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WO2025135246A1 - Système de recherche d'itinéraire optimale pour uav de livraison en tenant compte de la connectivité avec une station de base et du coût de transfert - Google Patents

Système de recherche d'itinéraire optimale pour uav de livraison en tenant compte de la connectivité avec une station de base et du coût de transfert Download PDF

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Publication number
WO2025135246A1
WO2025135246A1 PCT/KR2023/021266 KR2023021266W WO2025135246A1 WO 2025135246 A1 WO2025135246 A1 WO 2025135246A1 KR 2023021266 W KR2023021266 W KR 2023021266W WO 2025135246 A1 WO2025135246 A1 WO 2025135246A1
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WO
WIPO (PCT)
Prior art keywords
base station
terminal
movement path
measurement report
control center
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
PCT/KR2023/021266
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English (en)
Korean (ko)
Inventor
정재훈
임현성
이시현
김규영
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
LG Electronics Inc
Korea Advanced Institute of Science and Technology KAIST
Original Assignee
LG Electronics Inc
Korea Advanced Institute of Science and Technology KAIST
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 LG Electronics Inc, Korea Advanced Institute of Science and Technology KAIST filed Critical LG Electronics Inc
Priority to PCT/KR2023/021266 priority Critical patent/WO2025135246A1/fr
Publication of WO2025135246A1 publication Critical patent/WO2025135246A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/04—Large scale networks; Deep hierarchical networks
    • H04W84/06—Airborne or Satellite Networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications

Definitions

  • Wireless access systems are being widely deployed to provide various types of communication services such as voice and data.
  • wireless access systems are multiple access systems that can support communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.).
  • multiple access systems include CDMA (code division multiple access) systems, FDMA (frequency division multiple access) systems, TDMA (time division multiple access) systems, OFDMA (orthogonal frequency division multiple access) systems, and SC-FDMA (single carrier frequency division multiple access) systems.
  • enhanced mobile broadband (eMBB) communication technology is being proposed compared to the existing radio access technology (RAT).
  • RAT radio access technology
  • a communication system that considers services/UE (user equipment) that are sensitive to reliability and latency as well as mMTC (massive machine type communications) that connects a large number of devices and objects to provide various services anytime and anywhere is being proposed.
  • Various technology configurations are being proposed for this.
  • the present disclosure can provide a device and method for determining a movement path of a terminal based on connectivity with a base station and handover cost in a wireless communication system.
  • the present disclosure can provide a device and method for a control center connected through a backhaul network in a wireless communication system to determine a movement path of a terminal.
  • the present disclosure can provide a device and method for updating parameter values for determining a movement path in a wireless communication system.
  • the present disclosure can provide a device and method for determining a movement path based on the weights of edges forming a graph in a wireless communication system.
  • the present disclosure relates to a device and method for transmitting a plurality of transport blocks (TBs) as a single unit in a wireless communication system.
  • TBs transport blocks
  • a method for operating a terminal in a wireless communication system comprises the steps of: transmitting a setup request signal to a first base station; receiving a setup response signal from the first base station based on the setup request signal; setting a movement path based on the setup response signal; receiving a reference signal from a second base station; and transmitting a measurement report to the second base station based on the reference signal, wherein the movement path is determined based on a connectable range and a handover cost over which the terminal can perform communications for at least one base station, and the connectable range can be updated through the measurement report.
  • a method for operating a first base station in a wireless communication system comprises the steps of: receiving a setup request signal from a terminal; determining a movement path based on the setup request signal; transmitting a setup response signal including information about the movement path to the terminal; receiving a measurement report measured by the terminal from a second base station; and updating parameter values for determining the movement path based on the measurement report, wherein the movement path is determined based on a connectable range and a handover cost in which the terminal can perform communication with respect to at least one base station, and the connectable range can be updated through the measurement report.
  • a terminal in a wireless communication system, includes a transceiver, and a processor connected to the transceiver, wherein the processor controls to transmit a setup request signal to a first base station, receive a setup response signal from the first base station based on the setup request signal, set a movement path based on the setup response signal, receive a reference signal from a second base station, and transmit a measurement report to the second base station based on the reference signal, wherein the movement path is determined based on a connectable range in which the terminal can perform communication with respect to at least one base station, and a handover cost, and the connectable range can be updated through the measurement report.
  • a first base station includes a transceiver and a processor connected to the transceiver, wherein the processor receives a setup request signal from a terminal, determines a movement path based on the setup request signal, transmits a setup response signal including information about the movement path to the terminal, receives a measurement report measured by the terminal from a second base station, and controls updating parameter values for determining the movement path based on the measurement report, wherein the movement path is determined based on a connectable range in which the terminal can perform communication with respect to at least one base station, and a handover cost, and the connectable range can be updated through the measurement report.
  • a communication device comprises at least one processor, and at least one computer memory coupled to the at least one processor and storing instructions that direct operations when executed by the at least one processor, the operations including: transmitting a configuration request signal to a first base station, receiving a configuration response signal from the first base station based on the configuration request signal, setting a movement path based on the configuration response signal, receiving a reference signal from a second base station, and transmitting a measurement report to the second base station based on the reference signal, wherein the movement path is determined based on a connectable range and a handover cost over which the communication device can perform communications with respect to each of the at least one base station, and the connectable range can be updated through the measurement report.
  • a non-transitory computer-readable medium storing at least one instruction, the at least one instruction being executable by a processor, the at least one instruction controlling a device to transmit a setup request signal to a first base station, receive a setup response signal from the first base station based on the setup request signal, set a moving path based on the setup response signal, receive a reference signal from a second base station, and transmit a measurement report to the second base station based on the reference signal, wherein the moving path is determined based on a connectable range and a handover cost over which the device can perform communications for each of at least one base station, and the connectable range can be updated through the measurement report.
  • a movement path of a terminal can be efficiently determined based on connectivity with a base station and handover costs in a wireless communication system.
  • a movement path of a terminal can be determined with low complexity in a wireless communication system.
  • a movement path of a terminal can be determined by utilizing a graph generated based on the coverage radius of a base station.
  • the movement path of a terminal can be determined using the GIM-HO (generalized intersection method with hand-off, GIM-HO) technique.
  • GIM-HO generalized intersection method with hand-off, GIM-HO
  • FIG. 3 is a diagram illustrating another example of a wireless device applicable to the present disclosure.
  • FIG. 4 is a drawing showing an example of a portable device applicable to the present disclosure.
  • FIG. 5 is a drawing showing an example of a vehicle or autonomous vehicle applicable to the present disclosure.
  • FIG. 6 is a diagram showing an example of AI (Artificial Intelligence) applicable to the present disclosure.
  • AI Artificial Intelligence
  • FIG. 18 illustrates an example of signaling for a core network to optimize a movement path and transmit the optimized path to a first terminal according to one embodiment of the present disclosure.
  • FIG. 20 illustrates an example of searching for whether there is a path from a starting point to an ending point where connectivity of base stations is maintained according to one embodiment of the present disclosure.
  • the base station is meant as a terminal node of a network that directly communicates with a mobile station.
  • a specific operation described as being performed by the base station in this document may in some cases be performed by an upper node of the base station.
  • the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).
  • UE user equipment
  • MS mobile station
  • SS subscriber station
  • MSS mobile subscriber station
  • AMS advanced mobile station
  • the transmitter refers to a fixed and/or mobile node that provides data service or voice service
  • the receiver refers to a fixed and/or mobile node that receives data service or voice service.
  • a mobile station in the case of uplink, can be a transmitter and a base station can be a receiver.
  • a mobile station in the case of downlink, can be a receiver and a base station can be a transmitter.
  • Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of wireless access systems, namely IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5th generation (5G) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present disclosure may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.
  • 3GPP TS technical specification
  • embodiments of the present disclosure may be applied to other wireless access systems and are not limited to the above-described system.
  • they may be applied to systems applied after the 3GPP 5G NR system and are not limited to a specific system.
  • CDMA code division multiple access
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal frequency division multiple access
  • SC-FDMA single carrier frequency division multiple access
  • LTE may refer to technology after 3GPP TS 36.xxx Release 8.
  • LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A
  • LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro.
  • 3GPP NR may refer to technology after TS 38.xxx Release 15.
  • 3GPP 6G may refer to technology after TS Release 17 and/or Release 18. “xxx” refers to a standard document detail number.
  • LTE/NR/6G may be collectively referred to as a 3GPP system.
  • FIG. 1 is a diagram illustrating an example of a communication system applied to the present disclosure.
  • a communication system (100) applied to the present disclosure includes a wireless device, a base station, and a network.
  • the wireless device means a device that performs communication using a wireless access technology (e.g., 5G NR, LTE) and may be referred to as a communication/wireless/5G device.
  • the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Thing) device (100f), and an AI (artificial intelligence) device/server (100g).
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc.
  • the vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones).
  • UAVs unmanned aerial vehicles
  • the XR devices (100c) include augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc.
  • HMD head-mounted device
  • HUD head-up display
  • vehicles can communicate directly (e.g., V2V (vehicle to vehicle)/V2X (vehicle to everything) communication).
  • an IoT device (100f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or another wireless device (100a to 100f).
  • a first wireless device (200a) includes one or more processors (202a) and one or more memories (204a), and may additionally include one or more transceivers (206a) and/or one or more antennas (208a).
  • the processor (202a) controls the memory (204a) and/or the transceiver (206a), and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed in this document.
  • the processor (202a) may process information in the memory (204a) to generate first information/signal, and then transmit a wireless signal including the first information/signal via the transceiver (206a).
  • the processor (202a) may receive a wireless signal including second information/signal via the transceiver (206a), and then store information obtained from signal processing of the second information/signal in the memory (204a).
  • the memory (204a) may be connected to the processor (202a) and may store various information related to the operation of the processor (202a).
  • the memory (204a) may perform some or all of the processes controlled by the processor (202a), or may store software codes including instructions for performing the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document.
  • the processor (202a) and the memory (204a) may be part of a communication modem/circuit/chip designed to implement wireless communication technology (e.g., LTE, NR).
  • the transceiver (206a) may be connected to the processor (202a) and may transmit and/or receive wireless signals via one or more antennas (208a).
  • the transceiver (206a) may include a transmitter and/or a receiver.
  • the transceiver (206a) may be used interchangeably with an RF (radio frequency) unit.
  • a wireless device may also mean a communication modem/circuit/chip.
  • the processor (202b) may receive a wireless signal including fourth information/signals via the transceivers (206b), and then store information obtained from signal processing of the fourth information/signals in the memory (204b).
  • the memory (204b) may be connected to the processor (202b) and may store various information related to the operation of the processor (202b).
  • the memory (204b) may perform some or all of the processes controlled by the processor (202b), or may store software codes including instructions for performing the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in this document.
  • the processor (202b) and the memory (204b) may be part of a communication modem/circuit/chip designed to implement wireless communication technology (e.g., LTE, NR).
  • the transceiver (206b) may be connected to the processor (202b) and may transmit and/or receive wireless signals via one or more antennas (208b).
  • the transceiver (206b) may include a transmitter and/or a receiver.
  • the transceiver (206b) may be used interchangeably with an RF unit.
  • a wireless device may also mean a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors (202a, 202b).
  • one or more processors (202a, 202b) may implement one or more layers (e.g., functional layers such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), service data adaptation protocol (SDAP)).
  • layers e.g., functional layers such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), service data adaptation protocol (SDAP)).
  • PHY physical
  • MAC media access control
  • RLC radio link control
  • PDCP packet data convergence protocol
  • RRC radio resource control
  • SDAP service data adaptation protocol
  • One or more processors (202a, 202b) may generate one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed in this document.
  • One or more processors (202a, 202b) can generate messages, control information, data or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed herein.
  • One or more processors (202a, 202b) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, suggestions and/or methods disclosed herein and provide the signals to one or more transceivers (206a, 206b).
  • One or more processors (202a, 202b) can receive signals (e.g., baseband signals) from one or more transceivers (206a, 206b) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed herein.
  • the one or more processors (202a, 202b) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer.
  • the one or more processors (202a, 202b) may be implemented by hardware, firmware, software, 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
  • the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc.
  • the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in this document may be implemented using firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in this document may be included in one or more processors (202a, 202b) or stored in one or more memories (204a, 204b) and executed by one or more processors (202a, 202b).
  • the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.
  • One or more memories (204a, 204b) may be coupled to one or more processors (202a, 202b) and may store various forms of data, signals, messages, information, programs, codes, instructions, and/or commands.
  • the one or more memories (204a, 204b) may be comprised of read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drives, registers, cache memory, computer readable storage media, and/or combinations thereof.
  • the one or more memories (204a, 204b) may be located internally and/or externally to the one or more processors (202a, 202b). Additionally, the one or more memories (204a, 204b) may be coupled to the one or more processors (202a, 202b) via various technologies, such as wired or wireless connections.
  • One or more transceivers (206a, 206b) can transmit user data, control information, wireless signals/channels, etc., as described in the methods and/or flowcharts of this document, to one or more other devices.
  • One or more transceivers (206a, 206b) can receive user data, control information, wireless signals/channels, etc., as described in the descriptions, functions, procedures, suggestions, methods and/or flowcharts of this document, from one or more other devices.
  • one or more transceivers (206a, 206b) can be coupled to one or more processors (202a, 202b) and can transmit and receive wireless signals.
  • one or more processors (202a, 202b) can control one or more transceivers (206a, 206b) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (202a, 202b) may control one or more transceivers (206a, 206b) to receive user data, control information, or wireless signals from one or more other devices.
  • one or more transceivers (206a, 206b) may be coupled to one or more antennas (208a, 208b), and one or more transceivers (206a, 206b) may be configured to transmit and receive user data, control information, wireless signals/channels, and the like, as referred to in the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein, via one or more antennas (208a, 208b).
  • one or more antennas may be multiple physical antennas, or multiple logical antennas (e.g., antenna ports).
  • One or more transceivers (206a, 206b) may convert received user data, control information, wireless signals/channels, etc.
  • One or more transceivers (206a, 206b) may convert processed user data, control information, wireless signals/channels, etc. from baseband signals to RF band signals using one or more processors (202a, 202b).
  • one or more transceivers (206a, 206b) may include an (analog) oscillator and/or filter.
  • FIG. 3 is a diagram illustrating another example of a wireless device applied to the present disclosure.
  • the wireless device (300) corresponds to the wireless device (200a, 200b) of FIG. 2, and may be composed of various elements, components, units/units, and/or modules.
  • the wireless device (300) may include a communication unit (310), a control unit (320), a memory unit (330), and additional elements (340).
  • the communication unit may include a communication circuit (312) and a transceiver(s) (314).
  • the communication circuit (312) may include one or more processors (202a, 202b) and/or one or more memories (204a, 204b) of FIG. 2.
  • the transceiver(s) (314) may include one or more transceivers (206a, 206b) and/or one or more antennas (208a, 208b) of FIG. 2.
  • the control unit (320) is electrically connected to the communication unit (310), the memory unit (330), and the additional elements (340) and controls overall operations of the wireless device.
  • the control unit (320) may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit (330).
  • control unit (320) may transmit information stored in the memory unit (330) to an external device (e.g., another communication device) via a wireless/wired interface through the communication unit (310), or store information received from an external device (e.g., another communication device) via a wireless/wired interface in the memory unit (330).
  • an external device e.g., another communication device
  • store information received from an external device e.g., another communication device
  • the additional element (340) may be configured in various ways depending on the type of the wireless device.
  • the additional element (340) may include at least one of a power unit/battery, an input/output unit, a driving unit, and a computing unit.
  • the wireless device (300) may be implemented in the form of a robot (FIG. 1, 100a), a vehicle (FIG. 1, 100b-1, 100b-2), an XR device (FIG. 1, 100c), a portable device (FIG. 1, 100d), a home appliance (FIG. 1, 100e), an IoT device (FIG.
  • Wireless devices may be mobile or stationary, depending on the use/service.
  • various elements, components, units/parts, and/or modules within the wireless device (300) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (310).
  • the control unit (320) and the communication unit (310) may be wired, and the control unit (320) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (310).
  • each element, component, unit/part, and/or module within the wireless device (300) may further include one or more elements.
  • the control unit (320) may be composed of one or more processor sets.
  • control unit (320) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc.
  • memory unit (330) may be composed of RAM, DRAM (dynamic RAM), ROM, flash memory, volatile memory, non-volatile memory, and/or a combination thereof.
  • FIG. 4 is a drawing illustrating an example of a portable device to which the present disclosure applies.
  • FIG. 4 illustrates an example of a mobile device to which the present disclosure applies.
  • the mobile device may include a smart phone, a smart pad, a wearable device (e.g., a smart watch, a smart glass), a portable computer (e.g., a laptop, etc.).
  • the mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).
  • MS mobile station
  • UT user terminal
  • MSS mobile subscriber station
  • SS subscriber station
  • AMS advanced mobile station
  • WT wireless terminal
  • the portable device (400) may include an antenna unit (408), a communication unit (410), a control unit (420), a memory unit (430), a power supply unit (440a), an interface unit (440b), and an input/output unit (440c).
  • the antenna unit (408) may be configured as a part of the communication unit (410).
  • Blocks 410 to 430/440a to 440c correspond to blocks 310 to 330/340 of FIG. 3, respectively.
  • the interface unit (440b) can include various ports (e.g., audio input/output ports, video input/output ports) for connection with external devices.
  • the input/output unit (440c) can input or output image information/signals, audio information/signals, data, and/or information input from a user.
  • the input/output unit (440c) can include a camera, a microphone, a user input unit, a display unit (440d), a speaker, and/or a haptic module.
  • the input/output unit (440c) obtains information/signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information/signals can be stored in the memory unit (430).
  • the communication unit (410) can convert the information/signals stored in the memory into wireless signals, and directly transmit the converted wireless signals to other wireless devices or to a base station.
  • the communication unit (410) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information/signals.
  • the restored information/signals can be stored in the memory unit (430) and then output in various forms (e.g., text, voice, image, video, haptic) through the input/output unit (440c).
  • FIG. 5 is a drawing illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.
  • FIG. 5 illustrates a vehicle or autonomous vehicle to which the present disclosure applies.
  • the vehicle or autonomous vehicle may be implemented as a mobile robot, a vehicle, a train, a manned/unmanned aerial vehicle (AV), a ship, etc., and is not limited to the form of a vehicle.
  • AV manned/unmanned aerial vehicle
  • a vehicle or autonomous vehicle may include an antenna unit (508), a communication unit (510), a control unit (520), a driving unit (540a), a power supply unit (540b), a sensor unit (540c), and an autonomous driving unit (540d).
  • the antenna unit (550) may be configured as a part of the communication unit (510). Blocks 510/530/540a to 540d correspond to blocks 410/430/440 of FIG. 4, respectively.
  • the communication unit (510) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), servers, etc.
  • the control unit (520) can control elements of a vehicle or an autonomous vehicle (500) to perform various operations.
  • the control unit (520) can include an electronic control unit (ECU).
  • FIG. 6 is a diagram illustrating an example of an AI device applied to the present disclosure.
  • the AI device may be implemented as a fixed device or a movable device, such as a TV, a projector, a smartphone, a PC, a laptop, a digital broadcasting terminal, a tablet PC, a wearable device, a set-top box (STB), a radio, a washing machine, a refrigerator, digital signage, a robot, a vehicle, etc.
  • a fixed device such as a TV, a projector, a smartphone, a PC, a laptop, a digital broadcasting terminal, a tablet PC, a wearable device, a set-top box (STB), a radio, a washing machine, a refrigerator, digital signage, a robot, a vehicle, etc.
  • STB set-top box
  • the AI device (600) may include a communication unit (610), a control unit (620), a memory unit (630), an input/output unit (640a/640b), a running processor unit (640c), and a sensor unit (640d).
  • Blocks 610 to 630/640a to 640d may correspond to blocks 310 to 330/340 of FIG. 3, respectively.
  • control unit (620) may collect history information including operation contents of the AI device (600) or user feedback on the operation, and store the information in the memory unit (630) or the learning processor unit (640c), or transmit the information to an external device such as an AI server (FIG. 1, 140).
  • the collected history information may be used to update a learning model.
  • the memory unit (630) can store data that supports various functions of the AI device (600).
  • the memory unit (630) can store data obtained from the input unit (640a), data obtained from the communication unit (610), output data of the learning processor unit (640c), and data obtained from the sensing unit (640).
  • the memory unit (630) can store control information and/or software codes necessary for the operation/execution of the control unit (620).
  • the input unit (640a) can obtain various types of data from the outside of the AI device (600).
  • the input unit (620) can obtain learning data for model learning, and input data to which the learning model is to be applied.
  • the input unit (640a) may include a camera, a microphone, and/or a user input unit.
  • the output unit (640b) may generate output related to vision, hearing, or touch.
  • the output unit (640b) may include a display unit, a speaker, and/or a haptic module.
  • the sensing unit (640) may obtain at least one of internal information of the AI device (600), surrounding environment information of the AI device (600), and user information using various sensors.
  • the sensing unit (640) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and/or a radar.
  • the learning processor unit (640c) can train a model composed of an artificial neural network using learning data.
  • the learning processor unit (640c) can perform AI processing together with the learning processor unit of the AI server (Fig. 1, 140).
  • the learning processor unit (640c) can process information received from an external device through the communication unit (610) and/or information stored in the memory unit (630).
  • the output value of the learning processor unit (640c) can be transmitted to an external device through the communication unit (610) and/or stored in the memory unit (630).
  • FIG. 7 is a diagram illustrating a method for processing a transmission signal applied to the present disclosure.
  • the transmission signal may be processed by a signal processing circuit.
  • the signal processing circuit (700) may include a scrambler (710), a modulator (720), a layer mapper (730), a precoder (740), a resource mapper (750), and a signal generator (760).
  • the operation/function of FIG. 7 may be performed in the processor (202a, 202b) and/or the transceiver (206a, 206b) of FIG. 2.
  • blocks 710 to 760 may be implemented in the processor (202a, 202b) and/or the transceiver (206a, 206b) of FIG. 2.
  • blocks 710 to 760 may be implemented in the processor (202a, 202b) of FIG. 2.
  • blocks 710 to 750 may be implemented in the processor (202a, 202b) of FIG. 2, and block 760 may be implemented in the transceiver (206a, 206b) of FIG. 2, and are not limited to the above-described embodiments.
  • the codeword can be converted into a wireless signal through the signal processing circuit (700) of FIG. 7.
  • the codeword is an encoded bit sequence of an information block.
  • the information block can include a transport block (e.g., a UL-SCH transport block, a DL-SCH transport block).
  • the wireless signal can be transmitted through various physical channels (e.g., a PUSCH, a PDSCH).
  • the codeword can be converted into a bit sequence scrambled by a scrambler (710).
  • the scramble sequence used for scrambling is generated based on an initialization value, and the initialization value can include ID information of a wireless device, etc.
  • the scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (720).
  • the modulation scheme can include pi/2-BPSK (pi/2-binary phase shift keying), m-PSK (m-phase shift keying), m-QAM (m-quadrature amplitude modulation), etc
  • the complex modulation symbol sequence can be mapped to one or more transmission layers by the layer mapper (730).
  • the modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by the precoder (740) (precoding).
  • the output z of the precoder (740) can be obtained by multiplying the output y of the layer mapper (730) by a precoding matrix W of N*M.
  • N is the number of antenna ports
  • M is the number of transmission layers.
  • the precoder (740) can perform precoding after performing transform precoding (e.g., DFT (discrete Fourier transform) transform) on the complex modulation symbols.
  • the precoder (740) can perform precoding without performing transform precoding.
  • the resource mapper (750) can map modulation symbols of each antenna port to time-frequency resources.
  • the time-frequency resources can include a plurality of symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain.
  • the signal generator (760) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna.
  • the signal generator (760) can include an inverse fast fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, etc.
  • IFFT inverse fast fourier transform
  • CP cyclic prefix
  • DAC digital-to-analog converter
  • the signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (710 to 760) of FIG. 7.
  • a wireless device e.g., 200a and 200b of FIG. 2
  • the received wireless signal can be converted into a baseband signal through a signal restorer.
  • the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module.
  • ADC analog-to-digital converter
  • FFT fast Fourier transform
  • the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process.
  • a signal processing circuit for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler and a decoder.
  • the 6G (wireless communication) system aims at (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities.
  • the vision of the 6G system can be divided into four aspects: "intelligent connectivity”, “deep connectivity”, “holographic connectivity”, and "ubiquitous connectivity", and the 6G system can satisfy the requirements as shown in Table 1 below. That is, Table 1 is a table showing the requirements of the 6G system.
  • 6G systems may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.
  • eMBB enhanced mobile broadband
  • URLLC ultra-reliable low latency communications
  • mMTC massive machine type communications
  • AI integrated communication tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.
  • FIG. 8 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.
  • 6G systems are expected to have 50 times higher simultaneous wireless communication connectivity than 5G wireless communication systems.
  • URLLC a key feature of 5G, is expected to become a more important technology in 6G communications by providing end-to-end delay of less than 1 ms.
  • 6G systems will have much better volumetric spectral efficiency than frequently used area spectral efficiency.
  • 6G systems can provide very long battery life and advanced battery technologies for energy harvesting, so that mobile devices in 6G systems may not need to be charged separately.
  • the most important and newly introduced technology in the 6G system is AI.
  • the 4G system did not involve AI.
  • the 5G system will support partial or very limited AI.
  • the 6G system will be fully AI-supported for automation.
  • Advances in machine learning will create more intelligent networks for real-time communications in 6G.
  • Introducing AI in communications can simplify and improve real-time data transmission.
  • AI can use numerous analyses to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.
  • AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication.
  • AI can also be a rapid communication in brain computer interface (BCI).
  • BCI brain computer interface
  • AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.
  • AI-based physical layer transmission means applying signal processing and communication mechanisms based on AI drivers rather than traditional communication frameworks in terms of fundamental signal processing and communication mechanisms. For example, it can include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanisms, and AI-based resource scheduling and allocation.
  • MIMO multiple input multiple output
  • Machine learning can be used for channel estimation and channel tracking, and power allocation, interference cancellation, etc. in the physical layer of the downlink (DL). Machine learning can also be used for antenna selection, power control, and symbol detection in MIMO systems.
  • Deep learning-based AI algorithms require a large amount of training data to optimize training parameters.
  • a large amount of training data is used offline. This is because static training on training data in a specific channel environment can cause a conflict between the dynamic characteristics and diversity of the wireless channel.
  • Machine learning refers to a series of operations that teach machines to create machines that can perform tasks that people can or cannot do.
  • Machine learning requires data and a learning model.
  • data learning methods can be broadly divided into three: supervised learning, unsupervised learning, and reinforcement learning.
  • Neural network learning is to minimize the error of the output.
  • Neural network learning is a process of repeatedly inputting learning data into the neural network, calculating the neural network output and target error for the learning data, and backpropagating the neural network error from the output layer of the neural network to the input layer in the direction of reducing the error, thereby updating the weights of each node of the neural network.
  • Supervised learning uses training data with correct answers labeled in the training data, while unsupervised learning may not have correct answers labeled in the training data. That is, for example, in the case of supervised learning for data classification, the training data may be data in which each category is labeled in the training data.
  • the labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data.
  • the calculated error is backpropagated in the neural network in the reverse direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation.
  • the amount of change in the connection weights of each node that is updated can be determined according to the learning rate.
  • the neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch).
  • the learning rate can be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of learning a neural network, a high learning rate can be used to allow the network to quickly achieve a certain level of performance, thereby increasing efficiency, while in the later stages of learning, a low learning rate can be used to increase accuracy.
  • the learning method may vary. For example, if the goal is to accurately predict data transmitted from the transmitter to the receiver in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.
  • the learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.
  • the neural network cores used in learning methods can be broadly divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN), and these learning models can be applied.
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN recurrent Boltzmann machines
  • THz communication can be applied in 6G systems.
  • the data transmission rate can be increased by increasing the bandwidth. This can be done by using sub-THz communication with a wide bandwidth and applying advanced massive MIMO technology.
  • FIG. 9 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure.
  • THz waves also known as sub-millimeter radiation, generally represent a frequency band between 0.1 THz and 10 THz with a corresponding wavelength in the range of 0.03 mm-3 mm.
  • the 100 GHz-300 GHz band range (Sub THz band) is considered to be a major part of the THz band for cellular communications. Adding the Sub-THz band to the mmWave band will increase the capacity of 6G cellular communications.
  • 300 GHz-3 THz is in the far-infrared (IR) frequency band.
  • the 300 GHz-3 THz band is a part of the optical band, but is at the boundary of the optical band, just behind the RF band. Therefore, this 300 GHz-3 THz band exhibits similarities with RF.
  • THz communications Key characteristics include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are essential).
  • the narrow beam widths generated by highly directional antennas reduce interference.
  • the small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This allows the use of advanced adaptive array techniques to overcome range limitations.
  • THz Terahertz
  • FIG. 10 is a diagram illustrating a THz communication method applicable to the present disclosure.
  • THz waves are located between the RF (Radio Frequency)/millimeter (mm) and infrared bands, and (i) compared to visible light/infrared rays, they penetrate non-metallic/non-polarizable materials well, and compared to RF/millimeter waves, they have a shorter wavelength, so they have high straightness and can enable beam focusing.
  • UAM Urban air mobility
  • UAM can be operated by integrating various advanced technologies such as autonomous flight technology, electric propulsion systems, and advanced air traffic management systems.
  • UAV unmanned aerial vehicle
  • the method applied to the present disclosure is not limited to UAVs. That is, it can be applied to all terminals that determine a movement path using wireless communication, such as drones, manned aircraft, and autonomous vehicles.
  • Unmanned aerial vehicles can be used for various purposes due to their advantages of free movement and low cost. Unmanned aerial vehicles can be used for various purposes such as transportation, data collection from flying base stations and IoT devices, and can be used for reconnaissance for military operations and for mounting on warships. In order to remotely control UAVs, a communication network and procedures for controlling UAVs are required.
  • a path can be set so that it can travel from the starting point to the destination in the shortest possible time.
  • the UAV While the UAV is flying, it must be connected to a ground base station for safety and route updates. Therefore, it is important for the operator to determine that the path that the UAV moves is the shortest distance or travel time, but the operator must also consider the function of preventing the state in which the communication connection with the control center cannot be maintained due to factors such as route loss and securing line-of-sight (LoS).
  • the operator can design the UAV to utilize cellular communication.
  • the UAV can communicate with a nearby base station.
  • the UAV can be connected to the control center using a backhaul network through the connected base station.
  • the base stations can act as a relay for the connection between the UAV and the control center. Therefore, the operator must devise a method for the UAV to search for an optimal movement path while maintaining a communication connection with the base station using cellular communication.
  • Reinforcement learning a type of machine learning, can be used to determine the optimal movement path.
  • Reinforcement learning is a learning method that enables optimal behavior in a given environment.
  • An agent performs a specific action in a given environment and receives a reward and the next state as a result.
  • the agent can modify its behavior pattern based on the reward, perform a new action again, and receive a reward. By repeating this, the agent can perform an action that can obtain the optimal reward.
  • UAVs can also perform reinforcement learning as agents of reinforcement learning.
  • the reward of reinforcement learning is related to the movement of the UAV and can be set as a value for optimal path search.
  • Path search technology based on reinforcement learning has the advantage of being able to search for a path even in situations where there is not much prior information about the communication environment. However, path search technology based on reinforcement learning cannot always guarantee that it can search for the optimal path, and since it is highly complex, it consumes a lot of computing resources.
  • the theoretical method is a method of solving a problem by setting variables and mathematical formulas for problem solving and finding a solution that maximizes the mathematical formula as an objective function, or finding a solution to a specific equation.
  • Representative examples of theoretical methods include convex optimization, graph theory, and dynamic programming.
  • the theoretical method has less complexity than the approach using reinforcement learning.
  • the problem of maintaining the connectivity of the UAV and finding a path can be solved through convex optimization or graph theory.
  • the convex optimization method is a method of finding the values of variables that minimize or maximize a given convex function.
  • a gradient-based approach can be used to determine the point where the convex function is maximized or minimized in a certain range.
  • Representative methods that use convex optimization or graph theory include exhaustive search (ES), exhaustive search with fixed association (ES-FA), and exhaustive search with quantization (ES-Q).
  • the ES technique is a convex optimization-based search method that searches all possible paths, and has a high complexity of NP-hard.
  • the ES-FA technique is performed similarly to the ES technique, but the complexity can be reduced by considering the path while fixing the order of visiting base stations.
  • the ES-Q technique is a method that searches for a path based on a graph whose vertices are quantized points in the overlapping area where the connectivity of each base station pair can be maintained.
  • the ES-Q technique compresses the graph form into a finite number of points and lines that are important when searching for a path by simplifying cellular communication and movement paths.
  • the ES-FA and ES-Q techniques have a complexity of NP-easy, but do not guarantee an optimal path.
  • the intersection technique is a graph theory-based path search method that uses a finite number of intersections as vertices due to the boundaries of each base station and the communication range.
  • the intersection technique compresses the communication and movement environment in a graph form and fixes the visiting order of the base stations. Therefore, the intersection technique has NP-easy complexity, but does not guarantee the optimal path.
  • path search techniques that consider the constraints of the communication interruption period, path search techniques that consider the cooperation of multiple UAVs, and path search techniques using 3D building maps and 3D radio maps are being considered.
  • Dijkstra's algorithm is an algorithm that searches for the shortest path between vertices in a graph.
  • Dijkstra's algorithm can be widely used in artificial satellites, GPS (global positioning system), etc.
  • Dijkstra's algorithm uses the fact that the shortest distance is composed of several sub-shortest distance sums. Therefore, Dijkstra's algorithm is performed by updating the shortest path known so far rather than searching all paths. Since Dijkstra's algorithm can be implemented with low complexity, it can be used efficiently even for large-sized graphs.
  • the above-described techniques have the disadvantage of not being able to find the optimal path or having a high complexity that is NP-hard when the optimal path search is possible.
  • the above-described techniques do not consider the loss due to handover between base stations when searching for the path.
  • highly mobile entities such as UAVs and autonomous vehicles may experience a degradation of communication performance due to handover, and the frequency of disconnection with the base station increases, so the loss must be considered when searching for the path.
  • handover refers to the procedure for a specific device to change the base station to maintain the communication connection when the base station must be changed due to reasons such as channel deterioration. Handover can be referred to by other terms such as handoff and is not limited to a specific name.
  • the present disclosure proposes a method for a UAV to maintain connectivity with a base station in a cellular network when performing a transport mission from a starting point to a destination point.
  • a UAV can perform a transport mission by using an optimal path determined by considering the cost due to handover in order to perform the transport mission.
  • an optimal path search method having a complexity of NP-easy for determining an optimal path search.
  • Fig. 11 illustrates an example of a transportation environment of a UAV (1110) according to one embodiment of the present disclosure. Referring to Fig. 11, it is possible to know a communication environment that may change on a route in which a UAV (1110) transports goods from a starting point to a destination point, and to determine the meaning of parameter values to be used below.
  • a three-dimensional coordinate notation consisting of the x-axis, y-axis, and z-axis is used to indicate the location in the following.
  • the UAV (1110) is located at a starting point in a terrestrial cellular network where M base stations (1120#1 to 1120#3) exist. From the destination point Assume that you are tasked with delivering goods to .
  • the second base station is It is assumed that all base stations are connected to a control center (1130) that can remotely control the UAV (1110) through a backhaul network, etc. At this time, the control center (1130) can determine the movement path of the UAV (1110).
  • UAV (1110) must maintain a connection with at least one base station to perform transportation tasks.
  • the present disclosure is The location of UAV (1110) in It is to be expressed as . Also, the speed of UAV (1110) is , the horizontal coordinate of the starting point is , the horizontal coordinate of the Mth base station (1120#M) is , the horizontal coordinate of the destination point is and visual Location of UAV (1110) in It is expressed as .
  • the UAV (1110) receives a signal from a base station when the SINR (signal to interference plus noise ratio) value is below a specific threshold value.
  • the UAV (1110) can be connected to the base station.
  • the UAV (1110) can be connected to the base station if the SINR of the signal received by the base station is higher than a specific threshold value.
  • the channel between the UAV (1110) and the base stations (1120#1 to 1120#3) can be assumed as a LoS path acquisition probability model.
  • the channel environment considers only large-scale fading.
  • large-scale fading mainly means a frequency change caused by geographical diffusion, absorption or scattering due to obstacles such as buildings and trees when a signal is transmitted from a transmitter to a receiver.
  • the LoS path acquisition probability can be set to increase as the elevation angle between the UAV (1110) and the base station increases.
  • the elevation angle means the angle that the connection line between the UAV (1110) and the base station makes with respect to the horizon. In other words, the higher the UAV (1110) is located, the easier it is to secure a LoS path toward the base station while avoiding other obstacles.
  • the UAV (1110) has a maximum horizontal distance from each base station that can be connected.
  • the maximum connectable radius is the maximum radius at which the UAV (1110) can maintain connectivity with the base station, and is not limited by any special term.
  • the maximum connectable radius may be referred to as the coverage radius of the base station. If the interference effect is considered, the UAV (1110) can be connected to the base station if the following [Mathematical Formula 1] is satisfied.
  • various parameters may be considered to determine the transport path.
  • the loss caused by performing a handover between base stations while the UAV (1110) moves may be considered.
  • the UAV (1110) performs a handover procedure a time may occur during which the connection with the base station is lost.
  • devices moving at a fast speed such as the UAV (1110) may take a longer time to complete the handover than devices moving at a slow speed.
  • the probability of the handover procedure failing may increase due to the fast speed. Therefore, in order to determine the path of the UAV (1110), the path search decision method needs to be designed to consider the loss caused by the handover.
  • the handover cost which is the loss or cost incurred when performing one handover.
  • the handover cost uses the same unit as the transportation work time. That is, the goal for route search in transportation work can be to establish a movement path and handover strategy that minimizes the weighted sum of the time required to perform the transportation work and the number of handovers.
  • the UAV (1210) can move from the starting point to the destination point within a range that does not go out of the coverage of the base stations (1220#1 to 1220#3). If the UAV goes out of the coverage of the base station, the communication connection may be interrupted. In addition, a handover procedure may be performed in an area where the coverage of the first base station (1220#1) and the coverage of the second base station (1220#2) overlap, and a handover cost may be incurred.
  • the coverage radius of each base station may be determined as a value obtained by subtracting the amount of reduction due to interference from the maximum connectable radius. That is, the objective function may be set by considering the starting point and the destination point, the speed of the UAV, and maintaining connectivity with the base station. For example, the objective function and constraints as shown in [Table 2] below may be used.
  • Is is an impulse function defined so that it can mean the total number of handovers.
  • GIM-HO generalized intersection method with hand-off
  • Fig. 13 illustrates an example of an operation procedure of the GIM-HO technique according to one embodiment of the present disclosure.
  • the control center can generate a graph and determine an optimal path to perform the GIM-HO technique.
  • Phase 1 The control center determines whether the path can be found. The control center determines whether the UAV is at the starting point. From the destination point It checks whether there is a route that can be delivered while maintaining connectivity with the base station. If there is at least one possible route, the control center proceeds with the following procedure, and if there is no route, it declares that the route search is impossible and terminates the algorithm. For example, if there is at least one route that does not go beyond the range of connection with the base station, as in Step 1 of Fig. 13, the following steps are performed.
  • Step 4 The control center searches for an optimal path and a moving path that can maintain a connection to the base station.
  • the control center generates a weighted graph using the graph vertices and edges found in the second and third preparation steps. Thereafter, the control center searches for an optimal path from a starting point to an arrival point using Dijkstra's algorithm.
  • the control center searches for an optimal base station connection strategy based on the optimal path and the base station connection order for each edge searched in Step 3. For example, as shown in FIG. 13, the UAV can move along the first line segment while maintaining a connection with the first base station (1320#2), move along the second line segment while maintaining a connection with the second base station, and move along the third line segment while maintaining a connection with the third base station.
  • the control center determines the starting point and the destination based on the customer's requirements requesting the transport mission.
  • the customer's requirements can be conveyed in various ways.
  • the control center can receive the customer's requirements entered by the user in the UAV through the base station.
  • the control center can receive the customer's requirements entered from a separate server.
  • the control center can determine the speed of the UAV.
  • the method of determining the speed can be determined in various ways and is not limited to a specific method.
  • the control center can determine the maximum critical speed at which the UAV can reach the speed for safety. Therefore, if the speed needs to be reduced due to rain or other reasons, the maximum critical speed can be set low.
  • the control center can decide to use the highest speed value within the limit that complies with the legal speed limit regulations of the area where the transportation work is performed.
  • the delivery map maintained by the control center can include legal speed limit values by area.
  • the success rate of handover may change depending on the speed of the UAV.
  • the control center may determine the handover cost based on the success rate of the handover.
  • the method of determining the handover cost may change depending on the parameter value affecting the handover success rate or the operator's purpose. For example, the handover cost may consider the channel environment index and the importance of the connectivity of the transport task, and may be determined as shown in the following [Mathematical Formula 2].
  • the weighting for speed can adjust the cost that increases the handover cost compared to speed. Therefore, if the handover is sensitive to speed, the weighting for speed can be set high. For example, Values such as can be used.
  • control center can determine parameter values for using the GIM-HO technique.
  • the procedure of the GIM-HO technique can be expressed in the following manner [Table 3].
  • the optimal path can be searched using the GIM-HO technique.
  • the GIM-HO technique uses the parameter values determined in the preparation step described above as input. Then, Step 1 to Step 4 of the GIM-HO technique described above are performed.
  • Step 1 the control center can check whether there is a route that can be delivered while maintaining connectivity with the base station using the function ChkFea. A detailed description of the function ChkFea is described later in [Table 4].
  • Step 2 the control center establishes a set of vertices of the graph.
  • Step 3 the control center establishes a set of edges of the graph.
  • the control center must check whether each edge candidate line segment is included in the set of all possible ranges of connection of all base stations, and must search for the base station connection order within the confirmed edges.
  • the function ChkOutHO can be used to calculate the minimum weight.
  • the function CHkOutHO is described later in [Table 5].
  • Step 4 the control center uses Dijkstra's algorithm to find the optimal path and the corresponding base station connection strategy. When Steps 1 to 4 are all performed, the control center outputs the optimal UAV path. and base station connection strategy can be obtained.
  • Fig. 14 illustrates an example of an optimal base station connection strategy corresponding to a given path according to one embodiment of the present disclosure.
  • the optimal path is determined to include an edge I edge ( u 0 , x 1 ) connecting a starting point u 0 and x 1 and an edge I edge ( x 1 , u f ) connecting x 1 and a destination point u F .
  • the control center establishes the connection order within the base station.
  • the control center can decide that the trunk I edge ( u 0 , x 1 ) performs connection in the order of the first base station (1420#1) and the second base station (1420#2), and that the trunk I edge ( x 1, u f ) performs connection in the order of the third base station (1420#3) and the fourth base station (1420#4).
  • the control center establishes an optimal base station connection strategy.
  • the UAV can be set to be connected to the base station with which it is currently communicating for the longest time.
  • the UAV maintains a connection with the first base station (1420#1) up to the boundary of the coverage of the currently connected first base station (1420#1), and performs a handover to the second base station (1420#2) at the boundary of the coverage of the first base station (1420#1).
  • the UAV maintains a connection with the second base station (1420#2), and performs a handover to the third base station (1420#3) at x 1 , which is the boundary of the coverage of the second base station.
  • the UAV maintains a connection with the third base station (1420#3), and performs a handover to the fourth base station (1420#4) at the boundary of the coverage of the third base station (1420#3). After that, you can arrive at u f which is within the coverage area of the 4th base station (1420#4).
  • the function ChkFea is the starting point From the destination point It can be used as a function to check whether there is a path that can perform transportation work while maintaining connectivity with the base station.
  • the function ChkFea is the starting point , destination point , and the location of each base station , maximum connectable radius and reduction in the radius of connection due to interference from base stations
  • the function ChkFea operates based on graph theory. That is, the vertex set of the graph G to be used in the function ChkFea is determined based on the starting point, the destination point, and the location of each base station.
  • the edge set corresponds to a pair of vertices that can be moved directly without connecting to other base stations.
  • the function ChkFea generates an unweighted graph G with the vertex and edge sets. After that, the function ChkFea generates an unweighted graph G with the starting point And the destination can determine whether or not the graph is connected.
  • the method for determining whether or not the graph is connected can be implemented in various ways and is not limited to a specific method.
  • the function ChkFea can determine whether or not the graph is connected using the breadth first search (BFS) algorithm.
  • the BFS algorithm is a search algorithm that first visits adjacent vertices from the starting vertex.
  • the BFS algorithm can search for the connection between the vertices of the graph with low complexity.
  • the function ChkFea outputs whether or not the starting point and the destination point are connected.
  • ChkFea outputs whether or not the search is possible if the starting point and the destination point are connected. can be set as the output value, and if the start point and the destination point are not connected, it means that navigation is not possible. can be set as an output value.
  • the function ChkOutHO can be used to calculate the minimum weight for each edge.
  • the function ChkOutHO can be expressed as shown in [Table 5] below.
  • the input of the function ChkOutHO is two vertices of the line segment x 1 , x 2 , and the destination point to specify the edge candidates. , the location of each base station , maximum connectable radius and reduction in the radius of connection due to interference from base stations .
  • the function ChkOutHO checks whether the input edge candidate is included in the set of all base stations' connectable ranges, and if the condition is satisfied, it can find the optimal base station visit order and minimum number of handovers corresponding to the edge.
  • the function ChkOutHO uses a safe interval to check whether the edge candidate is included in the set of connectable ranges.
  • the safe interval is a part where connectivity with the base station is confirmed.
  • the safe interval can be updated by adding additional parts where connectivity is confirmed.
  • the function ChkOutHO searches the connectable ranges of base stations where connectivity is confirmed from the starting point, and updates the range where connectivity is confirmed by adding the safe interval.
  • the safe interval can be initialized to an initial setting value.
  • the initial setting value can be a set that does not include any part, or it can include a confirmed safe interval that is already known.
  • the function ChkOutHO can be set to minimize the number of base stations that the UAV must connect to in order to move a given line segment in order to reduce the number of handovers. That is, the number of handovers required for the UAV to maintain connectivity with at least one base station can be set to be the minimum.
  • the base station can be set to be selected so that the longest length can be added to the safe section when the safe section is updated.
  • the function ChkOutHO indicates that the edge set includes the line segment when the safe section becomes identical to the input line segment. In the opposite case, the function ChkOutHO indicates that the edge set does not contain the corresponding line segment. Prints out . When outputting, the output of ChkOutHO is the optimal base station visit order of the edge. and the optimal number of handovers may include.
  • the optimal base station visit order and the minimum number of handovers corresponding to a specific edge can be found through the function ChkOutHO in the GIM-HO technique.
  • the GIM-HO technique can find the optimal transport path and base station connection strategy through the method expressed in [Table 3] above. In addition, the time complexity of the GIM-HO technique is am.
  • FIG. 15 illustrates an example of a method for a terminal to receive an optimized movement path according to one embodiment of the present disclosure.
  • the terminal can receive an optimized movement path from a control center and perform transportation work, etc.
  • step S1501 the terminal transmits a setup request signal to the first base station.
  • the setup request signal may include a message requesting a movement path to the control center.
  • the setup request signal may include information required by the control center.
  • control center may need at least one of the following information in the preparation phase: the starting point, the destination point, the location of each base station, the maximum connectable radius, the amount of connectable radius reduction due to interference by each base station, the speed of the terminal, and the handover cost. Accordingly, among the above-described information, information determined by the terminal or known by the terminal may be transmitted to the control center.
  • the starting point and the destination point can be determined by the control center or the terminal.
  • the setting request signal can include the starting point and the destination point.
  • the terminal can transmit the speed information to the first base station.
  • the terminal can determine the moving speed based on at least one of the moving environment or the loading level of the transported goods.
  • the terminal can transmit a setting request signal including the speed of the terminal to the first base station.
  • the terminal can transmit information about the speed in a separate message.
  • the terminal can transmit a terminal capability message including the speed of the terminal to the first base station.
  • the first base station that receives the setup request message transmits the setup request message to the control center through the backhaul network.
  • the control center determines the optimal movement path between the starting point and the destination point based on the received setup request message.
  • the optimal movement path can be determined based on the coverage and handover cost of the base stations.
  • the control center can determine the optimal movement path based on the number of handovers and the connectivity with the base station.
  • the optimal movement path can be performed based on the above-described GIM-HO technique. Therefore, the control center can determine the optimal movement path through the algorithm described above through [Table 3] to [Table 5].
  • the optimal movement path can include at least one edge.
  • the edge can be generated based on the intersections where the starting point, the destination point, and the boundary of the above-described connectable range intersect.
  • the edge considered for determining the optimal path must be included in the connectable range of the base stations.
  • the control center can determine the path where the sum of the weights of the edges connecting the starting point to the destination point is minimum as the optimal movement path.
  • the weight of each edge can be determined based on the product of the time required for the terminal to move along the edge and the number of handovers and the handover cost.
  • the handover cost can be determined in the form of [Mathematical Formula 2].
  • the weight of each edge can be set so that the number of handovers required for the terminal to maintain connectivity with at least one base station is minimized.
  • the terminal receives a configuration response signal from the first base station.
  • the control center transmits information about an optimal path determined based on the configuration information signal to the first base station.
  • the first base station transmits a configuration response signal to the terminal in order to convey the optimal movement path received from the control center. Therefore, the configuration response signal may include information about the optimal movement path. If there is no path that can move from a starting point to a destination while maintaining connectivity with at least one base station, the configuration response signal may include information about the impossibility of path search.
  • step S1505 the terminal sets a movement path based on the setting response signal.
  • the terminal can determine the movement path based on the setting response signal received from the first base station. If the terminal is a UAV, the movement path of the terminal can be set based on information about the optimal movement path included in the setting response signal. If the terminal is a case where a user can control it, such as a self-driving car, the terminal can present multiple paths including the optimal movement path to the user based on the setting response signal. Thereafter, the terminal sets the movement path based on the path selected by the user.
  • the terminal receives a reference signal from the second base station.
  • the terminal can receive a reference signal from the second base station for channel estimation.
  • the terminal transmits a measurement report to the second base station.
  • the measurement report may include a SINR value measured based on a reference signal and a location of the terminal.
  • the location of the terminal may be measured based on GPS.
  • the second base station transmits the received measurement report to the control center through a backhaul network.
  • the control center may update parameter values for determining a movement path based on the received measurement report. For example, the control center may use the received measurement report to determine a reduction in the connectable radius. can be updated. Therefore, the control center can update the base station connection radius based on the measurement report.
  • the procedure can be performed again from step S1501. In this case, the control center can determine the terminal's movement path based on the updated parameter values.
  • the control center and the first base station can be integrated and operated. That is, the first base station can perform the function of the control center, and the first base station can determine the optimal path using the procedure. Therefore, all of the procedures related to the above-described control center can be integrated and operated by the first base station.
  • the signaling between the first base station and the control center is omitted, and the signaling between the second base station and the control center can be replaced with the signaling between the second base station and the first base station.
  • FIG. 16 illustrates an example of a procedure in which a control center transmits a movement path to a terminal according to one embodiment of the present disclosure.
  • the control center can receive an optimized movement path from a terminal and perform transportation work, etc.
  • step S1601 the control center receives a configuration request signal from the terminal.
  • the first base station can transmit the configuration request signal received from the terminal to the control center.
  • the configuration request signal can include a message requesting a movement path from the control center.
  • the configuration request signal can include information required by the control center.
  • control center may need at least one of the following information in the preparation phase: the starting point, the destination point, the location of each base station, the maximum connectable radius, the amount of connectable radius reduction due to interference by each base station, the speed of the terminal, and the handover cost. Accordingly, among the above-described information, information determined by the terminal or known by the terminal may be transmitted to the control center.
  • the starting point and the destination point can be determined by the control center or the terminal.
  • the setting request signal can include the starting point and the destination point.
  • the terminal can transmit the speed information to the first base station.
  • the terminal can determine the moving speed based on at least one of the moving environment or the loading level of the transported goods.
  • the terminal can transmit a setting request signal including the speed of the terminal to the first base station.
  • information about the speed can be transmitted as a separate message.
  • the terminal can transmit a terminal capability message including the speed of the terminal to the first base station.
  • the control center determines the movement path.
  • the control center determines the optimal movement path between the starting point and the destination point based on the received setup request message.
  • the optimal movement path can be determined based on the coverage of the base stations and the handover cost.
  • the control center can determine the optimal movement path based on the number of handovers and the connectivity with the base stations.
  • the control center can determine the optimal movement path based on the above-described GIM-HO technique.
  • the first base station can directly determine the optimal path through the GIM-HO technique.
  • step S1605 the control center transmits a setting response signal to the terminal.
  • the control center transmits information about the optimal path determined based on the setting information signal to the first base station.
  • the first base station transmits the setting response signal to the terminal to convey the optimal movement path received from the control center. Therefore, the setting response signal may include information about the optimal movement path.
  • the control center receives a measurement report measured by the terminal from the second base station.
  • the control center can change parameter values for determining the movement path based on the received measurement report. For example, the control center can use the received measurement report to determine the amount of reduction in the radius of connection can be updated. If the terminal performs a new transport mission thereafter, the procedure can be performed again from step S1601. In this case, the control center can determine the movement path of the terminal based on the updated parameter values.
  • steps S1609 and 1611 may be performed. That is, in step S1609, the control center may set new parameter values and change the existing path based on the measurement report received in step S1607. For example, the control center may set the current terminal's location as the starting location, set the arrival location as the transport target point, and search for a new path.
  • FIG. 17 illustrates an example of signaling for optimizing a movement path of a terminal according to one embodiment of the present disclosure.
  • the first base station (1720#1) and the control center can be operated in an integrated manner. Accordingly, the first base station (1720#1) can determine an optimized movement path and transmit the optimized movement path to the terminal.
  • the first terminal (1710) transmits a setup request message to the first base station (1720#1).
  • the setup request message may include information for the first base station (1720#1) to determine a movement path.
  • the first base station (1720#1) transmits a setup response message to the first terminal (1710).
  • the setup response message may include a movement path determined by the first base station (1720#1).
  • steps S1715 and S1717 can be performed.
  • the first base station (1720#1) updates the moving path using the updated parameters and transmits the updated moving path to the first terminal (1710).
  • the amount of reduction in the connectable radius may be changed based on the measurement report transmitted by the second terminal to the first base station (1720#1). That is, the second terminal may transmit the measurement report to the first base station (1720#1), and the first base station (1720#1) may update the parameter values for setting the moving path and determine the updated moving path of the first terminal (1710).
  • FIG. 18 illustrates an example of signaling for optimizing a movement path and transmitting the optimized path to a first terminal (1810) by a core network (1830) according to one embodiment of the present disclosure.
  • the core network (1830) is not limited to a specific name. Accordingly, the core network (1830) may be referred to as a control center, and may refer to a device having a function separate from a base station and having a function of determining a movement path of the first terminal (1810).
  • step S1801 the first terminal (1810) transmits a setup request message to the core network (1830).
  • the setup request message may include information for the core network (1830) to determine a movement path.
  • the first base station (1820#1) may receive the setup request message from the first terminal (1810) and transmit the setup request message to the core network (1830).
  • the core network (1830) sets the movement path of the terminal based on the setup request message.
  • the movement path of the terminal can be determined through the KIM-HO technique.
  • step S1805 the core network (1830) transmits a configuration response message to the first terminal (1810).
  • the configuration response message may include a movement path determined by the first base station (1820#1).
  • the first base station (1820#1) may receive the configuration response message from the core network (1830) and transmit the configuration response message to the first terminal (1810).
  • step S1807 the first terminal (1810) sets the movement path on which it will move based on the received setting response message.
  • the terminal can set the movement path to the same path as the movement path included in the setting response message. Thereafter, the first terminal (1810) performs transportation work, etc. along the corresponding movement path.
  • the first terminal (1810) performs a measurement report procedure.
  • the first terminal (1810) may perform a measurement procedure along a moving path to perform transportation tasks, etc. Accordingly, when the first terminal (1810) is connected to the first base station (1820#1), it may receive a reference signal from the first base station (1820#1) and transmit a measurement report to the first base station (1820#1).
  • the first terminal (1810) is connected to the second base station (1820#2) while moving, it may receive a reference signal from the second base station (1820#2) and transmit a measurement report to the second base station (1820#2).
  • the first base station (1820#1) and the second base station (1820#2) transmit the measurement reports received from the terminals to the core network (1830).
  • the core network (1830) may update parameter values for path setting based on the received measurement reports. For example, the core network (1830) uses the received measurement report to determine the amount of reduction in the reachable radius. can be renewed.
  • steps S1811 and S1813 may be performed.
  • the core network (1830) can update the movement path using the updated parameters and transmit the updated movement path to the first terminal (1810).
  • the terminal is not limited to a specific device.
  • it may be a device that needs to figure out a movement path in order to move a specific section, as well as a UAV.
  • the terminal may include an autonomous vehicle, a drone, a robot, etc.
  • the terminal does not necessarily have to be operated unmanned, and a user using the terminal may receive multiple paths from the control center and select one of the paths to set a movement path.
  • the terminal may transport objects or people through an optimized movement path.
  • the GIM-HO technique proposed in this disclosure can find an optimal path compared to the exhaustive search method, while having relatively low time complexity.
  • a comparison of the GIM-HO method with other path finding techniques is as shown in [Table 6] below.
  • Optimal path search technique (*improved by reflecting hand-off) Time complexity order Maximum performance difference Exhaustive search (ES) 0 ES with fixed association (ES-FA) ES with quantization (ES-Q) Intersection technique Proposed GIM-HO technique 0
  • the ES-FA and intersection techniques can be said to have a large difference in performance from the proposed GIM-HO technique.
  • the maximum difference in performance is Value-independent and adjustable parameters for path optimization There is a difference in performance depending on the value.
  • the performance difference increases with M , resulting in significant performance degradation compared to GIM-HO.
  • the performance difference decreases with M and eventually converges to 0, but the time complexity order becomes larger than that of the optimal technique, the GIM-HO technique. Therefore, the GIM-HO technique proposed in this disclosure can be seen to be superior to other techniques when comprehensively comparing the time complexity order and the maximum difference in performance.
  • the proposed GIM-HO technique can produce the following effects.
  • the GIM-HO technique can secure the connection with the base station and determine the moving path by considering the handover cost between base stations.
  • the GIM-HO technique can find the optimal path with the complexity of NP-easy for the path search problem given in [Table 2].
  • the GIM-HO technique can solve the path search problem given in [Table 2] more efficiently than other techniques.
  • FIG. 19 illustrates an example of a simulation result in a real environment according to an embodiment of the present disclosure.
  • FIG. 19 shows the results of comparing the GIM-HO technique, the ES technique, the ES-FA technique, the ES-Q technique, and the intersection technique.
  • the evaluation index means the weighted sum of the mission execution time and the number of handovers. It can be seen that the ES technique with the highest complexity has the lowest evaluation index.
  • the proposed GIM-HO technique also has the same evaluation index as the ES technique. The remaining techniques except for the ES technique and the GIM-HO technique have relatively higher evaluation indexes, which means that the delivery time increases further.
  • FIG. 19 shows the simulation results that the GIM-HO technique can search for the same optimal path as the ES technique, and the remaining techniques search for different paths than the ES technique.
  • the GIM-HO technique can check whether there is a path that maintains the connectivity of the base station from the starting point to the destination point, as shown in Fig. 20.
  • the SINR threshold value If the reachable radius per base station is very high and small, a state in which path finding is impossible may occur, and the GIM-HO technique can detect this.
  • Figure 21 illustrates an example of a path search result by handover cost using the GIM-HO technique according to one embodiment of the present disclosure.
  • the handover cost Simulations were performed by changing the handover cost to 0s, 5s, and 20s.
  • Figure 21 shows that the evaluation index also increases as the handover cost increases.
  • the route is selected in the direction of reducing the number of handovers.
  • the transport route is determined to reduce the number of handovers even if it operates a longer distance as the handover cost increases.
  • the examples of the proposed methods described above can also be included as one of the implementation methods of the present disclosure, and thus can be considered as a kind of proposed methods.
  • the proposed methods described above can be implemented independently, but can also be implemented in the form of a combination (or merge) of some of the proposed methods.
  • Information on whether the proposed methods are applied can be defined as a rule so that the base station notifies the terminal through a predefined signal (e.g., a physical layer signal or a higher layer signal).
  • embodiments of the present disclosure can be applied to various applications such as autonomous vehicles and drones.

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Abstract

Selon la présente divulgation, un procédé de fonctionnement d'un terminal permettant de déterminer un itinéraire de déplacement d'un terminal dans un système de communication sans fil comprend les étapes consistant à : transmettre un signal de demande de configuration à une première station de base ; recevoir un signal de réponse de configuration de la première station de base sur la base du signal de demande de configuration ; définir un itinéraire de déplacement sur la base du signal de réponse de configuration ; recevoir un signal de référence d'une seconde station de base ; et transmettre un rapport de mesure à la seconde station de base d'après le signal de référence, l'itinéraire de déplacement étant déterminé d'après un coût de transfert et une plage de connexion, dans laquelle le terminal peut établir une communication pour au moins une station de base, la plage de connexion pouvant être mise à jour par le biais du rapport de mesure.
PCT/KR2023/021266 2023-12-21 2023-12-21 Système de recherche d'itinéraire optimale pour uav de livraison en tenant compte de la connectivité avec une station de base et du coût de transfert Pending WO2025135246A1 (fr)

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