. The method may also involve the apparatus: (1) generating a model output from a positioning model based on the first channel delay profile having the adjusted first path timing as a model input; or (2) reporting the first channel delay profile having the adjusted first path timing to a network.

[0008] In one aspect, an apparatus may include a transceiver that, during operation, wirelessly communicates with at least one network node of a wireless network. The apparatus may also include a processor in communication connection with the transceiver. The processor may perform operations during operation including: measuring, via the transceiver, a first channel delay distribution having a first path timing based on a first reference signal associated with a first network node. The processor may also perform operations including: adjusting the first path timing based on a time difference associated with a reference network node. The processor may also perform operations including: (1) generating a model output from a positioning model based on the first channel delay distribution having the adjusted first path timing as a model input; or (2) reporting the first channel delay distribution having the adjusted first path timing to a network via the transceiver.

[0009] It is worth noting that although the description provided herein may be in the context of certain radio access technologies, networks and network topologies (e.g., Long Term Evolution (LTE), LTE-Advanced, LTE-Advanced Pro, fifth generation systems (5G), new radio (NR), Internet of Things (IoT) and narrowband Internet of Things (NB-IoT), Industrial Internet of Things (IIoT), Beyond 5G (B5G) and Sixth Generation Systems (6G)), the proposed concepts, schemes and any variations / derivatives thereof may be implemented on, for and through any other type of radio access technologies, networks and network topologies. Therefore, the scope of the present invention is not limited to the examples described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are included to provide a further understanding of the invention and are incorporated into and constitute a part of the present invention. The accompanying drawings illustrate embodiments of the invention and together with the description are used to explain the principles of the invention. It is understood that in order to clearly illustrate the concepts of the present invention, the accompanying drawings are not necessarily drawn to scale, and some components shown may be shown in a scale that exceeds the size in the actual embodiment.

[0011] Figure 1 is a schematic diagram of an example scenario described according to an embodiment of the present invention.

[0012] Figure 2 is a schematic diagram of an example scenario described according to an embodiment of the present invention.

[0013] Figure 3 is a schematic diagram of an example scenario described according to an embodiment of the present invention.

[0014] Figure 4 is a schematic diagram of an example scenario described according to an embodiment of the present invention.

[0015] Figure 5 is a schematic diagram of an example scenario described according to an embodiment of the present invention.

[0016] Figure 6 is a schematic diagram of an example scenario described according to an embodiment of the present invention.

[0017] Figure 7 is a block diagram of an exemplary communication system described according to an embodiment of the present invention.

[0018] Figure 8 is a flowchart of an example process described according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Detailed embodiments and implementations of the claimed subject matter are disclosed herein. However, it should be understood that the disclosed embodiments and implementations are merely illustrations of the claimed subject matter, which can be implemented in various forms. However, the present invention can be implemented in many different forms and should not be construed as being limited to the exemplary embodiments and implementations set forth herein. On the contrary, these multiple exemplary embodiments and implementations are provided so that the description of the present invention is comprehensive and complete, and will fully convey the scope of the present invention to those skilled in the art. In the following description, the details of well-known features and technologies may be omitted to avoid unnecessarily obscuring the presented embodiments and implementations.

[0020] Overview

[0021] Embodiments of the present invention relate to various techniques, methods, schemes and / or solutions for artificial intelligence / machine learning (AI / ML) positioning models for relative time inputs associated with devices in mobile communications. According to the present invention, multiple possible solutions are implemented individually or in combination. That is, although these possible solutions are described separately below, two or more of these possible solutions may also be implemented in combination or in another manner.

[0022] Regarding the AI / ML positioning model of the present invention, a model input with relative time can be introduced to improve the accuracy of the model output. In particular, the network can notify the user equipment (UE) of the reference network node. The UE can measure the channel delay distribution with multiple path timings based on one or more reference signals (for example, a downlink positioning reference signal (DL-PRS) transmitted from one or more target network nodes (for example, a target transmission reception point (TRP)). More specifically, after one or more target network nodes transmit the reference signal, the UE can receive the reference signal through multiple paths with different path timings, and then measure the channel delay distribution based on the reference signal. For each path timing, the UE can use the time difference associated with the reference network node as a reference and adjust each path timing by the time difference.

[0023] Then, for the UE-based AI / ML positioning model (i.e., the AI / ML positioning model is used at the UE end), the UE can generate a model output through the AI / ML positioning model based on the channel delay distribution with adjusted path timing. In other words, the UE can input the channel delay distribution with adjusted path timing into the AI / ML positioning model to generate a model output, such as the UE position. For the UE-assisted AI / ML positioning model (i.e., the AI / ML positioning model is used in the network), the UE can report the channel delay distribution with adjusted path timing to the network so that the network can determine the model output through the positioning model based on the channel delay distribution with adjusted path timing as the model input. In other words, after receiving the channel delay distribution with adjusted path timing, the network can input the channel delay distribution with adjusted path timing into the AI / ML positioning model to generate a model output, such as the UE position.

[0024] Figure 1 An example scenario 100 under the scheme is described according to an embodiment of the present invention. Scenario 100 involves at least one network node and UE, which may be part of a wireless communication network (e.g., an LTE network, a 5G / NR network, an IoT network, or a 6G network). Scenario 100 describes a current network framework in which a UE may connect to a network node. In some embodiments, the UE may include a positioning reference unit (PRU) and the network node may include a TRP. The network may inform the UE which is the reference network node.

[0025] Figure 2 An example scenario 200 under the scheme is described according to an embodiment of the present invention. In some embodiments, the UE may measure a reference channel delay distribution with a reference path timing based on a reference signal associated with a reference network node. In particular, the reference network node may transmit a reference signal (e.g., a downlink positioning reference signal (DL-PRS)) to the UE. The UE may receive the reference signal at the reference path timing via a path and measure the reference channel delay distribution based on the reference signal. The UE may determine a time difference as the difference between the reference path timing and a receive boundary (i.e., an Rx boundary representing t=0). In some cases, the Rx boundary is the starting timing of a time interval of a channel delay distribution inferred by the UE based on certain signals. After determining the time difference, the time difference associated with the reference network node may be used as a reference for determining relative time.

[0026] In addition, the UE may measure a first channel delay distribution having a first path timing based on a first reference signal associated with the first network node. In particular, the first network node may transmit the first reference signal to the UE. The UE may receive the first reference signal at the first path timing through the path and measure the first channel delay distribution based on the first reference signal. Then, the UE may adjust the first path timing by the time difference. After adjusting the first path timing, the UE may: (1) generate a model output through the AI / ML positioning model based on the first channel delay distribution having the adjusted first path timing with respect to the UE-based AI / ML positioning model; or (2) report the first channel delay distribution having the adjusted first path timing to the network with respect to the UE-assisted positioning model so that the network determines the model output through the AI / ML positioning model. In some cases, the channel delay distribution may include at least one of a channel impulse response (CIR), a power delay profile (PDP), and a delay profile (DP).

[0027] Figure 3 An example scenario 300 is described in accordance with an embodiment of the present invention. In some embodiments, the time interval (ie, time domain samples) corresponding to the first channel delay profile may be from T=0 (Rx boundary) to T=N t -1. When the reference path timing is earlier than the first path timing within the time interval, adjusting the first path timing by the time difference may include shifting the first path timing by the time difference. More specifically, shifting the first path timing by the time difference may include advancing the first path timing by the time difference. For example, when the first path timing is X and the time difference is Y, the adjusted first path timing is equal to a value obtained by subtracting Y from X (i.e., the adjusted first path timing is (XY)).

[0028] Figure 4 An example scenario 400 is described in accordance with an embodiment of the present invention. In some embodiments, the time interval (ie, time domain samples) corresponding to the first channel delay profile may be from T=0 (Rx boundary) to T=N t When the reference path timing is later than the first path timing within the time interval, adjusting the first path timing by the time difference may include cyclically moving the first path timing by the time difference within the time interval. For example, when the first path timing is A and the time difference is B, the adjusted first path timing is equal to subtracting B from A and adding N. t The obtained value (i.e., the adjusted first path timing is (A-B+N t )).

[0029] In some embodiments, before an artificial intelligence / machine learning (AI / ML) positioning model is put into use, it is necessary to train the AI / ML positioning model with some training data. In particular, according to some machine learning schemes, an AI / ML positioning model having multiple training model inputs and training model output pairs can be generated (e.g., at a network or user equipment (UE) end). Each training model input includes a training channel delay distribution having at least one training path timing adjusted by a training time difference. For example, each of some training model inputs includes a training channel delay distribution having a training path timing adjusted by a training time difference, and each of some training model inputs includes a training channel delay distribution having multiple training path timings all adjusted by training time differences. Each training model output may include a training location. Therefore, after training, the AI / ML positioning model may receive a channel delay distribution having at least one path timing adjusted by a time difference, and then generate a location.

[0030] Figure 5 An example scenario 500 under the scheme is described according to an embodiment of the present invention. In some embodiments, the UE can measure a reference channel delay profile with a reference path timing based on a reference signal associated with a reference network node. In particular, the reference network node can transmit the reference signal to the UE. The UE can receive the reference signal through a path of the reference path timing and measure the reference channel delay profile based on the reference signal. The UE can determine a time difference as a difference between the reference path timing and a receive (Rx) boundary. After determining the time difference, the time difference associated with the reference network node can be used as a basis for determining relative time.

[0031] In addition, the UE can measure the timing of multiple paths (ie, Figure 5 In particular, the first network node may transmit a first reference signal to the UE. The UE may transmit a first reference signal to the UE through different path timings (i.e., Figure 5The UE receives a first reference signal through multiple paths (e.g., a line-of-sight (LOS) path or a non-line-of-sight (NLOS) path) of the UE (shown as "A" path timing, "B" path timing, and "C" path timing) and measures a first channel delay distribution based on the first reference signal. Then, for each path timing, the UE may adjust the path timing by the time difference. After adjusting the path timing, the UE may: (1) generate a model output by the AI / ML positioning model based on the first channel delay distribution with the adjusted path timing with respect to a UE-based AI / ML positioning model; or (2) report the first channel delay distribution with the adjusted path timing to the network with respect to a UE-assisted positioning model so that the network determines the model output through the AI / ML positioning model.

[0032] Figure 6 An example scenario 600 of the scheme is described according to an embodiment of the present invention. In some embodiments, the UE may measure a reference channel delay profile having a reference path timing based on a reference signal associated with a reference network node. In particular, the reference network node may transmit a reference signal to the UE. The UE may receive the reference signal through a path of the reference path timing and measure the reference channel delay profile based on the reference signal. The UE may determine a time difference as a difference between the reference path timing and a receive (Rx) boundary. After determining the time difference, the time difference associated with the reference network node may be used as a basis for determining a relative time.

[0033] In addition, the UE may: (1) measure a first channel delay profile having a first path timing based on a first reference signal associated with the first network node; and (2) measure a second channel delay profile having the first path timing based on a second reference signal associated with the second network node. In particular, the first network node may transmit a first reference signal to the UE. The UE may receive the first reference signal through a path of the first path timing associated with the first network node, and measure the first channel delay profile based on the first reference signal. The UE may then adjust the first path timing associated with the first network node by a time difference. The second network node may transmit a second reference signal to the UE. The UE may receive the second reference signal through a path of the first path timing associated with the second network node, and measure the second channel delay profile based on the second reference signal. The UE may then adjust the first path timing associated with the second network node by a time difference.

[0034] After adjusting the first path timing, the UE may: (1) with respect to a UE-based AI / ML positioning model, generate a model output by the AI / ML positioning model based on the first channel delay distribution with the adjusted first path timing associated with the first network node and the second channel delay distribution with the adjusted first path timing associated with the second network node; or (2) with respect to a UE-assisted positioning model, report to the network the first channel delay distribution with the adjusted first path timing associated with the first network node and the second channel delay distribution with the adjusted first path timing associated with the second network node, so that the network determines the model output through the AI / ML positioning model.

[0035] Illustrative Embodiments

[0036] Figure 7 An example communication system 700 is described according to an embodiment of the present invention, and the system has an example communication device 710 and an example network device 720. Each of the communication device 710 and the network device 720 can perform various functions to implement the schemes, technologies, processes and methods described herein related to the artificial intelligence / machine learning (AI / ML) positioning model of relative time input related to devices in mobile communications, including the scenarios / schemes described above and the process 800 described below.

[0037] The communication device 710 may be part of an electronic device, which may be a UE such as a portable or mobile device, a wearable device, an in-vehicle device or vehicle, a wireless communication device, or a computing device. For example, the communication device 710 may be implemented in a smartphone, a smart watch, a personal digital assistant, an electronic control unit in a vehicle, a digital camera, or a computing device such as a tablet, a laptop, or a notebook computer. The communication device 710 may also be part of a machine-type device, which may be an IoT, NB-IoT, IIoT device such as a fixed or static device, a home device, a wired communication device, or a computing device. For example, the communication device 710 may be implemented in a smart thermostat, a smart refrigerator, a smart door lock, a wireless speaker, or a home control center. Alternatively, the communication device 710 may be implemented in the form of one or more integrated circuit (IC) chips, such as, but not limited to, one or more single-core processors, one or more multi-core processors, one or more reduced instruction set computing (RISC) processors, or one or more complex instruction set computing (CISC) processors. The communication device 710 may include Figure 7 The communication device 710 may also include one or more other components not related to the solution proposed by the present invention (e.g., an internal power supply, a display device and / or a user interface device), so for the sake of brevity and simplicity, these components of the communication device 710 are not shown in FIG. Figure 7It is not shown in the figure and is not described below.

[0038] The network device 720 may be part of a network device, which may be a network node, such as a satellite, a base station, a small base station, a router, or a gateway. For example, the network device 720 may be implemented in an eNodeB in an LTE network, in a gNB in ​​a 5G / NR, IoT, NB-IoT, or IIoT network, or in a satellite or base station in a 6G network. Alternatively, the network device 720 may be implemented in the form of one or more integrated circuit (IC) chips, such as, but not limited to, one or more single-core processors, one or more multi-core processors, one or more reduced instruction set computing (RISC) processors, or one or more complex instruction set computing (CISC) processors. The network device 720 may include Figure 7 The network device 720 may also include one or more other components not related to the solution proposed by the present invention (e.g., an internal power supply, a display device, and / or a user interface device). Therefore, for the sake of brevity and simplicity, these components of the network device 720 are not shown in FIG. Figure 7 It is not shown in the figure and is not described below.

[0039] In one aspect, each of the processors 712 and 722 may be implemented in the form of one or more single-core processors, one or more multi-core processors, or one or more CISC or RISC processors. That is, even though the singular term "processor" is used herein to refer to the processors 712 and 722, each of the processors 712 and 722 may include multiple processors in some embodiments and may include a single processor in other embodiments in accordance with the present invention. In another aspect, each of the processors 712 and 722 may be implemented in the form of hardware (and, optionally, firmware) having electronic components, which may include, for example but not limited to, one or more transistors, one or more diodes, one or more capacitors, one or more resistors, one or more inductors, one or more memristors, and / or one or more varactors configured and arranged to achieve the specific purposes of the present invention. In other words, according to various embodiments of the present invention, at least in some embodiments, each of the processor 712 and the processor 722 can be used as a dedicated machine specially designed, configured and arranged to perform specific tasks of an AI / ML positioning model using relative time input in a device (e.g., represented by the communication device 710) and a network (e.g., represented by the network device 720) according to an embodiment of the present invention.

[0040] In some embodiments, the communication device 710 may also include a transceiver 716 coupled to the processor 712, and capable of transmitting and receiving data. In some embodiments, the communication device 710 may also include a memory 714 connected to the processor 712, and can be accessed by the processor 712 and store data therein. In some embodiments, the network device 720 may also include a transceiver 726 connected to the processor 722, and can transmit and receive data. In some embodiments, the network device 720 may also include a memory 724 coupled to the processor 722, and can be accessed by the processor 722 and store data therein. Therefore, the communication device 710 and the network device 720 may communicate with each other through the transceiver 716 and the transceiver 726. For better understanding, the following describes the operation, functions and capabilities of the communication device 710 and the network device 720, which are provided in a mobile communication environment, wherein the communication device 710 is implemented in or as a communication device or UE, and the network device 720 is implemented in or as a network node (e.g., TRP) of the communication network.

[0041] Illustrative Process

[0042] Figure 8 is an example process 800 shown according to an embodiment of the present invention. Whether in part or in whole, process 800 may represent one aspect of the design, concept, scheme, system and method proposed for implementing the above-mentioned AI / ML positioning model using relative time input. More specifically, process 800 may represent one aspect of the implementation of the communication device 710 feature. Process 800 may include one or more operations, actions or functions shown by one or more of blocks 810-830. Although the blocks shown are discrete, depending on the desired implementation, the blocks in process 800 may be split into more blocks, combined into fewer blocks, or some blocks may be deleted. In addition, the blocks of process 800 may be arranged according to Figure 8 The process 800 may be performed in the order shown, or, alternatively, may be performed in a different order. The process 800 may also be repeated in part or in its entirety. The communication device 710, any suitable UE, or a device of its type may implement the process 800. For illustrative purposes only and not to limit the scope, the process 800 is described below in the context of the communication device 710. The process 800 may start at block 810.

[0043] At block 810 , the process 800 may involve the processor 712 of the communication device 710 measuring a first channel delay profile having a first path timing based on a first reference signal associated with a first network node. The process 800 may continue from block 810 to block 820 .

[0044] At block 820 , the process 800 may involve the processor 712 of the communication device 710 adjusting the first path timing by the time difference associated with the reference network node. The process 800 may continue from block 820 to block 830 .

[0045] At block 830 , process 800 may involve processor 712 of communication device 710 (1) generating a model output from a positioning model based on the first channel delay profile with the adjusted first path timing; or (2) reporting the first channel delay profile with the adjusted first path timing to a network.

[0046] In some implementations, the process 800 may further involve the processor 712 of the communication device 710 measuring the reference channel delay profile according to a reference signal associated with the reference network node. The process 800 may further involve the processor 712 of the communication device 710 determining the time difference as the difference between the reference path timing and the reception boundary.

[0047] In certain implementations, the process 800 may further involve the processor 712 of the communication device 710 shifting the first path timing by a time difference associated with a reference network node.

[0048] In certain implementations, the process 800 may further involve the processor 712 of the communication device 710 cyclically shifting the first path timing by a time difference associated with a reference network node within a time interval.

[0049] In some implementations, process 800 may further involve the processor 712 of the communication device 710 generating a positioning model based on a machine learning scheme, comprising a plurality of training model input and training model output pairs, each training model input comprising a training channel delay distribution having at least one training path timing adjusted by a training time difference, and each training model output comprising a training position.

[0050] In some implementations, the process 800 may further involve the processor 712 of the communication device 710 measuring a first channel delay profile having a first path timing and a second path timing associated with the first network node based on a first reference signal associated with the first network node. The process 800 may further involve the processor 712 of the communication device 710 adjusting the first path timing and the second path timing based on a time difference associated with a reference network node. The process 800 may further involve the processor 712 of the communication device 710: (1) generating a model output by a positioning model based on the first channel delay profile having the adjusted first path timing and the adjusted second path timing; or (2) reporting the first channel delay profile having the adjusted first path timing and the adjusted second path timing to the network to determine the model output generated by the positioning model.

[0051] In some implementations, the process 800 may further involve the processor 712 of the communication device 710 measuring a second channel delay profile having a first path timing associated with the second network node based on a second reference signal associated with the second network node. The process 800 may further involve the processor 712 of the communication device 710 adjusting the first path timing associated with the second network node based on a time difference associated with the reference network node. The process 800 may further involve the processor 712 of the communication device 710: (1) generating a model output by a positioning model based on the first channel delay profile having the adjusted first path timing associated with the first network node and the second channel delay profile having the adjusted first path timing associated with the second network node; or (2) reporting the first channel delay profile having the adjusted first path timing associated with the first network node and the second channel delay profile having the adjusted first path timing associated with the second network node to the network to determine the model output generated by the positioning model.

[0052] In certain implementations, the first channel delay profile includes at least one of a channel impulse response (CIR), a power delay profile (PDP), and a delay profile (DP).

[0053] In certain implementations, the first reference signal includes a positioning reference signal.

[0054] In certain implementations, an apparatus includes a positioning reference unit (PRU).

[0055] Additional Notes

[0056] The subject matter described herein sometimes shows different components contained in or connected to different other components. However, it should be understood that the multiple depicted architectures are only examples, and in fact many other architectures that implement the same function can be implemented. In a conceptual sense, any arrangement of components that implement the same function is effectively "associated", so that the desired function is realized. Therefore, regardless of the architecture or intermediate components, any two components combined to implement a specific function herein can be regarded as "associated" with each other, so that the desired function is realized. Similarly, any two components so associated can also be regarded as "operationally connected" or "operationally coupled" to each other to achieve the desired function, and any two components that can be so associated can also be regarded as "operationally connected" to each other to achieve the desired function. The specific examples of coupling in operation include but are not limited to components that can be matched and / or physically interact and / or components that can interact wirelessly and / or wirelessly interact and / or components that can interact logically and / or logically interact.

[0057] Further, with respect to any plural and / or singular terms used in this article, those skilled in the art may convert from plural to singular and / or from singular to plural when appropriate for context and / or application. For clarity, various singular / plural reciprocities may be explicitly described herein.

[0058] Furthermore, those skilled in the art will understand that, in general, the terms used herein and particularly in the appended claims (e.g., the bodies of the appended claims) are generally intended to be "open" terms, e.g., the term "comprising" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "including" should be interpreted as "including but not limited to," and the like. Those skilled in the art will also understand that if a specific number of an introduced claim recitation is intended, such intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, to aid understanding, the appended claims may contain use of the introductory phrases "at least one" and "one or more." However, the use of such phrases should not be interpreted as implying that any particular claim included in a claim recitation through the introduction of the indefinite article "a" or "an" will be limited to embodiments containing only one such recitation, even when the same claim contains the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" or "an", for example, "a and / or an" should be interpreted as meaning "at least one" or "one or more", and the same applies to the use of definite articles used to introduce claim recitations. In addition, even if a specific number of introduced claim recitations is explicitly recited, a person skilled in the art will recognize that such recitation should be interpreted as meaning at least the recited number, for example, the unmasked recitation of "two recitations" means at least two recitations or two or more recitations in the absence of other modifiers. Furthermore, where a convention similar to “at least one of A, B, and C, etc.” is used, a person skilled in the art will understand that in the sense of this convention, it is generally intended to be interpreted as such (e.g., “a system having at least one of A, B, and C” will include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). Where a convention similar to “at least one of A, B, or C, etc.” is used, a person skilled in the art will understand that in the sense of this convention, it is generally intended to be interpreted as such (e.g., “a system having at least one of A, B, or C” will include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). A person skilled in the art will also understand that any transitional words and / or phrases that actually indicate two or more alternatives, whether in the specification, claims, or drawings, should be understood to contemplate the possibility of including one of such items, any of such items, or both of such items. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."

[0059] As can be seen from the above, it can be understood that various embodiments of the present invention have been described herein for illustrative purposes, and various modifications can be made without departing from the scope and spirit of the present invention. Therefore, the various embodiments disclosed herein are not meant to be restrictive, and the true scope and spirit are determined by the appended claims.

Claims

1. A positioning model method using relative time input, characterized in that: include: Measuring, by a processor of the device, a first channel delay profile having a first path timing based on a first reference signal associated with a first network node; adjusting, by the processor, the first path timing based on a time difference associated with a reference network node; as well as The processor generates a model output based on the first channel delay distribution with the adjusted first path timing as a model input, or the processor reports the first channel delay distribution with the adjusted first path timing to a network.

2. The positioning model method using relative time input as claimed in claim 1, characterized in that: Further including: Measuring, by the processor, a reference channel delay profile having a reference path timing based on a reference signal associated with the reference network node; as well as The time difference is determined, by the processor, as a difference between the reference path timing and a receive boundary.

3. The positioning model method using relative time input as claimed in claim 1, characterized in that: The step of adjusting the first path timing by the time difference associated with the reference network node further comprises: The first path timing is shifted, by the processor, by the time difference associated with the reference network node.

4. The positioning model method using relative time input as claimed in claim 1, characterized in that: The step of adjusting the first path timing by the time difference associated with the reference network node further comprises: The first path timing is cyclically shifted, by the processor, within a time interval by the time difference associated with the reference network node.

5. The positioning model method using relative time input as claimed in claim 1, characterized in that: Further including: The processor generates the positioning model having multiple pairs of training model inputs and training model outputs according to a machine learning scheme, wherein each training model input includes a training channel delay distribution having at least one training path timing adjusted by a training time difference, and each training model output includes a training position.

6. The positioning model method using relative time input as claimed in claim 1, characterized in that: The step of measuring the first channel delay profile having the first path timing according to the first reference signal associated with the first network node further comprises: Measuring, by the processor, the first channel delay profile having the first path timing and the second path timing associated with the first network node based on the first reference signal associated with the first network node; The step of adjusting the first path timing by the time difference associated with the reference network node further includes: adjusting, by the processor, the first path timing and the second path timing using the time difference associated with the reference network node; Wherein, based on the first channel delay distribution with the adjusted first path timing as a model input, the step of generating the model output by the positioning model further comprises: generating, by the processor, the model output from the positioning model based on the first channel delay profile with the adjusted first path timing and the adjusted second path timing as a model input; Wherein, in order to determine the model output generated by the positioning model, the step of reporting the first channel delay distribution having the adjusted first path timing to the network further comprises: The first channel delay profile having the adjusted first path timing and the adjusted second path timing is reported by the processor to the network to determine the model output generated by the positioning model.

7. The positioning model method using relative time input as claimed in claim 1, characterized in that: Further including: measuring, by the processor, a second channel delay profile having a first path timing associated with a second network node based on a second reference signal associated with the second network node; adjusting, by the processor, the first path timing associated with the second network node based on the time difference associated with the reference network node; Wherein, based on the first channel delay distribution with the adjusted first path timing as a model input, the step of generating the model output by the positioning model further comprises: generating, by the processor, the model output by the positioning model based on the first channel delay profile having the adjusted first path timing associated with the first network node and the second channel delay profile having the adjusted first path timing associated with the second network node; Wherein, in order to determine the model output generated by the positioning model, the step of reporting the first channel delay distribution and the adjusted first path timing to the network further includes: The processor reports, to a network, the first channel delay profile having the adjusted first path timing associated with the first network node and the second channel delay profile having the adjusted first path timing associated with the second network node to determine the model output generated by the positioning model.

8. The positioning model method using relative time input as claimed in claim 1, characterized in that: The first channel delay profile includes at least one of a channel impulse response, a power delay profile, and a delay profile.

9. The positioning model method using relative time input as claimed in claim 1, characterized in that: The first reference signal includes a positioning reference signal.

10. The positioning model method using relative time input as claimed in claim 1, characterized in that: The apparatus comprises a positioning reference unit.

11. A positioning model apparatus using relative time input, comprising: a transceiver for wirelessly communicating with at least one network node during operation; as well as A processor is communicatively coupled to the transceiver such that during operation, the processor performs operations including: measuring, via the transceiver, a first channel delay profile having a first path timing based on a first reference signal associated with a first network node; adjusting the first path timing based on a time difference associated with a reference network node; and Based on the first channel delay profile with the adjusted first path timing as a model input, a model output is generated, or the first channel delay profile with the adjusted first path timing is reported by the processor to a network.

12. The positioning model device using relative time input as claimed in claim 11, characterized in that: During operation, the processor further performs operations including: measuring, via the transceiver, a reference channel delay profile having a reference path timing based on a reference signal associated with the reference network node; and The time difference is determined as the difference between the reference path timing and the receive boundary.

13. The positioning model device using relative time input as claimed in claim 11, characterized in that: The operation of adjusting the first path timing by the time difference associated with the reference network node further includes: The first path timing is shifted, by the processor, by the time difference associated with the reference network node.

14. The positioning model device using relative time input as claimed in claim 11, characterized in that: The operation of adjusting the first path timing by the time difference associated with the reference network node further includes: The first path timing is cyclically shifted, by the processor, within a time interval by the time difference associated with the reference network node.

15. The positioning model device using relative time input as claimed in claim 11, characterized in that: During operation, the processor further performs operations including: The processor generates the positioning model having multiple pairs of training model inputs and training model outputs according to a machine learning scheme, wherein each training model input includes a training channel delay distribution having at least one training path timing adjusted by a training time difference, and each training model output includes a training position.

16. The positioning model device using relative time input as claimed in claim 11, characterized in that: The operation of measuring the first channel delay profile having the first path timing according to the first reference signal associated with the first network node further comprises: measuring, via the transceiver, the first channel delay profile having the first path timing and the second path timing associated with the first network node based on the first reference signal associated with the first network node; The operation of adjusting the first path timing by the time difference associated with the reference network node further includes: adjusting the first path timing and the second path timing by the time difference associated with the reference network node; Wherein, based on the first channel delay distribution with the adjusted first path timing as a model input, the operation of generating the model output by the positioning model further includes: generating the model output by the positioning model based on the first channel delay profile with the adjusted first path timing and the adjusted second path timing as a model input; Wherein, to determine the model output generated by the positioning model, the operation of reporting the first channel delay distribution having the adjusted first path timing to the network further includes: The first channel delay profile having the adjusted first path timing and the adjusted second path timing is reported to the network via the transceiver to determine the model output generated by the positioning model.

17. The positioning model device using relative time input according to claim 11, characterized in that: During operation, the processor further performs operations including: measuring, via the transceiver, a second channel delay profile having a first path timing associated with a second network node based on a second reference signal associated with the second network node; adjusting the first path timing associated with the second network node based on the time difference associated with the reference network node; Wherein, based on the first channel delay distribution with the adjusted first path timing as a model input, the operation of generating the model output by the positioning model further includes: generating, by the positioning model, the model output based on the first channel delay profile having the adjusted first path timing associated with the first network node and the second channel delay profile having the adjusted first path timing associated with the second network node; Wherein, in order to determine the model output generated by the positioning model, the operation of reporting the first channel delay distribution and the adjusted first path timing to the network further includes: The first channel delay profile having the adjusted first path timing associated with the first network node and the second channel delay profile having the adjusted first path timing associated with the second network node are reported to a network via the transceiver to determine the model output generated by the positioning model.

18. The positioning model device using relative time input as claimed in claim 11, characterized in that: The first channel delay profile includes at least one of a channel impulse response, a power delay profile, and a delay profile.

19. The positioning model device using relative time input as claimed in claim 11, characterized in that: The first reference signal includes a positioning reference signal.

20. The positioning model device using relative time input according to claim 11, characterized in that: The apparatus comprises a positioning reference unit.

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