System, method, device, and program for achieving positioning accuracy through inter-device cooperation

Abstract

A method for adaptive positioning accuracy of a device in a telecommunications network may be provided. The method may be executed by one or more processors. The method may include transmitting, by a network element of the telecommunications network, a positioning trigger signal and a positioning reference signal to a user device, transmitting, by a user premises equipment for the user device, a sidelink reference signal to the user device, and receiving, by the network element, a ranging result report. The ranging result report includes distance and timing information calculated by the user device and related to the signal received by the user device. The method may further include transmitting, by the network element, a location of the user device to a core network element of the telecommunications network. The location of the user device is based on the machine-learned location inference model and the ranging result report.

Description

[Technical field]

[0001] The present disclosure relates to estimating the position of one or more user devices or automated guided vehicles in a telecommunications and / or wireless network. In particular, the present disclosure relates to methods, apparatus, and systems for estimating the position of one or more user devices or automated guided vehicles in a telecommunications and / or wireless network. [Background technology]

[0002] In telecommunication networks, accurate measurement of device location is very important for continuous and efficient provision of network services. Determining the location of user devices, autonomous guided vehicles, or portable network devices frequently uses Line Of Sight (LOS) based signaling methods. Such methods may be satisfactory in outdoor areas where LOS signals can be easily transmitted or received. However, in areas where it may be more difficult to transmit LOS signals, such as indoor areas or outdoor areas with obstructions, methods using LOS signaling may be very error prone.

[0003] Therefore, there is a need for a method for improving the positioning accuracy of a user device or an automated guided vehicle that may not be within line of sight of a telecommunications network.

[0004] Telecommunications networks are increasingly relying on cloud computing and artificial intelligence methods to handle the expansion of services, customers, and areas of operations. However, there has been no significant research examining how artificial intelligence methods may be utilized to improve the accuracy of user device location, especially in areas where line of sight is difficult to find. Summary of the Invention

[0005] According to an embodiment, a method for adaptive positioning accuracy of a device in a telecommunications network may be provided. The method may be executed by one or more processors. The method may include transmitting a positioning trigger signal and a positioning reference signal to a user device by a network element of the telecommunications network, transmitting a sidelink reference signal to the user device by a user premises equipment for the user device, and receiving a ranging result report by the network element. The ranging result report is calculated by the user device. The ranging result report includes distance and timing information related to signals received by the user device. The method may further include transmitting, by the network element, a location of the user device to a core network element of the telecommunications network. The location of the user device is based on the machine-learned location inference model and the ranging result report.

[0006] According to an embodiment, an apparatus for adaptive positioning accuracy of a device of a telecommunications network may be provided. The apparatus may include at least one memory configured to store computer program code and at least one processor configured to access the computer program code and operate as instructed by the computer program code. The program may include a first transmitting code configured to cause a first processor of the at least one processor to transmit a positioning trigger signal and a positioning reference signal. The first processor is part of a network element. The program may further include a second transmitting code configured to cause a second processor of the at least one processor to transmit a sidelink reference signal to a user device. The second processor is part of a user premises equipment. The program may further include a first receiving code configured to cause the first processor to receive a ranging result report. The ranging result report calculated by the user device includes distance and timing information associated with the signal received by the user device. The program may further include a third transmitting code configured to cause the first processor to transmit a location of the user device. The location of the usage device is based on the machine-learned location inference model and the ranging result report.

[0007] According to an embodiment, a non-transitory computer-readable medium may be provided. The non-transitory computer-readable medium may store a program that causes a computer to execute a process. The process may include transmitting, by a network element of a telecommunications network, a positioning trigger signal and a positioning reference signal to a user device, transmitting, by a user premises equipment for the user device, a sidelink reference signal to the user device, and receiving, by the network element, a ranging result report. The ranging result report is calculated by the user device. The ranging result report includes distance and timing information related to the signal received by the user device. The process may further include transmitting, by the network element, a location of the user device to a core network element of the telecommunications network. The location of the user device is based on the machine-learned location inference model and the ranging result report.

[0008] The features, advantages, and significance of exemplary embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which like reference numerals indicate like elements, and in which: [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is an example diagram of a network architecture in which the systems and / or methods described in this disclosure may be implemented.

[0010] [Diagram 2] 2 is an example diagram of components of the network architecture of FIG. 1 in accordance with an embodiment of the present disclosure.

[0011] [Diagram 3] 2 is an example diagram of components of the network architecture of FIG. 1 in accordance with an embodiment of the present disclosure.

[0012] [Figure 4]FIG. 1 is an example diagram of a network architecture in which the systems and / or methods described in this disclosure may be implemented.

[0013] [Diagram 5] FIG. 2 is an example workflow diagram illustrating an example process for determining a location of a user device in a telecommunications network, according to an embodiment of the present disclosure.

[0014] [Figure 6] FIG. 1 is an example diagram of a network architecture in which the systems and / or methods described in this disclosure may be implemented.

[0015] [Figure 7] FIG. 2 is an example workflow diagram illustrating an example process for determining a location of a user device in a telecommunications network, according to an embodiment of the present disclosure.

[0016] [Figure 8] FIG. 1 is an example diagram of a network architecture in which the systems and / or methods described in this disclosure may be implemented.

[0017] [Figure 9] FIG. 2 is an example workflow diagram illustrating an example process for determining a location of a user device in a telecommunications network, according to an embodiment of the present disclosure.

[0018] [Figure 10] 1 is an example flowchart illustrating an example process for determining a location of a user device in a telecommunications network, according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] The following detailed description of the exemplary embodiments refers to the accompanying drawings, in which the same reference numbers in different drawings may refer to the same or similar elements.

[0020] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practicing the implementations.

[0021] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement the systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0022] As is conventional in the art, the embodiments may be described and illustrated with blocks performing one or more described functions. These blocks, sometimes referred to herein as units or modules, may be physically implemented by analog or digital circuits, such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, etc., and may be driven by firmware and software. Optionally, they may be driven by firmware. The circuits may be embodied, for example, in one or more semiconductor chips or on a substrate support, such as a printed circuit board. The circuits included in the blocks may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuits), or by a combination of dedicated hardware for performing some functions of the block and a processor for performing other functions of the block. Each block of the embodiments may be physically separated into two or more interacting individual blocks. Similarly, the blocks of the embodiments may be physically combined into more complex blocks.

[0023] Although particular combinations of features are recited in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. Indeed, many of these features may be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim listed below may depend directly on only one claim, in the disclosure of possible implementations, each dependent claim includes all other claims recited in the claims.

[0024] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described. Additionally, the articles "a" and "an" as used herein are intended to include one or more items and may be used interchangeably with "one or more." When only one item is intended, the term "one" or similar language is used. Additionally, the terms "has," "have," "having," "include," "including," and the like, as used herein, are intended to be open-ended terms. Additionally, the phrase "based on" is intended to mean "based at least in part on," unless specifically noted otherwise.

[0025] As mentioned above, accurate positioning of user devices and network devices is important for providing continuous and efficient services to users of telecommunication networks. Determining the location of devices that are not necessarily in the line of sight (LOS) of the telecommunication network, e.g., devices located in indoor areas or in areas with obstructions, is highly error prone due to lack of LOS signaling. Position Reference Signals (PRS) may be primarily used by network elements (e.g., gNodeB, eNodeB, centralized units, distributed units) to determine the location of user devices. PRSs move in straight lines and may be redirected or reoriented when bouncing off surfaces. This reorientation often makes the relative time and distance measurements of PRSs inaccurate. The inaccuracies result in service disruptions to some customers, reducing customer satisfaction and increasing inefficiencies in the telecommunication network. Thus, a method or system is needed that can be used in either LOS or No Line Of Sight (NLOS) conditions.

[0026] The efficiency of telecommunications networks has been greatly improved by analyzing various aspects of the telecommunications networks using artificial intelligence methods. However, such artificial intelligence techniques have not yet been utilized for real-time positioning of user equipment in telecommunications networks. Thus, there may be a need for methods and systems, particularly those that utilize artificial intelligence, in the context of determining the location of network devices and / or user equipment in a communication system.

[0027] Embodiments of the present disclosure relate to an adaptive method of determining the real-time location of a user equipment, especially in areas where there may be many obstacles that make the use of PRS difficult. As mentioned above, PRS is a position reference signal that supports downlink / uplink positioning. According to embodiments, the location of a user equipment may be determined using a combination of one or more PRS from one or more network elements and Sidelink Reference Signals (SLRS) from one or more network devices whose locations are known.

[0028] The one or more network devices with known locations may include local network devices that provide network services. As an example, the network devices may include user premises equipment or fixed wireless access equipment that are mostly fixed in locations close to the user. In areas where it is difficult to use PRS because the network elements are not in the LOS of the user device, the fixed location network devices with known locations may be used to determine the location of the user equipment. The relative location of the user equipment from the fixed location network devices may be used to determine the relative location of the user equipment to the fixed location network devices. This relative location of the user equipment may then be used to find the location of the user equipment relative to the network elements (e.g., gNodeBs) by adjusting the relative location of the network devices to the network elements. The location of the user equipment may then be used to provide continuous and efficient service to the user equipment even when the user device is not in the LOS of the network elements.

[0029] According to some embodiments of the present disclosure, an adaptive method of determining a location of a user device can include using a machine-learned customizable location inference model. The location inference model can use multiple artificial intelligence methods to determine the precise location of the user device. The location inference model may be trained at a core network element or a central processor of the telecommunications network. The machine-learned location inference model can be deployed at a regional processor (e.g., a network element) or at the user equipment itself to infer the location of the user equipment. Transferring the location inference model to the network element or user equipment can reduce transmission delays and overheads when determining the location of the user equipment in the telecommunications network, and increase the overall efficiency of the process. Transferring the location inference model for inference only to the network element or user equipment can also allow for detailed customization of the local inference model of the location inference model while maintaining the privacy of the user. In essence, having a local inference model of the machine-learned location inference model at the network element or user equipment can result in more accurate and secure positioning of the user equipment by taking into account the specific patterns of the local network.

[0030] Embodiments of the present disclosure can enable real-time updates of the position and / or location of user equipment or network devices, such as Automated Guided Vehicles (AGVs) or Internet Of Things (IoT) enabled devices, efficiently without taxing the resources of a telecommunications network.

[0031] FIG. 1 is an example diagram of a network architecture 100 in which the systems and / or methods described in this disclosure may be implemented.

[0032] As shown in FIG. 1, the telecommunications network 100 may include a network element 101, one or more obstacles 105 (e.g., 105-1, 105-2, and 105-3), one or more user premise equipment 104 (e.g., 104-1 and 104-2), one or more user equipment, such as a user device 103, and an AGV 102. As shown in FIG. 1, the telecommunications network 100 may be located in an environment that includes multiple obstacles (obstacles 105). The obstacles may make direct LOS communication or positioning between the equipment (user device 103 or AGV 102) and the network element 101 difficult.

[0033] According to an embodiment of the present disclosure, the network element 101 can determine the position and / or location of the user equipment in order to continuously and efficiently provide network services in a telecommunications network. The network element can include any facility or equipment used to provide network services in a telecommunications network. As an example, the network element 101 can include cell towers, cell sites, base stations (e.g., gNodeB, eNodeB, central or distributed units, E-UTRA cells, etc.). In some embodiments, the network element 101 can periodically or adaptively update the user equipment location when a real-time location of the user equipment may be required. As an example, when media may be streamed or formed to the user device 103, knowing the location of the user equipment can improve the efficiency of media exchange between the network element 101 and the user device 103.

[0034] To determine or update the location of the user equipment, the network element may use a LOS signaling method. As an example, the network element may use the PRS to determine or update the location of the user equipment. Any known protocol may be used to define and configure the PRS. As an example, the New Radio Positioning Protocol A (NRPPa) may be used to define and configure the PRS. In some embodiments, the definition or configuration of the PRS may include a positioning frequency layer, a resource set, and a sequence ID. In some embodiments, the definition or configuration of the PRS may include a PRS positioning frequency layer that may include a set of PRS resource sets, each PRS resource set defining a set of PRS resources. In some embodiments, the definition or configuration of the PRS may include slot configuration parameters, and a carrier grid may be plotted at the slot level to highlight the slots in which the PRS resource sets reside. A slot may refer to a particular space in a radio frame, and the slot configuration parameters may include a higher layer sequence ID that may be mapped to a particular slot. According to some embodiments, the PRS may be used to transmit or determine timing information associated with the PRS being received or transmitted by a user device using an Observed Time Difference Of Arrival (OTDOA) parameter of the signal.

[0035] The environment of the user equipment may include multiple obstacles that may block a direct path between the network element and the user equipment. The obstacles may include man-made obstacles such as walls, buildings, or natural obstacles such as trees, mountains, hills, etc.

[0036] As mentioned above, a network element can use a PRS to determine the location of a user device. However, a PRS requires a direct LOS, and the presence of an obstacle that blocks the LOS between a network element and a user device may cause errors when determining the location of a user device. As an example, in FIG. 1, a user device 103 may not be within the LOS of a network element 101 due to an obstacle 105-1. Thus, when a network element broadcasts a PRS, the signal may bounce back significantly after colliding with the obstacle 105-1 or be diverted after colliding with the obstacle 105-3. Even if the PRS after colliding with the obstacle 105-3 reaches and communicates with the user device 103, the distance and directivity calculated based on the path of the PRS may be incorrect.

[0037] A telecommunications network may include one or more user premises equipment. User premises equipment may include any equipment that provides services related to a telecommunications network, such as a telephone, a modem, a router, a wireless access point device, an adapter for a network device, a Local Area Network (LAN), a Wide Area Network (WAN) device, etc. As an example, in telecommunications network 100, user premises equipment 104 (e.g., user premises equipment 104-1 or 104-2) may be a router, a modem, or a fixed wireless access device.

[0038] According to embodiments of the present disclosure, the user premises equipment may be located at either a fixed position or a position and / or location known to the telecommunications network. The user premises equipment may communicate with other network devices or user equipment using a Sidelink Reference Signal (SLRS) without going through a network element. As an example, the first user premises equipment 104-1 and the second user premises equipment 104-2 may communicate with each other, the user device 103 and the AGV 102 directly using the SLRS without going through the network element 101. The SLRS may be used to measure the distance between the user equipment and the user premises equipment based on Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), and / or beam information. In some embodiments, the user equipment may generate a ranging result report based on the SLRS. The SLRS may be defined and configured using any suitable or known protocol. In some embodiments, the user premises equipment can report the distance and / or directivity between the user premises equipment and the user equipment to a network element or a core network element. As described above, a combination of SLRS and PRS can be used to determine the location of the user equipment. To combine information from both PRS and SLRS when determining the location of the user equipment, the user premises equipment can correlate each PRS and each SLRS associated with the user equipment. In some embodiments, the ranging result report can be based on the SLRS and the associated / correlated PRS. As an example, the SLRS can be correlated with the PRS and the signals can be associated with the same location measurement time based, for example, on a first transaction ID associated with the PRS being the same as a second transaction ID associated with the SLRS.

[0039] According to some embodiments, the ranging result report may include one or more of a time lag between transmission and reception of the SLRS between the user equipment and the user premises equipment, location information associated with the user premises equipment, power information associated with the user equipment, power information associated with the user premises equipment, a time of arrival of the SLRS, or angle information associated with reception of the SLRS. In some embodiments, the ranging result report may include signal information associated with the PRS. The signal information may include at least one of sequence ID information associated with the PRS and a beam index received by the user equipment associated with the PRS.

[0040] A telecommunications network may include one or more user equipment. User equipment may be any user device that utilizes services provided by a telecommunications network. Examples of user equipment may include Industrial Internet Of Things (IIoT) devices (e.g., smart sensors, etc.), personal computing devices, user mobile devices, user Internet Of Things enabled devices, automated guided vehicles (e.g., drones, dedicated robots, autonomous vehicles, etc.). As an example, in telecommunications network 100, user equipment may include user device 103 and AGV 102. In some embodiments, user equipment may be controlled by a user of the telecommunications network to use network services or by an operator of the telecommunications network to maintain the telecommunications network.

[0041] In an exemplary embodiment, the first user premises equipment 104-1 or the second user premises equipment 104-2 can use the SLRS to determine the distance and / or directionality between the first user premises equipment 104-1 or the second user premises equipment 104-2 and the user device 103 and the AGV 102 based on the RSRP, RSSI, RSRQ, and / or beam information from the received SLRS.

[0042] According to an embodiment of the present disclosure, a core network element of a telecommunication may be used to determine the location of a user equipment in a LOS or NLOS setting. The core network element may include a central processing unit responsible for performing critical functions in a telecommunication network. The critical functions may include maintaining subscriber information, call switching, service authorization, and / or location services. In some embodiments, the core network element may trigger a process to determine the location of the user equipment. The network element may transmit a PRS to the user equipment upon receiving a positioning trigger signal from the core network element. Generally, since the core network element performs critical functions, it has robust computing capabilities (more storage, faster processors, more data, etc.) compared to the network element. Thus, in some embodiments, the core network element may be used by a location management function to train and / or deploy models to infer the location of the user equipment. The location management function may use the core network element to train a location inference model with data from the entire communication network to leverage the robust computing resources of the core network element.

[0043] In some embodiments, a telecommunications network may include a Location Management Function (LMF) that may train and deploy a machine-learned location inference model that determines a location of a user equipment. The LMF may train the location inference model using data from across the telecommunications network. In some embodiments, the LMF may train the location inference model for adaptive indoor positioning of the user equipment. According to an embodiment of the present disclosure, training data for training the location inference model may include SLRS measurements, estimated location of the user equipment, location of user premises equipment, PRS, sounding reference signals, and timing information associated with signals received by the user equipment.

[0044] In some embodiments, the LMF may also deploy machine learning or trained location inference models to the core network elements. In some other embodiments, the LMF may generate an inference model based on the machine learning location inference model and send the generated inference to the sub-location management function at the network element or user equipment during the location determination. The location of the user device may be calculated carefully by the network element or user equipment based on the inference model and the ranging result report generated by the user equipment. Forwarding the machine learning location inference model to the sub-LMF for inference only reduces the overall transmission delay and transmission overhead while determining the location of the user equipment.

[0045] In an example embodiment, the trained or machine-learned location inference model can be used with the PRS and SLRS to determine the real-time position and / or location of the user device 103. The first user premises equipment 104-1 can report the distance between the user premises equipment 104-1 and the user device 103 along with other information to a core network element of the telecommunications network. The reported distance between the first user premises equipment 104-1 and the user device 103 can be correlated to a PRS transmitted by the network element to the user device 103. The position or location of the user device can be determined based on the reported distance between the first user premises equipment 104-1 and the user device 103 and information related to the correlated PRS.

[0046] 2 is a diagram of an example environment 200 in which the methods and systems described herein may be implemented. As shown in FIG. 2, environment 200 may include a device 210, a platform 220, and a network 230. The devices of environment 200 may be interconnected by wired connections, wireless connections, or a combination of wired and wireless connections. In an embodiment, any of the functions of the elements included in telecommunications network 100 may be performed by any combination of elements shown in FIG. 2.

[0047] The devices 210 include one or more devices that can receive, generate, store, process, and / or provide information related to the platform 220. For example, the devices 210 may include computing devices (e.g., desktop computers, laptop computers, tablet computers, handheld computers, smart speakers, servers, etc.), mobile phones (e.g., smartphones, wireless telephones, etc.), wearable devices (e.g., smart glasses or smart watches), or similar devices. In some implementations, the devices 210 can receive information from and / or transmit information to the platform 220. In some embodiments, the devices 210 can include a network element 101, a user premises equipment 104, a user device 103, or an AGV 102.

[0048] Platform 220 includes one or more devices capable of providing network services, as described elsewhere herein. In some implementations, platform 220 may include a cloud server or a collection of cloud servers. In some implementations, platform 220 may be designed to be modular so that specific software components can be swapped out depending on specific needs. Thus, platform 220 can be easily and / or quickly reconfigured for different uses.

[0049] In some implementations, as shown, platform 220 may be hosted in a cloud computing environment 222. Notably, although the implementations described herein describe platform 220 as being hosted in a cloud computing environment 222, in some implementations platform 220 may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

[0050] Cloud computing environment 222 includes an environment that hosts platform 220. Cloud computing environment 222 may provide services such as computation, software, data access, storage, etc. that do not require end-user (e.g., device 210) knowledge of the physical location and configuration of the systems and / or devices that host platform 220. As shown, cloud computing environment 222 may include a collection of computing resources 224 (which may be referred to collectively or individually as “computing resources 224”).

[0051] The computing resources 224 include one or more personal computers, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, the computing resources 224 can host the platform 220. Cloud resources may include computational instances executing within the computing resources 224, storage devices provided within the computing resources 224, data transfer devices provided by the computing resources 224, etc. In some implementations, the computing resources 224 may communicate with other computing resources 224 via wired connections, wireless connections, or a combination of wired and wireless connections.

[0052] As further illustrated in FIG. 2, the computing resources 224 include a group of cloud resources, such as one or more Applications ("APPs") 224-1, one or more Virtual Machines ("VMs") 224-2, Virtualized Storage ("VSs") 224-3, one or more Hypervisors ("HYPs") 224-4, etc. The Applications 224-1 include one or more software applications that may be provided or accessed by the device 210. The Applications 224-1 may eliminate the need to install or run software applications on the device 210. For example, the Applications 224-1 may include software related to the platform 220 and / or any other software that may be provided through the cloud computing environment 222. In some implementations, one Application 224-1 may send or receive information to one or more other Applications 224-1 via the Virtual Machine 224-2.

[0053] The virtual machine 224-2 includes a software-implemented machine (e.g., a computer) that executes programs like a physical machine. The virtual machine 224-2 can be either a system virtual machine or a process virtual machine, depending on the application and the degree to which the virtual machine 224-2 matches an actual machine. A system virtual machine can provide a complete system platform that supports the execution of a complete Operating System ("OS"). A process virtual machine can execute a single program and support a single process. In some implementations, the virtual machine 224-2 can run on behalf of a user (e.g., the device 210) and manage the infrastructure of the cloud computing environment 222, such as data management, synchronization, or long-term data transfer.

[0054] Virtualized storage 224-3 includes one or more storage systems and / or one or more devices that use virtualization technology within the storage systems or devices of computing resources 224. In some implementations, in the case of a storage system, the types of virtualization may include block virtualization and file virtualization. Block virtualization may refer to the abstraction (or separation) of logical storage from physical storage such that the storage system can be accessed regardless of the physical storage or heterogeneous structure. The separation may allow administrators flexibility in the storage system as to how they manage the storage for end users. File virtualization may eliminate the dependency between data accessed at the file level and where the file is physically stored. This may enable optimization of storage usage, server consolidation, and / or performing non-disruptive file migrations.

[0055] Hypervisor 224-4 may provide hardware virtualization technology that allows multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer, such as computing resource 224. Hypervisor 224-4 may present a virtual operating platform to the guest operating systems and may manage the execution of the guest operating systems. Multiple instances of different operating systems may share virtualized hardware resources.

[0056] Network 230 may include one or more wired and / or wireless networks. For example, network 230 may include a cellular network (e.g., a fifth generation (5G) network, a Long-Term Evolution (LTE) network, a third generation (3G) network, a Code Division Multiple Access (CDMA) network, etc.), a Public Land Mobile Network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a private network, an ad-hoc network, an intranet, the Internet, an optical fiber-based network, etc., and / or a combination of these or other types of networks.

[0057] The number and arrangement of devices and networks shown in Figure 2 are provided as an example. In practice, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or devices and / or networks in a different arrangement than those shown in Figure 2. Furthermore, two or more devices shown in Figure 2 may be implemented within a single device, or a single device shown in Figure 2 may be implemented as multiple distributed devices. Additionally or alternatively, a set of devices (e.g., one or more devices) of environment 200 may perform one or more functions described as being performed by another set of devices of environment 200.

[0058] 3 is a diagram of example components of a device 300. The device 300 may correspond to the device 210 and / or the platform 220. As shown in FIG. 3, the device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340, an input component 350, an output component 360, and a communication interface 370.

[0059] The bus 310 includes components that enable communication between the components of the device 300. The processor 320 is implemented in hardware, firmware, or a combination of hardware and software. The processor 320 is a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Accelerated Processing Unit (APU), a microprocessor, a microcontroller, a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), or another type of processing component. In some implementations, the processor 320 includes one or more processors that can be programmed to perform functions. The memory 330 includes a Random Access Memory (RAM), a Read Only Memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions for use by the processor 320.

[0060] The storage component 340 stores information and / or software related to the operation and use of the device 300. For example, the storage component 340 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or other types of non-transitory computer-readable storage media along with a corresponding drive. The input component 350 includes components that enable the device 300 to receive information via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone), and the like. Additionally or alternatively, the input component 350 may include sensors for sensing information (e.g., a Global Positioning System (GPS) component, an accelerometer, a gyroscope, and / or an actuator). Output components 360 include components that provide output information from device 300 (eg, a display, a speaker, and / or one or more Light-Emitting Diodes (LEDs)).

[0061] The communications interface 370 includes transceiver-like components (e.g., a transceiver and / or a separate receiver and transmitter) that enable the device 300 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communications interface 370 may enable the device 300 to receive information from another device and / or provide information to another device. For example, the communications interface 370 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a Radio Frequency (RF) interface, a Universal Serial Bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0062] The device 300 may perform one or more processes described herein. The device 300 may perform these processes in response to the processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as the memory 330 and / or the storage component 340. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or across multiple physical storage devices.

[0063] Software instructions may be loaded into memory 330 and / or storage component 340 from another computer-readable medium or from another device via communications interface 370. The software instructions stored in memory 330 and / or storage component 340, when executed, may cause processor 320 to perform one or more of the processes described herein.

[0064] Additionally, or instead, hardwired circuitry may be used in place of, or in combination with, software instructions to perform one or more of the processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0065] The number and arrangement of components shown in Figure 3 are provided as an example. In practice, device 300 may include additional, fewer, different, or differently arranged components than those shown in Figure 3. Additionally or alternatively, a set of components (e.g., one or more components) of device 300 may perform one or more functions described as being performed by another set of components of device 300.

[0066] In an embodiment, any one of the modules or components of FIG. 1 may be implemented by or using any one of the elements shown in FIGS.

[0067] 4, 6 and 8 are exemplary diagrams of network architectures for adaptive positioning accuracy of user equipment in telecommunications networks.

[0068] As shown in FIG. 4 , the telecommunications network 400 may include a network element 101 , a core network element 405 , a regional network element (Regional NE) 401 , user premises equipment 104 , and a user device 103 .

[0069] In an embodiment, the LMF may be included in a core network element. The LMF may be configured to train a location inference model and deploy the trained location inference model to the core network element. The core network element may use the deployed machine learning location inference model to determine / calculate a location of the user equipment. In some embodiments, the core network element may use ranging result reports generated by the user equipment to determine / calculate a location of the user equipment.

[0070] As an example, the LMF may be located in the core network element 405, and the LMF may be configured to train and deploy the trained location reasoning model at the core network element 405. The machine-learned location reasoning model may be deployed to the core network element 405, such that the core network element 405 can determine the location of the user device 103 based on the machine-learned location reasoning model. In some embodiments, the core network element 405 may determine the location of the user device 103 based on a ranging result report generated by the user device 103, which the core network element 405 may receive from the user device 103.

[0071] In the case where the LMF may be located in a core network element, and the core network element calculates the location of the user equipment, an example workflow for determining the location of the user equipment is shown in FIG.

[0072] As seen in FIG. 5, workflow process 500 may illustrate one or more operations for determining the location of a user equipment when the LMF may be located in a core network element and the core network element calculates the location of the user equipment.

[0073] At operation 505, the network element may transmit a positioning trigger signal (PTS) to the user equipment. In some embodiments, at operation 505, the network element may transmit the positioning trigger signal based on or subsequent to receipt of an initial trigger signal from a core network element. The positioning trigger signal may be a broadcast signal indicating the start of process 500. In some embodiments, the positioning trigger signal may be the same as the initial trigger signal.

[0074] As an example, the network element 101 can transmit a positioning trigger signal to the user device 103. In some embodiments, the network element 101 can transmit the positioning trigger signal to the user device 103 based on or subsequent to receiving an initial trigger signal from the core network element 405.

[0075] In operation 510, the network element may transmit a Positioning Reference Signal (PRS) to the user equipment. In some embodiments, the PRS may be transmitted in the same operation as the PTS. According to embodiments of the present disclosure, the PRS may include information regarding the PRS resource and resource set, sequence ID information associated with the PRS, or a beam index received by the user device associated with the PRS (the user device to which the PRS may be transmitted). In some embodiments, the PRS may also include a transaction ID, which is associated with the position measurement time or user device whose position is being determined and / or calculated. As mentioned above, embodiments of the present disclosure relate to determining the position of a user equipment / user device utilizing a combination of a PRS and a SLRS. Correlation between PRS and SLRS associated with the same device may be required to ensure that signals associated with the same user equipment / user device are being used to determine the position.

[0076] As an example, the network element 101 may transmit a PRS to the user device 103. In some embodiments, the PRS may include a first transaction ID that may be used to correlate the PRS and the SLRS. In an embodiment, the first transaction ID may be associated with the time (hour) at which the location measurement was triggered. In some other embodiments, the first transaction ID may be associated with the user device 103 whose location is being determined.

[0077] In operation 515, the user premises equipment may transmit a Sidelink Reference Signal (SLRS) to the user equipment whose position is being determined. Correlation between the PRS and the SLRS, or correlated positioning reference signal information, may be required or signaled to ensure that signals associated with the same user equipment / user device are being used to determine the position of the user equipment / user device. In some embodiments, the SLRS may also include a transaction ID, which is associated with the position measurement time or user device whose position is being determined and / or calculated. In some other embodiments, the SLRS may include correlated PRS information, which may include information related to the PRS transmitted by the network element to the user equipment.

[0078] As an example, the user premises equipment 104 may transmit an SLRS to the user device 103. In some embodiments, the SLRS may include a second transaction ID that may be used to correlate the PRS and the SLRS. In an embodiment, the second transaction ID may be associated with the time (hour) at which the location measurement was triggered. In some other embodiments, the second transaction ID may be associated with the user device 103 whose location is being determined.

[0079] In operation 520, the user equipment may generate a ranging result report based on the SLRS. In some embodiments, the ranging result report may include one or more distance calculations between the user equipment and the user premises equipment based on the SLRS. The ranging result report may include correlated positioning reference signal information, where the correlated positioning reference signal information may include information associated with a positioning reference signal having a first transaction ID that is the same as a second transaction ID associated with the sidelink reference signal. In some embodiments, the first transaction ID and the second transaction ID are associated with the same position measurement time or the same user device. In some embodiments, the ranging result report may include Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), and / or beam information associated with the received SLRS. The RSRP, RSSI, RSRQ, and / or beam index may be used to measure the distance or directionality between the user equipment and the user premises equipment.

[0080] In operation 525, the user equipment may transmit the generated ranging result report to a core network element. As an example, the user device 103 may transmit the ranging result report to the core network element 405.

[0081] In operation 530, the core network element may determine and / or calculate a position of the user equipment based on the ranging result report and the machine learning position inference model. According to some embodiments of the present disclosure, a Location Management Function (LMF) may be located in the core network element and may be configured to train and deploy the trained position inference model. The machine learning position inference model may be deployed in the core network element so that the core network element can use it to determine and / or calculate the position of the user equipment. The determined and / or calculated position of the user equipment may be an initial position of the user equipment or an updated and / or revised position of the user equipment. In some embodiments, the position and / or location of the user equipment determined by the core network element may be an absolute position (e.g., geographic coordinates) of the user equipment or may be a relative position of the user equipment with respect to the core network element or the nearest network element.

[0082] As an example, the core network element 405 can determine and / or calculate the location of the user device 103 based on ranging result reports received from the user device 103 and a machine-learned location inference model trained and deployed by the LMF at the core network element 405.

[0083] In operation 535, the core network element and / or the network element may transmit the determined location of the user equipment throughout the telecommunications network, as appropriate.

[0084] Referring now to FIG. 6, as shown in FIG. 6, a telecommunications network 600 may include a network element 101, a core network element 405, a regional network element 401, a user premises equipment 104, and a user device 103.

[0085] In an embodiment, the LMF may be included in a core network element. However, the LMF may be configured only to train the location inference model based on data generated throughout the telecommunications network. Thus, the core network element may only include a model training stage of the location inference model. The core network may generate an inference model based on the machine-learned location inference model trained by the LMF. The core network element may transmit the inference model to lower layer nodes of the telecommunications network 600. Moving the generated inference model to a lower layer may reduce positioning latency and reduce network traffic load for collecting information for learning. In some embodiments, the network element may receive a ranging result report from the user equipment. The network element may use the inference model and the ranging result report to determine and / or calculate the location of the user equipment.

[0086] As an example, the core network element 405 can transmit the inference model to the sub-LMF at the network element 101, and the network element 101 can not train the transferred inference model but only apply it to determine the location of the user device 103. Since the inference model can be located at the sub-LMF of the network element 101, the network element 101 can determine the location of the user device 103 based on the transferred inference model. In some embodiments, the network element 101 can determine the location of the user device 103 based on a ranging result report generated by the user device 103, which the network element 101 can receive from the user device 103.

[0087] Although the LMF may be located in a core network element, in the case where the network element calculates the location of the user equipment, an exemplary workflow for determining the location of the user equipment is shown in FIG.

[0088] As seen in FIG. 7, workflow process 700 may illustrate one or more operations for determining the location of a user equipment where the LMF may be located in a core network element and the network element calculates the location of the user equipment.

[0089] Operations 705 to 720 of the workflow process 700 are similar to operations 505 to 520 of the workflow process 500 .

[0090] In operation 725, the user equipment may transmit the generated ranging result report to a network element. Thus, in contrast to operation 525, the ranging result report may be transmitted to the network element. As an example, the user device 103 may transmit the ranging result report to the network element 101.

[0091] In operation 730, the network element may determine and / or calculate a position of the user equipment based on the ranging result report and the forwarded inference model, where the forwarded inference model is based on the machine-learned position inference model. According to some embodiments of the present disclosure, a location management function (LMF) may be located in the core network element and may be configured to train the position inference model. However, the LMF may be configured to generate the inference model based on the machine-learned position inference model. The core network element may forward only the inference model to the network element. The inference model may be used to determine the position of the user equipment, and the inference model may be deployed to the network element, so that the network element can use it to determine and / or calculate the position of the user equipment. The determined and / or calculated position of the user equipment may be an initial position of the user equipment, or an updated and / or revised position of the user equipment. In some embodiments, the position and / or location of the user equipment determined by the network element may be an absolute position of the user equipment (e.g., actual geographic coordinates) or may be a relative position of the user equipment with respect to the core network element or the nearest network element.

[0092] As an example, the network element 101 can determine and / or calculate the location of the user device 103 based on a ranging result report received from the user device 103 and a forwarded inference model deployed in a sub-LMF included in the network element 101.

[0093] In operation 735, the network element may transmit the location of the user device determined by the network element to the core network element. The location of the user device may be based on the inference model and the ranging result report. In some embodiments, the network element may broadcast the location of the user device determined by the network element to the telecommunications network as needed.

[0094] Referring now to FIG. 8, as shown in FIG. 8, a telecommunications network 800 may include a network element 101, a core network element 405, a regional network element 401, a user premises equipment 104, and a user device 103.

[0095] In an embodiment, the LMF may be included in a core network element. However, the LMF may be configured only to train the location inference model based on data generated throughout the telecommunications network. Thus, the core network element may only include the model-training phase of the location inference model. The core network may generate an inference model based on the machine-learned location inference model trained by the LMF. The core network element may transmit the inference model to a lower layer node of the telecommunications network 600. Moving the generated inference model to a lower layer may reduce positioning latency and reduce network traffic load for collecting information for learning. As an example, the core network element may transmit the inference model to a user equipment whose location is being determined in the telecommunications network 600.

[0096] In some embodiments, the user equipment can generate a ranging result report, and the user equipment can calculate its own position using the ranging result report and the transferred inference model.

[0097] As an example, the core network element 405 can transmit the inference model to the sub-LMF of the user device 103, and the user device 103 can not train the transferred inference model and can only apply it to determine its own location. Since the inference model can be located in the sub-LMF of the user device 103, the user device 103 can determine its own location based on the transferred inference model. In some embodiments, the user device 103 can determine its own location based on its own (the user device's) ranging result report.

[0098] An example workflow 900 for determining the location of a user equipment is shown in FIG. 9, where the LMF may be located in a core network element, but the user equipment calculates the location of the user equipment.

[0099] As seen in FIG. 9, workflow process 900 may illustrate one or more operations for determining the location of a user equipment where the LMF may be located in a core network element and the user equipment calculates its own location.

[0100] Operations 905 - 915 of the workflow process 900 are similar to operations 505 - 515 of the workflow process 500 and operations 705 - 715 of the workflow process 700 .

[0101] In operation 920, the user equipment may generate a ranging result report based on the SLRS. In some embodiments, the ranging result report may include one or more distance calculations between the user equipment and the user premises equipment based on the SLRS. The ranging result report may include correlated positioning reference signal information, where the correlated positioning reference signal information may include information associated with a positioning reference signal having a first transaction ID that is the same as a second transaction ID associated with the sidelink reference signal. In some embodiments, the first transaction ID and the second transaction ID are associated with the same position measurement time or the same user device. In some embodiments, the ranging result report may include Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), and / or beam information associated with the received SLRS. The RSRP, RSSI, RSRO, and / or beam index may be used to measure the distance or directionality between the user equipment and the user premises equipment.

[0102] In operation 920, in some embodiments, the ranging result report generated by the user equipment may include a determination and / or calculation of a location of itself (user equipment) based on the ranging result report and the forwarded inference model, where the forwarded inference model is based on a machine-learned location inference model. According to some embodiments of the present disclosure, a Location Management Function (LMF) may be located in a core network element and may be configured to train the location inference model. However, the LMF may be configured to generate the inference model based on the machine-learned location inference model. The core network element may forward only the inference model to the user equipment. The inference model may be used to determine the location of the user equipment, and the inference model may be deployed to the user equipment, so that the user equipment uses the ranging result report and the inference model to determine the location of itself (user equipment).

[0103] The determined and / or calculated position of the user equipment may be an initial position of the user equipment or an updated and / or revised position of the user equipment. In some embodiments, the position and / or location of the user equipment determined by the user equipment may be an absolute position (e.g., geographic coordinates) of the user equipment or may be a relative position of the user equipment with respect to a core network element or a nearest network element.

[0104] As an example, the user device 103 can generate a ranging result report for itself (the user device) and can determine and / or calculate its own location based on the ranging result report that the user device 103 generated for itself and a transferred inference model based on the trained location inference model.

[0105] At operation 955, the user equipment may transmit its determined location across a telecommunications network, if desired. By way of example, the user device 103 may transmit its determined location across a telecommunications network, if desired.

[0106] FIG. 10 is an example flowchart illustrating an example process 1000 for determining a location of a user device in a telecommunications network, according to an embodiment of the present disclosure.

[0107] According to an embodiment, operation 1010 may include transmitting, by a network element of the telecommunications network, a positioning trigger signal and a positioning reference signal to the user device. In some embodiments, the positioning trigger signal may be transmitted by the network element upon receiving an initial trigger signal from a core network element of the telecommunications network.

[0108] According to an embodiment, operation 1015 may include transmitting, by the user premises equipment, a sidelink reference signal to the user device. Any known format or protocol may be used to generate and configure the sidelink reference signal.

[0109] According to an embodiment, operation 1020 may include receiving a ranging result report by a network element. The ranging result report may be calculated by the user device. The ranging result report may include distance and timing information related to signals received by the user device. In some embodiments, the ranging result report may be calculated by the user device and may include a calculation of a distance between the user device and the user premises equipment based on information of the sidelink reference signal and the correlated positioning reference signal.

[0110] The information of the correlated positioning reference signal may include information associated with a positioning reference signal having a first transaction ID that is the same as a second transaction ID associated with the sidelink reference signal. In some embodiments, the first transaction ID and the second transaction ID may be associated with a position measurement time or a user device. According to an embodiment, the ranging result report may include one or more of the following: a time lag between transmission and reception of the sidelink reference signal between the user device and the user premises equipment, position information related to the user premises equipment, power information related to the user device, power information related to the user premises equipment, a time of arrival of the sidelink reference signal, or angle information related to reception of the sidelink reference signal. According to an embodiment of the present disclosure, the ranging result report may also include signal information related to the positioning reference signal. The signal information may include at least one of sequence ID information related to the positioning reference signal and a beam index received by the user device related to the positioning reference signal.

[0111] According to an embodiment, operation 1025 may include transmitting, by the network element, a location of the user device to a core network element of the telecommunications network, where the location of the user device may be based on the machine-learned location inference model and the ranging result report. According to an embodiment, the machine-learned location inference model may be trained using a location management function, which may be part of the core network element.

[0112] In some embodiments, the inference model may be based on a machine-learned location inference model and may be sent to a sub-location management function of the network element, and the location of the user device may be calculated by the network element based on the inference model and the ranging result report. In some other embodiments, the inference model based on the machine-learned location inference model may be sent to a sub-location management function of the user device, and the location of the user device may be calculated by the user device based on the inference model and the ranging result report, and the ranging result report may be calculated by the user device, and may further include the location of the user device.

[0113] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practicing the implementations.

[0114] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail of integration. Furthermore, one or more of the above components described above may be implemented as instructions stored on a computer-readable medium and executable by at least one processor (and / or may include at least one processor). The computer-readable medium may include a computer-readable non-transitory storage medium(s) having computer-readable program instructions for causing a processor to perform operations.

[0115] A computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved ridge structures having instructions recorded therein, and any suitable combination thereof. As used herein, computer-readable storage media should not be interpreted as being a transitory signal itself, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or an electrical signal transmitted through a wire.

[0116] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to the respective computing / processing device or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device.

[0117] The computer readable program code / instructions for performing the operations may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, Instruction-Set-Architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for an integrated circuit, or object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) can execute computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry to perform an aspect or operation.

[0118] These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to generate a machine such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for performing the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium capable of directing a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium on which the instructions are stored includes a product including instructions implementing aspects of the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0119] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to execute a series of operational steps to generate a computer-implemented process, such that the instructions executing on the computer, other programmable data processing apparatus, or other device perform the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or part of an instruction that includes one or more executable instructions for implementing a specified logical function. The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions described in the blocks may be performed in a different order than that described in the figures. For example, two blocks shown in succession may actually be executed simultaneously or substantially simultaneously, or the blocks may sometimes be executed in the reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, as well as combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a dedicated hardware-based system that performs the specified functions or operations or executes a combination of dedicated hardware and computer instructions.

[0121] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

Claims

1. 1. A method for adaptive positioning accuracy of a device in a telecommunications network, the method being executed by one or more processors, the method comprising: transmitting, by a network element of the telecommunications network, a positioning trigger signal and a positioning reference signal to a user device; transmitting, by a user premises equipment for the user device, a sidelink reference signal to the user device; receiving, by the network element, a ranging result report, the ranging result report being calculated by the user device, the ranging result report including at least one of distance and timing information associated with signals received by the user device; The method further includes transmitting, by the network element, a location of the user device to a core network element of the telecommunications network, the location of the user device being based on a machine-learned location inference model and the ranging result report.

2. The method of claim 1 , wherein the positioning trigger signal is transmitted by a core network element of the telecommunications network in response to receiving an initial trigger signal from the network element.

3. 2. The method of claim 1, wherein the machine-learned location inference model is trained using a location management function, the location management function being part of the core network element.

4. 4. The method of claim 3, wherein an inference model based on the machine-learned location inference model is sent to a sub-location management function of the network element, and the location of the user device is calculated by the network element based on the inference model and the ranging result report.

5. The method of claim 3, wherein an inference model based on the machine-learned location inference model is sent to a sub-location management function of the user device, and the location of the user device is calculated by the user device based on the inference model and the ranging result report, and the ranging result report calculated by the user device further includes the location of the user device.

6. 2. The method of claim 1, wherein the ranging result report calculated by the user device comprises a calculation of a distance between the user device and the user premises equipment based on the sidelink reference signal and a correlated positioning reference signal information, the correlated positioning reference signal information comprising information associated to a positioning reference signal having a first transaction ID that is the same as a second transaction ID associated to the sidelink reference signal, the first transaction ID and the second transaction ID being associated to a position measurement time or to the user device.

7. 7. The method of claim 6, wherein the ranging result report further comprises one or more of the following: a time lag between transmission and reception of the sidelink reference signal between the user device and the user premises equipment, location information relating to the user premises equipment, power information relating to the user device, power information relating to the user premises equipment, a time of arrival of the sidelink reference signal, or angle information relating to reception of the sidelink reference signal.

8. 8. The method of claim 7, wherein the ranging result report further includes signal information associated with the positioning reference signal, the signal information including at least one of sequence ID information associated with the positioning reference signal and a beam index received by the user device associated with the positioning reference signal.

9. The method of claim 3 , wherein the machine-learned location reasoning model is specifically trained for adaptive indoor positioning of the user device.

10. 4. The method of claim 3, wherein the machine-learned location inference model is trained using sidelink channel measurements, an estimated position of the user device, a position of the user premises equipment, the positioning reference signals, and the timing information related to the signals received by the user device.

11. The method of claim 1 , wherein the location of the user device comprises a location of the user device or a modified location of the user device.

12. 1. An apparatus for adaptive positioning accuracy of a device in a telecommunications network, the apparatus comprising: at least one memory configured to store computer program code; at least one processor configured to access the computer program code and to operate as instructed by the computer program code; The computer program code comprises: a first transmission code configured to cause a first processor of the at least one processor to transmit a positioning trigger signal and a positioning reference signal, the first processor being part of a network element; The computer program code further comprises: and second transmission code configured to cause a second processor of the at least one processor to transmit a sidelink reference signal to a user device, the second processor being part of a user premises equipment; The computer program code further comprises: a first receiving code configured to cause the first processor to receive a ranging result report, the ranging result report being calculated by the user device, the ranging result report including distance and timing information associated with signals received by the user device; The computer program code further comprises: and third transmitting code configured to cause the first processor to transmit a location of the user device, the location of the usage device being based on a machine-learned location inference model and the ranging result report.

13. 13. The apparatus of claim 12, wherein the machine-learned location inference model is trained using a location management function, the location management function being part of a core network element.

14. 13. The apparatus of claim 12, wherein the ranging result report calculated by the user device comprises a calculation of a distance between the user device and the user premises equipment based on the sidelink reference signal and a correlated positioning reference signal information, the correlated positioning reference signal information comprising information associated to a positioning reference signal having a first transaction ID that is the same as a second transaction ID associated to the sidelink reference signal, the first transaction ID and the second transaction ID being associated to a position measurement time or to the user device.

15. 14. The apparatus of claim 13, wherein an inference model based on the machine-learned location inference model is sent to a sub-location management function of the network element, and the location of the user device is calculated by the first processor based on the inference model and the ranging result report.

16. The apparatus of claim 13 , wherein an inference model based on the machine-learned location inference model is sent to a sub-location management function of the user device, and the location of the user device is calculated by the user device based on the inference model and the ranging result report, and the ranging result report calculated by the user device further includes the location of the user device.

17. A non-transitory computer-readable medium having stored thereon a program for causing a computer system to execute a process, the process comprising: transmitting, by a network element of a telecommunications network, a positioning trigger signal and a positioning reference signal to a user device; transmitting, by a user premises equipment for the user device, a sidelink reference signal to the user device; receiving, by the network element, a ranging result report, the ranging result report being calculated by the user device, the ranging result report including distance and timing information associated with signals received by the user device; The process further includes transmitting, by the network element, a location of the user device to a core network element of the telecommunications network, the location of the user device based on a machine-learned location inference model and the ranging result report.

18. 20. The non-transitory computer-readable medium of claim 17, wherein the machine-learned location inference model is trained using a location management function, the location management function being part of the core network element.

19. 20. The non-transitory computer-readable medium of claim 18, wherein an inference model based on the machine-learned location inference model is sent to a sub-location management function of the network element, and the location of the user device is calculated by the network element based on the inference model and the ranging result report.

20. 20. The non-transitory computer-readable medium of claim 18, wherein an inference model based on the machine-learned location inference model is sent to a sub-location management function of the user device, and the location of the user device is calculated by the user device based on the inference model and the ranging result report, and the ranging result report calculated by the user device also includes the location of the user device.

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