WO2022129690A1 - Estimation d'intégrité de positionnement - Google Patents

Estimation d'intégrité de positionnement Download PDF

Info

Publication number
WO2022129690A1
WO2022129690A1 PCT/FI2021/050867 FI2021050867W WO2022129690A1 WO 2022129690 A1 WO2022129690 A1 WO 2022129690A1 FI 2021050867 W FI2021050867 W FI 2021050867W WO 2022129690 A1 WO2022129690 A1 WO 2022129690A1
Authority
WO
WIPO (PCT)
Prior art keywords
positioning information
terminal device
positioning
devices
integrity
Prior art date
Application number
PCT/FI2021/050867
Other languages
English (en)
Inventor
Philippe Sehier
Daejung YOON
Diomidis Michalopoulos
Original Assignee
Nokia Technologies Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nokia Technologies Oy filed Critical Nokia Technologies Oy
Publication of WO2022129690A1 publication Critical patent/WO2022129690A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/396—Determining accuracy or reliability of position or pseudorange measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009—Transmission of position information to remote stations
    • G01S5/0018—Transmission from mobile station to base station
    • G01S5/0036—Transmission from mobile station to base station of measured values, i.e. measurement on mobile and position calculation on base station
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0236—Assistance data, e.g. base station almanac
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0244—Accuracy or reliability of position solution or of measurements contributing thereto
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G01S5/0263—Hybrid positioning by combining or switching between positions derived from two or more separate positioning systems
    • G01S5/0264—Hybrid positioning by combining or switching between positions derived from two or more separate positioning systems at least one of the systems being a non-radio wave positioning system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/10—Integrity
    • H04W12/104—Location integrity, e.g. secure geotagging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/20—Integrity monitoring, fault detection or fault isolation of space segment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/485—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an optical system or imaging system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology

Definitions

  • the following exemplary embodiments relate to wireless communication and to positioning.
  • Positioning technologies may be used to estimate a position of a device. It is desirable to estimate the uncertainty associated with position estimates in order to determine whether the position estimate is reliable or not.
  • an apparatus comprising at least one processor, and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to: obtain a set of positioning information associated with communication between a terminal device and a plurality of devices; obtain one or more position estimates associated with the terminal device; and estimate a positioning integrity based at least partly on the one or more position estimates and the set of positioning information.
  • an apparatus comprising means for: obtaining a set of positioning information associated with communication between a terminal device and a plurality of devices; obtaining one or more position estimates associated with the terminal device; and estimating a positioning integrity based at least partly on the one or more position estimates and the set of positioning information.
  • a method comprising obtaining a set of positioning information associated with communication between a terminal device and a plurality of devices; obtaining one or more position estimates associated with the terminal device; and estimating a positioning integrity based at least partly on the one or more position estimates and the set of positioning information.
  • a computer program comprising instructions for causing an apparatus to perform at least the following: obtain a set of positioning information associated with communication between a terminal device and a plurality of devices; obtain one or more position estimates associated with the terminal device; and estimate a positioning integrity based at least partly on the one or more position estimates and the set of positioning information.
  • a computer readable medium comprising program instructions for causing an apparatus to perform at least the following: obtain a set of positioning information associated with communication between a terminal device and a plurality of devices; obtain one or more position estimates associated with the terminal device; and estimate a positioning integrity based at least partly on the one or more position estimates and the set of positioning information.
  • a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the following: obtain a set of positioning information associated with communication between a terminal device and a plurality of devices; obtain one or more position estimates associated with the terminal device; and estimate a positioning integrity based at least partly on the one or more position estimates and the set of positioning information.
  • a system comprising at least a location management function, a terminal device, and a plurality of devices.
  • the terminal device is configured to: communicate with the plurality of devices; and transmit, to the location management function, a first set of positioning information associated with the communication with the plurality of devices.
  • the plurality of devices are configured to: communicate with the terminal device; and transmit, to the location management function, a second set of positioning information associated with the communication with the terminal device.
  • the location management function is configured to: receive the first set of positioning information from the terminal device; receive the second set of positioning information from the plurality of devices; obtain one or more position estimates associated with the terminal device; and estimate a positioning integrity based at least partly on the one or more position estimates, the first set of positioning information, and the second set of positioning information.
  • the estimation of the positioning integrity may be based at least partly on a quality associated with the first set of positioning information and the second set of positioning information.
  • a system comprising at least a location management function, a terminal device, and a plurality of devices.
  • the terminal device comprises means for: communicating with the plurality of devices; and transmitting, to the location management function, a first set of positioning information associated with the communication with the plurality of devices.
  • the plurality of devices comprise means for: communicating with the terminal device; and transmitting, to the location management function, a second set of positioning information associated with the communication with the terminal device.
  • the location management function comprises means for: receiving the first set of positioning information from the terminal device; receiving the second set of positioning information from the plurality of devices; obtaining one or more position estimates associated with the terminal device; and estimating a positioning integrity based at least partly on the one or more position estimates, the first set of positioning information, and the second set of positioning information.
  • the estimation of the positioning integrity may be based at least partly on a quality associated with the first set of positioning information and the second set of positioning information.
  • FIG. 1 illustrates an exemplary embodiment of a cellular communication network
  • FIG. 2 illustrates a positioning measurement scheme
  • FIGS. 3a and 3b illustrate integrity loss detection schemes
  • FIGS. 4 and 5 illustrate signalling diagrams according to some exemplary embodiment
  • FIG. 6 illustrates a flow chart according to an exemplary embodiment
  • FIG. 7 illustrates a simplified block diagram of an integrity check function according to an exemplary embodiment
  • FIGS. 8 and 9 illustrate apparatuses according to some exemplary embodiments.
  • exemplary embodiments will be described using, as an example of an access architecture to which the exemplary embodiments may be applied, a radio access architecture based on long term evolution advanced (LTE Advanced, LTE-A) or new radio (NR, 5G), without restricting the exemplary embodiments to such an architecture, however. It is obvious for a person skilled in the art that the exemplary embodiments may also be applied to other kinds of communications networks having suitable means by adjusting parameters and procedures appropriately.
  • LTE Advanced long term evolution advanced
  • NR new radio
  • UMTS universal mobile telecommunications system
  • E-UTRAN long term evolution radio access network
  • LTE long term evolution
  • WLAN wireless local area network
  • Wi-Fi worldwide interoperability for microwave access
  • Bluetooth® personal communications services
  • PCS personal communications services
  • WCDMA wideband code division multiple access
  • UWB ultra-wideband
  • sensor networks mobile ad-hoc networks
  • MANETs mobile ad-hoc networks
  • IMS Internet Protocol multimedia subsystems
  • FIG. 1 depicts examples of simplified system architectures showing some elements and functional entities, all being logical units, whose implementation may differ from what is shown.
  • the connections shown in FIG. 1 are logical connections; the actual physical connections may be different. It is apparent to a person skilled in the art that the system may also comprise other functions and structures than those shown in FIG. 1.
  • FIG. 1 shows a part of an exemplifying radio access network.
  • FIG. 1 shows user devices 100 and 102 configured to be in a wireless connection on one or more communication channels in a cell with an access node [such as [e/g]NodeB] 104 providing the cell.
  • the physical link from a user device to a [e/g]NodeB may be called uplink or reverse link and the physical link from the [e/g]NodeB to the user device may be called downlink or forward link.
  • [e/g]NodeBs or their functionalities may be implemented by using any node, host, server or access point etc. entity suitable for such a usage.
  • a communication system may comprise more than one [e/g]NodeB, in which case the [e/g]NodeBs may also be configured to communicate with one another over links, wired or wireless, designed for the purpose. These links may be used for signaling purposes.
  • the [e/g]NodeB may be a computing device configured to control the radio resources of communication system it is coupled to.
  • the NodeB may also be referred to as a base station, an access point or any other type of interfacing device including a relay station capable of operating in a wireless environment.
  • the e/gjNodeB may include or be coupled to transceivers. From the transceivers of the (e/g)NodeB, a connection may be provided to an antenna unit that establishes bi-directional radio links to user devices.
  • the antenna unit may comprise a plurality of antennas or antenna elements.
  • the (e/g)NodeB may further be connected to core network 110 (CN or next generation core NGC).
  • core network 110 CN or next generation core NGC.
  • the counterpart on the CN side may be a serving gateway (S-GW, routing and forwarding user data packets), packet data network gateway (P-GW), for providing connectivity of user devices (UEs) to external packet data networks, or mobile management entity (MME), etc.
  • S-GW serving gateway
  • P-GW packet data network gateway
  • MME mobile management entity
  • the user device also called UE, user equipment, user terminal, terminal device, etc.
  • UE user equipment
  • user terminal terminal device
  • any feature described herein with a user device may be implemented with a corresponding apparatus, such as a relay node.
  • a relay node may be a layer 3 relay (self- backhauling relay) towards the base station.
  • the user device may refer to a portable computing device that includes wireless mobile communication devices operating with or without a subscriber identification module (SIM), including, but not limited to, the following types of devices: a mobile station (mobile phone), smartphone, personal digital assistant (PDA), handset, device using a wireless modem (alarm or measurement device, etc.), laptop and/or touch screen computer, tablet, game console, notebook, and multimedia device.
  • SIM subscriber identification module
  • a user device may also be a nearly exclusive uplink only device, of which an example may be a camera or video camera loading images or video clips to a network.
  • a user device may also be a device having capability to operate in Internet of Things (loT) network which is a scenario in which objects may be provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
  • the user device may also utilize cloud.
  • a user device may comprise a small portable device with radio parts (such as a watch, earphones or eyeglasses) and the computation may be carried out in the cloud.
  • the user device (or in some exemplary embodiments a layer 3 relay node) may be configured to perform one or more of user equipment functionalities.
  • the user device may also be called a subscriber unit, mobile station, remote terminal, access terminal, user terminal, terminal device, or user equipment (UE) just to mention but a few names or apparatuses.
  • CPS cyberphysical system
  • ICT devices sensors, actuators, processors microcontrollers, etc.
  • Mobile cyber physical systems in which the physical system in question may have inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals.
  • apparatuses have been depicted as single entities, different units, processors and/or memory units (not all shown in FIG. 1) may be implemented.
  • 5G may enable using multiple input - multiple output (M1M0) antennas, many more base stations or nodes than the LTE (a so-called small cell concept), including macro sites operating in co-operation with smaller stations and employing a variety of radio technologies depending on service needs, use cases and/or spectrum available.
  • 5G mobile communications may support a wide range of use cases and related applications including video streaming, augmented reality, different ways of data sharing and various forms of machine type applications (such as (massive) machine-type communications (mMTC), including vehicular safety, different sensors and real-time control.
  • 5G may be expected to have multiple radio interfaces, namely below 6GHz, cmWave and mmWave, and also being integratable with existing legacy radio access technologies, such as the LTE.
  • Integration with the LTE may be implemented, at least in the early phase, as a system, where macro coverage may be provided by the LTE, and 5G radio interface access may come from small cells by aggregation to the LTE.
  • 5G may support both inter-RAT operability (such as LTE-5G) and inter-Rl operability inter-radio interface operability, such as below 6GHz - cmWave, below 6GHz - cmWave - mmWave).
  • inter-RAT operability such as LTE-5G
  • inter-Rl operability inter-radio interface operability such as below 6GHz - cmWave, below 6GHz - cmWave - mmWave.
  • One of the concepts considered to be used in 5G networks may be network slicing in which multiple independent and dedicated virtual subnetworks [network instances) may be created within the same infrastructure to run services that have different requirements on latency, reliability, throughput and mobility.
  • the current architecture in LTE networks may be fully distributed in the radio and fully centralized in the core network.
  • the low latency applications and services in 5G may require to bring the content close to the radio which leads to local break out and multi-access edge computing [MEC).
  • 5G may enable analytics and knowledge generation to occur at the source of the data. This approach may require leveraging resources that may not be continuously connected to a network such as laptops, smartphones, tablets and sensors.
  • MEC may provide a distributed computing environment for application and service hosting. It may also have the ability to store and process content in close proximity to cellular subscribers for faster response time.
  • Edge computing may cover a wide range of technologies such as wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing also classifiable as local cloud/fog computing and grid/mesh computing, dew computing, mobile edge computing, cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented and virtual reality, data caching, Internet of Things [massive connectivity and/or latency critical), critical communications [autonomous vehicles, traffic safety, realtime analytics, time-critical control, healthcare applications).
  • the communication system may also be able to communicate with other networks, such as a public switched telephone network or the Internet 112, or utilize services provided by them.
  • the communication network may also be able to support the usage of cloud services, for example at least part of core network operations may be carried out as a cloud service [this is depicted in FIG. 1 by "cloud" 114).
  • the communication system may also comprise a central control entity, or a like, providing facilities for networks of different operators to cooperate for example in spectrum sharing.
  • Edge cloud may be brought into radio access network (RAN) by utilizing network function virtualization (NFV) and software defined networking (SDN).
  • RAN radio access network
  • NFV network function virtualization
  • SDN software defined networking
  • Using edge cloud may mean access node operations to be carried out, at least partly, in a server, host or node operationally coupled to a remote radio head or base station comprising radio parts. It may also be possible that node operations will be distributed among a plurality of servers, nodes or hosts.
  • Application of cloudRAN architecture may enable RAN real time functions being carried out at the RAN side (in a distributed unit, DU 104) and non-real time functions being carried out in a centralized manner (in a centralized unit, CU 108).
  • 5G (or new radio, NR) networks may be designed to support multiple hierarchies, where MEC servers may be placed between the core and the base station or nodeB (gNB). It should be appreciated that MEC may be applied in 4G networks as well.
  • 5G may also utilize satellite communication to enhance or complement the coverage of 5G service, for example by providing backhauling.
  • Possible use cases may be providing service continuity for machine-to-machine (M2M) or Internet of Things (loT) devices or for passengers on board of vehicles, or ensuring service availability for critical communications, and future railway/maritime/aeronautical communications.
  • Satellite communication may utilize geostationary earth orbit (GEO) satellite systems, but also low earth orbit (LEO) satellite systems, in particular mega-constellations (systems in which hundreds of (nano) satellites are deployed). Each satellite 106 in the megaconstellation may cover several satellite-enabled network entities that create on- ground cells.
  • the on-ground cells may be created through an on-ground relay node 104 or by a gNB located on-ground or in a satellite. It is obvious for a person skilled in the art that the depicted system is only an example of a part of a radio access system and in practice, the system may comprise a plurality of (e/g)NodeBs, the user device may have an access to a plurality of radio cells and the system may also comprise other apparatuses, such as physical layer relay nodes or other network elements, etc. At least one of the (e/g)NodeBs or may be a Home(e/g)nodeB.
  • Radio cells may be macro cells (or umbrella cells) which may be large cells having a diameter of up to tens of kilometers, or smaller cells such as micro-, femto- or picocells.
  • the (e/g)NodeBs of FIG. 1 may provide any kind of these cells.
  • a cellular radio system may be implemented as a multilayer network including several kinds of cells. In multilayer networks, one access node may provide one kind of a cell or cells, and thus a plurality of (e/g)NodeBs may be required to provide such a network structure.
  • a network which may be able to use “plug-and-play" (e/g)Node Bs may include, in addition to Home (e/g)NodeBs (H(e/g)nodeBs), a home node B gateway, or HNB-GW (not shown in FIG. 1).
  • HNB-GW HNB Gateway
  • HNB-GW which may be installed within an operator’s network, may aggregate traffic from a large number of HNBs back to a core network.
  • Positioning may be a key enabler for various use cases for 5G. By obtaining information relating to accurate positions of devices, applications such as location-based services, autonomous driving, and industrial loT maybe fulfilled by 5G systems. Positioning may be provided, for example, by global navigation satellite system, GNSS, techniques such as the global positioning system, GPS, but satellite-based positioning may not be able to provide positioning with sufficient accuracy for some indoor applications, such as factory automation or warehouse management.
  • GNSS global navigation satellite system
  • techniques such as the global positioning system, GPS
  • satellite-based positioning may not be able to provide positioning with sufficient accuracy for some indoor applications, such as factory automation or warehouse management.
  • Radio access technology, RAT, dependent positioning techniques based on downlink and/or uplink signals provide an alternative to GNSS positioning techniques.
  • a location server such as a location management function, LMF, or a component such as a location management component, LMC, deployed in the core network or RAN may be responsible for requesting and collecting measurements associated with a positioning reference signal, PRS, and/or a sounding reference signal, SRS, obtained by a UE or gNB, depending on whether a downlink or uplink positioning technique is used.
  • the position of the target device may be estimated by the location server. The accuracy of the position estimate depends on the quality of the measurements reported by the UE and/or gNB.
  • integrity may be defined as a metric for the trustworthiness of the position estimation.
  • integrity is the measure of trust that can be placed in the correctness of the information supplied by a navigation system. Integrity may include the ability of a system to provide timely warnings to user receivers in case of failure, when the system should not be used for navigation. Emerging applications relying on high-precision positioning technology in autonomous applications, such as automotive, may require high integrity and reliability for position-related data, in addition to high accuracy.
  • the integrity metric may be used by the user to determine whether the positioning information is reliable or not. If the integrity metric reveals an unreliable measurement, it should not be used by the application or the user.
  • the integrity metric may be used by many applications, such as air navigation, autonomous driving, as well as positioning of automated guided vehicles, AGVs, and unmanned aerial vehicles, UAVs.
  • Protection level may be defined as an estimate of the maximum possible error in the position estimate, and it may be an output from positioning.
  • Alert limit may be defined as the maximum error that the system can tolerate. The alert limit may depend on the application, such as autonomous driving or aeronautics, and it may be part of the system requirements.
  • Integrity risk may be defined as the probability that the true error exceeds the protection level, and it may be a safety requirement. In other words, the integrity risk is the probability that the position error is larger than the alert limit, and the user is not warned within the time to alert, TTA.
  • Availability may be defined as the probability that the protection level is less than or equal to the alert limit, and it may be a performance requirement.
  • Integrity alarms should be raised with a high reliability. When the application or user receives this type of alarm, it indicates that the information is erroneous, and thus should not be used. To ensure sufficient service continuity, the number of integrity loss events per time event should be as low as possible. As an example, less than 10-7 events per hour may be required in aeronautics applications.
  • the radio channel may be subject to multipath, noise, and several interference sources.
  • there may be faults in the equipment such as an incorrect time reference, clock drift, and/or miscalibration.
  • Another possible cause for integrity loss may be intentional jamming. Users may try to dissimulate or tamper their localizations by using a jammer or other techniques.
  • Some exemplary embodiments may provide an integrity check with an improved level of performance and reliability, which may also be used for terrestrial-based positioning techniques.
  • Some exemplary embodiments may exploit the global coherency of available measurements in order to estimate if there is an integrity loss. For example, some or all of the following positioning information may be correlated: uplink and/or downlink time difference of arrival, TDoA, angle of arrival, AoA, and/or angle of departure, AoD, UE AoD and/or AoA, enhanced cell identity (eCID) information, GNSS information, WiFi-based positioning information, LiFi (light fidelity) based positioning information, Bluetooth-based positioning information, UWB positioning information, inertial measurement unit (1MU) information, local sensor information, and/or measurement reports provided by neighboring devices.
  • FIG. 2 illustrates a positioning measurement scheme.
  • GNSS 205 may also be combined with other measurements and information sources, if available.
  • Integrity loss may also be detected with a smaller set of measurements, as illustrated in FIGS. 3a and 3b.
  • FIGS. 3a and 3b illustrate integrity loss detection schemes using TDoA measurements.
  • FIG. 3a there are four gNBs 311, 312, 313, 314 and a single crossing point cluster, and the estimated position 310 of the UE corresponds with the true position 310 of the UE.
  • FIG. 3b there are four gNBs 321, 322, 323, 324, but an error is introduced on the time-of-arrival measurement associated with the first base station 321. There are multiple crossing points, and the estimated position 325 of the UE deviates substantially from the true position 326 of the UE. Thus, integrity loss events may be detected by using time-of-arrival measurements with a plurality of gNBs for redundancy.
  • Machine learning may be used to enable integrity loss detection for a large set of measurements comprising heterogeneous data from different sources. If a significant breach of coherency is detected in a measurement set, a loss of integrity may be declared. The reliability of the alarm increases with the dimension of the set.
  • the measurement sets may be obtained by using field measurements, such as drive tests, or by accumulating previous measurements associated with other UEs after verifying the validity of the positioning data.
  • Some exemplary embodiments provide an integrity verification technique using supervised learning in the form of an artificial neural network, which may be used to check that the input from different sources demonstrates sufficient coherency levels.
  • the artificial neural network may also determine the estimated position of the device, as well as the accuracy of the estimated position.
  • the labelled inputs used for training the artificial neural network may comprise the exact known positions of the device.
  • Supervised learning is an area of machine learning that may be used for learning a function that maps an input to an output based on exemplary input-output pairs, which may be referred to as labelled training data.
  • FIG. 4 illustrates a signalling diagram according to an exemplary embodiment.
  • the LMF 401 comprises an integrity check function.
  • a dedicated functional entity may also be used for the integrity check function.
  • a first gNB 402 transmits 411 a first PRS to a UE 404.
  • a second gNB 403 transmits 412 a second PRS to the UE 404.
  • the UE performs timing measurements based on the time of arrival of the first PRS and the second PRS.
  • the timing measurements may include the power and delays of several paths in addition to the line-of-sight path.
  • the UE transmits 413 a first set of positioning information to an LMF 401.
  • the first set of positioning information may comprise, for example, the timing measurements, angular measurements, reference signal received power (RSRP) measurements, and/or the UE position estimated by the UE itself.
  • RSRP reference signal received power
  • the UE may estimate its position by using terrestrial means, such as downlink TDoA, based at least partly on the first PRS and/or the second PRS.
  • the RSRP measurements may be used for eClD.
  • the RSRP measurements may provide additional positioning information to further secure the integrity flag.
  • the UE transmits 414 a first SRS to the first gNB.
  • the UE transmits 415 a second SRS to the second gNB.
  • a GNSS satellite 405 transmits 416 one or more positioning signals to the UE.
  • the UE estimates the position of the UE based at least partly on the one or more positioning signals received from the GNSS satellite, and transmits 417 the GNSS-based position estimate to the LMF.
  • the first gNB performs timing measurements based on the first SRS, and transmits 418, to the LMF, a second set of positioning information comprising at least the timing measurements performed by the first gNB.
  • the second set of positioning information may also comprise the AoA and/or AoD of the beam used by the first gNB to communicate with the UE.
  • the timing measurements may include the power and delays of several paths in addition to the line-of-sight path.
  • the second gNB performs timing measurements based on the second SRS, and transmits 419, to the LMF, a third set of positioning information comprising at least the timing measurements performed by the second gNB.
  • the third set of positioning information may also comprise the AoA and/or AoD of the beam used by the second gNB to communicate with the UE.
  • the timing measurements may include the power and delays of several paths in addition to the line-of-sight path.
  • the LMF performs a first integrity estimation 420, which may also be referred to as a first integrity check, based at least partly on one or more of the following: the timing measurements performed by the UE, the timing measurements performed by the first gNB, the timing measurements performed by the second gNB, the AoA and/or AoD reported by the gNBs, and/or other measurements, such as downlink AoA at the UE side and/or measurements done via sidelink.
  • the input data for the first integrity check may comprise the application ID and its related requirements. However, the application ID may not be needed if the requirements are applicable to all applications, or if a single application is considered. The reliability of the integrity check may be increased by using a higher amount of input data.
  • the integrity check may be performed by using a trained artificial neural network comprised in the LMF, for example.
  • the LMF may request additional information.
  • the LMF may request 421 a capability report from the UE to inquire the positioning capabilities supported by the UE.
  • the UE may then transmit 422 the capability report to the LMF, wherein the capability report indicates the positioning capabilities supported by the UE.
  • the LMF may then transmit 423 a request to provide additional positioning information that is available based on the reported UE positioning capabilities.
  • the LTE positioning protocol, LPP may be used to request the capability report as well as to report the positioning capabilities and the additional positioning information.
  • the additional positioning information may comprise, for example, Wi-Fi based positioning information, Bluetooth-based positioning information, information provided by one or more local sensors, such as 1MU, comprised in the UE, proximity radar information, and/or camera information.
  • the UE may transmit 424 the additional positioning information to the LMF.
  • the LMF may then perform a second integrity estimation 425, which may also be referred to as a complementary integrity check, based at least partly on the additional positioning information.
  • more than two gNBs may be used to provide a PRS to the UE, and to provide timing measurements to the LMF.
  • the UE may perform the integrity check(s).
  • the UE may obtain information from the LMF for performing the integrity check(s).
  • FIG. 5 illustrates a signalling diagram according to an exemplary embodiment, wherein a single integrity check is performed.
  • an LMF 501 requests 511 a capability report from a UE 504 to inquire the positioning capabilities supported by the UE.
  • the UE then transmits 512 the capability report to the LMF, wherein the capability report indicates the positioning capabilities supported by the UE.
  • a first gNB 502 transmits 513 a first PRS to the UE.
  • a second gNB 503 transmits 514 a second PRS to the UE.
  • the UE performs timing measurements based on the time of arrival of the first PRS and the second PRS.
  • the timing measurements may include the power and delays of several paths in addition to the line-of-sight path.
  • the UE transmits 515 a first set of positioning information to the LMF.
  • the first set of positioning information may comprise, for example, the timing measurements, angular measurements, RSRP measurements, and/or the UE position estimated by the UE itself.
  • the UE may estimate its position by using terrestrial means, such as downlink TDoA, based at least partly on the first PRS and/or the second PRS.
  • the UE transmits 516 a first SRS to the first gNB.
  • the UE transmits 517 a second SRS to the second gNB.
  • a GNSS satellite 505 transmits 518 one or more positioning signals to the UE.
  • the UE estimates the position of the UE based at least partly on the one or more positioning signals received from the GNSS satellite, and transmits 519 the GNSS-based position estimate to the LMF.
  • the first gNB performs timing measurements based on the first SRS, and transmits 520, to the LMF, a second set of positioning information comprising at least the timing measurements performed by the first gNB.
  • the second set of positioning information may also comprise the AoA and/or AoD of the beam used by the first gNB to communicate with the UE.
  • the timing measurements may include the power and delays of several paths in addition to the line-of-sight path.
  • the second gNB performs timing measurements based on the second SRS, and transmits 521, to the LMF, a third set of positioning information comprising at least the timing measurements performed by the second gNB.
  • the third set of positioning information may also comprise the AoA and/or AoD of the beam used by the second gNB to communicate with the UE.
  • the timing measurements may include the power and delays of several paths in addition to the line-of-sight path.
  • the UE transmits 522 additional positioning information to the LMF.
  • the additional positioning information may comprise LPP-based positioning information, Wi-Fi based positioning information, Bluetooth-based positioning information, information provided by one or more local sensors, such as 1MU, comprised in the UE, proximity radar information, and/or camera information.
  • the LMF performs an integrity estimation 523, which may also be referred to as an integrity check, based at least partly on one or more of the following: the additional positioning information, the timing measurements performed by the UE, the timing measurements performed by the first gNB, the timing measurements performed by the second gNB, the AoA and/or AoD reported by the gNBs, and/or other measurements, such as downlink AoA at the UE side and/or measurements done via sidelink.
  • the functions and/or blocks described above by means of FIG. 5 are in no absolute chronological order, and some of them may be performed simultaneously or in an order differing from the described one. Other functions and/or blocks may also be executed between them or within them.
  • FIG. 6 illustrates a flow chart according to an exemplary embodiment.
  • a set of positioning information associated with communication between a terminal device and a plurality of devices is obtained 601.
  • the set of positioning information may be received from the terminal device and/or from the plurality of devices.
  • the plurality of devices may comprise, for example, one or more base stations, one or more other terminal devices, and/or one or more Wi-Fi access points.
  • One or more position estimates associated with the terminal device are obtained 602.
  • the one or more position estimates may be obtained by estimating the position of the terminal device based on the set of positioning information, and/or by receiving from the terminal device one or more position estimates determined by the terminal device.
  • the one or more position estimates may comprise a terrestrial-based position estimate, a satellite-based position estimate, a sensor-based position estimate, and/or an image-based position estimate.
  • a positioning integrity is estimated 603 based at least partly on the one or more position estimates and the set of positioning information.
  • the integrity estimation may further be based at least partly on a quality associated with the set of positioning information, i.e. the quality of the measurements comprised in the set of positioning information.
  • the set of positioning information may comprise one or more of the following: timing measurements, angular measurements, such as AoD and/or AoA, signal power measurements, such as RSRP, WiFi-based positioning information, light fidelity based positioning information, Bluetooth-based positioning information, ultra-wideband positioning information, sensor information, inertial measurement unit information, proximity radar information, camera information, enhanced cell identity information, and/or global navigation satellite system information.
  • FIG. 7 illustrates a simplified block diagram of an integrity check function according to an exemplary embodiment.
  • the integrity check function uses an artificial neural network based on supervised learning.
  • the first block 701 illustrates the training phase of the artificial neural network
  • the second block 702 illustrates the inference phase of the trained artificial neural network.
  • the artificial neural network is trained by using labelled training data, i.e. exemplary pairs of input and output data, wherein the labelled training data comprises a pre-defined set of positioning information, i.e. exemplary input data, and a pre-defined set of values indicating an integrity level, i.e. exemplary output data, associated with the pre-defined set of positioning information.
  • the labelled training data for the artificial neural network may be generated as follows. A cartography of a geographical area may be obtained by means of field measurements, such as drive tests, performed by a trusted entity, for example. Timing measurements and positioning information are collected.
  • the labelled training data is constituted from these inputs, combined with the true position of the UE as well as an integrity check flag.
  • labelled training data may be obtained by using measurements previously performed by other UEs, after integrity verification.
  • the labelled training data may be obtained from simulations. For example, environment models with ray tracing tools may be used for this purpose.
  • a mix of integer and non-integer labelled data may be used to train the artificial neural network. Additional labelled data may be added continuously in order to track environmental changes, which may affect the integrity. Alternatively, reinforcement learning may be used for continuously tracking the environmental changes. Reinforcement learning is an area of machine learning, wherein the software agent learns from experience by taking actions in an environment by trial and error. In reinforcement learning, the goal of the software agent may be to maximize a cumulative reward resulting from its actions. In other words, reinforcement learning differs from supervised learning in that in reinforcement learning there is no pre-provided labelled training data comprising exemplary input-output pairs.
  • the artificial neural network is trained to provide a binary output, i.e. 0 or 1, for a set of labelled training data comprising input data and the expected output for the input data. For example, if the input data does not reveal an integrity loss, the artificial neural network may be trained to provide an output value of 0. Conversely, for non-integer data sets, the artificial neural network may be trained to provide an output value of 1.
  • the goal of the training may be to minimize a loss function on a list of labelled data.
  • the training of the artificial neural network may be performed by a supervised learning process, wherein the output of the artificial neural network is compared against a set of coherent and non-coherent values, thereby providing feedback on the integrity level of a given set of measurements. Then, for the set of measurements that pass the coherency integrity test, the integrity check function provides an indication of coherency, which further indicates to the UE that given integrity levels are met.
  • a soft output corresponding to a low or high integrity probability may be used.
  • the application requirements such as protection level, may also be included in the training process in order to raise the integrity loss flag in accordance with the requirements. Accordingly, the artificial neural network may be trained to deliver several indications corresponding to the application requirements.
  • sets of input data for example sets of positioning information
  • the output value, for example 0 or 1 of the artificial neural network indicates whether the set is coherent or not. For example, an output value of 0 may indicate high probability of integrity, whereas an output value of 1 may indicate a high probability of integrity loss.
  • the artificial neural network may be based on unsupervised learning.
  • a technical advantage provided by some exemplary embodiments may be that they may provide a positioning integrity check with improved reliability by exploiting a plurality of measurements from different sources.
  • some exemplary embodiments may be scalable and flexible, as they may be able to adapt to various situations. Machine learning enables continuous learning for adapting to changes in the environment.
  • some exemplary embodiments may provide robustness against malicious UEs.
  • some exemplary embodiments may enable a tradeoff between consumption of resources in the air interface and integrity reliability performance.
  • Some exemplary embodiments enable training an LMF, or an artificial neural network, for these purposes.
  • FIG. 8 illustrates an apparatus 800, which may be an apparatus such as, or comprised in, a terminal device, according to an exemplary embodiment.
  • a terminal device may also be referred to as a UE herein.
  • the apparatus 800 comprises a processor 810.
  • the processor 810 interprets computer program instructions and processes data.
  • the processor 810 may comprise one or more programmable processors.
  • the processor 810 may comprise programmable hardware with embedded firmware and may, alternatively or additionally, comprise one or more application specific integrated circuits, ASICs.
  • the processor 810 is coupled to a memory 820.
  • the processor is configured to read and write data to and from the memory 820.
  • the memory 820 may comprise one or more memory units.
  • the memory units may be volatile or non-volatile. It is to be noted that in some exemplary embodiments there may be one or more units of non-volatile memory and one or more units of volatile memory or, alternatively, one or more units of non-volatile memory, or, alternatively, one or more units of volatile memory.
  • Volatile memory may be for example RAM, DRAM or SDRAM.
  • Non-volatile memory may be for example ROM, PROM, EEPROM, flash memory, optical storage or magnetic storage. In general, memories may be referred to as non-transitory computer readable media.
  • the memory 820 stores computer readable instructions that are executed by the processor 810. For example, non-volatile memory stores the computer readable instructions and the processor 810 executes the instructions using volatile memory for temporary storage of data and/or instructions.
  • the computer readable instructions may have been pre-stored to the memory 820 or, alternatively or additionally, they may be received, by the apparatus, via an electromagnetic carrier signal and/or may be copied from a physical entity such as a computer program product. Execution of the computer readable instructions causes the apparatus 800 to perform one or more of the functionalities described above.
  • a "memory” or “computer-readable media” or “computer-readable medium” may be any non-transitory media or medium or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer.
  • the apparatus 800 may further comprise, or be connected to, an input unit 830.
  • the input unit 830 may comprise one or more interfaces for receiving input.
  • the one or more interfaces may comprise for example one or more sensors, such as temperature, motion and/or orientation sensors, one or more cameras, one or more accelerometers, one or more microphones, one or more buttons and/or one or more touch detection units. Further, the input unit 830 may comprise an interface to which external devices may connect to.
  • the apparatus 800 may also comprise an output unit 840.
  • the output unit may comprise or be connected to one or more displays capable of rendering visual content such as a light emitting diode, LED, display, a liquid crystal display, LCD and a liquid crystal on silicon, LCoS, display.
  • the output unit 840 may further comprise one or more audio outputs.
  • the one or more audio outputs may be for example loudspeakers.
  • the apparatus 800 further comprises a connectivity unit 850.
  • the connectivity unit 850 enables wireless connectivity to one or more external devices.
  • the connectivity unit 850 comprises at least one transmitter and at least one receiver that may be integrated to the apparatus 800 or that the apparatus 800 may be connected to.
  • the at least one transmitter comprises at least one transmission antenna, and the at least one receiver comprises at least one receiving antenna.
  • the connectivity unit 850 may comprise an integrated circuit or a set of integrated circuits that provide the wireless communication capability for the apparatus 800.
  • the wireless connectivity may be a hardwired application specific integrated circuit, ASIC.
  • the connectivity unit 850 may comprise one or more components such as a power amplifier, digital front end, DFE, analog-to-digital converter, ADC, digital-to-analog converter, DAC, frequency converter, (de) modulator, and/or encoder/decoder circuitries, controlled by the corresponding controlling units.
  • apparatus 800 may further comprise various components not illustrated in FIG. 8.
  • the various components may be hardware components and/or software components.
  • the apparatus 900 of FIG. 9 illustrates an exemplary embodiment of an apparatus such as, or comprised in, a base station such as a gNB, or an apparatus comprising an LMF.
  • the apparatus may comprise, for example, a circuitry or a chipset applicable for realizing some of the described exemplary embodiments.
  • the apparatus 900 may be an electronic device comprising one or more electronic circuitries.
  • the apparatus 900 may comprise a communication control circuitry 910 such as at least one processor, and at least one memory 920 including a computer program code (software) 922 wherein the at least one memory and the computer program code (software) 922 are configured, with the at least one processor, to cause the apparatus 900 to carry out some of the exemplary embodiments described above.
  • the memory 920 may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and/or removable memory.
  • the memory may comprise a configuration database for storing configuration data.
  • the configuration database may store a current neighbour cell list, and, in some exemplary embodiments, structures of the frames used in the detected neighbour cells.
  • the apparatus 900 may further comprise a communication interface 930 comprising hardware and/or software for realizing communication connectivity according to one or more communication protocols.
  • the communication interface 930 may provide the apparatus with radio communication capabilities to communicate in the cellular communication system.
  • the communication interface may, for example, provide a radio interface to terminal devices.
  • the apparatus 900 may further comprise another interface towards a core network such as the network coordinator apparatus and/or to the access nodes of the cellular communication system.
  • the apparatus 900 may further comprise a scheduler 940 that is configured to allocate resources.
  • circuitry may refer to one or more or all of the following: a. hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and b. combinations of hardware circuits and software, such as (as applicable): i. a combination of analog and/or digital hardware circuit(s) with software/firmware and ii. any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone, to perform various functions) and c. hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (for example firmware) for operation, but the software may not be present when it is not needed for operation.
  • hardware circuit(s) and or processor(s) such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (for example firmware) for operation, but the software may not be present when it is not needed
  • circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its or their] accompanying software and/or firmware.
  • circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.
  • the techniques and methods described herein may be implemented by various means. For example, these techniques may be implemented in hardware [one or more devices], firmware [one or more devices], software [one or more modules], or combinations thereof.
  • the apparatus [es] of exemplary embodiments may be implemented within one or more application-specific integrated circuits [ASICs], digital signal processors [DSPs], digital signal processing devices [DSPDs], programmable logic devices [PLDs], field programmable gate arrays [FPGAs], graphics processing units [GPUs], processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
  • ASICs application-specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • GPUs graphics processing units
  • processors controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a
  • the implementation can be carried out through modules of at least one chipset [for example procedures, functions, and so on] that perform the functions described herein.
  • the software codes may be stored in a memory unit and executed by processors.
  • the memory unit may be implemented within the processor or externally to the processor. In the latter case, it can be communicatively coupled to the processor via various means, as is known in the art.
  • the components of the systems described herein may be rearranged and/or complemented by additional components in order to facilitate the achievements of the various aspects, etc., described with regard thereto, and they are not limited to the precise configurations set forth in the given figures, as will be appreciated by one skilled in the art.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Computer Security & Cryptography (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

L'invention concerne un procédé pour l'estimation d'une intégrité de positionnement. Un ensemble d'informations de positionnement (515) associées à une communication entre un dispositif terminal (504) et une pluralité de dispositifs est obtenu. Une ou plusieurs estimations de position associées au dispositif terminal sont obtenues. Une intégrité de positionnement est estimée (523) sur la base, au moins en partie, des une ou plusieurs estimations de position et de l'ensemble d'informations de positionnement (515).
PCT/FI2021/050867 2020-12-17 2021-12-10 Estimation d'intégrité de positionnement WO2022129690A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FI20206326A FI20206326A1 (en) 2020-12-17 2020-12-17 Assessment of positioning integrity
FI20206326 2020-12-17

Publications (1)

Publication Number Publication Date
WO2022129690A1 true WO2022129690A1 (fr) 2022-06-23

Family

ID=82058998

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/FI2021/050867 WO2022129690A1 (fr) 2020-12-17 2021-12-10 Estimation d'intégrité de positionnement

Country Status (2)

Country Link
FI (1) FI20206326A1 (fr)
WO (1) WO2022129690A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116170752A (zh) * 2023-04-18 2023-05-26 厦门大学 基于深组合辅助的5g多径间歇性跟踪装置和方法
EP4306984A1 (fr) * 2022-07-13 2024-01-17 Rohde & Schwarz GmbH & Co. KG Procédé de positionnement géographique d'un dispositif récepteur, et dispositif récepteur
WO2024074363A1 (fr) * 2022-10-03 2024-04-11 Telefonaktiebolaget Lm Ericsson (Publ) Surveillance d'événement d'intégrité pour positionnement basé sur ia/ml

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009149417A1 (fr) * 2008-06-06 2009-12-10 Skyhook Wireless, Inc. Procédé et système pour déterminer l’emplacement utilisant un satellite hybride et un système de positionnement wlan par sélection de la meilleure solution wlan-ps
US20200225358A1 (en) * 2019-01-10 2020-07-16 Raytheon Company Detection of spoofing and meaconing for geolocation positioning system signals
WO2020223684A1 (fr) * 2019-05-01 2020-11-05 Swift Navigation, Inc. Systèmes et procédés de positionnement de satellite a intégrité élevée

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009149417A1 (fr) * 2008-06-06 2009-12-10 Skyhook Wireless, Inc. Procédé et système pour déterminer l’emplacement utilisant un satellite hybride et un système de positionnement wlan par sélection de la meilleure solution wlan-ps
US20200225358A1 (en) * 2019-01-10 2020-07-16 Raytheon Company Detection of spoofing and meaconing for geolocation positioning system signals
WO2020223684A1 (fr) * 2019-05-01 2020-11-05 Swift Navigation, Inc. Systèmes et procédés de positionnement de satellite a intégrité élevée

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NG Radio Access Network (NG-RAN); Stage 2 functional specification of User Equipment (UE) positioning in NG-RAN (Release 16)", 3GPP STANDARD; TECHNICAL SPECIFICATION; 3GPP TS 38.305, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG2, no. V16.2.0, 2 October 2020 (2020-10-02), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , pages 1 - 117, XP051961363 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4306984A1 (fr) * 2022-07-13 2024-01-17 Rohde & Schwarz GmbH & Co. KG Procédé de positionnement géographique d'un dispositif récepteur, et dispositif récepteur
WO2024074363A1 (fr) * 2022-10-03 2024-04-11 Telefonaktiebolaget Lm Ericsson (Publ) Surveillance d'événement d'intégrité pour positionnement basé sur ia/ml
CN116170752A (zh) * 2023-04-18 2023-05-26 厦门大学 基于深组合辅助的5g多径间歇性跟踪装置和方法
CN116170752B (zh) * 2023-04-18 2023-08-15 厦门大学 基于深组合辅助的5g多径间歇性跟踪装置和方法

Also Published As

Publication number Publication date
FI20206326A1 (en) 2022-06-18

Similar Documents

Publication Publication Date Title
WO2022129690A1 (fr) Estimation d'intégrité de positionnement
US11243290B2 (en) Future position estimation for improved reliability of connectivity
JP2021141578A5 (fr)
US20230314625A1 (en) Ensuring location information is correct
US11997627B2 (en) Predicting clock drifting
US20230239829A1 (en) Enhancing positioning efficiency
WO2023041169A1 (fr) Positionnement de dispositif
US20230328682A1 (en) Determining timing offset for improved positioning accuracy
EP4191270A1 (fr) Positionnement de dispositif
US20240107620A1 (en) Positioning reference unit selection
EP4345488A1 (fr) Activation d'unité de référence de positionnement
WO2024094393A1 (fr) Détection de classification incorrecte d'indicateur de ligne de visée ou de non ligne de visée
US11856554B2 (en) Relaxation of UE measurements
US20240040534A1 (en) Positioning a device associated with multiple network subscriptions
WO2024033034A1 (fr) Informations de référence pour différence de temps de signal de référence
WO2024027905A1 (fr) Activation d'unité de référence de positionnement
WO2024039400A1 (fr) Mise à jour de modèle d'apprentissage automatique pour positionnement
WO2024032953A1 (fr) Configuration de signal de référence de positionnement de liaison latérale
WO2024023395A1 (fr) Détermination d'ancrage de positionnement
WO2024017516A1 (fr) Sélection de caractéristiques basées sur une largeur de bande et/ou un scénario
EP4128912A1 (fr) Surveillance et réglage temporels de réseau
WO2023117205A1 (fr) Positionnement de liaison latérale dans un système cellulaire
WO2023052014A1 (fr) Indication de changements de synchronisation de transmission
WO2024022589A1 (fr) Charge et suivi d'un dispositif radio passif
WO2024115942A1 (fr) Adaptation d'intervalle d'apprentissage de faisceau basée sur l'effet doppler

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21905885

Country of ref document: EP

Kind code of ref document: A1

DPE1 Request for preliminary examination filed after expiration of 19th month from priority date (pct application filed from 20040101)
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21905885

Country of ref document: EP

Kind code of ref document: A1