WO2022045390A1 - Procédé et appareil pour la mise en œuvre d'un codage de canal au moyen d'un terminal et d'une station de base dans un système de communication sans fil - Google Patents

Procédé et appareil pour la mise en œuvre d'un codage de canal au moyen d'un terminal et d'une station de base dans un système de communication sans fil Download PDF

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Publication number
WO2022045390A1
WO2022045390A1 PCT/KR2020/011340 KR2020011340W WO2022045390A1 WO 2022045390 A1 WO2022045390 A1 WO 2022045390A1 KR 2020011340 W KR2020011340 W KR 2020011340W WO 2022045390 A1 WO2022045390 A1 WO 2022045390A1
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Prior art keywords
neural network
terminal
information
channel
data
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PCT/KR2020/011340
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English (en)
Korean (ko)
Inventor
김성진
이종구
이명희
박재용
정익주
조민석
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엘지전자 주식회사
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Priority to KR1020227046103A priority Critical patent/KR20230051433A/ko
Priority to PCT/KR2020/011340 priority patent/WO2022045390A1/fr
Publication of WO2022045390A1 publication Critical patent/WO2022045390A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0009—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the channel coding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines

Definitions

  • the following description relates to a wireless communication system, and relates to a method and apparatus for transmitting and receiving data by performing channel coding by a terminal and a base station in a wireless communication system.
  • a wireless access system is a multiple access system that can support communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.).
  • Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, and a single carrier frequency (SC-FDMA) system. division multiple access) systems.
  • 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
  • an enhanced mobile broadband (eMBB) communication technology has been proposed compared to the existing radio access technology (RAT).
  • eMBB enhanced mobile broadband
  • RAT radio access technology
  • MTC Massive Machine Type Communications
  • the present disclosure may provide a method and apparatus for performing channel coding to transmit/receive signals of a terminal and a base station in a wireless communication system.
  • the present disclosure may provide a method and apparatus for performing channel coding through learning by a terminal and a base station in a wireless communication system.
  • the present disclosure may provide a method of operating a terminal in a wireless communication system.
  • the method of operation of the terminal includes receiving a pilot signal from a first device, and generating weight information for each of a neural network of the terminal and a neural network of the first device based on the pilot signal. Step, feeding back weight information on the neural network of the first device to the first device, applying weight information on the neural network of the terminal to the neural network of the terminal, coded through the neural network of the first device It may include receiving data from the first device and performing decoding on the data through a neural network of the terminal.
  • a terminal operating in a wireless communication system is operably connected to at least one transmitter, at least one receiver, at least one processor, and the at least one processor, and when executed, the at least one
  • the processor may include at least one memory storing instructions for performing a specific operation.
  • the specific operation is: receiving a pilot signal from the first device, and generating weight information for each of the neural network of the terminal and the neural network of the first device based on the pilot signal, , feeds back weight information on the neural network of the first device to the first device, applies the weight information on the neural network of the terminal to the neural network of the terminal, and transmits data coded through the neural network of the first device
  • the data may be received from the first device, and the data may be decoded through a neural network of the terminal.
  • the terminal may communicate with at least one of a mobile terminal, a network, and an autonomous vehicle other than a vehicle including the terminal.
  • the terminal may obtain statistical information on the channel based on the plurality of received pilot signals through an artificial intelligence processing method.
  • the artificial intelligence processing method is a generative adversarial networks (GANs) method
  • GANs generative adversarial networks
  • the terminal when the terminal acquires the statistical information based on the GANs method, the terminal is a first neural network of the GANs method Creates a channel based on statistical characteristics based on Weight information on the neural network of the first device and weight information on the neural network of the terminal may be acquired through the second neural network.
  • GANs generative adversarial networks
  • data generated by the first device is converted based on a first channel coding scheme, and the converted data is converted into transmission data through a neural network of the first device to establish a wireless channel is received by the terminal through, the terminal transforms the received data through a neural network of the terminal, and decodes the converted data based on the first channel coding scheme to obtain data.
  • the first channel coding scheme may be an outer coding scheme.
  • the neural network of the terminal includes an internal neural network and an external neural network
  • the neural network of the first device includes an internal neural network and an external neural network
  • an internal neural network of the terminal and an external neural network of the first device The internal neural network may be a neural network that is learned based on long-term characteristics
  • the external neural network of the terminal and the external neural network of the first device may be neural networks that are learned based on short-term characteristics.
  • the pilot signal includes a first pilot signal and a second pilot signal, wherein the first pilot signal is a pilot signal for learning the long-term characteristic, and the second pilot signal includes: It may be a pilot signal for learning about the short-term characteristic.
  • the terminal receives a plurality of the first pilot signals from the first device, obtains statistical information about a channel based on the received plurality of first pilot signals, and Based on the statistical information, weight information on the internal neural network of the first device and weight information on the internal neural network of the terminal may be acquired.
  • the terminal may feed back weight information on the internal neural network of the first device to the first device.
  • the second pilot signal is received from the first device, instantaneous channel information is obtained based on the received second pilot signal, and the first channel information is obtained based on the instantaneous channel information.
  • weight information on the external neural network of the first device and weight information on the external neural network of the terminal may be obtained.
  • the terminal may feed back weight information on the external neural network of the first device to the first device, and apply the weight information on the external neural network of the terminal to the terminal.
  • the first device may be any one of a terminal, a base station, and a device capable of data transmission.
  • the terminal may perform learning for channel coding in consideration of the channel environment.
  • the terminal when the terminal performs learning for channel coding, the terminal may perform learning in consideration of long-term characteristics and short-term characteristics.
  • the terminal when the terminal performs learning for channel coding, the terminal may perform learning while fixing some weights.
  • the terminal may perform channel coding in consideration of a long-term channel environment and a short-term channel environment.
  • FIG. 1 is a diagram illustrating an example of a communication system applicable to the present disclosure.
  • FIG. 2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.
  • FIG. 3 is a diagram illustrating another example of a wireless device applicable to the present disclosure.
  • FIG. 4 is a diagram illustrating an example of a portable device applicable to the present disclosure.
  • FIG. 5 is a diagram illustrating an example of a vehicle or autonomous driving vehicle applicable to the present disclosure.
  • FIG. 6 is a view showing an example of a movable body applicable to the present disclosure.
  • FIG. 7 is a diagram illustrating an example of an XR device applicable to the present disclosure.
  • FIG. 8 is a view showing an example of a robot applicable to the present disclosure.
  • AI Artificial Intelligence
  • FIG. 10 is a diagram illustrating physical channels applicable to the present disclosure and a signal transmission method using the same.
  • FIG. 11 is a diagram illustrating a control plane and a user plane structure of a radio interface protocol applicable to the present disclosure.
  • FIG. 12 is a diagram illustrating a method of processing a transmission signal applicable to the present disclosure.
  • FIG. 13 is a diagram illustrating a structure of a radio frame applicable to the present disclosure.
  • FIG. 14 is a diagram illustrating a slot structure applicable to the present disclosure.
  • 15 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.
  • 16 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure.
  • 17 is a diagram illustrating a THz communication method applicable to the present disclosure.
  • FIG. 18 is a diagram illustrating a THz wireless communication transceiver applicable to the present disclosure.
  • FIG. 19 is a diagram illustrating a method for generating a THz signal applicable to the present disclosure.
  • 20 is a diagram illustrating a wireless communication transceiver applicable to the present disclosure.
  • 21 is a diagram illustrating a structure of a transmitter applicable to the present disclosure.
  • 22 is a diagram illustrating a modulator structure applicable to the present disclosure.
  • FIG. 23 is a diagram illustrating a neural network applicable to the present disclosure.
  • 24 is a diagram illustrating an activation node in a neural network applicable to the present disclosure.
  • 25 is a diagram illustrating a method of calculating a gradient using a chain rule applicable to the present disclosure.
  • 26 is a diagram illustrating a learning model based on RNN applicable to the present disclosure.
  • 27 is a view showing an autoencoder applicable to the present disclosure.
  • FIG. 28 is a diagram illustrating a neural network-based communication system applicable to the present disclosure.
  • 29 is a diagram illustrating a method of performing channel coding based on a neural network applicable to the present disclosure.
  • FIG. 30 is a diagram illustrating a method in which a transmitter and a receiver applicable to the present disclosure transmit and receive signals based on a neural network.
  • 31 is a diagram illustrating a transfer learning method applicable to the present disclosure.
  • 32 is a diagram illustrating a method of performing channel coding based on a neural network applicable to the present disclosure.
  • FIG 33 is a diagram illustrating a method in which a transmitter and a receiver applicable to the present disclosure transmit and receive signals based on a neural network.
  • 34 is a diagram illustrating a terminal operation method applicable to the present disclosure.
  • each component or feature may be considered optional unless explicitly stated otherwise.
  • Each component or feature may be implemented in a form that is not combined with other components or features.
  • some components and/or features may be combined to configure an embodiment of the present disclosure.
  • the order of operations described in embodiments of the present disclosure may be changed. Some configurations or features of one embodiment may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments.
  • the base station has a meaning as a terminal node of a network that directly communicates with the mobile station.
  • a specific operation described as being performed by the base station in this document may be performed by an upper node of the base station in some cases.
  • the 'base station' is a term such as a fixed station, a Node B, an eNB (eNode B), a gNB (gNode B), an ng-eNB, an advanced base station (ABS) or an access point (access point).
  • eNode B eNode B
  • gNode B gNode B
  • ng-eNB ng-eNB
  • ABS advanced base station
  • access point access point
  • a terminal includes a user equipment (UE), a mobile station (MS), a subscriber station (SS), a mobile subscriber station (MSS), It may be replaced by terms such as a mobile terminal or an advanced mobile station (AMS).
  • UE user equipment
  • MS mobile station
  • SS subscriber station
  • MSS mobile subscriber station
  • AMS advanced mobile station
  • a transmitting end refers to a fixed and/or mobile node that provides a data service or a voice service
  • a receiving end refers to a fixed and/or mobile node that receives a data service or a voice service.
  • the mobile station may be a transmitting end, and the base station may be a receiving end.
  • the mobile station may be the receiving end, and the base station may be the transmitting end.
  • Embodiments of the present disclosure are wireless access systems IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5G (5th generation) NR (New Radio) system, and 3GPP2 system among It may be supported by standard documents disclosed in at least one, and in particular, embodiments of the present disclosure are 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. can be
  • embodiments of the present disclosure may be applied to other wireless access systems, and are not limited to the above-described system. As an example, it may be applicable to a system applied after the 3GPP 5G NR system, and is 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 is 3GPP TS 36.xxx Release 8 or later
  • LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A
  • xxx Release 13 may be referred to as LTE-A pro.
  • 3GPP NR may mean technology after TS 38.xxx Release 15.
  • 3GPP 6G may mean technology after TS Release 17 and/or Release 18.
  • "xxx" means standard document detail number LTE/NR/6G may be collectively referred to as a 3GPP system.
  • 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 (eg, 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 extended reality (XR) device 100c, a hand-held device 100d, and a home appliance. appliance) 100e, an Internet of Things (IoT) device 100f, and an artificial intelligence (AI) device/server 100g.
  • a wireless access technology eg, 5G NR, LTE
  • XR extended reality
  • AI artificial intelligence
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous driving vehicle, a vehicle capable of performing inter-vehicle communication, and the like.
  • the vehicles 100b-1 and 100b-2 may include an unmanned aerial vehicle (UAV) (eg, a drone).
  • UAV unmanned aerial vehicle
  • the XR device 100c includes augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and includes a head-mounted device (HMD), a head-up display (HUD) provided in a vehicle, a television, It may be implemented in the form of a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.
  • the portable device 100d may include a smart phone, a smart pad, a wearable device (eg, smart watch, smart glasses), and a computer (eg, a laptop computer).
  • the home appliance 100e may include a TV, a refrigerator, a washing machine, and the like.
  • the IoT device 100f may include a sensor, a smart meter, and the like.
  • the base station 120 and the network 130 may be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices.
  • the wireless devices 100a to 100f may be connected to the network 130 through the base station 120 .
  • AI technology may be applied to the wireless devices 100a to 100f , and the wireless devices 100a to 100f may be connected to the AI server 100g through the network 130 .
  • the network 130 may be configured using a 3G network, a 4G (eg, LTE) network, or a 5G (eg, NR) network.
  • the wireless devices 100a to 100f may communicate with each other through the base station 120/network 130, but communicate directly without going through the base station 120/network 130 (eg, sidelink communication) You may.
  • the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication).
  • the IoT device 100f eg, a sensor
  • Wireless communication/connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 120 and the base station 120/base station 120 .
  • wireless communication/connection includes uplink/downlink communication 150a and sidelink communication 150b (or D2D communication), and communication between base stations 150c (eg, relay, integrated access backhaul (IAB)). This may be achieved through radio access technology (eg, 5G NR).
  • IAB integrated access backhaul
  • the wireless device and the base station/wireless device, and the base station and the base station may transmit/receive wireless signals to each other.
  • the wireless communication/connection 150a , 150b , 150c may transmit/receive signals through various physical channels.
  • various configuration information setting processes for transmission/reception of wireless signals various signal processing processes (eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.) , at least a part of a resource allocation process may be performed.
  • signal processing processes eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.
  • FIG. 2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.
  • a first wireless device 200a and a second wireless device 200b may transmit/receive wireless signals through various wireless access technologies (eg, LTE, NR).
  • ⁇ first wireless device 200a, second wireless device 200b ⁇ is ⁇ wireless device 100x, base station 120 ⁇ of FIG. 1 and/or ⁇ wireless device 100x, wireless device 100x) ⁇ can be matched.
  • the first wireless device 200a includes one or more processors 202a and one or more memories 204a, and may further 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 herein.
  • 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 through the transceiver 206a.
  • the processor 202a may receive the radio signal including the second information/signal through the transceiver 206a, and then store the information obtained from the 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 provide instructions for performing some or all of the processes controlled by the processor 202a, or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including
  • the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • a wireless communication technology eg, LTE, NR
  • the transceiver 206a may be coupled 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 a radio frequency (RF) unit.
  • RF radio frequency
  • a wireless device may refer to a communication modem/circuit/chip.
  • the second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b.
  • the processor 202b controls the memory 204b and/or the transceiver 206b and may be configured to implement the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed herein.
  • the processor 202b may process information in the memory 204b to generate third information/signal, and then transmit a wireless signal including the third information/signal through the transceiver 206b.
  • the processor 202b may receive the radio signal including the fourth information/signal through the transceiver 206b, and then store information obtained from signal processing of the fourth information/signal 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 provide instructions for performing some or all of the processes controlled by the processor 202b, or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including
  • the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • a wireless communication technology eg, LTE, NR
  • the transceiver 206b may be coupled to the processor 202b and may transmit and/or receive wireless signals via one or more antennas 208b.
  • Transceiver 206b may include a transmitter and/or receiver.
  • Transceiver 206b may be used interchangeably with an RF unit.
  • a wireless device may refer to 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) is one or more layers (eg, PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource) control) and a functional layer such as 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
  • the one or more processors 202a, 202b may be configured to process one or more protocol data units (PDUs) and/or one or more service data units (SDUs) according to the description, function, procedure, proposal, method, and/or flow charts disclosed herein. can create One or more processors 202a, 202b may generate messages, control information, data, or information according to the description, function, procedure, proposal, method, and/or operational flowcharts disclosed herein.
  • the one or more processors 202a, 202b generate a signal (eg, a baseband signal) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and/or methods disclosed herein.
  • a signal eg, a baseband signal
  • the one or more processors 202a, 202b may receive signals (eg, baseband signals) from one or more transceivers 206a, 206b, and may be described, functions, procedures, proposals, methods, and/or flowcharts of operation disclosed herein.
  • PDU, SDU, message, control information, data, or information may be acquired according to the above.
  • One or more processors 202a, 202b may be referred to as a controller, microcontroller, microprocessor, or microcomputer.
  • 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
  • firmware or software may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like.
  • the descriptions, functions, procedures, proposals, methods, and/or flow charts disclosed in this document provide that firmware or software configured to perform is included in one or more processors 202a, 202b, or stored in one or more memories 204a, 204b. It may be driven by the above processors 202a and 202b.
  • the descriptions, functions, procedures, proposals, methods, and/or flowcharts of operations disclosed herein may be implemented using firmware or software in the form of code, instructions, and/or a set of instructions.
  • One or more memories 204a, 204b may be coupled to one or more processors 202a, 202b and may store various types of data, signals, messages, information, programs, codes, instructions, and/or instructions.
  • One or more memories 204a, 204b may include 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 It may be composed of a combination of these.
  • One or more memories 204a, 204b may be located inside and/or external to one or more processors 202a, 202b. Additionally, one or more memories 204a, 204b may be coupled to one or more processors 202a, 202b through various technologies, such as wired or wireless connections.
  • the one or more transceivers 206a, 206b may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flowcharts of this document to one or more other devices.
  • the one or more transceivers 206a, 206b may receive user data, control information, radio signals/channels, etc. referred to in the descriptions, functions, procedures, suggestions, methods and/or flow charts, etc. disclosed herein, from one or more other devices. there is.
  • one or more transceivers 206a , 206b may be coupled to one or more processors 202a , 202b and may transmit and receive wireless signals.
  • one or more processors 202a, 202b may 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. Further, one or more transceivers 206a, 206b may be coupled with one or more antennas 208a, 208b, and the one or more transceivers 206a, 206b may be connected via one or more antennas 208a, 208b. , may be set to transmit and receive user data, control information, radio signals/channels, etc.
  • one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports).
  • the one or more transceivers 206a, 206b converts the received radio signal/channel, etc. from the RF band signal to process the received user data, control information, radio signal/channel, etc. using the one or more processors 202a, 202b. It can be converted into a baseband signal.
  • One or more transceivers 206a, 206b may convert user data, control information, radio signals/channels, etc. processed using one or more processors 202a, 202b from baseband signals to RF band signals.
  • one or more transceivers 206a, 206b may include (analog) oscillators and/or filters.
  • FIG. 3 is a diagram illustrating another example of a wireless device applied to the present disclosure.
  • a wireless device 300 corresponds to the wireless devices 200a and 200b of FIG. 2 , and includes various elements, components, units/units, and/or modules. ) can be composed of
  • the wireless device 300 may include a communication unit 310 , a control unit 320 , a memory unit 330 , and an additional element 340 .
  • the communication unit may include communication circuitry 312 and transceiver(s) 314 .
  • communication circuitry 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 element 340 and controls general operations of the wireless device.
  • the controller 320 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 330 .
  • control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or externally (eg, through the communication unit 310) Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 330 .
  • the additional element 340 may be configured in various ways according to 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 include a robot ( FIGS. 1 and 100a ), a vehicle ( FIGS. 1 , 100b-1 , 100b-2 ), an XR device ( FIGS. 1 and 100c ), and a mobile device ( FIGS. 1 and 100d ). ), home appliances (FIG. 1, 100e), IoT device (FIG.
  • the wireless device may be mobile or used in a fixed location depending on the use-example/service.
  • various elements, components, units/units, and/or modules in the wireless device 300 may be all interconnected through a wired interface, or at least some may be wirelessly connected through the communication unit 310 .
  • the control unit 320 and the communication unit 310 are connected by wire, and the control unit 320 and the first unit (eg, 130 , 140 ) are connected wirelessly through the communication unit 310 .
  • each element, component, unit/unit, and/or module within the wireless device 300 may further include one or more elements.
  • the controller 320 may include one or more processor sets.
  • control unit 320 may be configured as a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like.
  • memory unit 330 may include RAM, dynamic RAM (DRAM), ROM, flash memory, volatile memory, non-volatile memory, and/or a combination thereof. can be configured.
  • FIG. 4 is a diagram illustrating an example of a mobile device applied to the present disclosure.
  • the portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), and a portable computer (eg, a laptop computer).
  • 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 mobile device 400 includes 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 respectively correspond to blocks 310 to 330/340 of FIG. 3 .
  • the communication unit 410 may transmit and receive signals (eg, data, control signals, etc.) with other wireless devices and base stations.
  • the controller 420 may control components of the portable device 400 to perform various operations.
  • the controller 420 may include an application processor (AP).
  • the memory unit 430 may store data/parameters/programs/codes/commands necessary for driving the portable device 400 . Also, the memory unit 430 may store input/output data/information.
  • the power supply unit 440a supplies power to the portable device 400 and may include a wired/wireless charging circuit, a battery, and the like.
  • the interface unit 440b may support a connection between the portable device 400 and other external devices.
  • the interface unit 440b may include various ports (eg, an audio input/output port and a video input/output port) for connection with an external device.
  • the input/output unit 440c may receive or output image information/signal, audio information/signal, data, and/or information input from a user.
  • the input/output unit 440c may 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 (eg, touch, text, voice, image, video) input from the user, and the obtained information/signals are stored in the memory unit 430 . can be saved.
  • the communication unit 410 may convert the information/signal stored in the memory into a wireless signal, and transmit the converted wireless signal directly to another wireless device or to a base station. Also, after receiving a radio signal from another radio device or base station, the communication unit 410 may restore the received radio signal to original information/signal.
  • the restored information/signal may be stored in the memory unit 430 and output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 440c.
  • FIG. 5 is a diagram illustrating an example of a vehicle or autonomous driving vehicle applied to the present disclosure.
  • the vehicle or autonomous driving vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, and the like, but is not limited to the shape of the vehicle.
  • AV aerial vehicle
  • the vehicle or autonomous driving vehicle 500 includes 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 autonomous driving.
  • a unit 540d may be included.
  • the antenna unit 550 may be configured as a part of the communication unit 510 .
  • Blocks 510/530/540a to 540d respectively correspond to blocks 410/430/440 of FIG. 4 .
  • the communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) to and from external devices such as other vehicles, base stations (eg, base stations, roadside units, etc.), and servers.
  • the controller 520 may control elements of the vehicle or the autonomous driving vehicle 500 to perform various operations.
  • the controller 520 may include an electronic control unit (ECU).
  • the driving unit 540a may cause the vehicle or the autonomous driving vehicle 500 to run on the ground.
  • the driving unit 540a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like.
  • the power supply unit 540b supplies power to the vehicle or the autonomous driving vehicle 500 , and may include a wired/wireless charging circuit, a battery, and the like.
  • the sensor unit 540c may obtain vehicle state, surrounding environment information, user information, and the like.
  • the sensor unit 540c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, and a vehicle forward movement.
  • IMU inertial measurement unit
  • a collision sensor a wheel sensor
  • a speed sensor a speed sensor
  • an inclination sensor a weight sensor
  • a heading sensor a position module
  • a vehicle forward movement / may include a reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, and the like.
  • the autonomous driving unit 540d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set. technology can be implemented.
  • the communication unit 510 may receive map data, traffic information data, and the like from an external server.
  • the autonomous driving unit 540d may generate an autonomous driving route and a driving plan based on the acquired data.
  • the controller 520 may control the driving unit 540a to move the vehicle or the autonomous driving vehicle 500 along the autonomous driving path (eg, speed/direction adjustment) according to the driving plan.
  • the communication unit 510 may obtain the latest traffic information data from an external server non/periodically, and may acquire surrounding traffic information data from surrounding vehicles.
  • the sensor unit 540c may acquire vehicle state and surrounding environment information.
  • the autonomous driving unit 540d may update the autonomous driving route and driving plan based on the newly acquired data/information.
  • the communication unit 510 may transmit information about a vehicle location, an autonomous driving route, a driving plan, and the like to an external server.
  • the external server may predict traffic information data in advance using AI technology or the like based on information collected from the vehicle or autonomous vehicles, and may provide the predicted traffic information data to the vehicle or autonomous vehicles.
  • FIG. 6 is a diagram illustrating an example of a movable body applied to the present disclosure.
  • the moving object applied to the present disclosure may be implemented as at least any one of means of transport, train, aircraft, and ship.
  • the movable body applied to the present disclosure may be implemented in other forms, and is not limited to the above-described embodiment.
  • the mobile unit 600 may include a communication unit 610 , a control unit 620 , a memory unit 630 , an input/output unit 640a , and a position measurement unit 640b .
  • blocks 610 to 630/640a to 640b correspond to blocks 310 to 330/340 of FIG. 3 , respectively.
  • the communication unit 610 may transmit/receive signals (eg, data, control signals, etc.) with other mobile devices or external devices such as a base station.
  • the controller 620 may perform various operations by controlling the components of the movable body 600 .
  • the memory unit 630 may store data/parameters/programs/codes/commands supporting various functions of the mobile unit 600 .
  • the input/output unit 640a may output an AR/VR object based on information in the memory unit 630 .
  • the input/output unit 640a may include a HUD.
  • the position measuring unit 640b may acquire position information of the moving object 600 .
  • the location information may include absolute location information of the moving object 600 , location information within a driving line, acceleration information, and location information with a surrounding vehicle.
  • the position measuring unit 640b may include a GPS and various sensors.
  • the communication unit 610 of the mobile unit 600 may receive map information, traffic information, and the like from an external server and store it in the memory unit 630 .
  • the position measurement unit 640b may obtain information about the location of the moving object through GPS and various sensors and store it in the memory unit 630 .
  • the controller 620 may generate a virtual object based on map information, traffic information, and location information of a moving object, and the input/output unit 640a may display the generated virtual object on a window inside the moving object (651, 652). Also, the control unit 620 may determine whether the moving object 600 is normally operating within the driving line based on the moving object location information.
  • the control unit 620 may display a warning on the glass window of the moving object through the input/output unit 640a. Also, the control unit 620 may broadcast a warning message regarding the driving abnormality to surrounding moving objects through the communication unit 610 . Depending on the situation, the control unit 620 may transmit the location information of the moving object and information on the driving/moving object abnormality to the related organization through the communication unit 610 .
  • the XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smart phone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.
  • HMD head-up display
  • a television a smart phone
  • a computer a wearable device
  • a home appliance a digital signage
  • a vehicle a robot, and the like.
  • the XR device 700a may include a communication unit 710 , a control unit 720 , a memory unit 730 , an input/output unit 740a , a sensor unit 740b , and a power supply unit 740c .
  • blocks 710 to 730/740a to 740c may correspond to blocks 310 to 330/340 of FIG. 3 , respectively.
  • the communication unit 710 may transmit/receive signals (eg, media data, control signals, etc.) to/from external devices such as other wireless devices, portable devices, or media servers.
  • Media data may include images, images, and sounds.
  • the controller 720 may perform various operations by controlling the components of the XR device 700a.
  • the controller 720 may be configured to control and/or perform procedures such as video/image acquisition, (video/image) encoding, and metadata generation and processing.
  • the memory unit 730 may store data/parameters/programs/codes/commands necessary for driving the XR device 700a/creating an XR object.
  • the input/output unit 740a may obtain control information, data, etc. from the outside, and may output the generated XR object.
  • the input/output unit 740a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module.
  • the sensor unit 740b may obtain an XR device state, surrounding environment information, user information, and the like.
  • the sensor unit 740b includes a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, a red green blue (RGB) sensor, an infrared (IR) sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone and / or radar or the like.
  • the power supply unit 740c supplies power to the XR device 700a, and may include a wired/wireless charging circuit, a battery, and the like.
  • the memory unit 730 of the XR device 700a may include information (eg, data, etc.) necessary for generating an XR object (eg, AR/VR/MR object).
  • the input/output unit 740a may obtain a command to operate the XR device 700a from the user, and the controller 720 may drive the XR device 700a according to the user's driving command. For example, when the user intends to watch a movie or news through the XR device 700a, the controller 720 transmits the content request information through the communication unit 730 to another device (eg, the mobile device 700b) or can be sent to the media server.
  • another device eg, the mobile device 700b
  • the communication unit 730 may download/stream contents such as movies and news from another device (eg, the portable device 700b) or a media server to the memory unit 730 .
  • the controller 720 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, and metadata generation/processing for the content, and is acquired through the input/output unit 740a/sensor unit 740b It is possible to generate/output an XR object based on information about one surrounding space or a real object.
  • the XR device 700a is wirelessly connected to the portable device 700b through the communication unit 710 , and the operation of the XR device 700a may be controlled by the portable device 700b.
  • the portable device 700b may operate as a controller for the XR device 700a.
  • the XR device 700a may obtain 3D location information of the portable device 700b, and then generate and output an XR object corresponding to the portable device 700b.
  • the robot 800 may include a communication unit 810 , a control unit 820 , a memory unit 830 , an input/output unit 840a , a sensor unit 840b , and a driving unit 840c .
  • blocks 810 to 830/840a to 840c may correspond to blocks 310 to 330/340 of FIG. 3 , respectively.
  • the communication unit 810 may transmit and receive signals (eg, driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers.
  • the controller 820 may control components of the robot 800 to perform various operations.
  • the memory unit 830 may store data/parameters/programs/codes/commands supporting various functions of the robot 800 .
  • the input/output unit 840a may obtain information from the outside of the robot 800 and may output information to the outside of the robot 800 .
  • the input/output unit 840a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module.
  • the sensor unit 840b may obtain internal information, surrounding environment information, user information, and the like of the robot 800 .
  • the sensor unit 840b may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a radar, and the like.
  • the driving unit 840c may perform various physical operations, such as moving a robot joint. Also, the driving unit 840c may cause the robot 800 to travel on the ground or to fly in the air.
  • the driving unit 840c may include an actuator, a motor, a wheel, a brake, a propeller, and the like.
  • AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented as a device or a mobile device.
  • the AI device 900 includes a communication unit 910 , a control unit 920 , a memory unit 930 , input/output units 940a/940b , a learning processor unit 940c and a sensor unit 940d.
  • the communication unit 910 uses wired/wireless communication technology to communicate with external devices such as other AI devices (eg, FIGS. 1, 100x, 120, 140) or an AI server ( FIGS. 1 and 140 ) and wired/wireless signals (eg, sensor information, user input, learning model, control signal, etc.). To this end, the communication unit 910 may transmit information in the memory unit 930 to an external device or transmit a signal received from the external device to the memory unit 930 .
  • AI devices eg, FIGS. 1, 100x, 120, 140
  • an AI server FIGS. 1 and 140
  • wired/wireless signals eg, sensor information, user input, learning model, control signal, etc.
  • the controller 920 may determine at least one executable operation of the AI device 900 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the controller 920 may control the components of the AI device 900 to perform the determined operation. For example, the control unit 920 may request, search, receive, or utilize the data of the learning processor unit 940c or the memory unit 930, and may be a predicted operation among at least one executable operation or determined to be preferable. Components of the AI device 900 may be controlled to execute the operation.
  • control unit 920 collects history information including user feedback on the operation contents or operation of the AI device 900 and stores it in the memory unit 930 or the learning processor unit 940c, or the AI server ( 1 and 140), and the like may be transmitted to an external device.
  • the collected historical information may be used to update the learning model.
  • the memory unit 930 may store data supporting various functions of the AI device 900 .
  • the memory unit 930 may store data obtained from the input unit 940a , data obtained from the communication unit 910 , output data of the learning processor unit 940c , and data obtained from the sensing unit 940 .
  • the memory unit 930 may store control information and/or software codes necessary for the operation/execution of the control unit 920 .
  • the input unit 940a may acquire various types of data from the outside of the AI device 900 .
  • the input unit 920 may obtain training data for model learning, input data to which the learning model is applied, and the like.
  • the input unit 940a may include a camera, a microphone, and/or a user input unit.
  • the output unit 940b may generate an output related to sight, hearing, or touch.
  • the output unit 940b may include a display unit, a speaker, and/or a haptic module.
  • the sensing unit 940 may obtain at least one of internal information of the AI device 900 , surrounding environment information of the AI device 900 , and user information by using various sensors.
  • the sensing unit 940 may include a proximity sensor, an illumination 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, an optical sensor, a microphone, and/or a radar. there is.
  • the learning processor unit 940c may train a model composed of an artificial neural network by using the training data.
  • the learning processor unit 940c may perform AI processing together with the learning processor unit of the AI server ( FIGS. 1 and 140 ).
  • the learning processor unit 940c may process information received from an external device through the communication unit 910 and/or information stored in the memory unit 930 . Also, the output value of the learning processor unit 940c may be transmitted to an external device through the communication unit 910 and/or stored in the memory unit 930 .
  • a terminal may receive information from a base station through downlink (DL) and transmit information to a base station through uplink (UL).
  • Information transmitted and received between the base station and the terminal includes general data information and various control information, and various physical channels exist according to the type/use of the information they transmit and receive.
  • FIG. 10 is a diagram illustrating physical channels applied to the present disclosure and a signal transmission method using the same.
  • the terminal receives a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the base station, synchronizes with the base station, and can obtain information such as cell ID. .
  • P-SCH primary synchronization channel
  • S-SCH secondary synchronization channel
  • the terminal may receive a physical broadcast channel (PBCH) signal from the base station to obtain intra-cell broadcast information.
  • the UE may receive a downlink reference signal (DL RS) in the initial cell search step to check the downlink channel state.
  • DL RS downlink reference signal
  • the UE receives a physical downlink control channel (PDCCH) and a physical downlink control channel (PDSCH) according to physical downlink control channel information in step S1012 and receives a little more Specific system information can be obtained.
  • PDCCH physical downlink control channel
  • PDSCH physical downlink control channel
  • the terminal may perform a random access procedure, such as steps S1013 to S1016, to complete access to the base station.
  • the UE transmits a preamble through a physical random access channel (PRACH) (S1013), and RAR for the preamble through a physical downlink control channel and a corresponding physical downlink shared channel (S1013). random access response) may be received (S1014).
  • the UE transmits a physical uplink shared channel (PUSCH) using scheduling information in the RAR (S1015), and a contention resolution procedure such as reception of a physical downlink control channel signal and a corresponding physical downlink shared channel signal. ) can be performed (S1016).
  • PUSCH physical uplink shared channel
  • S1015 scheduling information in the RAR
  • a contention resolution procedure such as reception of a physical downlink control channel signal and a corresponding physical downlink shared channel signal.
  • the terminal After performing the procedure as described above, the terminal receives a physical downlink control channel signal and/or a physical downlink shared channel signal (S1017) and a physical uplink shared channel as a general uplink/downlink signal transmission procedure thereafter.
  • channel, PUSCH) signal and/or a physical uplink control channel (PUCCH) signal may be transmitted ( S1018 ).
  • UCI uplink control information
  • HARQ-ACK / NACK hybrid automatic repeat and request acknowledgment / negative-ACK
  • SR scheduling request
  • CQI channel quality indication
  • PMI precoding matrix indication
  • RI rank indication
  • BI beam indication
  • the UCI is generally transmitted periodically through the PUCCH, but may be transmitted through the PUSCH according to an embodiment (eg, when control information and traffic data are to be transmitted at the same time).
  • the UE may aperiodically transmit the UCI through the PUSCH.
  • FIG. 11 is a diagram illustrating a control plane and a user plane structure of a radio interface protocol applied to the present disclosure.
  • entity 1 may be a user equipment (UE).
  • the term "terminal" may be at least one of a wireless device, a portable device, a vehicle, a mobile body, an XR device, a robot, and an AI to which the present disclosure is applied in FIGS. 1 to 9 described above.
  • the terminal refers to a device to which the present disclosure can be applied and may not be limited to a specific device or device.
  • Entity 2 may be a base station.
  • the base station may be at least one of an eNB, a gNB, and an ng-eNB.
  • the base station may refer to an apparatus for transmitting a downlink signal to the terminal, and may not be limited to a specific type or apparatus. That is, the base station may be implemented in various forms or types, and may not be limited to a specific form.
  • Entity 3 may be a network device or a device performing a network function.
  • the network device may be a core network node (eg, a mobility management entity (MME), an access and mobility management function (AMF), etc.) that manages mobility.
  • the network function may mean a function implemented to perform a network function
  • entity 3 may be a device to which the function is applied. That is, the entity 3 may refer to a function or device that performs a network function, and is not limited to a specific type of device.
  • the control plane may refer to a path through which control messages used by a user equipment (UE) and a network to manage a call are transmitted.
  • the user plane may mean a path through which data generated in the application layer, for example, voice data or Internet packet data, is transmitted.
  • the physical layer which is the first layer, may provide an information transfer service to a higher layer by using a physical channel.
  • the physical layer is connected to the upper medium access control layer through a transport channel.
  • data may be moved between the medium access control layer and the physical layer through the transport channel.
  • Data can be moved between the physical layers of the transmitting side and the receiving side through a physical channel.
  • the physical channel uses time and frequency as radio resources.
  • a medium access control (MAC) layer of the second layer provides a service to a radio link control (RLC) layer, which is an upper layer, through a logical channel.
  • the RLC layer of the second layer may support reliable data transmission.
  • the function of the RLC layer may be implemented as a function block inside the MAC.
  • the packet data convergence protocol (PDCP) layer of the second layer may perform a header compression function that reduces unnecessary control information in order to efficiently transmit IP packets such as IPv4 or IPv6 in a narrow-bandwidth air interface.
  • PDCP packet data convergence protocol
  • a radio resource control (RRC) layer located at the bottom of the third layer is defined only in the control plane.
  • the RRC layer may be in charge of controlling logical channels, transport channels and physical channels in relation to configuration, re-configuration, and release of radio bearers (RBs).
  • RB may mean a service provided by the second layer for data transfer between the terminal and the network.
  • the UE and the RRC layer of the network may exchange RRC messages with each other.
  • a non-access stratum (NAS) layer above the RRC layer may perform functions such as session management and mobility management.
  • One cell constituting the base station may be set to one of various bandwidths to provide downlink or uplink transmission services to multiple terminals. Different cells may be configured to provide different bandwidths.
  • the downlink transmission channel for transmitting data from the network to the terminal includes a broadcast channel (BCH) for transmitting system information, a paging channel (PCH) for transmitting a paging message, and a downlink shared channel (SCH) for transmitting user traffic or control messages.
  • BCH broadcast channel
  • PCH paging channel
  • SCH downlink shared channel
  • a downlink multicast or broadcast service traffic or control message it may be transmitted through a downlink SCH or may be transmitted through a separate downlink multicast channel (MCH).
  • RACH random access channel
  • SCH uplink shared channel
  • a logical channel that is located above the transport channel and is mapped to the transport channel includes a broadcast control channel (BCCH), a paging control channel (PCCH), a common control channel (CCCH), a multicast control channel (MCCH), and a multicast (MTCH) channel. traffic channels), etc.
  • BCCH broadcast control channel
  • PCCH paging control channel
  • CCCH common control channel
  • MCCH multicast control channel
  • MTCH multicast
  • the transmission signal may be processed by a signal processing circuit.
  • the signal processing circuit 1200 may include a scrambler 1210 , a modulator 1220 , a layer mapper 1230 , a precoder 1240 , a resource mapper 1250 , and a signal generator 1260 .
  • the operation/function of FIG. 12 may be performed by the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 .
  • blocks 1010 to 1060 may be implemented in the processors 202a and 202b of FIG. 2 .
  • blocks 1210 to 1250 may be implemented in the processors 202a and 202b of FIG. 2
  • block 1260 may be implemented in the transceivers 206a and 206b of FIG. 2 , and the embodiment is not limited thereto.
  • the codeword may be converted into a wireless signal through the signal processing circuit 1200 of FIG. 12 .
  • the codeword is a coded bit sequence of an information block.
  • the information block may include a transport block (eg, a UL-SCH transport block, a DL-SCH transport block).
  • the radio signal may be transmitted through various physical channels (eg, PUSCH, PDSCH) of FIG. 10 .
  • the codeword may be converted into a scrambled bit sequence by the scrambler 1210 .
  • a scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device, and the like.
  • the scrambled bit sequence may be modulated by a modulator 1220 into a modulation symbol sequence.
  • the modulation method may include pi/2-binary phase shift keying (pi/2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), and the like.
  • the complex modulation symbol sequence may be mapped to one or more transport layers by a layer mapper 1230 .
  • Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 1240 (precoding).
  • the output z of the precoder 1240 may be obtained by multiplying the output y of the layer mapper 1230 by the precoding matrix W of N*M.
  • N is the number of antenna ports
  • M is the number of transport layers.
  • the precoder 1240 may perform precoding after performing transform precoding (eg, discrete fourier transform (DFT) transform) on the complex modulation symbols. Also, the precoder 1240 may perform precoding without performing transform precoding.
  • transform precoding eg, discrete fourier transform (DFT) transform
  • the resource mapper 1250 may map modulation symbols of each antenna port to a time-frequency resource.
  • the time-frequency resource may include a plurality of symbols (eg, a CP-OFDMA symbol, a DFT-s-OFDMA symbol) in the time domain and a plurality of subcarriers in the frequency domain.
  • the signal generator 1260 generates a radio signal from the mapped modulation symbols, and the generated radio signal may be transmitted to another device through each antenna.
  • the signal generator 1260 may include an inverse fast fourier transform (IFFT) module and a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like. .
  • IFFT inverse fast fourier transform
  • CP cyclic prefix
  • DAC digital-to-analog converter
  • the signal processing process for the received signal in the wireless device may be configured in reverse of the signal processing process 1210 to 1260 of FIG. 12 .
  • the wireless device eg, 200a or 200b of FIG. 2
  • the received radio signal may be converted into a baseband signal through a signal restorer.
  • the signal restorer may 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 may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a descrambling process.
  • the codeword may be restored to the original information block through decoding.
  • the signal processing circuit (not shown) for the received signal may include a signal restorer, a resource de-mapper, a post coder, a demodulator, a descrambler, and a decoder.
  • FIG. 13 is a diagram illustrating a structure of a radio frame applicable to the present disclosure.
  • Uplink and downlink transmission based on the NR system may be based on a frame as shown in FIG. 13 .
  • one radio frame has a length of 10 ms and may be defined as two 5 ms half-frames (HF).
  • One half-frame may be defined as 5 1ms subframes (subframe, SF).
  • One subframe is divided into one or more slots, and the number of slots in a subframe may depend on subcarrier spacing (SCS).
  • SCS subcarrier spacing
  • each slot may include 12 or 14 OFDM(A) symbols according to a cyclic prefix (CP).
  • CP cyclic prefix
  • each slot When a normal CP (normal CP) is used, each slot may include 14 symbols.
  • each slot may include 12 symbols.
  • the symbol may include an OFDM symbol (or a CP-OFDM symbol) and an SC-FDMA symbol (or a DFT-s-OFDM symbol).
  • Table 1 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to the SCS when the normal CP is used
  • Table 2 shows the number of slots per slot according to the SCS when the extended CSP is used. Indicates the number of symbols, the number of slots per frame, and the number of slots per subframe.
  • OFDM(A) numerology eg, SCS, CP length, etc.
  • OFDM(A) numerology eg, SCS, CP length, etc.
  • an (absolute time) interval of a time resource eg, SF, slot, or TTI
  • a TU time unit
  • NR may support multiple numerology (or subcarrier spacing (SCS)) to support various 5G services. For example, when SCS is 15kHz, it supports a wide area in traditional cellular bands, and when SCS is 30kHz/60kHz, dense-urban, lower latency and a wider carrier bandwidth, and when the SCS is 60 kHz or higher, it can support a bandwidth greater than 24.25 GHz to overcome phase noise.
  • SCS subcarrier spacing
  • the NR frequency band is defined as a frequency range of two types (FR1, FR2).
  • FR1 and FR2 may be configured as shown in the table below.
  • FR2 may mean a millimeter wave (mmW).
  • 6G (wireless) systems have (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to reduce energy consumption of battery-free IoT devices, (vi) ultra-reliable connections, and (vii) connected intelligence with machine learning capabilities.
  • the vision of the 6G system may have four aspects such as “intelligent connectivity”, “deep connectivity”, “holographic connectivity”, and “ubiquitous connectivity”, and the 6G system can satisfy the requirements shown in Table 4 below. That is, Table 4 is a table showing the requirements of the 6G system.
  • the above-described pneumatic numerology may be set differently.
  • a terahertz wave (THz) band may be used as a higher frequency band than the above-described FR2.
  • the SCS may be set to be larger than that of the NR system, and the number of slots may be set differently, and it is not limited to the above-described embodiment.
  • the THz band will be described later.
  • FIG. 14 is a diagram illustrating a slot structure applicable to the present disclosure.
  • One slot includes a plurality of symbols in the time domain. For example, in the case of a normal CP, one slot may include 7 symbols, but in the case of an extended CP, one slot may include 6 symbols.
  • a carrier includes a plurality of subcarriers (subcarrier) in the frequency domain.
  • a resource block may be defined as a plurality of (eg, 12) consecutive subcarriers in the frequency domain.
  • a bandwidth part is defined as a plurality of consecutive (P)RBs in the frequency domain, and may correspond to one numerology (eg, SCS, CP length, etc.).
  • a carrier may include a maximum of N (eg, 5) BWPs. Data communication is performed through the activated BWP, and only one BWP can be activated for one terminal.
  • N e.g. 5
  • Each element in the resource grid is referred to as a resource element (RE), and one complex symbol may be mapped.
  • RE resource element
  • the 6G system includes enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mmTC), AI integrated communication, and tactile Internet (tactile internet), high throughput (high throughput), high network capacity (high network capacity), high energy efficiency (high energy efficiency), low backhaul and access network congestion (low backhaul and access network congestion) and improved data security ( It may have key factors such as enhanced data security.
  • eMBB enhanced mobile broadband
  • URLLC ultra-reliable low latency communications
  • mmTC massive machine type communications
  • AI integrated communication e.g., eMBB
  • tactile Internet e internet
  • high throughput high network capacity
  • high energy efficiency high energy efficiency
  • low backhaul and access network congestion low backhaul and access network congestion
  • improved data security It may have key factors such as enhanced data security.
  • 15 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.
  • the 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than the 5G wireless communication system.
  • URLLC a key feature of 5G, is expected to become an even more important technology by providing an end-to-end delay of less than 1 ms in 6G communication.
  • the 6G system will have much better volumetric spectral efficiency, unlike the frequently used area spectral efficiency.
  • 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in 6G systems may not need to be charged separately.
  • new network characteristics in 6G may be as follows.
  • 6G is expected to be integrated with satellites to provide a global mobile population.
  • the integration of terrestrial, satellite and public networks into one wireless communication system could be very important for 6G.
  • AI may be applied in each step of a communication procedure (or each procedure of signal processing to be described later).
  • the 6G wireless network will deliver power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.
  • WIET wireless information and energy transfer
  • Small cell networks The idea of small cell networks was introduced to improve the received signal quality as a result of improved throughput, energy efficiency and spectral efficiency in cellular systems. As a result, small cell networks are essential characteristics for communication systems beyond 5G and Beyond 5G (5GB). Accordingly, the 6G communication system also adopts the characteristics of the small cell network.
  • Ultra-dense heterogeneous networks will be another important characteristic of 6G communication system.
  • a multi-tier network composed of heterogeneous networks improves overall QoS and reduces costs.
  • the backhaul connection is characterized as a high-capacity backhaul network to support high-capacity traffic.
  • High-speed fiber optics and free-space optics (FSO) systems may be possible solutions to this problem.
  • High-precision localization (or location-based service) through communication is one of the functions of the 6G wireless communication system. Therefore, the radar system will be integrated with the 6G network.
  • Softening and virtualization are two important functions that underlie the design process in 5GB networks to ensure flexibility, reconfigurability and programmability. In addition, billions of devices can be shared in a shared physical infrastructure.
  • AI The most important and newly introduced technology for 6G systems is AI.
  • AI was not involved in the 4G system.
  • 5G systems will support partial or very limited AI.
  • the 6G system will be AI-enabled for full automation.
  • Advances in machine learning will create more intelligent networks for real-time communication in 6G.
  • Incorporating AI into communications can simplify and enhance real-time data transmission.
  • AI can use numerous analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.
  • AI can also play an important role in M2M, machine-to-human and human-to-machine communication.
  • AI can be a rapid communication in the BCI (brain computer interface).
  • 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 a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism.
  • a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism.
  • deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanism It may include AI-based resource scheduling and allocation.
  • Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, and the like in a physical layer of a downlink (DL). In addition, machine learning may be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • DL downlink
  • machine learning may be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • Deep learning-based AI algorithms require large amounts of training data to optimize training parameters.
  • a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a wireless channel.
  • signals of a physical layer of wireless communication may be expressed as complex signals.
  • further research on a neural network for detecting a complex domain signal is needed.
  • Machine learning refers to a set of operations that trains a machine to create a machine that can perform tasks that humans can or cannot do.
  • Machine learning requires data and a learning model.
  • data learning methods can be roughly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.
  • Neural network learning is to minimize output errors. Neural network learning repeatedly inputs learning data into the neural network, calculates the output and target errors of the neural network for the training data, and backpropagates the neural network error from the output layer of the neural network to the input layer in the direction to reduce the error. ) to update the weight of each node in the neural network.
  • Supervised learning uses training data in which the correct answer is labeled in the training data, and in unsupervised learning, the correct answer may not be labeled in the training data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which categories are labeled for each of the training data.
  • the labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data.
  • the calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation.
  • the change amount of the connection weight of each node to be updated may be determined according to a learning rate.
  • the computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch).
  • the learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stage of learning a neural network, a high learning rate can be used to increase the efficiency by allowing the neural network to quickly obtain a certain level of performance, and in the late learning period, a low learning rate can be used to increase the accuracy.
  • the learning method may vary depending on the characteristics of the data. For example, when the purpose of accurately predicting data transmitted from a transmitter in a communication system is at a receiver, 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 considered. ) is called
  • the neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machine (RNN) methods. and such a learning model can be applied.
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN recurrent boltzmann machine
  • THz communication may be applied in the 6G system.
  • the data rate may be increased by increasing the bandwidth. This can be accomplished by using sub-THz communication with a wide bandwidth and applying advanced large-scale MIMO technology.
  • a THz wave also known as sub-millimeter radiation, generally represents a frequency band between 0.1 THz and 10 THz with a corresponding wavelength in the range of 0.03 mm-3 mm.
  • the 100GHz-300GHz band range (Sub THz band) is considered a major part of the THz band for cellular communication.
  • Sub-THz band Addition to mmWave band increases 6G cellular communication capacity.
  • 300GHz-3THz is in the far-infrared (IR) frequency band.
  • the 300GHz-3THz band is part of the broadband, but at the edge of the wideband, just behind the RF band. Thus, this 300 GHz-3THz band shows similarities to RF.
  • THz communication The main characteristics of THz communication include (i) widely available bandwidth to support very high data rates, and (ii) high path loss occurring at high frequencies (high directional antennas are indispensable).
  • the narrow beamwidth produced by the highly directional antenna reduces interference.
  • the small wavelength of the THz signal 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 nesting techniques that can overcome range limitations.
  • Optical wireless communication (OWC) technology is envisaged for 6G communication in addition to RF-based communication for all possible device-to-access networks. These networks connect to network-to-backhaul/fronthaul network connections.
  • OWC technology has already been used since the 4G communication system, but will be used more widely to meet the needs of the 6G communication system.
  • OWC technologies such as light fidelity, visible light communication, optical camera communication, and free space optical (FSO) communication based on a light band are well known technologies. Communication based on optical radio technology can provide very high data rates, low latency and secure communication.
  • Light detection and ranging (LiDAR) can also be used for ultra-high-resolution 3D mapping in 6G communication based on a wide band.
  • FSO The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network.
  • data transmission in an FSO system is similar to that of a fiber optic system. Therefore, FSO can be a good technology to provide backhaul connectivity in 6G systems along with fiber optic networks.
  • FSO supports high-capacity backhaul connections for remote and non-remote areas such as sea, space, underwater, and isolated islands.
  • FSO also supports cellular base station connectivity.
  • MIMO technology improves, so does the spectral efficiency. Therefore, large-scale MIMO technology will be important in 6G systems. Since the MIMO technology uses multiple paths, a multiplexing technique and a beam generation and operation technique suitable for the THz band should also be considered important so that a data signal can be transmitted through one or more paths.
  • Blockchain will become an important technology for managing large amounts of data in future communication systems.
  • Blockchain is a form of distributed ledger technology, which is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger.
  • the blockchain is managed as a peer-to-peer (P2P) network. It can exist without being managed by a centralized authority or server. Data on the blockchain is collected together and organized into blocks. Blocks are linked together and protected using encryption.
  • Blockchain in nature perfectly complements IoT at scale with improved interoperability, security, privacy, reliability and scalability. Therefore, blockchain technology provides several features such as interoperability between devices, traceability of large amounts of data, autonomous interaction of different IoT systems, and large-scale connection stability of 6G communication systems.
  • the 6G system integrates terrestrial and public networks to support vertical expansion of user communications.
  • 3D BS will be provided via low orbit satellites and UAVs. Adding a new dimension in terms of elevation and associated degrees of freedom makes 3D connections significantly different from traditional 2D networks.
  • Unmanned aerial vehicles or drones will become an important element in 6G wireless communications.
  • UAVs Unmanned aerial vehicles
  • a base station entity is installed in the UAV to provide cellular connectivity.
  • UAVs have certain features not found in fixed base station infrastructure, such as easy deployment, strong line-of-sight links, and degrees of freedom with controlled mobility.
  • the deployment of terrestrial communications infrastructure is not economically feasible and sometimes cannot provide services in volatile environments.
  • a UAV can easily handle this situation.
  • UAV will become a new paradigm in the field of wireless communication. This technology facilitates the three basic requirements of wireless networks: eMBB, URLLC and mMTC.
  • UAVs can also serve several purposes, such as improving network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, incident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.
  • Tight integration of multiple frequencies and heterogeneous communication technologies is very important in 6G systems. As a result, users can seamlessly move from one network to another without having to make any manual configuration on the device. The best network is automatically selected from the available communication technologies. This will break the limitations of the cell concept in wireless communication. Currently, user movement from one cell to another causes too many handovers in high-density networks, causing handover failures, handover delays, data loss and ping-pong effects. 6G cell-free communication will overcome all of this and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies and different heterogeneous radios of devices.
  • WIET Wireless information and energy transfer
  • WIET uses the same fields and waves as wireless communication systems.
  • the sensor and smartphone will be charged using wireless power transfer during communication.
  • WIET is a promising technology for extending the life of battery-charging wireless systems. Therefore, devices without batteries will be supported in 6G communication.
  • Autonomous wireless network is a function that can continuously detect dynamically changing environmental conditions and exchange information between different nodes.
  • sensing will be tightly integrated with communications to support autonomous systems.
  • the density of access networks in 6G will be enormous.
  • Each access network is connected by backhaul connections such as fiber optic and FSO networks.
  • backhaul connections such as fiber optic and FSO networks.
  • Beamforming is a signal processing procedure that adjusts an antenna array to transmit a radio signal in a specific direction.
  • Beamforming technology has several advantages, such as high signal-to-noise ratio, interference prevention and rejection, and high network efficiency.
  • Hologram beamforming (HBF) is a new beamforming method that is significantly different from MIMO systems because it uses a software-defined antenna. HBF will be a very effective approach for efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.
  • Big data analytics is a complex process for analyzing various large data sets or big data. This process ensures complete data management by finding information such as hidden data, unknown correlations and customer propensity. Big data is gathered from a variety of sources such as videos, social networks, images and sensors. This technology is widely used to process massive amounts of data in 6G systems.
  • the LIS is an artificial surface made of electromagnetic materials, and can change the propagation of incoming and outgoing radio waves.
  • LIS can be viewed as an extension of massive MIMO, but has a different array structure and operation mechanism from that of massive MIMO.
  • LIS is low in that it operates as a reconfigurable reflector with passive elements, that is, only passively reflects the signal without using an active RF chain. It has the advantage of having power consumption.
  • each of the passive reflectors of the LIS must independently adjust the phase shift of the incoming signal, it can be advantageous for a wireless communication channel.
  • the reflected signal can be gathered at the target receiver to boost the received signal power.
  • 17 is a diagram illustrating a THz communication method applicable to the present disclosure.
  • THz wave is located between RF (Radio Frequency)/millimeter (mm) and infrared band, (i) It transmits non-metal/non-polar material better than visible light/infrared light, and has a shorter wavelength than RF/millimeter wave, so it has high straightness. Beam focusing may be possible.
  • the frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band with low propagation loss due to absorption of molecules in the air.
  • Standardization discussion on THz wireless communication is being discussed centered on IEEE 802.15 THz working group (WG) in addition to 3GPP, and standard documents issued by TG (task group) (eg, TG3d, TG3e) of IEEE 802.15 are described in this specification. It can be specified or supplemented.
  • THz wireless communication may be applied to wireless recognition, sensing, imaging, wireless communication, THz navigation, and the like.
  • a THz wireless communication scenario may be classified into a macro network, a micro network, and a nanoscale network.
  • THz wireless communication can be applied to a vehicle-to-vehicle (V2V) connection and a backhaul/fronthaul connection.
  • V2V vehicle-to-vehicle
  • THz wireless communication in micro networks is applied to indoor small cells, fixed point-to-point or multi-point connections such as wireless connections in data centers, and near-field communication such as kiosk downloading.
  • Table 5 below is a table showing an example of a technique that can be used in the THz wave.
  • FIG. 18 is a diagram illustrating a THz wireless communication transceiver applicable to the present disclosure.
  • THz wireless communication may be classified based on a method for generating and receiving THz.
  • the THz generation method can be classified into an optical device or an electronic device-based technology.
  • the method of generating THz using an electronic device is a method using a semiconductor device such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a compound semiconductor HEMT (high electron mobility transistor) based
  • a monolithic microwave integrated circuit (MMIC) method using an integrated circuit a method using a Si-CMOS-based integrated circuit, and the like.
  • MMIC monolithic microwave integrated circuit
  • a doubler, tripler, or multiplier is applied to increase the frequency, and it is radiated by the antenna through the sub-harmonic mixer. Since the THz band forms a high frequency, a multiplier is essential.
  • the multiplier is a circuit that has an output frequency that is N times that of the input, matches the desired harmonic frequency, and filters out all other frequencies.
  • an array antenna or the like may be applied to the antenna of FIG. 18 to implement beamforming.
  • IF denotes an intermediate frequency
  • tripler and multiplier denote a multiplier
  • PA denotes a power amplifier
  • LNA denotes a low noise amplifier.
  • PLL represents a phase-locked loop.
  • FIG. 19 is a diagram illustrating a method for generating a THz signal applicable to the present disclosure.
  • FIG. 20 is a diagram illustrating a wireless communication transceiver applicable to the present disclosure.
  • the optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device.
  • the optical element-based THz signal generation technology is a technology that generates a high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. In this technology, it is easier to increase the frequency compared to the technology using only electronic devices, it is possible to generate a high-power signal, and it is possible to obtain a flat response characteristic in a wide frequency band.
  • a laser diode, a broadband optical modulator, and a high-speed photodetector are required to generate an optical device-based THz signal.
  • an optical coupler refers to a semiconductor device that transmits electrical signals using light waves to provide coupling with electrical insulation between circuits or systems
  • UTC-PD uni-travelling carrier photo- The detector
  • UTC-PD is one of the photodetectors, which uses electrons as active carriers and reduces the movement time of electrons by bandgap grading.
  • UTC-PD is capable of photodetection above 150GHz.
  • an erbium-doped fiber amplifier indicates an erbium-doped optical fiber amplifier
  • a photo detector indicates a semiconductor device capable of converting an optical signal into an electrical signal
  • the OSA indicates various optical communication functions (eg, , photoelectric conversion, electro-optical conversion, etc.) represents an optical module modularized into one component
  • DSO represents a digital storage oscilloscope.
  • FIG. 21 is a diagram illustrating a structure of a transmitter applicable to the present disclosure.
  • FIG. 22 is a diagram illustrating a modulator structure applicable to the present disclosure.
  • a phase of a signal may be changed by passing an optical source of a laser through an optical wave guide.
  • data is loaded by changing electrical characteristics through microwave contact or the like.
  • an optical modulator output is formed as a modulated waveform.
  • the photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal (nonlinear crystal), photoelectric conversion (O / E conversion) by a photoconductive antenna (photoconductive antenna), a bunch of electrons in the light beam (bunch of) THz pulses can be generated by, for example, emission from relativistic electrons.
  • a terahertz pulse (THz pulse) generated in the above manner may have a length in units of femtoseconds to picoseconds.
  • An O/E converter performs down conversion by using non-linearity of a device.
  • a number of contiguous GHz bands for fixed or mobile service use for the terahertz system are used. likely to use
  • available bandwidth may be classified based on oxygen attenuation of 10 ⁇ 2 dB/km in a spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of several band chunks may be considered.
  • the bandwidth (BW) becomes about 20 GHz.
  • Effective down conversion from the infrared band to the THz band depends on how the nonlinearity of the O/E converter is exploited. That is, in order to down-convert to a desired terahertz band (THz band), the O/E converter having the most ideal non-linearity for transfer to the terahertz band (THz band) is design is required. If an O/E converter that does not fit the target frequency band is used, there is a high possibility that an error may occur with respect to the amplitude and phase of the corresponding pulse.
  • a terahertz transmission/reception system may be implemented using one photoelectric converter. Although it depends on the channel environment, in a far-carrier system, as many photoelectric converters as the number of carriers may be required. In particular, in the case of a multi-carrier system using several broadbands according to the above-described spectrum usage-related scheme, the phenomenon will become conspicuous. In this regard, a frame structure for the multi-carrier system may be considered.
  • the down-frequency-converted signal based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame).
  • the frequency domain of the specific resource region may include a plurality of chunks. Each chunk may be composed of at least one component carrier (CC).
  • FIG. 23 is a diagram illustrating a neural network applicable to the present disclosure.
  • artificial intelligence technology may be introduced in a new communication system (e.g. 6G system).
  • artificial intelligence can utilize a neural network as a machine learning model modeled after the human brain.
  • the device may process the arithmetic operation consisting of 0 and 1, and may perform operations and communication based on this.
  • the device can process many arithmetic operations in a faster time and using less power than before.
  • humans cannot perform arithmetic operations as fast as devices.
  • the human brain may not be built to process only fast arithmetic operations.
  • humans can perform operations such as recognition and natural language processing.
  • the above-described operation is an operation for processing something more than arithmetic operation, and the current device may not be able to process to a level that a human brain can do. Therefore, it may be considered to make a system so that the device can achieve performance similar to that of a human in areas such as natural language processing and computer vision.
  • the neural network may be a model created based on the idea of mimicking the human brain.
  • the neural network may be a simple mathematical model made with the above-described motivation.
  • the human brain can consist of a huge number of neurons and synapses connecting them.
  • an action may be taken by selecting whether other neurons are also activated.
  • the neural network may define a mathematical model based on the above facts.
  • neurons are nodes, and it may be possible to create a network in which a synapse connecting neurons is an edge.
  • the importance of each synapse may be different. That is, it is necessary to separately define a weight for each edge.
  • the neural network may be a directed graph. That is, information propagation may be fixed in one direction. For example, when a non-directed edge is provided or the same direct edge is given in both directions, information propagation may occur recursively. Therefore, the result by the neural network can be complicated.
  • the neural network as described above may be a recurrent neural network (RNN). At this time, since RNN has the effect of storing past data, it is recently used a lot when processing sequential data such as voice recognition.
  • the multi-layer perceptron (MLP) structure may be a directed simple graph.
  • the MLP may be the above-described MLP unless there is a special mention of the layer below, but the present invention is not limited thereto.
  • the above-described network may be a feed-forward network, but is not limited thereto.
  • different neurons may be activated in an actual brain, and the result may be transmitted to the next neuron.
  • the result value can be activated by the neuron that makes the final decision, and the information is processed through it.
  • the above-described method is changed to a mathematical model, it may be possible to express an activation condition for input data as a function.
  • the above-described function may be referred to as an activate function.
  • the simplest activation function may be a function that sums all input data and compares it with a threshold value. For example, when the sum of all input data exceeds a specific value, the device may process information as activation. On the other hand, when the sum of all input data does not exceed a specific value, the device may process information as deactivation.
  • the activation function may have various forms.
  • Equation 1 may be defined for convenience of description. In this case, in Equation 1, it is necessary to consider not only the weight w but also the bias, and when this is taken into consideration, it can be expressed as Equation 2 below. However, since the vise (b) and the weight (w) are almost the same, only the weight is considered and described below. However, the present invention is not limited thereto. For example, the value is always 1 if you add Since is a vise, it is possible to assume a virtual input, and through this, the weight and the vise can be treated equally, and the present invention is not limited to the above-described embodiment.
  • a model based on the above can initially define the shape of a network composed of nodes and edges. After that, the model can define an activation function for each node.
  • the role of the parameter that adjusts the model takes on the weight of the edge, and finding the most appropriate weight may be a training goal of the mathematical model.
  • the following Equations 3 to 6 may be one form of the above-described activation function, and are not limited to a specific form.
  • the neural network may first determine activation of the next layer with respect to a given input, and may determine activation of the next layer according to the determined activation. Based on the above-described method, the interface may be determined by looking at the result of the last decision layer.
  • FIG. 24 is a diagram illustrating an activation node in a neural network applicable to the present disclosure.
  • as many decision nodes as the number of classes to be classified in the last layer may be created, and then a value may be selected by activating one of them.
  • a case in which activation functions of a neural network are non-linear and the functions are complexly configured while forming layers with each other may be considered.
  • weight optimization of the neural network may be non-convex optimization. Therefore, it may be impossible to find a global optimization of parameters of a neural network.
  • a method of convergence to an appropriate value using a gradient descent method may be used. For example, all optimization problems can be solved only when a target function is defined.
  • a method of minimizing a corresponding value by calculating a loss function between a target output actually desired in the final decision layer and an estimated output generated by the current network may be calculated.
  • the loss function may be as shown in Equations 7 to 9 below, but is not limited thereto.
  • Equations 7 to 9 may be loss functions for optimization.
  • the back propagation algorithm may be an algorithm capable of simply performing gradient calculation using a chain rule.
  • parallelization may also be easy.
  • memory can be saved through algorithm design. Therefore, the neural network update may use a backpropagation algorithm.
  • the backpropagation algorithm a loss is first calculated using the current parameters, and how much each parameter affects the corresponding loss can be calculated through the chain rule. An update may be performed based on the calculated value.
  • the backpropagation algorithm may be divided into two phases.
  • One may be a propagation phase, and the other may be a weight update phase.
  • an error or a change amount of each neuron may be calculated from a training input pattern.
  • the weight in the weight update phase, the weight may be updated using the previously calculated value.
  • specific phases may be as shown in Table 6 below.
  • FIG. 25 is a diagram illustrating a method of calculating a gradient using a chain rule applicable to the present disclosure.
  • a method for obtaining instead of calculating the corresponding value, it is a derivative value already calculated in the y-layer. with y-layers and only relevant to x can be used to calculate the desired value. If a parameter called x' exists under x, Wow using can be calculated. Therefore, what is needed in the backpropagation algorithm may be only two values of a derivative of a variable immediately preceding the parameter to be updated and a value obtained by differentiating the immediately preceding variable with the current parameter.
  • SDG stochastic gradient descent
  • a neural network that processes a complex number may have a number of advantages, such as a neural network description or a parameter expression.
  • a complex value neural network there may be points to be considered compared to a real neural network that processes real numbers.
  • the activation function As an example, for example, the “sigmoid function of Equation 3 ”, if t is a complex number, In the case of , f(t) becomes 0, so it is not differentiable. Therefore, activation functions generally used in real neural networks cannot be applied to complex neural networks without restrictions.
  • Equation 10 may be derived by “Liouville’s theorem” based on Table 7.
  • Equation 11 the form of the plurality of activation functions
  • Activation functions such as “sigmoid function” and “hyperbolic tangent function” used in real neural networks can be used.
  • CNNs Convolution neural networks
  • CNN may be a type of neural network mainly used in speech recognition or image recognition, but is not limited thereto.
  • CNN is configured to process multi-dimensional array data, and is specialized in multi-dimensional array processing such as color images. Therefore, most techniques using deep learning in the image recognition field can be performed based on CNN.
  • image data is processed as it is. In other words, since the entire image is considered as one data and received as input, the correct performance may not be obtained if the image position is slightly changed or distorted as above without finding the characteristics of the image.
  • CNN can process an image by dividing it into several pieces, not one piece of data. As described above, even if the image is distorted, the CNN can extract the partial characteristics of the image, so that the correct performance can be achieved. CNN may be defined in terms as shown in Table 9 below.
  • RNNs Recurrent neural networks
  • the RNN may be a type of artificial neural network in which hidden nodes are connected by directional edges to form a directed cycle.
  • the RNN may be a model suitable for processing data that appears sequentially, such as voice and text. Since RNN is a network structure that can accept input and output regardless of sequence length, it has the advantage of being able to create various and flexible structures according to needs.
  • a structure proposed to solve the “vanishing gradient” problem may be a long-short term memory (LSTM) and a gated recurrent unit (GRU). That is, the RNN may have a structure in which feedback exists compared to CNN.
  • LSTM long-short term memory
  • GRU gated recurrent unit
  • FIG. 27 is a view showing an autoencoder applicable to the present disclosure.
  • 28 to 30 are views showing a turbo autoencoder applicable to the present disclosure.
  • various attempts are being made to apply a neural network to a communication system.
  • an attempt to apply a neural network to a physical layer may mainly focus on optimizing a specific function of a receiver.
  • the channel decoder is configured as a neural network, the performance of the channel decoder may be improved.
  • a MIMO detector is implemented as a neural network in a MIMO system having a plurality of transmit/receive antennas, the performance of the MIMO system may be improved.
  • an autoencoder method may be applied.
  • the autoencoder configures both the transmitter and the receiver as a neural network, and performs optimization from an end-to-end point of view to improve performance. and may be configured as shown in FIG. 27 .
  • the communication system may operate in consideration of AI and machine learning based on deep learning technology.
  • channel coding may be performed based on machine learning.
  • a new channel coding scheme is introduced compared to the existing communication system as a channel coding scheme of LDPC (Low Density Parity Check Code) coding and polar coding.
  • the existing communication system performed channel coding through a turbo code or a tail-biting convolutional code (TBCC), and LDCP coding and polar coding could have better performance than the above-described coding schemes.
  • TBCC tail-biting convolutional code
  • LDCP coding and polar coding could have better performance than the above-described coding schemes.
  • the coding methods may be optimized and designed for an additive white gaussian noise (AWGN) channel.
  • AWGN additive white gaussian noise
  • the communication system may have to perform retransmission (e.g. HARQ, ARQ) to correct this.
  • the base station when the base station performs retransmission, the base station may need to store data to be retransmitted for retransmission.
  • the terminal receiving data from the base station may need to store the first received data in order to combine the previously received data with the retransmitted data.
  • the terminal and the base station may need to have a memory (memory).
  • the throughput of the communication system may decrease.
  • resources may be wasted based on the retransmission.
  • the communication system performs communication based on an encoder/decoder suitable for a link environment to reduce the transmission error occurrence probability, and reduce the retransmission rate and turn-around delay. can be reduced
  • a method of performing channel coding in consideration of a channel environment that varies when actual communication is performed is not considered. Accordingly, the following describes a method of effectively adapting a communication system to a wireless channel situation according to a wireless channel situation when channel coding is performed using a transmission/reception neural network communication system or an auto encoder.
  • the channel environment may be divided into a channel environment that gradually changes in a long-term and a channel environment that changes rapidly in a short-term such as fast fading.
  • a method for adapting to a radio channel in consideration of the above-described long-term changing channel environment and short-term changing channel characteristics will be described.
  • FIG. 28 is a diagram illustrating a neural network-based communication system applicable to the present disclosure.
  • FIG. 28 may be a detailed diagram of a communication method based on the auto-encoder based on FIG. 27 described above.
  • the conventional autoencoder communication method may perform communication using a transmitter neural network (or a transmitter neural net, 2810) and a receiver neural network (or a receiver neural net, 2830).
  • the transmitter neural network 2810 transmits the target signal ( ) to transmit signal ( ) can play a role in converting
  • the transmitting end transmits the converted signal ( ) may be transmitted to the receiving end through the wireless channel 2820 .
  • the transmission signal ( ) is the received signal ( ) can be received by the receiving end.
  • the receiving part neural network 2830 of the receiving end receives the received signal ( ) from the decoded signal ( ) can play a role in acquiring
  • the transmitter neural network 2810 and the receiver neural network 2830 need to perform learning in consideration of the actual channel environment change.
  • the characteristics of the radio channel may vary depending on the situation in mobile communication. Therefore, even if the neural network of the transmitter 2810 and the neural network 2830 of the receiver perform learning, if they are not adaptively optimized in an actual communication situation, there may be a limit in ensuring excellent performance of the auto-encoder. Accordingly, a method of applying a neural network (or a neural net) in consideration of a change in a wireless channel environment will be described below.
  • the transmitting end may be at least one of a terminal and a base station.
  • the transmitting end may be any one of apparatuses for transmitting a signal based on FIGS. 4 to 9 described above. That is, the transmitting end may refer to a device for transmitting a signal, and is not limited to a specific terminal or device.
  • the receiving end may be at least one of a terminal and a base station.
  • the transmitting end may be any one of apparatuses for transmitting a signal based on FIGS. 4 to 9 described above. That is, the transmitting end may refer to a device for transmitting a signal, and is not limited to a specific terminal or device.
  • FIG. 29 is a diagram illustrating a method of performing channel coding based on a neural network applicable to the present disclosure.
  • the transmitter neural network and the receiver neural network may be trained through statistical simulation as shown in FIG. 29 .
  • the transmitter 2910 transmits a pilot transmission signal ( ) may be transmitted to the receiver 2930 .
  • the pilot signal is a signal that the transmitter 2910 and the receiver 2930 mutually recognize in advance, and may be a signal for measuring a channel environment.
  • the pilot signal passing through the wireless communication channel 2920 is transmitted to the receiver 2930 as a pilot reception signal ( ) can be received.
  • the receiver 2930 receives the received pilot signal ( ) may be acquired through the statistical channel prediction part 2933 to obtain cumulative statistical information.
  • the statistical channel prediction part 2933 of the receiver is How to create a statistical channel by accumulating for a certain period of time ( ⁇ ⁇ ) may be transferred to the simulation-based neural net weight update part 2934 .
  • the statistical channel prediction part 2933 is based on the following Equation 12, the received pilot signal ( ) from the channel ( ) can be measured.
  • K may be the length of the pilot symbol.
  • the neural net weight update part 2934 may generate weights for both the transmitter neural network (or the transmitter neural net, 2912 ) and the receiver neural network (or the receiver neural net, 2931 ) through a statistical channel. That is, the receiver 2930 may determine weights by accumulating pilot signals transmitted from the transmitter 2910 and learning the transmitter 2910 and the receiver 2930 . Thereafter, the receiver may transmit information necessary for the transmitter neural network 2912 among the generated weight information to the transmitter 2910 through feedback. Through this, the transmitter 2910 may update the transmitter neural network 2912 with the feedback weight.
  • the statistical channel prediction part 2933 may use an existing signal processing method. Also, as an example, the statistical channel prediction part 2933 may use an artificial intelligence processing method for high accuracy. As an example, when the statistical channel prediction part 2933 uses an artificial intelligence processing method, the statistical channel may be processed in a manner that increases the description of the nonlinear characteristics of the channel or the utilization of prior information on the channel.
  • the AI processing method may be a method using generative adversarial networks (GANs).
  • GAN generative adversarial networks
  • the statistical channel when the statistical channel is generated based on the GAN method, the statistical channel may be generated based on Equation 13 below.
  • the GAN can continuously generate and output statistically similar channel vectors.
  • two neural networks (or neural nets) can be used as D and G in Equation 13 below.
  • G may play a role in creating a channel based on statistical characteristics.
  • D may play a role of checking whether the generated channel is statistically similar to the existing channel.
  • the two neural networks described above may be trained competitively with each other based on Equation 13 below. After that, after learning is completed, the statistical characteristics of the channel data used for input and the statistical characteristics of the generated channel may be output very similarly.
  • the simulation-based neural net weight generation part 2934 in the receiver 2930 may generate weight information of the transmitter 2910 and the receiver 2930 through learning.
  • the simulation-based neural net weight generation part 2934 is a statistical channel generation method ( ⁇ ⁇ ) can be passed.
  • the simulation-based neural net weight generation part 2934 uses the received statistical channel generation method ( ⁇ ⁇ ), a received signal can be modeled through Equation 14 below.
  • the channel you created may be the added AWGN noise.
  • learning of the transmitter neural network 2912 and the receiver neural network 29310 may be performed based on Equation 15 below using the modeled signal.
  • each Wow It may be a weight of a neural network used for also, may be optimized based on Equation 16 below.
  • implementation of the optimization may use stochastic gradient descent (SGD) based on Equation 17 below.
  • SGD stochastic gradient descent
  • the SGD method can optimize the weights for the transmitter neural network 2912 and the receiver neural network 2931 by repeatedly performing Equation 17 below.
  • ⁇ may be a learning rate parameter.
  • the simulation-based neural net weight generation part 2934 may transmit information W_Rx applied to the receiver neural network 2931 to the receiver neural network 2931 . Thereafter, the transmitter 2910 and the receiver 2930 may communicate based on the learned neural network of the transmitter 2912 and the neural network of the receiver 2931 .
  • the transmitter 2910 may transmit a pilot symbol to the receiver 2930 and may perform learning based on accumulated statistical information on the pilot symbol, as described above.
  • the channel coding information may be information that changes from a long-term perspective.
  • the transmitter 2910 may transmit the pilot symbol to the receiver 2930 based on a constant period.
  • the receiver 2930 may obtain channel prediction information through the received pilot symbol and accumulate channel prediction information for a plurality of pilot symbols based on a predetermined period. Thereafter, the receiver 2930 may generate weight information for the transmitter neural network 2912 and the receiver neural network 2931 based on the accumulated channel prediction information.
  • the receiver 2930 may transmit weight information and related information of the transmitter neural network 2912 to the transmitter 2910 .
  • the receiver 2930 performs learning by accumulating channel prediction information measured through pilot symbols transmitted based on a predetermined period from a long-term perspective, and the receiver 2930 feeds back to the transmitter 2910 .
  • the amount of information may be small, but is not limited thereto.
  • the transmitter neural network 2912 and the receiver neural network 2931 may perform data transmission/reception based on the learned information.
  • the transmitter 2910 is a signal source ( ) through the channel coding encoder 2911 can be converted to
  • the transmitter neural network 2912 may receive the information learned by the receiver 2930 and may be in a state where learning is completed.
  • the receiver neural network 2931 may be in a state in which learning is completed by the receiver 2930 .
  • the transmit signal is received signal to the receiver 2930 through the wireless communication channel 2920 can be received as
  • the received signal is passed through the received neural network 2931 learned as described above.
  • the channel coding decoder (2932) can be decrypted as
  • the channel coding decoder 2932 may have an optional configuration. That is, the received signal Is can be
  • the transmitter 2910 and the receiver 2930 may perform data communication.
  • the receiver 2930 may be a terminal.
  • the terminal may perform the above-described operation in consideration of the main communication environment as an environment in which communication is mainly performed.
  • the terminal may perform the above-described operation in a communication environment mainly used as a specific indoor environment.
  • the above-described operation may be performed in an outdoor environment or another environment as the main communication environment of the terminal, and the embodiment is not limited thereto.
  • the transmitter 2910 and the receiver 2920 may be applied not only to cellular communication but also to other wireless communication systems such as WiFi communication or Vehicle to Everything (V2X). More specifically, when performing WiFi communication, the terminal as the receiver 2920 may perform WiFi communication in the main communication area of the terminal.
  • the transmitter 2910 may be an access point (AP).
  • AP access point
  • the UE may generate weights of the transmitter (AP) and the receiver based on the accumulated statistical channel prediction information based on the pilot signal, and transmit the information to the AP. Through this, the terminal can perform learning in the main channel environment.
  • the terminal when the terminal performs V2X communication, the terminal may perform communication under the control of the base station or perform communication without the control of the base station. For example, when the terminal performs communication with another terminal under the control of the base station, the terminal as a receiver generates weights of the transmitter and the receiver based on the accumulated statistical channel prediction information based on the pilot signal as described above, and for this Information can be transmitted to the transmitter.
  • the terminal since the terminal can perform communication with the base station as well as the terminal-to-device communication, it can receive a pilot signal from each of a plurality of transmitters and perform respective learning. That is, the above-described operation may be performed for each transmitter, and is not limited to the above-described embodiment.
  • the receiver terminal when performing inter-terminal communication without base station control, receives a pilot signal from the transmitter terminal, generates weights of the transmitter and the receiver based on statistical channel prediction information based on this, and information on this can be transmitted to the transmitter (terminal).
  • the receiver terminal receives a pilot signal from the transmitter terminal, generates weights of the transmitter and the receiver based on statistical channel prediction information based on this, and information on this can be transmitted to the transmitter (terminal).
  • the receiver terminal when performing inter-terminal communication without base station control, receives a pilot signal from the transmitter terminal, generates weights of the transmitter and the receiver based on statistical channel prediction information based on this, and information on this can be transmitted to the transmitter (terminal).
  • it is not limited to the above-described embodiment.
  • the receiver 2930 when the receiver 2930 performs learning on the transmitter neural network 2912 and the receiver neural network 2931, if the code size is large, learning may not be smooth. For example, when the receiver 2930 performs the above-described learning, the code size may affect the learning. Here, the learning complexity may increase exponentially based on the code size. In consideration of the above, when the receiver 2930 performs learning, the receiver 2930 may perform learning based on a limited code size. Accordingly, the learned code size may be limited, and the transmitter 2910 and the receiver 2930 may perform communication based on the limited code size.
  • the channel coding encoder 2911 of the transmitter may be included in the transmitter, and the channel coding decoder 2932 of the receiver may be included in the receiver, thereby adjusting the code size. That is, in the communication system, an existing channel code (e.g. outer code) may be applied to the outside of the auto-encoder, which is the transmitter neural network 2912 and the receiver neural network 2931 .
  • an existing channel code e.g. outer code
  • the transmitter 2910 and the receiver 2930 perform communication
  • average performance may be secured by the above-described existing channel code.
  • a portion that is flexibly changed in consideration of the channel environment may be covered through the above-described auto encoder.
  • both an existing channel coder and an auto-encoder may be applied to the transmitter 2910 and the receiver 2920 , thereby improving coding performance.
  • the channel coding encoder 2911 and the channel coding decoder 2932 may have optional components, and it may be possible to perform communication except for the above-described components.
  • FIG. 30 is a diagram illustrating a method in which a transmitter and a receiver applicable to the present disclosure transmit and receive signals based on a neural network.
  • a transmitter 3010 and a receiver 3020 may perform data communication and may perform learning based on the above-described FIG. 29 .
  • the transmitter 3010 and the receiver 3020 may transmit a pilot signal.
  • the pilot signal may be transmitted from a long-term perspective based on a constant period.
  • the receiver 3020 may perform channel prediction based on the received pilot signal.
  • the receiver 3020 may acquire statistical information by accumulating channel predictions for pilot signals transmitted over a long period of time based on a predetermined period. Through this, the receiver 3020 may generate weight information on the neural network applied to the transmitter and the neural network applied to the receiver based on the statistical information. Thereafter, the receiver 3020 may feed back weight information about the neural network applied to the transmitter to the transmitter 3010 .
  • the receiver 3020 may feed back weight information and information related to the transmitter neural network together to the transmitter 3010, and the embodiment is not limited thereto. Thereafter, the transmitter 3010 may apply the weight received from the receiver 3020 to the transmitter neural network. Thereafter, the transmitter 3010 may transmit data to the receiver 3020 based on the applied weight. (S3003) In this case, the receiver 3020 may detect data received by applying it to the learned receiver neural network based on the above description, and data communication may be performed through this.
  • 31 is a diagram illustrating a transfer learning method applicable to the present disclosure.
  • the characteristics of the communication channel can be divided into a long-term change characteristic according to a change in an environment (long-term) and a short-term change characteristic such as fading (short-term). For example, when the device performs learning as a whole based on artificial intelligence, learning must be performed for a long time, and short-term channel characteristics cannot be properly reflected.
  • FIG. 31 may be a diagram illustrating a case in which transfer learning is performed.
  • transfer learning may be a method in which weights in a specific layer of a neural network (or neural net) are fixed and only the remaining weights are learned. That is, when transfer learning is performed, the fixed weights can be learned in advance with more data.
  • weights that are not fixed can be trained with a new data set. That is, some weights are fixed and the learning time can be reduced by learning some weights, and through this, learning can be performed by reflecting short-term change characteristics. This will be described later.
  • 32 is a diagram illustrating a method of performing channel coding based on a neural network applicable to the present disclosure.
  • the learning of channel coding e.g. GAN structure
  • learning of channel characteristics can be efficiently performed based on transfer learning.
  • a portion of the channel component that does not change regardless of a user's location or viewpoint may be learned in advance as a long-term characteristic.
  • learning for the above-described channel coding learning may be variably performed on short-term characteristics that change in real time among channel components. That is, the learning can be performed in the form of changing only the rear part of the generative network or the discrimination network based on the part for the long-term characteristics learned in advance.
  • the transmitter 3210 transmits a pilot transmission signal ( ) may be transmitted to the receiver 3230 .
  • the pilot signal passing through the wireless communication channel is transmitted to the receiver 3230 as a pilot reception signal ( ) can be received.
  • the statistical channel prediction part 3234 of the receiver is How to create a statistical channel by accumulating for a certain period of time ( ⁇ ⁇ ) may be transmitted to the simulation-based neural net weight update part 3235 .
  • the statistical channel prediction part 3234 may operate based on Equation 18 below, which may be the same as in FIG. 29 described above. That is, the receiver 3230 transmits the pilot received signal ( ) from the channel ( ) can be measured.
  • K corresponds to the length of the pilot symbol.
  • the operation of the neural net weight generation part 3235 based on simulation of the receiver may be performed separately from long-term learning and short-term learning.
  • the operation of the neural net weight generation part 3235 based on the simulation of the receiver may perform each learning based on a long period and a short period.
  • the receiver's simulation-based neural net weight generating part 3235 is delivered through the statistical channel prediction part 3234.
  • the received signal may be modeled based on Equation 19 below.
  • the neural net weight generation part 3235 based on the simulation of the receiver uses the modeled signal and based on a binary cross entropy (BCE) loss function as shown in Equation 20 below, a transmitter neural network (or a transmitter neural net) and a receiver neural network (or a receiver) neural net) can be learned.
  • BCE binary cross entropy
  • each It may be a weight of a neural network used for Meanwhile, as an example, the loss function may be used as a mean square error (MSE) method as shown in Equation 21 below.
  • MSE mean square error
  • the loss function may perform optimization based on Equation 22 below.
  • the implementation of the above-described optimization may use stochastic gradient descent (SGD) as shown in Equation 23 below.
  • SGD stochastic gradient descent
  • the SGD scheme may optimize the weights of the transmitter neural network and the receiver neural network by repeatedly performing Equation 23.
  • ⁇ is the learning rate parameter.
  • the loss function may be used as in Equation 24 below by combining the above-described methods in order to properly harmonize the learning rate and the detection error optimization, and is not limited to the above-described embodiment.
  • the MSE can obtain the inter-bit distance, so that learning can be performed quickly.
  • BEC can obtain the error rate between two bits, so learning is slow but better performance can be guaranteed.
  • the value of a may be adjusted in consideration of the above description.
  • the weight of the MSE may be increased by setting a large value a.
  • the specific gravity of BCE may be increased by setting the value a to be small, and the present invention is not limited to the above-described embodiment.
  • weights may be optimized.
  • among the optimized weights can be applied to the two receiver neural networks 3231 and 3232 in the terminal (or receiver, 3230).
  • the neural network for signal processing corresponding to a long cycle is can be may be an inner neural network (3240).
  • neural networks for signal processing corresponding to short-term cycles can be may be an external neural network (3250).
  • the above-described learning based on short-term characteristics may not be updated. Since it is possible to update when learning a short-term signal processing neural network, it may not be updated when learning a signal processing corresponding to a long cycle in consideration of the long-term characteristics.
  • this is only one example and is not limited to the above-described embodiment.
  • the receiver (or terminal, 3230) may perform a weight update period in a short-term unit.
  • the receiver 3230 may model the received signal using the currently measured channel based on Equation 25 below.
  • the receiver 3230 may learn the transmitter neural network and the receiver neural network based on the loss function of Equation 26 using the modeled signal.
  • implementation of the optimization may use stochastic gradient descent (SGD) as shown in Equation 28 below.
  • SGD stochastic gradient descent
  • the SGD scheme may optimize the weights of the transmitter neural network and the receiver neural network by repeatedly performing Equation 28.
  • ⁇ is the learning rate parameter.
  • ⁇ is the learning rate parameter.
  • the loss function may be used as shown in Equation 29 below by combining the above-described methods in order to properly harmonize the learning rate and the detection error optimization, and is not limited to the above-described embodiment.
  • the MSE can obtain the inter-bit distance, so that learning can be performed quickly.
  • BEC can obtain the error rate between two bits, so learning is slow but better performance can be guaranteed.
  • the value of a may be adjusted in consideration of the foregoing.
  • the weight of the MSE may be increased by setting a large value a.
  • the specific gravity of BCE may be increased by setting the value a to be small, and the present invention is not limited to the above-described embodiment.
  • the secondary receiver neural network 3232 of the receiver may be applied to the secondary receiver neural network 3232 of the receiver (or terminal, 3230). also, may be fed back to the transmitter (or base station, 3210) by uplink signaling and applied to the primary transmitter neural network 3212 of the transmitter.
  • the transmitter 3210 may transmit a pilot symbol to the receiver 3230 and perform learning based on the pilot symbol.
  • the receiver 3230 may separately perform learning of a long-term characteristic as a channel characteristic that does not change frequently and a learning of a short-term characteristic as a channel characteristic that changes frequently, such as fading, as described above.
  • the receiver 3230 may perform learning based on accumulated statistical information on pilot symbols transmitted with a long period based on long-term characteristics.
  • the channel coding information may be information reflecting long-term characteristics.
  • the transmitter 3210 may transmit the pilot symbol to the receiver 3230 based on a constant period.
  • the receiver 3230 may obtain channel prediction information through the received pilot symbol and accumulate channel prediction information for a plurality of pilot symbols based on a predetermined period. Thereafter, the receiver 3230 performs the above-described internal neural network 3240 based on the accumulated channel prediction information, which is a weight for the secondary transmitter neural network 3213 . and a weight for the primary receiver neural network 3231 can be updated through learning. That is, the receiver 3230 may update weights for the internal neural network 3240 based on the long-term characteristic.
  • the receiver 3230 is an external neural network 3250 that is a weight for the primary transmitter neural network 3212 . and a weight for the secondary receiver neural network 3232 may not be updated. As an example, weights for the external neural network 3250 may be updated in a short-term signal update process, and may not be updated in a long-term signal processing process.
  • the receiver 3230 performs both weights for the inner neural network 3240 and the outer neural network 3250 ( ) may be updated, and it is not limited to the above-described embodiment.
  • the receiver 3230 may transmit at least one of weight information and related information of the secondary transmitter neural network 3213 as the internal neural network 3240 to the transmitter 3210 .
  • the transmitter 3210 may apply a weight based on the received information.
  • the receiver 3230 may perform learning based on pilot symbols transmitted in a short period based on short-term characteristics. For example, since the receiver 3230 learns short-term characteristics, the pilot signal may be transmitted only once. However, this is only one example, and the pilot signal may be transmitted a plurality of times, and the embodiment is not limited thereto.
  • the receiver 3230 may acquire channel prediction information through the received pilot symbol, and learn short-term characteristics through this.
  • the receiver 3230 is the above-described internal neural network 3240, which is a weight for the secondary transmitter neural network 3213. and a weight for the primary receiver neural network 3231 can perform learning in a fixed state. That is, the receiver 3230 may not update the weights for the internal neural network 3240 based on the long-term characteristic.
  • the receiver 3230 is an external neural network 3250 that is a weight for the primary transmitter neural network 3212 . and a weight for the secondary receiver neural network 3232 can be updated. That is, the receiver 3230 may fix weights for the long-term characteristic and update only the weights for the short-term characteristic. Thereafter, the receiver 3230 may transmit at least one of weight information and related information of the primary transmitter neural network 3212 as the external neural network 3250 to the transmitter 3210 . The transmitter 3210 may apply a weight based on the received information. As described above, the transmitter 3210 and the receiver 3230 may perform learning of long-term characteristics and learning of short-term characteristics, respectively, and through this, long-term changing channel characteristics and short-term changing channel characteristics are reflected can do.
  • the transmitter neural networks 3212 and 3213 and the receiver neural networks 3231 and 3232 may perform data transmission/reception based on the learned information.
  • the transmitter 3210 is a signal source ( ) through the channel coding encoder 3211 can be converted to
  • the transmitter neural networks 3212 and 3213 may receive the information learned by the receiver 3230 and have completed learning.
  • the receiver neural networks 3231 and 3232 may be in a state in which learning is completed by the receiver 2930 .
  • the transmit signal is the received signal to the receiver 3230 through the wireless communication channel 3220 can be received as
  • the received signal is passed through the received neural networks 2931 and 2932 learned as described above.
  • the channel coding decoder (3232) can be decrypted as
  • the channel coding decoder 3232 may have an optional configuration. That is, the received signal Is can be
  • the transmitter 3210 and the receiver 3230 may perform data communication.
  • the receiver 3230 may be a terminal.
  • the terminal may perform the above-described operation in consideration of the main communication environment as an environment in which communication is mainly performed.
  • the terminal may perform the above-described operation in a communication environment mainly used as a specific indoor environment.
  • the above-described operation may be performed in an outdoor environment or another environment as the main communication environment of the terminal, and the embodiment is not limited thereto.
  • the transmitter 3210 and the receiver 3220 may be applied to other wireless communication systems such as WiFi communication or Vehicle to Everything (V2X) as well as cellular communication. More specifically, when performing WiFi communication, the terminal as the receiver 2920 may perform WiFi communication in the main communication area of the terminal.
  • the transmitter 3210 may be an access point (AP).
  • AP access point
  • the UE may generate weights of the transmitter (AP) and the receiver based on the accumulated statistical channel prediction information based on the pilot signal, and transmit the information to the AP. Through this, the terminal can perform learning in the main channel environment.
  • the terminal when the terminal performs V2X communication, the terminal may perform communication under the control of the base station or perform communication without the control of the base station. For example, when the terminal performs communication with another terminal under the control of the base station, the terminal as a receiver generates weights of the transmitter and the receiver based on the accumulated statistical channel prediction information based on the pilot signal as described above, and for this Information can be transmitted to the transmitter.
  • the terminal since the terminal can perform communication with the base station as well as the terminal-to-device communication, it can receive a pilot signal from each of a plurality of transmitters and perform respective learning. That is, the above-described operation may be performed for each transmitter, and is not limited to the above-described embodiment.
  • the receiver terminal when performing inter-terminal communication without base station control, receives a pilot signal from the transmitter terminal, generates weights of the transmitter and the receiver based on statistical channel prediction information based on this, and information on this can be transmitted to the transmitter (terminal).
  • the receiver terminal receives a pilot signal from the transmitter terminal, generates weights of the transmitter and the receiver based on statistical channel prediction information based on this, and information on this can be transmitted to the transmitter (terminal).
  • the receiver terminal when performing inter-terminal communication without base station control, receives a pilot signal from the transmitter terminal, generates weights of the transmitter and the receiver based on statistical channel prediction information based on this, and information on this can be transmitted to the transmitter (terminal).
  • it is not limited to the above-described embodiment.
  • the receiver 3230 when the receiver 3230 performs learning on the transmitter neural networks 3212 and 3213 and the receiver neural networks 3231 and 3232, learning may not be smooth when the code size is large.
  • the code size may affect the learning.
  • the learning complexity may increase exponentially based on the code size.
  • the receiver 3230 when the receiver 3230 performs learning, the receiver 3230 may perform learning based on a limited code size. Accordingly, the learned code size may be limited, and the transmitter 3210 and the receiver 3230 may perform communication based on the limited code size.
  • the channel coding encoder 3211 of the transmitter may be included in the transmitter, and the channel coding decoder 3232 of the receiver may be included in the receiver, thereby adjusting the code size.
  • An existing channel code e.g. outer code
  • the transmitter neural network 3212 and the receiver neural network 3231 may be applied to the outside of the auto-encoder, which is the transmitter neural network 3212 and the receiver neural network 3231 .
  • the transmitter 3210 and the receiver 3230 perform communication, average performance may be secured by the above-described existing channel code.
  • a portion that is flexibly changed in consideration of the channel environment may be covered through the above-described auto encoder.
  • both an existing channel coder and an auto-encoder may be applied to the transmitter 3210 and the receiver 3220 , thereby improving coding performance.
  • the channel coding encoder 3211 and the channel coding decoder 3232 may be optional, and it may be possible to perform communication except for the above-described configuration.
  • FIG 33 is a diagram illustrating a method in which a transmitter and a receiver applicable to the present disclosure transmit and receive signals based on a neural network.
  • a transmitter 3310 and a receiver 3320 may perform data communication, and may perform learning based on the above-described FIG. 32 .
  • the transmitter 3310 and the receiver 3320 may transmit a pilot signal.
  • the pilot signal may be a pilot signal transmitted for learning about long-term characteristics based on a constant period.
  • the receiver 3320 may perform channel prediction for a long-term characteristic based on the received pilot signal.
  • the receiver 3320 may acquire statistical information by accumulating channel predictions for a pilot signal transmitted over a long period of time based on a predetermined period. Through this, the receiver 3320 may generate weight information on the neural network applied to the transmitter and the neural network applied to the receiver based on the statistical information. In this case, as an example, the receiver 3320 may generate a primary weight and a secondary weight based on a long-term characteristic.
  • the receiver 3320 sets the weights ( , ) and weights for the external neural network ( , ) can be updated.
  • the receiver 3320 sets the weights for the internal neural network ( , ), and the external neural network weights for short-term features ( , ) may not be updated, and it is not limited to the above-described embodiment.
  • the receiver 3320 may feed back weight information about the neural network applied to the transmitter to the transmitter 3310 .
  • the receiver 3320 may feed back weight information and information related to the transmitter neural network together to the transmitter 3310, and the embodiment is not limited thereto.
  • the receiver 3320 may transmit information on all the weights to the transmitter 3310 .
  • the receiver 3320 may transmit only information on the weights for the internal neural network to the transmitter 3310, and the embodiment is not limited thereto.
  • the transmitter 3310 may apply the weight received from the receiver 3320 to the neural network of the transmitter.
  • the transmitter 3310 may transmit a pilot signal to the receiver 3320 to learn short-term characteristics.
  • the short-term characteristic considers short-term channel change characteristics such as fading, and the pilot signal may be transmitted only once.
  • the pilot signal may be transmitted a plurality of times, and the embodiment is not limited thereto.
  • the receiver 3320 may generate and update weights for the receive channel-based external neural network based on the received pilot signal.
  • the receiver 3320 may fix weights for the internal neural network, which are weights for long-term characteristics, and update only the weights for the external neural network.
  • the receiver 3320 sets the weights for the internal neural network ( , ), the weights for the external neural network ( , ) may be updated based on the received pilot signal. Thereafter, the receiver 3320 may feed back weight information about the external neural network applied to the transmitter to the transmitter 3310 . (S3304) Here, as an example, the receiver 3320 may feed back weight information on the external neural network and information related to the transmitter neural network together to the transmitter 3310, and the embodiment is not limited thereto.
  • the transmitter 3310 may transmit data to the receiver 3320 based on the applied weight.
  • the receiver 3320 may detect the received data by applying the learned receiver neural network based on the above description.
  • the short-term characteristics described above take short-term channel changes into account, and after a predetermined time has elapsed, the transmitter 3310 may transmit the pilot signal again.
  • the receiver may fix weights for the internal neural network, which are weights for long-term characteristics, based on the received pilot signal, and update only the weights for the external neural network. That is, the receiver 3320 sets the weights for the internal neural network ( , ), the weights for the external neural network ( , ) may be updated again based on the received pilot signal. Thereafter, the receiver 3320 may feed back weight information about the external neural network applied to the transmitter to the transmitter 3310 .
  • the receiver 3320 may feed back weight information about the external neural network and information related to the transmitter neural network together to the transmitter 3310, and the embodiment is not limited thereto. Thereafter, the transmitter 3310 and the receiver 3320 may perform data communication based on the updated neural network. (S3308) Based on the above description, the transmitter 3310 and the receiver 3320 separately perform learning for a long-term characteristic and a learning for a short-term characteristic, thereby improving channel coding performance.
  • 34 is a diagram illustrating a terminal operation method applicable to the present disclosure.
  • the terminal may receive a pilot signal from the first device.
  • the first device may be at least one of the terminal, the base station, and the devices of FIGS. 4 to 9 described above. That is, the first device may refer to a device capable of transmitting data, and is not limited to the above-described embodiment.
  • the terminal may be a receiving end that receives data. That is, in the above-described FIGS. 28 to 33, the receiver may be a terminal.
  • the first device may be the transmitter described above with reference to FIGS. 28 to 33 .
  • the terminal when the terminal receives a pilot signal, the terminal may generate weight information for each of the neural network of the terminal and the neural network of the first device. (S3420) Thereafter, the terminal may feed back weight information on the neural network of the first device to the first device. (S3430) In this case, as an example, the terminal may feed back information related to the neural network of the first device together with weight information, and the embodiment is not limited thereto. Next, the terminal may apply weight information on the neural network of the terminal to the neural network of the terminal. (S3440) As described above, each weight information generated in the terminal may be applied to each neural network.
  • the first device may perform coding on data through a neural network as a transmitting end, and transmit it to a receiving end through a wireless channel.
  • the terminal may receive data through the aforementioned radio channel as a receiving end.
  • the terminal may perform decoding on the data received through the neural network of the terminal, and obtain the data.
  • the first device and the terminal may perform communication.
  • the terminal may receive a plurality of pilot signals.
  • the plurality of pilot signals may be transmitted to the terminal for a long time based on a constant period.
  • the terminal may obtain information about the channel through each of the received pilot signals.
  • the terminal may obtain information on each channel based on a plurality of pilot signals, and through this, may obtain statistical information on the channel.
  • the statistical information on the channel may be performed in the above-described statistical channel prediction part, but is not limited thereto.
  • the terminal may acquire weight information on the neural network of the first device and weight information on the neural network of the terminal based on the obtained statistical information.
  • the statistical channel prediction part that processes the statistical information as described above may acquire statistical information about the channel based on a plurality of pilot signals received through the artificial intelligence processing method.
  • the AI processing method may be a generative adversarial networks (GANs) method.
  • GANs generative adversarial networks
  • the terminal acquires statistical information based on the GANs method
  • the terminal generates a channel based on the statistical characteristics based on the first neural network of the GANs method, and the channels generated based on the second neural network of the GANs method. can examine the statistical similarity of Through this, the terminal may obtain statistical information and generate weight information on the neural network of the first device and weight information on the neural network of the terminal, as described above.
  • data generated in the first device may be converted based on the first channel coding scheme, and the converted data may be converted into transmission data through the neural network of the first device and transmitted to the terminal through a wireless channel.
  • channel coding may be performed before the neural network is applied.
  • the learning efficiency may decrease as the code size increases, as described above.
  • the UE may limit the code size to be learned, and channel coding may be performed first before applying the neural network in consideration of the above points.
  • the terminal may obtain data by converting data received through a wireless channel through a neural network, and decoding the converted data based on a first channel coding method.
  • the first channel coding scheme may be an outer coding scheme, but is not limited thereto.
  • the neural network of the terminal and the neural network of the first device may be composed of an internal neural network and an external neural network, which may be the same as in FIGS. 32 and 33 described above.
  • the internal neural network of the terminal and the internal neural network of the first device may be neural networks that are learned based on organ characteristics.
  • the external neural network of the terminal and the external neural network of the first device may be neural networks that are learned based on short-term characteristics.
  • the first device may transmit the first pilot signal and the second pilot signal to the terminal.
  • the first pilot signal may be a pilot signal for learning a long-term characteristic
  • the second pilot signal may be a pilot signal for learning about a short-term characteristic.
  • the terminal may receive a plurality of first pilot signals from the first device.
  • the first pilot signal is a pilot signal in consideration of a long-term change of a channel, it may be transmitted based on a long period and may be obtained in advance of the second pilot signal.
  • the terminal may obtain statistical information about the channel based on the plurality of received first pilot signals.
  • the terminal may acquire weight information on the internal neural network of the first device and weight information on the internal neural network of the terminal based on the statistical information.
  • the terminal may further acquire weight information on the external neural network of the first device and weight information on the external neural network of the terminal based on the statistical information, as described above.
  • the terminal may feed back weight information for the neural network of the first device to the first device.
  • the terminal may feed back only weight information on the internal neural network of the first device.
  • the terminal acquires weight information on the external neural network of the first device together with weight information on the internal neural network of the first device
  • the terminal receives weight information on the internal neural network of the first device and the external neural network of the first device It is possible to feed back all of the weight information for , and it is not limited to the above-described embodiment.
  • the first device and the terminal can check the weight information of the neural network based on the long-term characteristic. Thereafter, the terminal may receive the second pilot signal from the first device. In this case, the second pilot signal may be transmitted only once as a pilot signal for measuring short-term characteristics, but is not limited thereto. Thereafter, the terminal may acquire instantaneous channel information based on the received second pilot signal. The terminal may acquire weight information on the external neural network of the first device and weight information on the external neural network of the terminal based on the instantaneous channel information. However, as described above, when the terminal acquires information on the external neural network, weight information on the internal neural network of the terminal and weight information on the internal neural network of the first device may be in a fixed state.
  • the terminal may fix the weight for the long-term characteristic, and obtain and update the weight for the short-term characteristic. Thereafter, the terminal may feed back weight information on the external neural network of the first device to the first device, and apply the weight information on the external neural network of the terminal to the terminal. Thereafter, the first device and the terminal may perform data communication, as described above.
  • examples of the above-described proposed method may also be included as one of the implementation methods of the present disclosure, it is clear that they may be regarded as a kind of proposed method.
  • the above-described proposed methods may be implemented independently, or may be implemented in the form of a combination (or merge) of some of the proposed methods.
  • Rules can be defined so that the base station informs the terminal of whether the proposed methods are applied or not (or information on the rules of the proposed methods) through a predefined signal (eg, a physical layer signal or a higher layer signal). there is.
  • Embodiments of the present disclosure may be applied to various wireless access systems.
  • various radio access systems there is a 3rd Generation Partnership Project (3GPP) or a 3GPP2 system.
  • 3GPP 3rd Generation Partnership Project
  • 3GPP2 3rd Generation Partnership Project2
  • Embodiments of the present disclosure may be applied not only to the various radio access systems, but also to all technical fields to which the various radio access systems are applied. Furthermore, the proposed method can be applied to mmWave and THz communication systems using very high frequency bands.
  • embodiments of the present disclosure may be applied to various applications such as free-running vehicles and drones.

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Abstract

La présente divulgation concerne un procédé de fonctionnement d'un terminal et d'une station de base dans un système de communication sans fil, et un appareil prenant en charge le procédé. Selon un mode de réalisation applicable à la présente divulgation, le procédé de fonctionnement d'un terminal peut comprendre les étapes consistant à : recevoir un signal pilote en provenance d'un premier dispositif ; générer des informations de pondération sur un réseau de neurones artificiels d'un terminal et un réseau de neurones artificiels d'un premier dispositif, respectivement, sur la base du signal pilote ; renvoyer les informations de pondération sur le réseau de neurones artificiels du premier dispositif vers le premier dispositif ; appliquer les informations de pondération sur le réseau de neurones artificiels du terminal vers le réseau de neurones artificiels du terminal ; recevoir des données codées par l'intermédiaire du réseau de neurones artificiels du premier dispositif en provenance du premier dispositif ; et décoder les données par l'intermédiaire du réseau de neurones artificiels du terminal.
PCT/KR2020/011340 2020-08-25 2020-08-25 Procédé et appareil pour la mise en œuvre d'un codage de canal au moyen d'un terminal et d'une station de base dans un système de communication sans fil WO2022045390A1 (fr)

Priority Applications (2)

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KR1020227046103A KR20230051433A (ko) 2020-08-25 2020-08-25 무선 통신 시스템에서 단말 및 기지국의 채널 코딩 수행 방법 및 장치
PCT/KR2020/011340 WO2022045390A1 (fr) 2020-08-25 2020-08-25 Procédé et appareil pour la mise en œuvre d'un codage de canal au moyen d'un terminal et d'une station de base dans un système de communication sans fil

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Citations (5)

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KR101992053B1 (ko) * 2018-03-22 2019-06-21 세종대학교산학협력단 적응적 앙상블 지도학습 기반의 딥 뉴럴 네트워크를 이용한 siso-ofdm 채널 추정 장치 및 그 방법
US20190229785A1 (en) * 2018-01-19 2019-07-25 At&T Intellectual Property I, L.P. Facilitating semi-open loop based transmission diversity for uplink transmissions for 5g or other next generation networks
KR20200032245A (ko) * 2017-08-31 2020-03-25 마이크론 테크놀로지, 인크. 협력 학습 신경망 및 시스템
WO2020092391A1 (fr) * 2018-10-29 2020-05-07 Board Of Regents, The University Of Texas System Récepteurs ofdm à basse résolution par apprentissage profond
KR102124316B1 (ko) * 2018-12-11 2020-06-18 한국교통대학교산학협력단 차량사물통신 환경에서의 효율적인 데이터 할당 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200032245A (ko) * 2017-08-31 2020-03-25 마이크론 테크놀로지, 인크. 협력 학습 신경망 및 시스템
US20190229785A1 (en) * 2018-01-19 2019-07-25 At&T Intellectual Property I, L.P. Facilitating semi-open loop based transmission diversity for uplink transmissions for 5g or other next generation networks
KR101992053B1 (ko) * 2018-03-22 2019-06-21 세종대학교산학협력단 적응적 앙상블 지도학습 기반의 딥 뉴럴 네트워크를 이용한 siso-ofdm 채널 추정 장치 및 그 방법
WO2020092391A1 (fr) * 2018-10-29 2020-05-07 Board Of Regents, The University Of Texas System Récepteurs ofdm à basse résolution par apprentissage profond
KR102124316B1 (ko) * 2018-12-11 2020-06-18 한국교통대학교산학협력단 차량사물통신 환경에서의 효율적인 데이터 할당 방법

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