WO2023068713A1 - Dispositif et procédé pour protocole de distribution d'enchevêtrement, comprenant une réparation d'erreur de bascule de phase dans un système de communication - Google Patents

Dispositif et procédé pour protocole de distribution d'enchevêtrement, comprenant une réparation d'erreur de bascule de phase dans un système de communication Download PDF

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WO2023068713A1
WO2023068713A1 PCT/KR2022/015778 KR2022015778W WO2023068713A1 WO 2023068713 A1 WO2023068713 A1 WO 2023068713A1 KR 2022015778 W KR2022015778 W KR 2022015778W WO 2023068713 A1 WO2023068713 A1 WO 2023068713A1
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qubit
phase
error
parity
phase flip
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PCT/KR2022/015778
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Korean (ko)
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김자영
이호재
이상림
안병규
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엘지전자 주식회사
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Priority to KR1020247006227A priority Critical patent/KR20240090130A/ko
Publication of WO2023068713A1 publication Critical patent/WO2023068713A1/fr

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    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H03M13/05—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
    • H03M13/11—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits using multiple parity bits
    • H03M13/1102—Codes on graphs and decoding on graphs, e.g. low-density parity check [LDPC] codes
    • H03M13/1105—Decoding
    • H03M13/1108—Hard decision decoding, e.g. bit flipping, modified or weighted bit flipping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
    • G06N10/70—Quantum error correction, detection or prevention, e.g. surface codes or magic state distillation
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/01—Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H03M13/033—Theoretical methods to calculate these checking codes
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H03M13/05—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
    • H03M13/13—Linear codes
    • H03M13/15—Cyclic codes, i.e. cyclic shifts of codewords produce other codewords, e.g. codes defined by a generator polynomial, Bose-Chaudhuri-Hocquenghem [BCH] codes
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/61—Aspects and characteristics of methods and arrangements for error correction or error detection, not provided for otherwise
    • H03M13/615—Use of computational or mathematical techniques
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/65—Purpose and implementation aspects
    • H03M13/6522—Intended application, e.g. transmission or communication standard
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/70—Photonic quantum communication
    • 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

Definitions

  • This disclosure relates to a communication system. Specifically, the present disclosure relates to an apparatus and method for an entanglement distribution protocol including phase flip error correction in a communication system.
  • quantum cryptographic communication Due to the advent of quantum computers, it has become possible to hack existing cryptographic systems based on mathematical complexity (eg, RSA, AES, etc.). To prevent hacking, quantum cryptographic communication is proposed.
  • the present disclosure relates to a method of performing entanglement error correction on a phase flip error generated by a Pauli X channel in a quantum communication system. More specifically, the correlation between two qubits is recognized as information rather than the individual state of each qubit constituting the entanglement state, and the entanglement already known in the entanglement distribution process of quantum teleportation It is to minimize the resources consumed for entanglement error correction by sufficiently utilizing state information and achieve an effective error suppression effect.
  • the present disclosure provides an apparatus and method for an entanglement distribution protocol including phase flip error correction in a communication system.
  • the present disclosure provides an apparatus and method for performing entanglement error correction on a phase flip error generated by a Pauli Z channel in a quantum communication system.
  • the present disclosure recognizes the phase correlation of two qubits as information rather than the individual state of each qubit constituting the entanglement state in a quantum communication system, and entanglement distribution of quantum teleportation ) process, we provide a device and method for minimizing the resources consumed for entanglement error correction and achieving an effective error suppression effect by fully utilizing the known entanglement state information.
  • a first qubit and an entanglement state of the first node with respect to a phase flip channel Identifying phase correlation between second qubits of a second node constituting , determining a first parity value based on the phase correlation, determining whether a phase flip error occurs based on the first parity value, determining a second parity value based on the phase correlation when it is determined that the phase flip error occurs based on the first parity value determining whether the phase flip error occurs based on the second parity value, and when it is determined based on the first parity value that the phase flip error occurs, the first qubit or the second qubit
  • a method is provided that includes performing error correction by a phase flip operation on qubits.
  • a transceiver and at least one processor are included, and the at least one processor is a phase flip channel of the first node with respect to a phase flip channel. Identify the phase correlation between the first qubit and the second qubit of the second node constituting the entanglement state, and based on the phase correlation, a first parity value value), determining whether a phase flip error occurs based on the first parity value, and when it is determined that the phase flip error occurs based on the first parity value, the phase correlation When a second parity value is determined based on the relationship, whether or not the phase flip error occurs based on the second parity value, and it is determined based on the first parity value that the phase flip error occurs, the A first node configured to perform error correction by a phase flip operation on the first qubit or the second qubit is provided.
  • non-transitory computer readable media storing one or more instructions
  • the one or more instructions based on being executed by one or more processors, perform operations.
  • the above operations are phase correlation between the first qubit of the first node and the second qubit of the second node constituting an entanglement state with respect to a phase flip channel Identifying a phase correlation, determining a first parity value based on the phase correlation, determining whether a phase flip error occurs based on the first parity value determining a second parity value based on the phase correlation when it is determined that the phase flip error occurs based on the first parity value; the phase flip error based on the second parity value Determining whether a phase flip error has occurred based on the first parity value;
  • a computer readable medium comprising performing error correction is provided.
  • the present disclosure may provide an apparatus and method for an entanglement distribution protocol including phase flip error correction in a communication system.
  • the present disclosure may provide an apparatus and method for performing entanglement error correction on a phase flip error generated by a Pauli Z channel in a quantum communication system.
  • the present disclosure recognizes the phase correlation of two qubits as information rather than the individual state of each qubit constituting the entanglement state in a quantum communication system, and entanglement distribution of quantum teleportation ) process, it is possible to provide an apparatus and method for minimizing the resources consumed for entanglement error correction and achieving an effective error suppression effect by fully utilizing the known entanglement state information.
  • NG-RAN New Generation Radio Access Network
  • 2 is a diagram illustrating functional division between NG-RAN and 5GC.
  • 3 is a diagram illustrating an example of a 5G usage scenario.
  • FIG. 4 is a diagram showing an example of a communication structure that can be provided in a 6G system.
  • FIG. 5 is a diagram schematically illustrating an example of a perceptron structure.
  • FIG. 6 is a diagram schematically illustrating an example of a multilayer perceptron structure.
  • FIG. 7 is a diagram schematically illustrating an example of a deep neural network.
  • FIG. 8 is a diagram schematically illustrating an example of a convolutional neural network.
  • FIG. 9 is a diagram schematically illustrating an example of a filter operation in a convolutional neural network.
  • FIG. 10 is a diagram schematically illustrating an example of a neural network structure in which a cyclic loop exists.
  • FIG. 11 is a diagram schematically illustrating an example of an operating structure of a recurrent neural network.
  • FIG. 12 is a diagram showing an example of an electromagnetic spectrum.
  • FIG. 13 is a diagram illustrating an example of a THz communication application.
  • FIG. 14 is a diagram illustrating an example of an electronic element-based THz wireless communication transceiver.
  • 15 is a diagram illustrating an example of a method of generating a THz signal based on an optical element.
  • 16 is a diagram showing an example of an optical element-based THz wireless communication transceiver.
  • 17 is a diagram showing the structure of a photoinc source-based transmitter.
  • 18 is a diagram showing the structure of an optical modulator.
  • 20 is a diagram showing an example of a quantum circuit for generating a bell state in a system applicable to the present disclosure.
  • 21 is a diagram showing an example of a bell state measurement circuit in a system applicable to the present disclosure.
  • FIG. 22 is a diagram showing an example of a quantum teleportation system in a system applicable to the present disclosure.
  • FIG. 23 is a diagram illustrating an example of spontaneous parametric down-conversion in a system applicable to the present disclosure.
  • 24 is a diagram showing an example of an atomic excitation method in an optical cavity using a laser pulse in a system applicable to the present disclosure.
  • 25 is a diagram showing an example of a simultaneous excitation method of two atoms using a laser pulse in a system applicable to the present disclosure.
  • 26 is a diagram showing an example of incompleteness that deteriorates a quantum teleportation process in a system applicable to the present disclosure.
  • FIG. 27 is a diagram illustrating an example of a quantum channel model in a system applicable to the present disclosure.
  • FIG. 28 is a diagram showing an example of Pauli-I, Pauli-Z, Pauli-X, and Pauli-Y gates in a system applicable to the present disclosure.
  • 29 is a diagram showing an example of an error correction circuit of a 3-qubit bit flip code in a system applicable to the present disclosure.
  • FIG. 30 is a diagram showing an example of an error correction circuit of a 3-qubit phase flip code in a system applicable to the present disclosure.
  • FIG. 31 is a diagram showing an example of a Shor code error correction circuit in a system applicable to the present disclosure.
  • 32 is a diagram illustrating an example of a 3-qubit iterative code-based entanglement error correction process in a system applicable to the present disclosure.
  • FIG. 33 is a diagram showing an example of a suppressed entanglement error rate of a 3-qubit iterative code-based entanglement error correction process in a system applicable to the present disclosure.
  • 34 is a diagram illustrating an example of a phase flip error correction protocol of an entanglement distribution process in a system applicable to the present disclosure.
  • 35 is a diagram showing an example of a phase flip error correction circuit in an entanglement distribution process in a system applicable to the present disclosure.
  • FIG. 36 is a diagram showing a result of minimizing the number of gates used based on an equivalent circuit for the circuit of FIG. 35 in a system applicable to the present disclosure.
  • FIG. 37 is a diagram showing an example of a result of analysis of a suppressed entanglement error rate of a technique according to an embodiment of the present disclosure and a conventional technique in a system applicable to the present disclosure.
  • FIG. 38 is a diagram showing an example of an analysis result of the average number of refined entanglement pairs consumed by a technique according to an embodiment of the present disclosure and a conventional technique in a system applicable to the present disclosure.
  • 39 is a diagram showing an example of a simulation environment configuration of a proposed method using an IBM quantum simulator in a system applicable to the present disclosure.
  • FIG. 40 is a diagram illustrating an example of simulation results of a suppressed entanglement error rate of a technique according to an embodiment of the present disclosure and a conventional technique in a system applicable to the present disclosure.
  • 40 is a diagram showing an example of an entangled state encoded using a repetition code in a conventional manner.
  • 41 is a diagram illustrating an example of entanglement distribution error correction in a system applicable to the present disclosure.
  • 43 is a diagram illustrating examples of an operation process of a first node in a system applicable to the present disclosure.
  • 46 illustrates another example of a wireless device applicable to various embodiments of the present disclosure.
  • 49 illustrates a portable device applied to various embodiments of the present disclosure.
  • 50 illustrates a vehicle or autonomous vehicle applied to various embodiments of the present disclosure.
  • FIG. 52 illustrates an XR device applied to various embodiments of the present disclosure.
  • 53 illustrates a robot applied to various embodiments of the present disclosure.
  • a or B may mean “only A”, “only B”, or “both A and B”. In other words, in various embodiments of the present disclosure, “A or B” may be interpreted as “A and/or B”.
  • “A, B, or C” means “only A,” “only B,” “only C,” or “any of A, B, and C. It may mean "any combination of A, B and C”.
  • a slash (/) or a comma (comma) used in various embodiments of the present disclosure may mean “and/or”.
  • A/B can mean “A and/or B”.
  • A/B may mean “only A”, “only B”, or “both A and B”.
  • A, B, C may mean "A, B or C”.
  • “at least one of A and B” may mean “only A”, “only B”, or “both A and B”.
  • the expression "at least one of A or B” or “at least one of A and/or B” can be interpreted the same as “at least one of A and B”.
  • At least one of A, B and C means “only A”, “only B”, “only C”, or “A” , B and C (any combination of A, B and C)". Also, “at least one of A, B or C” or “at least one of A, B and/or C” means It can mean “at least one of A, B and C”.
  • parentheses used in various embodiments of the present disclosure may mean “for example”. Specifically, when indicated as “control information (PDCCH)”, “PDCCH” may be suggested as an example of “control information”. In other words, "control information" of various embodiments of the present disclosure is not limited to "PDCCH”, and “PDDCH” may be suggested as an example of "control information”. Also, even when displayed as “control information (ie, PDCCH)”, “PDCCH” may be suggested as an example of “control information”.
  • new radio access technology new RAT, NR
  • next-generation communication As more and more communication devices require greater communication capacity, a need for improved mobile broadband communication compared to conventional radio access technology (RAT) has emerged.
  • massive machine type communications MTC
  • MTC massive machine type communications
  • communication system design considering reliability and latency-sensitive services/terminals is being discussed.
  • next-generation wireless access technologies considering enhanced mobile broadband communication, massive MTC, URLLC (Ultra-Reliable and Low Latency Communication), etc. is being discussed, and in various embodiments of the present disclosure, for convenience,
  • the technology is called new RAT or NR.
  • NG-RAN New Generation Radio Access Network
  • an NG-RAN may include a gNB and/or an eNB that provides user plane and control plane protocol termination to a UE.
  • 1 illustrates a case including only gNB.
  • gNB and eNB are connected to each other through an Xn interface.
  • the gNB and the eNB are connected to a 5G Core Network (5GC) through an NG interface.
  • 5GC 5G Core Network
  • AMF access and mobility management function
  • UPF user plane function
  • 2 is a diagram illustrating functional division between NG-RAN and 5GC.
  • the gNB is inter-cell radio resource management (Inter Cell RRM), radio bearer management (RB control), connection mobility control (Connection Mobility Control), radio admission control (Radio Admission Control), measurement setup and provision (Measurement configuration & provision) and dynamic resource allocation.
  • AMF can provide functions such as NAS security and idle state mobility handling.
  • UPF may provide functions such as mobility anchoring and PDU processing.
  • Session Management Function (SMF) may provide functions such as terminal IP address allocation and PDU session control.
  • 3 is a diagram illustrating an example of a 5G usage scenario.
  • the 5G usage scenario shown in FIG. 3 is just an example, and technical features of various embodiments of the present disclosure may also be applied to other 5G usage scenarios not shown in FIG. 3 .
  • the three major requirements areas of 5G are (1) enhanced mobile broadband (eMBB) area, (2) massive machine type communication (mMTC) area, and ( 3) It includes the ultra-reliable and low latency communications (URLLC) area.
  • eMBB enhanced mobile broadband
  • mMTC massive machine type communication
  • URLLC ultra-reliable and low latency communications
  • Some use cases may require multiple areas for optimization, while other use cases may focus on just one key performance indicator (KPI).
  • KPI key performance indicator
  • eMBB focuses on overall improvements in data rate, latency, user density, capacity and coverage of mobile broadband access.
  • eMBB targets a throughput of around 10 Gbps.
  • eMBB goes far beyond basic mobile Internet access, and covers rich interactive work, media and entertainment applications in the cloud or augmented reality.
  • Data is one of the key drivers of 5G, and we may not see dedicated voice services for the first time in the 5G era.
  • voice is expected to be handled simply as an application using the data connection provided by the communication system.
  • the main causes of the increased traffic volume are the increase in content size and the increase in the number of applications requiring high data rates.
  • Streaming services audio and video
  • interactive video and mobile internet connections will become more widely used as more devices connect to the internet.
  • Cloud storage and applications are rapidly growing in mobile communication platforms, which can be applied to both work and entertainment.
  • Cloud storage is a particular use case driving the growth of uplink data rates.
  • 5G is also used for remote work in the cloud, requiring much lower end-to-end latency to maintain a good user experience when tactile interfaces are used.
  • cloud gaming and video streaming are other key factors driving the demand for mobile broadband capabilities.
  • Entertainment is essential on smartphones and tablets everywhere, including in highly mobile environments such as trains, cars and planes.
  • Another use case is augmented reality for entertainment and information retrieval.
  • augmented reality requires very low latency and instantaneous amount of data.
  • mMTC is designed to enable communication between high-volume, low-cost devices powered by batteries, and is intended to support applications such as smart metering, logistics, field and body sensors.
  • mMTC targets 10 years of batteries and/or 1 million devices per square kilometer.
  • mMTC enables seamless connectivity of embedded sensors in all fields and is one of the most anticipated 5G use cases. Potentially, IoT devices are predicted to reach 20.4 billion by 2020.
  • Industrial IoT is one area where 5G is playing a key role enabling smart cities, asset tracking, smart utilities, agriculture and security infrastructure.
  • URLLC enables devices and machines to communicate with high reliability, very low latency and high availability, making it ideal for vehicular communications, industrial controls, factory automation, remote surgery, smart grid and public safety applications.
  • URLLC targets latency on the order of 1 ms.
  • URLLC includes new services that will transform industries through ultra-reliable/low-latency links, such as remote control of critical infrastructure and autonomous vehicles. This level of reliability and latency is essential for smart grid control, industrial automation, robotics, and drone control and coordination.
  • 5G can complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) as a means of delivering streams rated at hundreds of megabits per second to gigabits per second.
  • FTTH fiber-to-the-home
  • DOCSIS cable-based broadband
  • Such high speeds may be required to deliver TV at resolutions of 4K and beyond (6K, 8K and beyond) as well as virtual reality (VR) and augmented reality (AR).
  • VR and AR applications include almost immersive sports events. Certain applications may require special network settings. For example, in the case of VR games, game companies may need to integrate their core servers with the network operator's edge network servers to minimize latency.
  • Automotive is expected to be an important new driver for 5G, with many use cases for mobile communications to vehicles. For example, entertainment for passengers requires both high capacity and high mobile broadband. The reason is that future users will continue to expect high-quality connections regardless of their location and speed.
  • Another use case in the automotive sector is augmented reality dashboards.
  • Drivers can identify objects in the dark above what they are viewing through the front window via an augmented reality contrast board.
  • the augmented reality dashboard displays overlaid information to inform the driver about the distance and movement of objects.
  • wireless modules will enable communication between vehicles, exchange of information between vehicles and supporting infrastructure, and exchange of information between vehicles and other connected devices (eg devices carried by pedestrians).
  • a safety system can help reduce the risk of an accident by guiding the driver through an alternate course of action to make driving safer.
  • the next step will be remotely controlled or self-driving vehicles. This requires very reliable and very fast communication between different autonomous vehicles and/or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving activities, leaving drivers to focus only on traffic anomalies that the vehicle itself cannot identify. The technological requirements of autonomous vehicles require ultra-low latency and ultra-high reliability to increase traffic safety to levels that humans cannot achieve.
  • Smart cities and smart homes will be embedded with high-density wireless sensor networks.
  • a distributed network of intelligent sensors will identify conditions for cost- and energy-efficient maintenance of a city or home.
  • a similar setup can be done for each household.
  • Temperature sensors, window and heating controllers, burglar alarms and appliances are all connected wirelessly. Many of these sensors typically require low data rates, low power and low cost.
  • real-time HD video for example, may be required in certain types of devices for surveillance.
  • a smart grid interconnects these sensors using digital information and communication technologies to gather information and act on it. This information can include supplier and consumer behavior, enabling the smart grid to improve efficiency, reliability, affordability, sustainability of production and distribution of fuels such as electricity in an automated manner.
  • the smart grid can also be viewed as another low-latency sensor network.
  • the health sector has many applications that can benefit from mobile communications.
  • the communication system may support telemedicine, which provides clinical care at a remote location. This can help reduce barriers to distance and improve access to health services that are not consistently available in remote rural areas. It is also used to save lives in critical care and emergencies.
  • Mobile communication-based wireless sensor networks can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.
  • Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Thus, the possibility of replacing cables with reconfigurable wireless links is an attractive opportunity for many industries. However, achieving this requires that wireless connections operate with comparable latency, reliability and capacity to cables, and that their management be simplified. Low latency and very low error probability are the new requirements that need to be connected with 5G.
  • Logistics and freight tracking is an important use case for mobile communications enabling the tracking of inventory and packages from anywhere using location-based information systems.
  • Logistics and freight tracking use cases typically require low data rates, but may require wide range and reliable location information.
  • next-generation communication eg. 6G
  • 6G next-generation communication
  • 6G (radio communications) systems are characterized by (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 lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities.
  • the vision of the 6G system can be four aspects such as intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing an example of requirements for a 6G system.
  • 6G systems include Enhanced mobile broadband (eMBB), Ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, Tactile internet, High throughput, High network capacity, High energy efficiency, Low backhaul and It can have key factors such as access network congestion and enhanced data security.
  • eMBB Enhanced mobile broadband
  • URLLC Ultra-reliable low latency communications
  • mMTC massive machine-type communication
  • AI integrated communication Tactile internet
  • High throughput High network capacity
  • High energy efficiency High energy efficiency
  • Low backhaul Low backhaul and It can have key factors such as access network congestion and enhanced data security.
  • FIG. 4 is a diagram showing an example of a communication structure that can be provided in a 6G system.
  • 6G systems are expected to have 50 times higher simultaneous radiocommunication connectivity than 5G radiocommunication systems.
  • URLLC a key feature of 5G, will become even more important in 6G communications by providing end-to-end latency of less than 1 ms.
  • the 6G system will have much better volume spectral efficiency as opposed to the frequently used area spectral efficiency.
  • 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices will not need to be charged separately in 6G systems.
  • New network characteristics in 6G may be as follows.
  • 6G is expected to be integrated with satellites to serve the global mobile population. Integration of terrestrial, satellite and public networks into one wireless communication system is critical for 6G.
  • 6G wireless networks will transfer 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 has been introduced to improve received signal quality resulting in improved throughput, energy efficiency and spectral efficiency in cellular systems. As a result, small cell networks are an essential feature of 5G and Beyond 5G (5GB) and beyond communication systems. Therefore, the 6G communication system also adopts the characteristics of the small cell network.
  • Ultra-dense heterogeneous networks will be another important feature of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.
  • a backhaul connection is characterized by a high-capacity backhaul network to support high-capacity traffic.
  • High-speed fiber and free space optical (FSO) systems may be possible solutions to this problem.
  • High-precision localization (or location-based service) through communication is one of the features of 6G wireless communication systems.
  • radar systems will be integrated with 6G networks.
  • Softwarization and virtualization are two important features fundamental to 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 the 6G system 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.
  • Introducing AI in communications can simplify and enhance real-time data transmission.
  • AI can use a plethora of 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 machine-to-machine, machine-to-human and human-to-machine communications.
  • AI can be a rapid communication in 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 fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and may include allocations, etc.
  • 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 downlink (DL) physical layer. Machine learning can also be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • DL downlink
  • AI algorithms based on deep learning require a lot 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 radio channel.
  • Machine learning refers to a set of actions that train a machine to create a machine that can do tasks that humans can or cannot do.
  • Machine learning requires data and a running model.
  • data learning methods can be largely classified into three types: supervised learning, unsupervised learning, and reinforcement learning.
  • Neural network training is aimed at minimizing errors in the output.
  • Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. ) to update the weight of each node in the neural network.
  • Supervised learning uses training data in which correct answers are labeled in the learning data, and unsupervised learning may not have correct answers labeled in the learning data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and an error may be calculated by comparing the output (category) of the neural network and the label of the training data. The calculated error is back-propagated in a 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.
  • a reverse direction ie, from the output layer to the input layer
  • the amount of change in the connection weight of each updated node may be determined according to a learning rate.
  • the neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch).
  • the learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of neural network learning to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.
  • the learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of the receiver is to accurately predict data transmitted by the transmitter in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.
  • the learning model corresponds to the human brain, and the most basic linear model can be 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). there is.
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN recurrent Boltzmann Machine
  • An artificial neural network is an example of connecting several perceptrons.
  • FIG. 5 is a diagram schematically illustrating an example of a perceptron structure.
  • the huge artificial neural network structure may extend the simplified perceptron structure shown in FIG. 5 and apply input vectors to different multi-dimensional perceptrons.
  • an input value or an output value is referred to as a node.
  • the perceptron structure shown in FIG. 5 can be described as being composed of a total of three layers based on input values and output values.
  • An artificial neural network in which H number of (d + 1) dimensional perceptrons exist between the 1st layer and the 2nd layer and K number of (H + 1) dimensional perceptrons between the 2nd layer and the 3rd layer can be expressed as shown in FIG. 6 .
  • FIG. 6 is a diagram schematically illustrating an example of a multilayer perceptron structure.
  • the layer where the input vector is located is called the input layer
  • the layer where the final output value is located is called the output layer
  • all the layers located between the input layer and the output layer are called hidden layers.
  • three layers are disclosed, but when counting the number of layers of an actual artificial neural network, the number of layers is counted excluding the input layer, so a total of two layers can be considered.
  • the artificial neural network is composed of two-dimensionally connected perceptrons of basic blocks.
  • the above-described input layer, hidden layer, and output layer can be jointly applied to various artificial neural network structures such as CNN and RNN, which will be described later, as well as multi-layer perceptrons.
  • CNN neural network
  • RNN multi-layer perceptrons
  • DNN deep neural network
  • FIG. 7 is a diagram schematically illustrating an example of a deep neural network.
  • the deep neural network shown in FIG. 7 is a multi-layer perceptron consisting of 8 hidden layers + 8 output layers.
  • the multilayer perceptron structure is expressed as a fully-connected neural network.
  • a fully-connected neural network there is no connection relationship between nodes located on the same layer, and a connection relationship exists only between nodes located on adjacent layers.
  • DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, so it can be usefully applied to identify the correlation characteristics between inputs and outputs.
  • the correlation characteristic may mean a joint probability of input and output.
  • FIG. 8 is a diagram schematically illustrating an example of a convolutional neural network.
  • nodes located inside one layer are arranged in a one-dimensional vertical direction.
  • the nodes are two-dimensionally arranged with w nodes horizontally and h nodes vertically (convolutional neural network structure of FIG. 8).
  • a weight is added for each connection in the connection process from one input node to the hidden layer, so a total of h ⁇ w weights must be considered. Since there are h ⁇ w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.
  • the convolutional neural network of FIG. 8 has a problem in that the number of weights increases exponentially according to the number of connections, so instead of considering all mode connections between adjacent layers, it is assumed that there is a filter with a small size, and FIG. 9 As shown in , weighted sum and activation function calculations are performed for overlapping filters.
  • FIG. 9 is a diagram schematically illustrating an example of a filter operation in a convolutional neural network.
  • One filter has weights corresponding to the number of filters, and learning of weights can be performed so that a specific feature on an image can be extracted as a factor and output.
  • a 3 ⁇ 3 size filter is applied to the 3 ⁇ 3 area at the top left of the input layer, and the weighted sum and activation function calculations are performed on the corresponding node, and the resulting output value is stored in z22.
  • the filter While scanning the input layer, the filter moves by a certain distance horizontally and vertically, performs weighted sum and activation function calculations, and places the output value at the position of the current filter.
  • This operation method is similar to the convolution operation for images in the field of computer vision, so the deep neural network of this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer.
  • a neural network having a plurality of convolutional layers is referred to as a deep convolutional neural network (DCNN).
  • the number of weights can be reduced by calculating a weighted sum by including only nodes located in a region covered by the filter from the node where the current filter is located. This allows one filter to be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which a physical distance in a 2D area is an important criterion. Meanwhile, in the CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through a convolution operation of each filter.
  • FIG. 10 is a diagram schematically illustrating an example of a neural network structure in which a cyclic loop exists.
  • a recurrent neural network assigns an element (x1(t), x2(t), ,..., xd(t)) of any line t on a data sequence to a fully connected neural network.
  • the immediately preceding time point t-1 inputs the hidden vector (z1(t-1), z2(t-1),..., zH(t-1)) together to calculate the weighted sum and activation function structure that is applied.
  • the reason why the hidden vector is transmitted to the next time point in this way is that information in the input vector at previous time points is regarded as being accumulated in the hidden vector of the current time point.
  • FIG. 11 is a diagram schematically illustrating an example of an operating structure of a recurrent neural network.
  • the recurrent neural network operates in a sequence of predetermined views with respect to an input data sequence.
  • the hidden vector (z1(1),z2(1),.. .,zH(1)) is input together with the input vector of time 2 (x1(2),x2(2),...,xd(2)), and the vector of the hidden layer (z1( 2),z2(2) ,...,zH(2)). This process is repeated until time point 2, time point 3, ,..., point T.
  • a deep recurrent neural network a recurrent neural network
  • Recurrent neural networks are designed to be usefully applied to sequence data (eg, natural language processing).
  • Deep Q-Network As a neural network core used as a learning method, in addition to DNN, CNN, and RNN, Restricted Boltzmann Machine (RBM), deep belief networks (DBN), and Deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.
  • RBM Restricted Boltzmann Machine
  • DNN deep belief networks
  • Deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.
  • 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 fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and may include allocations, etc.
  • the data rate can be increased by increasing the bandwidth. This can be done using sub-THz communication with wide bandwidth and applying advanced massive MIMO technology.
  • THz waves also known as submillimeter radiation, typically represent a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm.
  • the 100 GHz-300 GHz band range (sub THz band) is considered a major part of the THz band for cellular communications.
  • 6G cellular communication capacity increases when added to the sub-THz band mmWave band.
  • 300 GHz-3 THz is in the far-infrared (IR) frequency band.
  • the 300 GHz-3 THz band is part of the broad band, but is at the border of the wide band, just behind the RF band. Thus, this 300 GHz-3 THz band exhibits similarities to RF.
  • FIG. 12 is a diagram showing an example of an electromagnetic spectrum.
  • THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly 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 incorporated into devices and BSs operating in this band. This enables advanced adaptive array technology to overcome range limitations.
  • OWC technology is intended for 6G communications in addition to RF-based communications for all possible device-to-access networks. These networks access network-to-backhaul/fronthaul network connections.
  • OWC technology is already in use after the 4G communication system, but will be more widely used to meet the needs of the 6G communication system.
  • OWC technologies such as light fidelity, visible light communication, optical camera communication, and FSO communication based on a wide band are already well-known technologies. Communications based on optical wireless technology can provide very high data rates, low latency and secure communications.
  • LiDAR can also be used for ultra-high resolution 3D mapping in 6G communication based on broadband.
  • FSO The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network.
  • data transmission in FSO systems is similar to fiber optic systems. Therefore, FSO can be a good technology to provide backhaul connectivity in 6G systems along with fiber optic networks.
  • FSO supports high-capacity backhaul connectivity for remote and non-remote locations such as ocean, space, underwater and isolated islands.
  • FSO also supports cellular BS connections.
  • MIMO technology improves, so does the spectral efficiency. Therefore, massive MIMO technology will be important in 6G systems. Since MIMO technology uses multiple paths, multiplexing technology and beam generation and operation technology suitable for the THz band must be considered as important so that data signals can be transmitted through more than one path.
  • Blockchain will be an important technology for managing large amounts of data in future communication systems.
  • Blockchain is a form of distributed ledger technology, where a distributed ledger is a database that is distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger.
  • Blockchain is managed as a peer-to-peer network. It can exist without being managed by a centralized authority or server. Data on a blockchain is collected together and organized into blocks. Blocks are linked together and protected using cryptography.
  • Blockchain is the perfect complement to the IoT at scale with inherently improved interoperability, security, privacy, reliability and scalability.
  • blockchain technology provides multiple capabilities such as interoperability between devices, traceability of large amounts of data, autonomous interaction of other IoT systems, and large-scale connection reliability in 6G communication systems.
  • the 6G system integrates terrestrial and air 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 height and related degrees of freedom makes 3D connections quite different from traditional 2D networks.
  • UAVs Unmanned Aerial Vehicles
  • BS entities are installed on UAVs to provide cellular connectivity.
  • UAVs have certain features not found in fixed BS infrastructures, such as easy deployment, strong line-of-sight links, and degrees of freedom with controlled mobility.
  • UAVs can easily handle this situation.
  • UAVs will become a new paradigm in the field of wireless communication. This technology facilitates three basic requirements of a wireless network: eMBB, URLLC and mMTC.
  • UAVs can also support multiple purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, accident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.
  • the tight integration of multiple frequencies and heterogeneous communication technologies is critical for 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 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, resulting in 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 uses the same fields and waves as wireless communication systems.
  • sensors and smartphones will be charged using wireless power transfer during communication.
  • WIET is a promising technology for extending the lifetime of battery charging wireless systems.
  • battery-less devices will be supported in 6G communications.
  • Autonomous radio networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes.
  • sensing will be tightly integrated with communications to support autonomous systems.
  • Beamforming is a signal processing procedure that adjusts an antenna array to transmit radio signals in a specific direction.
  • Beamforming technology has several advantages such as high call-to-noise ratio, interference avoidance and rejection, and high network efficiency.
  • Hologram beamforming (HBF) is a new beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. 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 analysis 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 preferences. Big data is collected from various sources such as videos, social networks, images and sensors. This technology is widely used to process massive data in 6G systems.
  • LIS is an artificial surface made of electromagnetic materials and can change the propagation of incoming and outgoing radio waves.
  • LIS can be seen as an extension of massive MIMO, but its array structure and operating mechanism are different from massive MIMO.
  • LIS also has low power consumption in that it operates as a reconfigurable reflector with passive elements, i.e. it only passively reflects signals without using an active RF chain.
  • each passive reflector of the LIS must independently adjust the phase shift of an incident signal, it may be advantageous for a wireless communication channel. By properly adjusting the phase shift through the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.
  • THz Terahertz
  • THz waves are located between RF (Radio Frequency)/millimeter (mm) and infrared bands, and (i) transmit non-metal/non-polarizable materials better than visible light/infrared rays, and have a shorter wavelength than RF/millimeter waves and have high straightness. Beam focusing may be possible.
  • the photon energy of the THz wave is only a few meV, it is harmless to the human body.
  • a 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 molecular absorption in the air.
  • Standardization discussions on THz wireless communication are being discussed centering on the IEEE 802.15 THz working group in addition to 3GPP, and standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) embody the contents described in various embodiments of the present disclosure. or can be supplemented.
  • THz wireless communication may be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, and the like.
  • FIG. 13 is a diagram illustrating an example of a THz communication application.
  • THz wireless communication scenarios can be classified into macro networks, micro networks, and nanoscale networks.
  • THz wireless communication can be applied to vehicle-to-vehicle connections and backhaul/fronthaul connections.
  • THz wireless communication 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. It can be.
  • Table 2 below is a table showing an example of a technique that can be used in THz waves.
  • THz wireless communication can be classified based on the method for generating and receiving THz.
  • the THz generation method can be classified as an optical device or an electronic device based technology.
  • FIG. 14 is a diagram illustrating an example of an electronic element-based THz wireless communication transceiver.
  • Methods of generating THz using electronic devices include a method using a semiconductor device such as a Resonant Tunneling Diode (RTD), a method using a local oscillator and a multiplier, and an integrated circuit based on a compound semiconductor HEMT (High Electron Mobility Transistor).
  • a MMIC Monolithic Microwave Integrated Circuits
  • a doubler, a tripler, or a multiplier is applied to increase the frequency, and the signal is radiated by the antenna after passing through the subharmonic mixer. Since the THz band forms high frequencies, a multiplier is essential.
  • the multiplier is a circuit that makes the output frequency N times greater than the input, matches the desired harmonic frequency, and filters out all other frequencies.
  • beamforming may be implemented by applying an array antenna or the like to the antenna of FIG. 14 .
  • IF denotes an intermediate frequency
  • tripler and multipler denote a multiplier
  • PA denotes a power amplifier
  • LNA denotes a low noise amplifier
  • PLL denotes a phase-locked circuit (Phase -Locked Loop).
  • 15 is a diagram illustrating an example of a method of generating a THz signal based on an optical element.
  • 16 is a diagram showing an example of an optical element-based THz wireless communication transceiver.
  • Optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device.
  • An optical element-based THz signal generation technology is a technology that generates an ultra-high speed optical signal using a laser and an optical modulator and converts it into a THz signal using an ultra-high speed photodetector. Compared to a technique using only an electronic device, this technique can easily increase the frequency, generate a high-power signal, and obtain a flat response characteristic in a wide frequency band.
  • a laser diode, a broadband light modulator, and a high-speed photodetector are required to generate a THz signal based on an optical device. In the case of FIG.
  • an optical coupler refers to a semiconductor device that transmits an electrical signal using light waves in order to provide electrical isolation and coupling between circuits or systems
  • UTC-PD Uni-Traveling Carrier Photo- Detector is one of the photodetectors, which uses electrons as active carriers and reduces the movement time of electrons through bandgap grading.
  • UTC-PD is capable of photodetection above 150 GHz.
  • EDFA Erbium-Doped Fiber Amplifier
  • PD Photo Detector
  • OSA various optical communication functions (photoelectric conversion, electric light conversion, etc.)
  • DSO Digital Storage oscilloscope
  • the structure of the photoelectric converter (or photoelectric converter) will be described with reference to FIGS. 17 and 18 .
  • 17 is a diagram showing the structure of a photoinc source-based transmitter.
  • 18 is a diagram showing the structure of an optical modulator.
  • a phase or the like of a signal may be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing electrical characteristics through a microwave contact or the like. Accordingly, the optical modulator output is formed as a modulated waveform.
  • a photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal, an O/E conversion by a photoconductive antenna, and a bundle of electrons in light flux.
  • THz pulses can be generated according to emission from relativistic electrons, etc.
  • a THz pulse generated in the above manner may have a unit length of femto second to pico second.
  • An O/E converter uses non-linearity of a device to perform down conversion.
  • 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. As an example of the framework, if the length of a THz pulse for one carrier is set to 50 ps, the bandwidth (BW) becomes about 20 GHz.
  • Effective down conversion from the IR band to the THz band depends on how to utilize the nonlinearity of the O/E converter. That is, in order to down-convert to the desired terahertz band (THz band), the photoelectric converter (O / E converter) having the most ideal non-linearity to move to the corresponding terahertz band (THz band) 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 will 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, as many photoelectric converters as the number of carriers may be required in a multi-carrier system. In particular, in the case of a multi-carrier system using several broadbands according to a plan related to the above-mentioned spectrum use, the phenomenon will be conspicuous.
  • a frame structure for the multi-carrier system may be considered.
  • a signal down-frequency converted based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame).
  • the frequency domain of the specific resource domain may include a plurality of chunks. Each chunk may consist of at least one component carrier (CC).
  • a quantum key distribution (QKD) transmitter 1910 may perform communication by being connected to a QKD receiver 1920 through a public channel and a quantum channel.
  • QKD quantum key distribution
  • the QKD transmitter 1910 may supply the secret key to the encryptor 1930, and the QKD receiver 1920 may also supply the secret key to the decryptor 1940.
  • plain text may be input/output to the encryptor 1930, and the encryptor 1930 may transmit data encrypted with a secret symmetric key (via an existing communication network) to the decryptor 1940.
  • plain text may be input/output to the decoder 1940.
  • quantum cryptographic communication A more detailed description of quantum cryptographic communication is as follows.
  • a signal is carried using a single photon, which is the smallest unit of light, unlike conventional communication methods that communicate by wavelength or amplitude. While stability of conventional cryptosystems is mostly guaranteed by the complexity of mathematical algorithms, quantum cryptographic communication is guaranteed stability as long as the physical laws of quantum mechanics are not broken because it is based on the unique properties of quantum.
  • the most representative quantum key distribution protocol is the BB84 protocol proposed by C. H. Bennett and G. Brassard in 1984.
  • the BB84 protocol information on states such as polarization and phase of photons is carried, and by using the characteristics of both, it is theoretically possible to share a secret key absolutely safely.
  • Table 3 shows an example of the BB84 protocol that generates a secret key by loading information on the polarization state between Alice at the sending side and Bob at the receiving side.
  • the overall flow of the BB84 protocol is as follows.
  • Bob measures and stores the polarization signal transmitted by Alice with the selected polarizer.
  • Alice and Bob keep only bits with the same polarizer and remove bits with different polarizers to obtain a secret key.
  • the present disclosure relates to a method for performing entanglement error correction on a phase flip error caused by a Pauli Z channel in a quantum communication system. More specifically, the correlation between two qubits is recognized as information rather than the individual state of each qubit constituting the entanglement state, and the entanglement already known in the process of entanglement distribution of quantum teleportation It is to minimize the resources consumed for entanglement error correction by fully utilizing state information and achieve an effective error suppression effect.
  • the Bell state is the simplest example of quantum entanglement and refers to the following four quantum states formed by two qubits in a maximally entangled state. This can be viewed as a maximally entangled basis of the 4-dimensional Hilbert space for two qubits, which is called a Bell basis.
  • 20 is a diagram showing an example of a quantum circuit for generating a bell state in a system applicable to the present disclosure.
  • the Bell state can be generated through a quantum circuit of two qubits composed of a Hadamard gate and a CNOT gate (controlled not gate).
  • a Hadamard gate and a CNOT gate (controlled not gate).
  • Table 4 shows the input and output states of the bell state generation circuit.
  • 21 is a diagram showing an example of a bell state measurement circuit in a system applicable to the present disclosure.
  • Bell states form an orthogonal basis
  • Bell state measurement the state of two qubits is to find out which of the four quantum entanglement states defined by the Bell state belongs to. If the order of the CNOT gate and the Hadamard gate is reversed in the Bell state generation circuit of FIG. 20, the Bell state measurement circuit shown in FIG. 21 is obtained.
  • the measurement results shown in Table 5 can be obtained for the four quantum entanglement states corresponding to the Bell state. Table 5 shows the input and output states of the bell state measurement circuit.
  • FIG. 22 is a diagram showing an example of a quantum teleportation system in a system applicable to the present disclosure.
  • Quantum teleportation is a technology that transmits quantum information from a sender at a specific location to a receiver at a certain distance. Contrary to the original meaning of the word ‘Teleport’, in quantum teleportation, the transmission of quantum information between carriers is performed, not the transmission of actual carriers, while the carriers on both sides are fixed. An entangled quantum state, that is, a Bell state, is required for the instant movement of such information, and based on this, a statistical correlation is given between separate physical systems. Because for every change that one of the two entangled particles undergoes, the other undergoes the same change, so the two particles behave as if they were in a single quantum state.
  • FIG. 22 is a schematic diagram of a quantum teleportation protocol using photons.
  • a classical channel capable of transmitting two classical bits
  • a Bell state (entangled state) generating device for moving two particles in a Bell state to a transceiver station in different locations
  • Resources of the bell state measurement device of the transmitting end and the unitary operation device of the receiving end are required.
  • the operation of the protocol is as follows.
  • Entanglement generation An entangled state of two qubits is generated through a Bell state generator.
  • Entanglement distribution In the generated entanglement state, one qubit is moved to the position of sender Alice (A) and the other qubit to the position of receiver Bob (B) through the quantum channel.
  • Quantum post-processing Based on the two bits of information received from Alice, Bob performs a unitary operation on the remaining one qubit of the Bell state he has, and the quantum information that Alice wants to transmit
  • Entanglement creation and distribution functions are key components of quantum teleportation. Since Alice and Bob are nodes located far apart, the entanglement generation that occurs at any one location must be supplemented with an entanglement distribution function that “moves” one of the entangled particles to the other.
  • the adoption of photons as flying qubits, that is, entanglement carriers, has already been widely agreed upon by related academic circles. Photons exhibit moderate decoherence due to their relatively small interaction with the environment, and have the advantage of being easily controllable through standard optical components as well as being capable of high-speed, low-loss transmission.
  • FIG. 23 is a diagram illustrating an example of spontaneous parametric down-conversion in a system applicable to the present disclosure.
  • 24 is a diagram showing an example of an atomic excitation method in an optical cavity using a laser pulse in a system applicable to the present disclosure.
  • 25 is a diagram showing an example of a simultaneous excitation method of two atoms using a laser pulse in a system applicable to the present disclosure.
  • the spontaneous mediated down-conversion method of FIG. 23 uses a characteristic in which a photon beam is sometimes split into polarization entangled photon pairs when a laser beam is projected onto a nonlinear crystal. Since this method creates an entangled pair between photons, Alice and Bob convert the photon they each receive into a matter qubit using a flying-matter transducer.
  • an atom in an optical cavity is excited by using a laser pulse on the Alice side, and the photon emitted as a result is incident into the optical cavity on the Bob side through a quantum channel, thereby creating a distance between two remote atoms.
  • this method it can be seen that entanglement between atoms and photons is initially generated, and the entanglement between atoms is converted into entanglement between atoms through the medium of photons.
  • Figure 25 shows the Bell state measurement at a third node, which can be referred to as a repeater, for the two photons emitted from both sides as a result when Alice and Bob simultaneously excite atoms in their optical resonators using laser pulses, respectively. It shows the way in which entanglement is formed between two atoms by performing It can be seen that entanglement between atoms and photons is converted into entanglement between atoms using entanglement swapping.
  • 26 is a diagram showing an example of incompleteness that deteriorates a quantum teleportation process in a system applicable to the present disclosure.
  • quantum communication processes can also be affected by the quality of transmitted information due to the imperfections that exist in real-world environments. 22 expresses the quantum teleportation process in an ideal environment as a closed physical system, but since the actual quantum teleportation process is affected by unwanted interactions with the surrounding environment, it should be expressed as an open physical system. do. This interaction with the environment causes an irreversible change process in the quantum state, which is called a decoherence process. This decoherence process affects not only the known quantum state transfer process, but also the entanglement generation and distribution process that must precede quantum teleportation. Another source of imperfection involved in the quantum teleportation process is a series of quantum operations performed on quantum states. Contamination in the quantum computation process is a factor that exacerbates the incompleteness of quantum teleportation.
  • 26 is a schematic diagram of the relationship between various imperfections that affect the fidelity of qubits transmitted through quantum teleportation. Regardless of the specific cause of performance degradation, the imperfections inherent in quantum systems result in a change from a pure quantum state to a mixed quantum state. In the field of quantum information science, dealing with these quantum imperfections is one of the very core tasks, but until now, imperfection modeling in the quantum domain to accurately capture the effects of various imperfections involved in the quantum teleportation process remains an unsolved problem. Remains.
  • FIG. 27 is a diagram illustrating an example of a quantum channel model in a system applicable to the present disclosure.
  • FIG. 28 is a diagram showing an example of Pauli-I, Pauli-Z, Pauli-X, and Pauli-Y gates in a system applicable to the present disclosure.
  • Environmental coherence can be described as an unwanted interaction of a qubit with its environment, more specifically entanglement, which perturbs the coherent superposition of the underlying quantum state.
  • the qubit or quantum system
  • This type of decoherence process can be modeled through an amplitude damping channel.
  • Another example of environmental decoherence is a model known as dephasing or phase damping, which characterizes loss of quantum information without loss of energy, e.g. scattering of photons, perturbation of electronic states due to stray charges. etc. may occur.
  • the amplitude and phase decay channels can be approximated as a Pauli channel NP, which maps the input state with density operator ⁇ to the state
  • I, X, Y, and Z correspond to the single-qubit Pauli operator of FIG. 28, and p x , p y , and p z are probabilities of Pauli X, Pauli Y, and Pauli Z errors.
  • Bit flip errors corresponding to the Pauli X channel and bit-phase flip errors corresponding to the Pauli Y channel are related to amplitude decay, and phase flip errors corresponding to the Pauli Z channel are caused by phase decay.
  • the most practical quantum system is an asymmetric channel, in which one of the following bit-flip, phase-flip, or bit-phase flip errors predominates.
  • 29 is a diagram showing an example of an error correction circuit of a 3-qubit bit flip code in a system applicable to the present disclosure.
  • the 3-qubit bit-flip code is a quantum error-correcting code that can protect information from single-bit flip errors occurring in the Pauli X channel.
  • the structure of the 3-qubit bit flip code has a shape similar to that of the repetition code among existing error correction codes.
  • the 3-qubit bit flip code encodes one 1-qubit information into a space composed of 3-qubits, and the encoding process is as follows.
  • ⁇ > a
  • 1> becomes
  • ⁇ > a
  • the codeword encoded by the 3-qubit bit flip code is transmitted to the receiver in one of the following four cases according to the position where the error occurred while being transmitted to the receiver through the single-bit flip error channel.
  • FIG. 30 is a diagram showing an example of an error correction circuit of a 3-qubit phase flip code in a system applicable to the present disclosure.
  • the 3-qubit phase-flip code is a quantum error-correction code technique that protects information from single-phase flip errors occurring in the Pauli Z channel.
  • the construction of the 3-qubit phase flip code is similar to that of the 3-qubit bit flip code.
  • the codeword of the 3-qubit phase-flip code exists in a space composed of
  • any 1-qubit state is encoded as
  • ⁇ a
  • - ⁇ have a relationship in which each other is flipped by the Z operator. This is similar to
  • FIG. 31 is a diagram showing an example of a Shor code error correction circuit in a system applicable to the present disclosure.
  • the coding process of the Shor code is performed by applying a 3-qubit bit flip process to each qubit after performing the coding process of the 3-qubit phase flip code.
  • a bit flip error and a phase flip error generated in a channel are individually determined and each error is corrected, thereby correcting the entire error.
  • 32 is a diagram illustrating an example of a 3-qubit iterative code-based entanglement error correction process in a system applicable to the present disclosure.
  • errors due to quantum channels always occur not only in the process of transmitting arbitrary qubits but also in the process of entanglement distribution, and even in the process of storing quantum information, errors may occur in response to the external environment. Errors in the entanglement distribution process can lead to errors in information transmission when an arbitrary qubit is to be transmitted through quantum teleportation using the corresponding entanglement pair. It is important to increase the fidelity of the pair.
  • FIG. 32 shows a process of correcting an entanglement error based on the 3-qubit repetitive code of FIG. 29 .
  • Blue dots in the diagram represent memory qubits
  • gray dots represent auxiliary qubits
  • connected gray dots represent refined entangled pairs.
  • Alice and Bob each prepare 3 memory qubits and 3 auxiliary qubits (step 1).
  • the memory qubits on the Alice and Bob sides are each fault-tolerantly encoded. is initialized (step 2).
  • Each memory qubit of Alice and Bob is paired and a teleportation-based CNOT operation using the refined entanglement pair is performed.
  • coded entanglement for bit flip error correction introduced in FIG. 29 pair is created in the state of (step 4).
  • the suppressed entanglement error probability of the conventional technique can be obtained as follows.
  • FIG. 33 is a diagram showing an example of a suppressed entanglement error rate of a 3-qubit iterative code-based entanglement error correction process in a system applicable to the present disclosure.
  • FIG. 33 is a schematic diagram of the suppressed entanglement error rate of the conventional entanglement error correction process in contrast to the case where encoding is not applied (no encoding) based on the above equation. It can be seen that the entanglement error correction process also shows the entanglement error suppression effect in the 0 ⁇ p_e ⁇ 0.5 region, which is the effective operating region of the 3-qubit repetitive code.
  • the conventional entanglement error correction process can achieve some degree of entanglement error suppression effect compared to the case where no encoding is applied, but this is because the encoding technique for an arbitrary quantum state other than the entanglement state is borrowed as it is. It is necessary to examine more closely whether this process is sufficiently efficient for error correction for entangled states.
  • the entanglement distribution process is a resource allocation process in which entanglement pairs to be used for information transmission are divided prior to information transmission in quantum teleportation. It should be treated as known information, not unknown information.
  • each qubit itself is regarded as information and error correction is performed, but in the error correction process for an entangled state, the correlation between two qubits rather than the state of each qubit It differs in that it must be processed as information.
  • the present disclosure proposes a technique for performing entanglement error correction on a phase flip error caused by a Pauli Z channel.
  • the proposal of the present disclosure recognizes the correlation between the phase states of two qubits as information rather than the individual phase states of each qubit in the entanglement state, and corrects entanglement errors by fully utilizing the entanglement state information already known in the entanglement distribution process. It is to minimize the resources consumed in and achieve an effective error suppression effect.
  • We propose a method and apparatus for performing error correction by generating parity using the correlation of phase states of two qubits constituting an entangled pair and determining an error according to the parity value.
  • entanglement distribution may be performed between two remote nodes named Alice and Bob.
  • ⁇ AB ideal ⁇ of the entangled pair that Alice and Bob want to share can be
  • ⁇ + ⁇ (
  • Alice and Bob can use refined physical entanglement pairs to perform teleportation-based CNOT operations between memory qubits for entanglement state storage and remote memory qubits for entanglement distribution.
  • a phase flip error may occur due to the reaction of the entangled state with the external environment. At this time, the probability that a phase flip error occurs in each physical qubit can be referred to as p e .
  • 34 is a diagram illustrating an example of a phase flip error correction protocol of an entanglement distribution process in a system applicable to the present disclosure.
  • the first qubit B is paired with Alice's memory qubit A and used to store the entangled state
  • the second qubit P 1 and the third qubit P 2 are respectively the first It is used to store the first parity bit and the second parity bit.
  • Alice's memory qubit A is initialized to
  • + ⁇ (
  • Bob's memory qubits B, P 1 , and P 2 are initialized to
  • Alice and Bob's auxiliary qubits pair up to form three pairs of refined physical entanglements (step 2).
  • a teleportation-based CNOT operation is performed on Alice's memory qubit A and Bob's first memory qubit B, which exhausts the first refined entanglement pair, resulting in entanglement between the two memory qubits.
  • Bob's second memory qubit P 1 calculates and stores the first parity value as the teleportation-based CNOT operation with qubit A's phase information A p and the CNOT operation of qubit B's phase information and B p are sequentially applied. (step 3).
  • a p and B p can be obtained by taking the Hadamard operation on qubit A and qubit B, and the relative phase difference between
  • Bob may transmit classical information indicating that the entanglement distribution process including phase flip error correction has been completed to Alice through the classical channel.
  • 35 is a diagram showing an example of a phase flip error correction circuit in an entanglement distribution process in a system applicable to the present disclosure.
  • each qubit is initialized to
  • qubit A is initialized to
  • the first CNOT gate drawn as a red line in step 3, represents a teleportation-based CNOT operation that exhausts the refined entanglement pair, through which entanglement is formed between qubits A and B.
  • the second and third CNOT gates in step 3 show the process of calculating the first parity value for qubit P 1 by using the correlation of the phase information of qubit A and qubit B, which are fault-tolerantly entangled. . It can be seen that the Hadamard operation was performed on each qubit before the CNOT operation with P 1 to extract the phase information of qubit A and qubit B, and the Hadamard operation to return each qubit to its original state after the CNOT operation is taken once again. In the embodiment of FIG. 34, since qubits P 1 and P 2 are located on the Bob side, the CNOT operation between qubit B, P 1 or P 2 and qubit A is a teleportation-based teleportation-based operation that consumes refined entanglement resources.
  • P 1 stores 0 as a parity value for the case where the phase information of qubits A and B are both
  • step 3 the process of storing the measurement result for P 1 in a classical register is included, which conditionally performs the second parity check process corresponding to step 4 depending on the result of step 3. This is to save refined entangled pair resources consumed in step 4.
  • the fact that the two CNOT gates constituting step 4 are connected to classical resistors indicates that the process of step 4 is conditional on the result of the P 1 value measured in step 3. If resource saving is not required, the P 1 measurement process in step 3 is omitted, and the two conditional CNOT gates constituting step 4 are qubits A and P 1 and qubits B and P 1 as control qubits, respectively. It can also be configured by substituting two Toffoli gates.
  • the process of calculating the second parity value in step 4, as in step 3, also sequentially performs the CNOT operation using the phase information of qubits A and B as control qubits and qubit P 2 as the target qubit.
  • step 5 the phase flip error is corrected through a CNOT operation with qubit P 2 storing the second parity value calculated in step 4 as a control bit and qubit B as a target bit.
  • the Hadamard operation is performed on qubit B before and after the CNOT operation.
  • the fact that the parity value is 1 means that only one of the qubits A and B causes a phase flip error, resulting in an entangled state of
  • ⁇ + ⁇ (
  • ⁇ - ⁇ (
  • error correction may be performed on qubit B stored in the same location as memory qubits P 1 and P 2 in which parity information is stored.
  • FIG. 36 is a diagram showing a result of minimizing the number of gates used based on an equivalent circuit for the circuit of FIG. 35 in a system applicable to the present disclosure.
  • the probability p e is the probability of a phase flip error occurring in each physical qubit
  • the probability p e 'of an error in the entangled state is equal to the probability that a phase flip error occurs in only one of qubits A and B. can be obtained together.
  • the case of error correction failure in the proposed error correction technique can be divided into two types: a case where an error does not occur but incorrect error correction is performed, and a case where an error occurs but is not properly detected.
  • the probability of performing erroneous error correction even though no error occurs is (1-p e ')p e 2 because errors occur in both parity checks.
  • the second parity check is not performed (because even if it is performed, it does not affect the final decision), the probability at this time is p e It can be obtained with 'p e .
  • the final decision was determined to be no error, and the probability in this case was p e '(1-p e ) becomes p e . Therefore, in summary, the suppressed entanglement error rate of the proposed error correction technique can be obtained as follows.
  • the proposed technique conditionally performs the second parity check step corresponding to step 4 according to the result of the first parity check, it has the effect of saving refined entangled pair resources compared to the conventional technique.
  • the number of refined entangled pair resources consumed by is obtained as follows.
  • FIG. 37 is a diagram showing an example of a result of analysis of a suppressed entanglement error rate of a technique according to an embodiment of the present disclosure and a conventional technique in a system applicable to the present disclosure.
  • FIG. 38 is a diagram showing an example of an analysis result of the average number of refined entanglement pairs consumed by a technique according to an embodiment of the present disclosure and a conventional technique in a system applicable to the present disclosure.
  • FIG. 37 and 38 are graphs showing analysis results for the suppressed entanglement error rate of the proposed technique and the conventional technique and the average number of refined entanglement pairs consumed by the proposed technique and the conventional technique, respectively.
  • the conventional technique corresponds to the error correction technique based on the 3-qubit repeating code shown in FIG. 33 .
  • FIG. 37 shows an error suppression effect that is superior to that of the conventional technique in the region of 0 ⁇ pe ⁇ 0.5 , and in FIG.
  • FIG. 32 and FIG. 34 it can be seen that the conventional technique uses a total of 6 memory qubits for both Alice and Bob, but the proposed technique uses a total of 4 memory qubits. Therefore, it can be seen that the proposed technique shows a better error suppression effect even though it consumes less resources than the conventional technique in terms of refined entangled pairs and memory qubits.
  • 39 is a diagram showing an example of a simulation environment configuration of a proposed method using an IBM quantum simulator in a system applicable to the present disclosure.
  • a quantum state E having a probability of being 1 is generated using a U gate, and a phase flip error is generated by performing a Pauli Z operation when the measurement result of E is 1 for each qubit.
  • FIG. 40 is a diagram showing an example of simulation results of a suppressed entanglement error rate of a technique according to an embodiment of the present disclosure and a conventional technique in a system applicable to the present disclosure.
  • the present disclosure proposes a technique for performing entanglement error correction on a phase flip error caused by a Pauli Z channel.
  • the proposal of the present disclosure recognizes the correlation of two qubits as information rather than the individual state of each qubit in the entanglement state, and fully utilizes the entanglement state information already known in the entanglement distribution process, resulting in refined entanglement compared to the conventional technique. It consumes less resources in terms of pairs and memory qubits while achieving better error suppression.
  • 41 is a diagram showing an example of an entangled state encoded using a repetition code in a conventional method.
  • the conventional approach considers the individual phase state of each qubit as information. In addition, it does not utilize correlation information between two qubits that is already known in the entangled state. Alice and Bob respectively perform error correction on their own qubits, consuming unnecessary resources.
  • FIG. 42 is a diagram illustrating an example of entanglement distribution error correction in a system applicable to the present disclosure.
  • a method regards a correlation between phase states of two qubits as information. Efficient error suppression can be achieved by sufficiently utilizing correlation information, which is information known to Alice and Bob. It is possible to reduce unnecessary operations and minimize resource consumption by aiming at error correction for entangled states rather than error correction for each qubit.
  • 43 is a diagram illustrating examples of an operation process of a first node in a system applicable to the present disclosure.
  • a method performed by a first node in a communication system is provided.
  • the first node may correspond to Alice and the second node may correspond to Bob.
  • the first node may correspond to Bob and the second node may correspond to Alice.
  • step S4301 the first node determines the phase between the first qubit of the first node and the second qubit of the second node constituting an entanglement state with respect to the phase flip channel Identify phase correlations.
  • step S4302 a first parity value is determined based on the first phase correlation.
  • step S4303 the first node determines whether a phase flip error occurs based on the first parity value.
  • step S4304 when the occurrence of the phase flip error is determined based on the first parity value, the first node determines a second parity value based on the phase correlation.
  • step S4305 the first node determines whether the phase flip error has occurred based on the second parity value.
  • step S4306 when the occurrence of the phase flip error is determined based on the first parity value, the first node performs an error by a phase flip operation on the first qubit or the second qubit. Perform error correction.
  • the method may further include not performing the phase flip operation based on the fact that no phase flip error has occurred.
  • the embodiment of FIG. 43 may include generating a plurality of parity values based on the phase correlation; determining occurrence of the phase flip error when a majority of parity values among the plurality of parity values are related to the occurrence of the phase flip error; The method may further include performing error correction by performing a phase flip operation on the first qubit or the second qubit when the generation of the phase flip error is determined based on the plurality of parity values.
  • the embodiment of FIG. 43 determines that the phase flip error has not occurred when half of the parity values among the plurality of parity values are related to the occurrence of the phase flip error. step; The method may further include not performing the phase flip operation based on that the phase flip error does not occur.
  • the first parity value and the second parity value are selected as either
  • the determining of the first parity value may include setting the phase information of the first qubit and the phase information of the second qubit as a control qubit, and setting the first parity qubit to a target queue. It can be performed by a controlled not gate (CNOT) operation that sets bits.
  • CNOT controlled not gate
  • the determining of the second parity value may include setting the phase information of the first qubit and the phase information of the second qubit as a control qubit, and setting the second parity qubit to a target queue. It can be performed by the CNOT operation to set bits.
  • the embodiment of FIG. 43 may include performing error correction by a phase flip operation on the first qubit or the second qubit; and performing a phase flip on the first qubit or the second qubit through a Pauli Z operation.
  • a first node is provided in a wireless communication system.
  • the first node may include a transceiver and at least one processor, and the at least one processor may be configured to perform the operating method of the first node according to FIG. 43 .
  • an apparatus for controlling a first node in a wireless communication system includes at least one processor; and at least one memory operably connected to the at least one processor.
  • the at least one memory may be configured to store instructions for performing the operating method of the first node according to FIG. 43 based on execution by the at least one processor.
  • one or more non-transitory computer readable media storing one or more instructions.
  • the one or more commands based on being executed by one or more processors, perform operations, and the operations may include the operating method of the first node according to FIG. 43 .
  • a communication system 1 applied to various embodiments of the present disclosure includes a wireless device, a base station, and a network.
  • the wireless device means a device that performs communication using a radio access technology (eg, 5G New RAT (NR), Long Term Evolution (LTE)), and may be referred to as a communication/wireless/5G device.
  • wireless devices include robots 100a, vehicles 100b-1 and 100b-2, XR (eXtended Reality) devices 100c, hand-held devices 100d, and home appliances 100e. ), an Internet of Thing (IoT) device 100f, and an AI device/server 400.
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, and the like.
  • the vehicle may include an Unmanned Aerial Vehicle (UAV) (eg, a drone).
  • UAV Unmanned Aerial Vehicle
  • XR devices include Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR) devices, Head-Mounted Devices (HMDs), Head-Up Displays (HUDs) installed in vehicles, televisions, smartphones, It may be implemented in the form of a computer, wearable device, home appliance, digital signage, vehicle, robot, and the like.
  • a portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), a computer (eg, a laptop computer, etc.), and the like.
  • Home appliances may include a TV, a refrigerator, a washing machine, and the like.
  • IoT devices may include sensors, smart meters, and the like.
  • a base station and a network may also be implemented as a wireless device, and a specific wireless device 200a may operate as a base station/network node to other wireless devices.
  • the wireless devices 100a to 100f may be connected to the network 300 through the base station 200 .
  • AI Artificial Intelligence
  • the network 300 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 200/network 300, but may also communicate directly (eg, sidelink communication) without going through the base station/network.
  • the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication).
  • IoT devices eg, sensors
  • IoT devices may directly communicate with other IoT devices (eg, sensors) or other wireless devices 100a to 100f.
  • Wireless communication/connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 200 and the base station 200/base station 200.
  • wireless communication/connection refers to various wireless connections such as uplink/downlink communication 150a, sidelink communication 150b (or D2D communication), and inter-base station communication 150c (e.g. relay, Integrated Access Backhaul (IAB)).
  • IAB Integrated Access Backhaul
  • Wireless communication/connection (150a, 150b, 150c) allows wireless devices and base stations/wireless devices, and base stations and base stations to transmit/receive radio signals to/from each other.
  • the wireless communication/connection 150a, 150b, and 150c may transmit/receive signals through various physical channels.
  • transmission of radio signals /
  • various signal processing processes eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.
  • resource allocation processes etc.
  • NR supports a number of numerologies (or subcarrier spacing (SCS)) to support various 5G services.
  • SCS subcarrier spacing
  • the SCS when the SCS is 15 kHz, it supports a wide area in traditional cellular bands, and when the SCS is 30 kHz/60 kHz, dense-urban, lower latency and a wider carrier bandwidth, and when the SCS is 60 kHz or higher, a bandwidth greater than 24.25 GHz is supported to overcome phase noise.
  • the NR frequency band may be defined as a frequency range of two types (FR1 and FR2).
  • the number of frequency ranges may be changed, and for example, the frequency ranges of the two types (FR1 and FR2) may be shown in Table 7 below.
  • FR1 may mean “sub 6 GHz range”
  • FR2 may mean “above 6 GHz range” and may be called millimeter wave (mmW) .
  • mmW millimeter wave
  • FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 8 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included in FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, and may be used, for example, for vehicle communication (eg, autonomous driving).
  • the first wireless device 100 and the second wireless device 200 may transmit and receive radio signals through various radio access technologies (eg, LTE, NR).
  • ⁇ the first wireless device 100, the second wireless device 200 ⁇ is the ⁇ wireless device 100x, the base station 200 ⁇ of FIG. 44 and/or the ⁇ wireless device 100x, the wireless device 100x ⁇ can correspond.
  • the first wireless device 100 includes one or more processors 102 and one or more memories 104, and may additionally include one or more transceivers 106 and/or one or more antennas 108.
  • the processor 102 controls the memory 104 and/or the transceiver 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or flowcharts of operations disclosed herein.
  • the processor 102 may process information in the memory 104 to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 106.
  • the processor 102 may receive a radio signal including the second information/signal through the transceiver 106, and then store information obtained from signal processing of the second information/signal in the memory 104.
  • the memory 104 may be connected to the processor 102 and may store various information related to the operation of the processor 102 .
  • memory 104 may perform some or all of the processes controlled by processor 102, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them.
  • the processor 102 and memory 104 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 106 may be coupled to the processor 102 and may transmit and/or receive wireless signals via one or more antennas 108 .
  • the transceiver 106 may include a transmitter and/or a receiver.
  • the transceiver 106 may be used interchangeably with a radio frequency (RF) unit.
  • RF radio frequency
  • a wireless device may mean a communication modem/circuit/chip.
  • the second wireless device 200 includes one or more processors 202, one or more memories 204, and may further include one or more transceivers 206 and/or one or more antennas 208.
  • Processor 202 controls memory 204 and/or transceiver 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein.
  • the processor 202 may process information in the memory 204 to generate third information/signal, and transmit a radio signal including the third information/signal through the transceiver 206.
  • the processor 202 may receive a radio signal including the fourth information/signal through the transceiver 206 and store information obtained from signal processing of the fourth information/signal in the memory 204 .
  • the memory 204 may be connected to the processor 202 and may store various information related to the operation of the processor 202 .
  • memory 204 may perform some or all of the processes controlled by processor 202, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them.
  • the processor 202 and memory 204 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 206 may be coupled to the processor 202 and may transmit and/or receive wireless signals via one or more antennas 208 .
  • the transceiver 206 may include a transmitter and/or a receiver.
  • the transceiver 206 may be used interchangeably with an RF unit.
  • a wireless device may mean a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors 102, 202.
  • one or more processors 102, 202 may implement one or more layers (eg, functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP).
  • One or more processors 102, 202 may generate one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) in accordance with the descriptions, functions, procedures, proposals, methods and/or operational flow charts disclosed herein.
  • PDUs Protocol Data Units
  • SDUs Service Data Units
  • processors 102, 202 may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flow diagrams disclosed herein.
  • One or more processors 102, 202 generate PDUs, SDUs, messages, control information, data or signals (e.g., baseband signals) containing information according to the functions, procedures, proposals and/or methods disclosed herein , can be provided to one or more transceivers 106, 206.
  • One or more processors 102, 202 may receive signals (eg, baseband signals) from one or more transceivers 106, 206, and descriptions, functions, procedures, proposals, methods, and/or flowcharts of operations disclosed herein PDUs, SDUs, messages, control information, data or information can be obtained according to these.
  • signals eg, baseband signals
  • One or more processors 102, 202 may be referred to as a controller, microcontroller, microprocessor or microcomputer.
  • One or more processors 102, 202 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.
  • Firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein may be included in one or more processors 102, 202 or stored in one or more memories 104, 204 and It can be driven by the above processors 102 and 202.
  • the descriptions, functions, procedures, suggestions, methods and/or operational flow charts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.
  • One or more memories 104, 204 may be coupled with one or more processors 102, 202 and may store various types of data, signals, messages, information, programs, codes, instructions and/or instructions.
  • One or more memories 104, 204 may be comprised of ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer readable storage media, and/or combinations thereof.
  • One or more memories 104, 204 may be located internally and/or external to one or more processors 102, 202. Additionally, one or more memories 104, 204 may be coupled to one or more processors 102, 202 through various technologies, such as wired or wireless connections.
  • One or more transceivers 106, 206 may transmit user data, control information, radio signals/channels, etc., as referred to in the methods and/or operational flow charts herein, to one or more other devices.
  • One or more transceivers 106, 206 may receive user data, control information, radio signals/channels, etc. referred to in descriptions, functions, procedures, proposals, methods and/or operational flow charts, etc. disclosed herein from one or more other devices. there is.
  • one or more transceivers 106 and 206 may be connected to one or more processors 102 and 202 and transmit and receive wireless signals.
  • one or more processors 102, 202 may control one or more transceivers 106, 206 to transmit user data, control information, or radio signals to one or more other devices. Additionally, one or more processors 102, 202 may control one or more transceivers 106, 206 to receive user data, control information, or radio signals from one or more other devices. In addition, one or more transceivers 106, 206 may be coupled with one or more antennas 108, 208, and one or more transceivers 106, 206 via one or more antennas 108, 208, as described herein, function. , procedures, proposals, methods and / or operation flowcharts, etc. can 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).
  • One or more transceivers (106, 206) convert the received radio signals/channels from RF band signals in order to process the received user data, control information, radio signals/channels, etc. using one or more processors (102, 202). It can be converted into a baseband signal.
  • One or more transceivers 106 and 206 may convert user data, control information, and radio signals/channels processed by one or more processors 102 and 202 from baseband signals to RF band signals.
  • one or more of the transceivers 106, 206 may include (analog) oscillators and/or filters.
  • 46 illustrates another example of a wireless device applicable to various embodiments of the present disclosure.
  • a wireless device may include at least one processor 102, 202, at least one memory 104, 204, at least one transceiver 106, 206, and one or more antennas 108, 208. there is.
  • the processors 102 and 202 and the memories 104 and 204 are separated, but in the example of FIG. 46, the processor Note that (102, 202) includes the memory (104, 204).
  • the signal processing circuit 1000 may include a scrambler 1010, a modulator 1020, a layer mapper 1030, a precoder 1040, a resource mapper 1050, and a signal generator 1060.
  • the operations/functions of FIG. 47 may be performed by processors 102 and 202 and/or transceivers 106 and 206 of FIG. 45 .
  • the hardware elements of FIG. 47 may be implemented in processors 102 and 202 and/or transceivers 106 and 206 of FIG.
  • blocks 1010-1060 may be implemented in the processors 102 and 202 of FIG. 45 .
  • blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 45
  • block 1060 may be implemented in the transceivers 106 and 206 of FIG. 45 .
  • the codeword may be converted into a radio signal through the signal processing circuit 1000 of FIG. 47 .
  • a codeword is an encoded bit sequence of an information block.
  • Information blocks may include transport blocks (eg, UL-SCH transport blocks, DL-SCH transport blocks).
  • Radio signals may be transmitted through various physical channels (eg, PUSCH, PDSCH).
  • the codeword may be converted into a scrambled bit sequence by the scrambler 1010.
  • 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.
  • the scrambled bit sequence may be modulated into a modulation symbol sequence by modulator 1020.
  • the modulation scheme 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 the layer mapper 1030.
  • Modulation symbols of each transport layer may be mapped to the corresponding antenna port(s) by the precoder 1040 (precoding).
  • the output z of the precoder 1040 can be obtained by multiplying the output y of the layer mapper 1030 by the N*M precoding matrix W.
  • N is the number of antenna ports and M is the number of transport layers.
  • the precoder 1040 may perform precoding after performing transform precoding (eg, DFT transformation) on complex modulation symbols. Also, the precoder 1040 may perform precoding without performing transform precoding.
  • the resource mapper 1050 may map modulation symbols of each antenna port to time-frequency resources.
  • the time-frequency resource may include a plurality of symbols (eg, CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain.
  • the signal generator 1060 generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to other devices through each antenna.
  • the signal generator 1060 may include an inverse fast Fourier transform (IFFT) module, 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 to the signal processing process 1010 to 1060 of FIG. 47 .
  • wireless devices eg, 100 and 200 of FIG. 45
  • 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 de-scramble process.
  • a signal processing circuit for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.
  • a wireless device may be implemented in various forms according to usage-examples/services (see FIG. 44).
  • wireless devices 100 and 200 correspond to the wireless devices 100 and 200 of FIG. 45, and include various elements, components, units/units, and/or modules. ) can be configured.
  • the wireless devices 100 and 200 may include a communication unit 110 , a control unit 120 , a memory unit 130 and an additional element 140 .
  • the communication unit may include communication circuitry 112 and transceiver(s) 114 .
  • communication circuitry 112 may include one or more processors 102, 202 of FIG. 45 and/or one or more memories 104, 204.
  • transceiver(s) 114 may include one or more transceivers 106, 206 of FIG. 45 and/or one or more antennas 108, 208.
  • the control unit 120 is electrically connected to the communication unit 110, the memory unit 130, and the additional element 140 and controls overall operations of the wireless device.
  • the control unit 120 may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit 130.
  • the controller 120 transmits the information stored in the memory unit 130 to the outside (eg, another communication device) through the communication unit 110 through a wireless/wired interface, or transmits the information stored in the memory unit 130 to the outside (eg, another communication device) through the communication unit 110.
  • Information received through a wireless/wired interface from other communication devices) may be stored in the memory unit 130 .
  • the additional element 140 may be configured in various ways according to the type of wireless device.
  • the additional element 140 may include at least one of a power unit/battery, an I/O unit, a driving unit, and a computing unit.
  • the wireless device may be a robot (Fig. 44, 100a), a vehicle (Fig. 44, 100b-1, 100b-2), an XR device (Fig. 44, 100c), a mobile device (Fig. 44, 100d), a home appliance. (FIG. 44, 100e), IoT device (FIG.
  • wireless devices can be mobile or used in a fixed location depending on the use-case/service.
  • various elements, components, units/units, and/or modules in the wireless devices 100 and 200 may be entirely interconnected through a wired interface or at least partially connected wirelessly through the communication unit 110.
  • the control unit 120 and the communication unit 110 are connected by wire, and the control unit 120 and the first units (eg, 130 and 140) are connected through the communication unit 110.
  • the control unit 120 and the first units eg, 130 and 140
  • each element, component, unit/unit, and/or module within the wireless device 100, 200 may further include one or more elements.
  • the control unit 120 may be composed of one or more processor sets.
  • the controller 120 may include 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.
  • the memory unit 130 may include random access memory (RAM), dynamic RAM (DRAM), read only memory (ROM), flash memory, volatile memory, and non-volatile memory. volatile memory) and/or a combination thereof.
  • FIG. 48 An implementation example of FIG. 48 will be described in more detail with reference to drawings.
  • a portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), and a portable computer (eg, a laptop computer).
  • a 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
  • a portable device 100 includes an antenna unit 108, a communication unit 110, a control unit 120, a memory unit 130, a power supply unit 140a, an interface unit 140b, and an input/output unit 140c. ) may be included.
  • the antenna unit 108 may be configured as part of the communication unit 110 .
  • Blocks 110 to 130/140a to 140c respectively correspond to blocks 110 to 130/140 of FIG. 48 .
  • the communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) with other wireless devices and base stations.
  • the controller 120 may perform various operations by controlling components of the portable device 100 .
  • the control unit 120 may include an application processor (AP).
  • the memory unit 130 may store data/parameters/programs/codes/commands necessary for driving the portable device 100 .
  • the memory unit 130 may store input/output data/information.
  • the power supply unit 140a supplies power to the portable device 100 and may include a wired/wireless charging circuit, a battery, and the like.
  • the interface unit 140b may support connection between the portable device 100 and other external devices.
  • the interface unit 140b may include various ports (eg, audio input/output ports and video input/output ports) for connection with external devices.
  • the input/output unit 140c may receive or output image information/signal, audio information/signal, data, and/or information input from a user.
  • the input/output unit 140c may include a camera, a microphone, a user input unit, a display unit 140d, a speaker, and/or a haptic module.
  • the input/output unit 140c obtains information/signals (eg, touch, text, voice, image, video) input from the user, and the acquired information/signals are stored in the memory unit 130.
  • the communication unit 110 may convert the information/signal stored in the memory into a wireless signal, and directly transmit the converted wireless signal to another wireless device or to a base station.
  • the communication unit 110 may receive a radio signal from another wireless device or a base station and then restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 130, it may be output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 140c.
  • 50 illustrates a vehicle or autonomous vehicle applied to various embodiments of the present disclosure.
  • Vehicles or autonomous vehicles may be implemented as mobile robots, vehicles, trains, manned/unmanned aerial vehicles (AVs), ships, and the like.
  • AVs manned/unmanned aerial vehicles
  • a vehicle or autonomous vehicle 100 includes an antenna unit 108, a communication unit 110, a control unit 120, a driving unit 140a, a power supply unit 140b, a sensor unit 140c, and an autonomous driving unit.
  • a portion 140d may be included.
  • the antenna unit 108 may be configured as part of the communication unit 110 .
  • Blocks 110/130/140a to 140d respectively correspond to blocks 110/130/140 of FIG. 48 .
  • the communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) with external devices such as other vehicles, base stations (e.g. base stations, roadside base stations, etc.), servers, and the like.
  • the controller 120 may perform various operations by controlling elements of the vehicle or autonomous vehicle 100 .
  • the controller 120 may include an Electronic Control Unit (ECU).
  • the driving unit 140a may drive the vehicle or autonomous vehicle 100 on the ground.
  • the driving unit 140a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like.
  • the power supply unit 140b supplies power to the vehicle or autonomous vehicle 100, and may include a wired/wireless charging circuit, a battery, and the like.
  • the sensor unit 140c may obtain vehicle conditions, surrounding environment information, and user information.
  • the sensor unit 140c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, and a vehicle forward.
  • IMU inertial measurement unit
  • /Can include a reverse sensor, battery sensor, fuel sensor, tire sensor, steering sensor, temperature sensor, humidity sensor, ultrasonic sensor, illuminance sensor, pedal position sensor, and the like.
  • the autonomous driving unit 140d 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 and driving. technology can be implemented.
  • the communication unit 110 may receive map data, traffic information data, and the like from an external server.
  • the autonomous driving unit 140d may generate an autonomous driving route and a driving plan based on the acquired data.
  • the controller 120 may control the driving unit 140a so that the vehicle or autonomous vehicle 100 moves along the autonomous driving path according to the driving plan (eg, speed/direction adjustment).
  • the communicator 110 may non-/periodically obtain the latest traffic information data from an external server and obtain surrounding traffic information data from surrounding vehicles.
  • the sensor unit 140c may acquire vehicle state and surrounding environment information.
  • the autonomous driving unit 140d may update an autonomous driving route and a driving plan based on newly acquired data/information.
  • the communication unit 110 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 based on information collected from the vehicle or self-driving vehicles, and may provide the predicted traffic information data to the vehicle or self-driving vehicles.
  • a vehicle may be implemented as a means of transportation, a train, an air vehicle, a ship, and the like.
  • the vehicle 100 may include a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a, and a position measurement unit 140b.
  • blocks 110 to 130/140a to 140b correspond to blocks 110 to 130/140 of FIG. X3, respectively.
  • the communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) with other vehicles or external devices such as base stations.
  • the controller 120 may perform various operations by controlling components of the vehicle 100 .
  • the memory unit 130 may store data/parameters/programs/codes/commands supporting various functions of the vehicle 100 .
  • the input/output unit 140a may output an AR/VR object based on information in the memory unit 130.
  • the input/output unit 140a may include a HUD.
  • the location measurement unit 140b may obtain location information of the vehicle 100 .
  • the location information may include absolute location information of the vehicle 100, location information within a driving line, acceleration information, and location information with neighboring vehicles.
  • the location measurement unit 140b may include GPS and various sensors.
  • the communication unit 110 of the vehicle 100 may receive map information, traffic information, and the like from an external server and store them in the memory unit 130 .
  • the location measurement unit 140b may acquire vehicle location information through GPS and various sensors and store it in the memory unit 130 .
  • the controller 120 may generate a virtual object based on map information, traffic information, vehicle location information, etc., and the input/output unit 140a may display the created virtual object on a window in the vehicle (1410, 1420).
  • the controller 120 may determine whether the vehicle 100 is normally operated within the driving line based on the vehicle location information. When the vehicle 100 abnormally deviate from the driving line, the controller 120 may display a warning on a window in the vehicle through the input/output unit 140a. In addition, the controller 120 may broadcast a warning message about driving abnormality to surrounding vehicles through the communication unit 110 .
  • the controller 120 may transmit vehicle location information and information on driving/vehicle abnormalities to related agencies through the communication unit 110 .
  • the XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.
  • HMD head-up display
  • the XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.
  • HUD head-up display
  • the XR device 100a may include a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a, a sensor unit 140b, and a power supply unit 140c.
  • blocks 110 to 130/140a to 140c correspond to blocks 110 to 130/140 of FIG. X3, respectively.
  • the communication unit 110 may transmit/receive signals (eg, media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers.
  • Media data may include video, image, sound, and the like.
  • the controller 120 may perform various operations by controlling components of the XR device 100a.
  • the controller 120 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 130 may store data/parameters/programs/codes/commands necessary for driving the XR device 100a/creating an XR object.
  • the input/output unit 140a may obtain control information, data, etc. from the outside and output the created XR object.
  • the input/output unit 140a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module.
  • the sensor unit 140b may obtain XR device status, surrounding environment information, user information, and the like.
  • the sensor unit 140b may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is.
  • the power supply unit 140c supplies power to the XR device 100a and may include a wired/wireless charging circuit, a battery, and the like.
  • the memory unit 130 of the XR device 100a may include information (eg, data, etc.) necessary for generating an XR object (eg, AR/VR/MR object).
  • the input/output unit 140a may obtain a command to operate the XR device 100a from a user, and the control unit 120 may drive the XR device 100a according to the user's driving command. For example, when a user tries to watch a movie, news, etc. through the XR device 100a, the control unit 120 transmits content request information to another device (eg, the mobile device 100b) or through the communication unit 130. can be sent to the media server.
  • another device eg, the mobile device 100b
  • the communication unit 130 can be sent to the media server.
  • the communication unit 130 may download/stream content such as movies and news from another device (eg, the portable device 100b) or a media server to the memory unit 130 .
  • the control unit 120 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, metadata generation/processing, etc. for content, and acquisition through the input/output unit 140a/sensor unit 140b.
  • An XR object may be created/output based on information about a surrounding space or a real object.
  • the XR device 100a is wirelessly connected to the portable device 100b through the communication unit 110, and the operation of the XR device 100a may be controlled by the portable device 100b.
  • the mobile device 100b may operate as a controller for the XR device 100a.
  • the XR device 100a may acquire 3D location information of the portable device 100b and then generate and output an XR object corresponding to the portable device 100b.
  • Robot 53 illustrates a robot applied to various embodiments of the present disclosure. Robots may be classified into industrial, medical, household, military, and the like depending on the purpose or field of use.
  • the robot 100 may include a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a, a sensor unit 140b, and a driving unit 140c.
  • blocks 110 to 130/140a to 140c correspond to blocks 110 to 130/140 of FIG. X3, respectively.
  • the communication unit 110 may transmit/receive signals (eg, driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers.
  • the controller 120 may perform various operations by controlling components of the robot 100 .
  • the memory unit 130 may store data/parameters/programs/codes/commands supporting various functions of the robot 100.
  • the input/output unit 140a may obtain information from the outside of the robot 100 and output the information to the outside of the robot 100 .
  • the input/output unit 140a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module.
  • the sensor unit 140b may obtain internal information of the robot 100, surrounding environment information, user information, and the like.
  • the sensor unit 140b may include a proximity sensor, an illuminance 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 140c may perform various physical operations such as moving a robot joint. In addition, the driving unit 140c may make the robot 100 drive on the ground or fly in the air.
  • the driving unit 140c may include actuators, motors, wheels, brakes, propellers, and the like.
  • AI devices include fixed or mobile devices such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, and vehicles. It can be implemented with possible devices and the like.
  • the AI device 100 includes a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a/140b, a running processor unit 140c, and a sensor unit 140d.
  • a communication unit 110 can include Blocks 110-130/140a-140d correspond to blocks 110-130/140 of Figure X3, respectively.
  • the communication unit 110 transmits wired/wireless signals (eg, sensor information, user input, learning) to other AI devices (eg, FIG. W1, 100x, 200, 400) or external devices such as the AI server 200 using wired/wireless communication technology. models, control signals, etc.) can be transmitted and received.
  • the communication unit 110 may transmit information in the memory unit 130 to an external device or transmit a signal received from the external device to the memory unit 130 .
  • the controller 120 may determine at least one feasible operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the controller 120 may perform the determined operation by controlling components of the AI device 100 . For example, the controller 120 may request, retrieve, receive, or utilize data from the learning processor unit 140c or the memory unit 130, and may perform a predicted operation among at least one feasible operation or an operation determined to be desirable. Components of the AI device 100 may be controlled to execute an operation. In addition, the control unit 120 collects history information including user feedback on the operation contents or operation of the AI device 100 and stores it in the memory unit 130 or the running processor unit 140c, or the AI server ( It can be transmitted to an external device such as FIG. W1, 400). The collected history information can be used to update the learning model.
  • the memory unit 130 may store data supporting various functions of the AI device 100 .
  • the memory unit 130 may store data obtained from the input unit 140a, data obtained from the communication unit 110, output data from the learning processor unit 140c, and data obtained from the sensing unit 140.
  • the memory unit 130 may store control information and/or software codes necessary for operation/execution of the control unit 120 .
  • the input unit 140a may obtain various types of data from the outside of the AI device 100.
  • the input unit 120 may obtain learning data for model learning and input data to which the learning model is to be applied.
  • the input unit 140a may include a camera, a microphone, and/or a user input unit.
  • the output unit 140b may generate an output related to sight, hearing, or touch.
  • the output unit 140b may include a display unit, a speaker, and/or a haptic module.
  • the sensing unit 140 may obtain at least one of internal information of the AI device 100, surrounding environment information of the AI device 100, and user information by using various sensors.
  • the sensing unit 140 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is.
  • the learning processor unit 140c may learn a model composed of an artificial neural network using learning data.
  • the running processor unit 140c may perform AI processing together with the running processor unit of the AI server (FIG. W1, 400).
  • the learning processor unit 140c may process information received from an external device through the communication unit 110 and/or information stored in the memory unit 130 .
  • the output value of the learning processor unit 140c may be transmitted to an external device through the communication unit 110 and/or stored in the memory unit 130.

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Abstract

Selon divers modes de réalisation de la présente divulgation est prévu un procédé pour faire fonctionner un premier nœud dans un système de communication, comprenant les étapes consistant à : identifier, par rapport à un canal à bascule de phase, une corrélation de phase entre un premier bit quantique d'un premier nœud et un second bit quantique d'un second nœud configurant un état d'enchevêtrement ; déterminer une première valeur de parité sur la base de la corrélation de phase ; déterminer, sur la base de la première valeur de parité, si une erreur de bascule de phase s'est produite ; déterminer une seconde valeur de parité sur la base de la corrélation de phase, s'il est déterminé sur la base de la première valeur de parité que l'erreur de bascule de phase s'est produite ; déterminer, sur la base de la seconde valeur de parité, si l'erreur de bascule de phase s'est produite ; et réaliser une correction d'erreur au moyen d'une opération de bascule de phase pour le premier bit quantique ou le second bit quantique, s'il est déterminé sur la base de la première valeur de parité que la bascule de phase s'est produite.
PCT/KR2022/015778 2021-10-18 2022-10-17 Dispositif et procédé pour protocole de distribution d'enchevêtrement, comprenant une réparation d'erreur de bascule de phase dans un système de communication WO2023068713A1 (fr)

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