Apparatus and method for determining route of entanglement routing in quantum communication system

Abstract

According to various embodiments of the present disclosure, an operation method of a first node in a communication system comprises the steps of: transmitting at least one synchronization signal to a plurality of nodes constituting a plurality of routes in a network; transmitting control information to the plurality of nodes; receiving network information from the plurality of nodes; receiving a request for entanglement routing (ER) between a second node and a third node from the second node; performing, on the basis of the network information, the ER for determining a specific route between the second node and the third node on the basis of first factor information dependent on a distance value between the second node and the third node, second factor information not dependent on the distance value, and a scaling factor for the first factor information or the second factor information.

Description

Device and method for determining a path of entanglement routing in a quantum communication system

[0001] The present disclosure relates to a device and method for determining a path for entanglement routing in a quantum communication system. Specifically, the present disclosure relates to a device and method for performing adaptive operation of an optimal routing algorithm according to the network scale when searching for a path for entanglement routing between any two source nodes and destination nodes connected via a multi-hop path in a quantum communication system.

[0002]

[0003] In a quantum network environment, there is generally no direct quantum channel between a source node and a destination node that can share entanglement through direct transmission. Therefore, technologies for multi-hop quantum information transmission are required. Research on multi-hop quantum information transmission broadly encompasses two approaches. The first is a multi-hop quantum teleportation technique, which performs quantum teleportation on a hop-by-hop basis, similar to routing in classical communications, so that information can travel to the destination node through hop-by-hop transmission. The other method is a multi-hop entanglement exchange method. This method first forms entanglement between the source and destination nodes by performing multiple entanglement exchanges on multi-hop links, and then utilizes this entanglement to perform quantum teleportation. This allows actual data to be transmitted to the destination node without having to directly traverse the entire multi-hop path.

[0004] Performing multi-hop quantum teleportation or multi-hop entanglement exchange requires an algorithm that finds the optimal path among multiple multi-hop paths connecting the source and destination nodes. Generally, the shortest path has the highest entanglement rate and the fewest hops. Therefore, when performing entanglement routing via a single path, using a shortest path algorithm can maximize network resource efficiency. However, considering that entanglement routing is a resource allocation process for quantum information transmission, the entanglement rate achievable via a single path may not be sufficient depending on the traffic characteristics of the data to be transmitted. In this case, network resources consisting of multiple paths must be allocated to entanglement routing. Conventional entanglement routing techniques individually select each path based on metrics such as entanglement success rate. This approach inevitably reduces resource efficiency compared to using a single shortest path, as it includes suboptimal paths that have more hops or lower entanglement rates than the shortest path. Therefore, when performing entanglement routing based on multiple paths, an algorithm is needed to select the shortest path that has the potential to improve network resource efficiency by considering organic utilization between them.

[0005]

[0006] To solve the above-described problems, the present disclosure provides a device and method for determining a path of entanglement routing in a quantum communication system.

[0007] The present disclosure provides a device and method for performing adaptive operation of an optimal path algorithm according to the network scale in searching a path for entanglement routing between any two source nodes and destination nodes connected through a multi-hop path in a quantum communication system.

[0008] The present disclosure provides a device and method for adaptively operating an evaluation function for distance-dependent factors by distinguishing between factors that are dependent on the distance between a source node and a destination node and factors that are not, among factors considered in an optimal path algorithm in a quantum communication system.

[0009] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0010]

[0011] According to various embodiments of the present disclosure, a method for operating a first node in a communication system is provided, the method comprising: transmitting at least one synchronization signal to a plurality of nodes constituting a plurality of paths in a network; transmitting control information to the plurality of nodes; receiving network information from the plurality of nodes; receiving a request for entanglement routing (ER) between a second node and a third node from the second node; and performing the ER to determine a specific path between the second node and the third node based on first factor information that depends on a distance value between the second node and the third node, second factor information that does not depend on the distance value, or a scaling factor for the first factor information or the second factor information, based on the network information.

[0012] According to various embodiments of the present disclosure, a method for operating a second node in a communication system is provided, the method comprising: receiving at least one synchronization signal from a first node; receiving control information from the first node; receiving network information from a plurality of nodes, wherein the network information is also transmitted to the first node; transmitting a request for entanglement routing (ER) between the second node and a third node to the first node; and performing the ER to determine a specific path between the second node and the third node based on the network information, based on first factor information that depends on a distance value between the second node and the third node, second factor information that does not depend on the distance value, or a scaling factor for the first factor information or the second factor information.

[0013] According to various embodiments of the present disclosure, in a communication system, a first node is provided, comprising: a transceiver; at least one processor; and at least one memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, wherein the operations include all steps of a method of operating the first node according to various embodiments of the present disclosure.

[0014] According to various embodiments of the present disclosure, in a communication system, a second node is provided, comprising: a transceiver; at least one processor; and at least one memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, wherein the operations include all steps of a method of operating the second node according to various embodiments of the present disclosure.

[0015] According to various embodiments of the present disclosure, a control device for controlling a first node in a communication system is provided, comprising: at least one processor; and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on being executed by the at least one processor, wherein the operations include all steps of an operating method of the first node according to various embodiments of the present disclosure.

[0016] According to various embodiments of the present disclosure, a control device for controlling a second node in a communication system is provided, comprising: at least one processor; and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on being executed by the at least one processor, wherein the operations include all steps of an operating method of the second node according to various embodiments of the present disclosure.

[0017] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions, based on being executed by one or more processors, perform operations, the operations comprising all steps of a method of operating a first node according to various embodiments of the present disclosure, are provided.

[0018] According to various embodiments of the present disclosure, there is provided one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions, when executed by one or more processors, perform operations, the operations comprising all steps of a method of operating a second node according to various embodiments of the present disclosure.

[0019]

[0020] To solve the above-described problems, the present disclosure can provide a device and method for determining a path of entanglement routing in a quantum communication system.

[0021] The present disclosure can provide a device and method for performing adaptive operation of an optimal path algorithm according to a network scale in searching a path for entanglement routing between any two source nodes and destination nodes connected through a multi-hop path in a quantum communication system.

[0022] The present disclosure can provide a device and method for adaptively operating an evaluation function for a distance-dependent factor by distinguishing between factors that are dependent on the distance between a source node and a destination node and factors that are not, among factors considered in an optimal path algorithm in a quantum communication system.

[0023]

[0024] The accompanying drawings are intended to aid in understanding the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0025] Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.

[0026] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0027] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0028] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0029] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0030] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0031] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0032] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0033] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0034] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0035] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0036] Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.

[0037] Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.

[0038] Figure 14 is a diagram illustrating an example of a THz communication application.

[0039] Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.

[0040] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0041] Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

[0042] Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.

[0043] Figure 19 is a drawing showing the structure of an optical modulator.

[0044] FIG. 20 is a diagram illustrating an example of a quantum circuit for generating a bell state in a system applicable to the present disclosure.

[0045] FIG. 21 is a diagram illustrating an example of a bell state measurement circuit in a system applicable to the present disclosure.

[0046] FIG. 22 is a diagram illustrating an example of a quantum teleportation system applicable to the present disclosure.

[0047] FIG. 23 is a diagram illustrating an example of spontaneous parametric down-conversion in a system applicable to the present disclosure.

[0048] FIG. 24 is a diagram illustrating an example of an atomic excitation method in an optical cavity using a laser pulse in a system applicable to the present disclosure.

[0049] FIG. 25 is a diagram illustrating an example of a method for simultaneously exciting two atoms using laser pulses in a system applicable to the present disclosure.

[0050] FIG. 26 is a diagram illustrating an example of an imperfection that degrades the quantum teleportation process in a system applicable to the present disclosure.

[0051] FIG. 27 is a diagram illustrating an example of a quantum channel model in a system applicable to the present disclosure.

[0052] FIG. 28 is a diagram illustrating an example of Pauli-I, Pauli-Z, Pauli-X, and Pauli-Y gates in a system applicable to the present disclosure.

[0053] FIG. 29 is a diagram illustrating an example of a quantum communication network model in a system applicable to the present disclosure.

[0054] FIG. 30 is a diagram illustrating an example of a process for applying adaptive parameters of an optimal path algorithm in a system applicable to the present disclosure.

[0055] FIG. 31 is a diagram illustrating an example of a process for applying adaptive parameters of an optimal path algorithm in a system applicable to the present disclosure.

[0056] FIG. 32 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.

[0057] FIG. 33 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0058] FIG. 34 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0059] FIG. 35 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0060] FIG. 36 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0061] Figure 37 illustrates a signal processing circuit for a transmission signal.

[0062] FIG. 38 illustrates another example of a wireless device applicable to various embodiments of the present disclosure.

[0063] FIG. 39 illustrates a mobile device applicable to various embodiments of the present disclosure.

[0064] FIG. 40 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0065] FIG. 41 illustrates a vehicle applicable to various embodiments of the present disclosure.

[0066] FIG. 42 illustrates an XR device applicable to various embodiments of the present disclosure.

[0067] FIG. 43 illustrates a robot applicable to various embodiments of the present disclosure.

[0068] FIG. 44 illustrates an AI device applicable to various embodiments of the present disclosure.

[0069]

[0070] In 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.” For example, in various embodiments of the present disclosure, “A, B or C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.”

[0071] In various embodiments of the present disclosure, a slash ( / ) or a comma may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B, or C."

[0072] In various embodiments of the present disclosure, “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” Furthermore, in various embodiments of the present disclosure, the expressions “at least one of A or B” or “at least one of A and / or B” may be interpreted as equivalent to “at least one of A and B.”

[0073] Additionally, in various embodiments of the present disclosure, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0074] Additionally, parentheses used in various embodiments of the present disclosure may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in various embodiments of the present disclosure is not limited to "PDCCH", and "PDDCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0075] Technical features individually described in a single drawing in various embodiments of the present disclosure may be implemented individually or simultaneously.

[0076]

[0077] The following technologies can be used in various wireless access systems, such as CDMA, FDMA, TDMA, OFDMA, and SC-FDMA. CDMA can be implemented using wireless technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (Global System for Mobile communications) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented using wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is a part of UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is a part of E-UMTS (Evolved UMTS) that uses E-UTRA, and LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A / LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

[0078]

[0079] For clarity, the description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical spirit of the present disclosure is not limited thereto. LTE refers to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR refers to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. “xxx” refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system. For background technology, terms, abbreviations, etc. used in the description of the present disclosure, reference may be made to matters described in standard documents published prior to the present disclosure. For example, reference may be made to the following documents.

[0080]

[0081] 3GPP LTE

[0082] - 36.211: Physical channels and modulation

[0083] - 36.212: Multiplexing and channel coding

[0084] - 36.213: Physical layer procedures

[0085] - 36.300: Overall description

[0086] - 36.331: Radio Resource Control (RRC)

[0087] 3GPP NR

[0088] - 38.211: Physical channels and modulation

[0089] - 38.212: Multiplexing and channel coding

[0090] - 38.213: Physical layer procedures for control

[0091] - 38.214: Physical layer procedures for data

[0092] - 38.300: NR and NG-RAN Overall Description

[0093] - 38.331: Radio Resource Control (RRC) protocol specification

[0094]

[0095] Physical Channel and Frame Structure

[0096] Physical channels and general signal transmission

[0097] Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.

[0098] In a wireless communication system, a terminal receives information from a base station via the downlink (DL) and transmits it to the base station via the uplink (UL). The information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0099]

[0100] When a terminal is powered on or enters a new cell, it performs an initial cell search operation, such as synchronizing with the base station (S11). To this end, the terminal receives a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS) from the base station to synchronize with the base station and obtain information such as a cell ID. Afterwards, the terminal can receive a Physical Broadcast Channel (PBCH) from the base station to obtain broadcast information within the cell. Meanwhile, the terminal can receive a Downlink Reference Signal (DL RS) during the initial cell search phase to check the downlink channel status.

[0101]

[0102] A terminal that has completed initial cell search can obtain more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) based on information contained in the PDCCH (S12).

[0103]

[0104] Meanwhile, when accessing a base station for the first time or when there are no radio resources for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (S13 to S16). To this end, the terminal may transmit a specific sequence as a preamble via a physical random access channel (PRACH) (S13 and S15) and receive a response message (RAR (Random Access Response) message) to the preamble via a PDCCH and a corresponding PDSCH. In the case of a contention-based RACH, a contention resolution procedure may additionally be performed (S16).

[0105]

[0106] The terminal that has performed the procedure described above can then perform PDCCH / PDSCH reception (S17) and physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) transmission (S18) as general uplink / downlink signal transmission procedures. In particular, the terminal can receive downlink control information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and different formats can be applied depending on the purpose of use.

[0107]

[0108] Meanwhile, the control information that the terminal transmits to the base station via the uplink or that the terminal receives from the base station may include downlink / uplink ACK / NACK signals, CQI (Channel Quality Indicator), PMI (Precoding Matrix Index), RI (Rank Indicator), etc. The terminal may transmit the above-described control information such as CQI / PMI / RI via PUSCH and / or PUCCH.

[0109]

[0110] Structure of uplink and downlink channels

[0111] Downlink channel structure

[0112] The base station transmits a related signal to the terminal through a downlink channel described below, and the terminal receives the related signal from the base station through a downlink channel described below.

[0113]

[0114] (1) Physical Downlink Shared Channel (PDSCH)

[0115] PDSCH carries downlink data (e.g., DL-shared channel transport block, DL-SCH TB) and applies modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM. Codewords are generated by encoding the TBs. PDSCH can carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and modulation symbols generated from each codeword are mapped to one or more layers (Layer mapping). Each layer is mapped to resources along with a Demodulation Reference Signal (DMRS), generated as an OFDM symbol signal, and transmitted through the corresponding antenna port.

[0116]

[0117] (2) Physical downlink control channel (PDCCH)

[0118] The PDCCH carries downlink control information (DCI) and employs modulation methods such as QPSK. A PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs), depending on the Aggregation Level (AL). Each CCE is comprised of six Resource Element Groups (REGs). Each REG is defined by one OFDM symbol and one (P)RB.

[0119] The UE obtains DCI transmitted via the PDCCH by performing decoding (also known as blind decoding) on ​​a set of PDCCH candidates. The set of PDCCH candidates decoded by the UE is defined as a PDCCH search space set. The search space set may be a common search space or a UE-specific search space. The UE can obtain DCI by monitoring PDCCH candidates within one or more search space sets established by the MIB or higher layer signaling.

[0120]

[0121] Uplink channel structure

[0122] The terminal transmits a related signal to the base station through the uplink channel described below, and the base station receives the related signal from the terminal through the uplink channel described below.

[0123] (1) Physical Uplink Shared Channel (PUSCH)

[0124] PUSCH carries uplink data (e.g., UL-shared channel transport block, UL-SCH TB) and / or uplink control information (UCI), and is transmitted based on a CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform, a DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform, etc. When the PUSCH is transmitted based on a DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. For example, when transform precoding is disabled (e.g., transform precoding is disabled), the UE transmits the PUSCH based on the CP-OFDM waveform, and when transform precoding is enabled (e.g., transform precoding is enabled), the UE can transmit the PUSCH based on the CP-OFDM waveform or the DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants in DCI, or semi-statically scheduled (configured grant) based on higher layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)). PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0125] (2) Physical Uplink Control Channel (PUCCH)

[0126] PUCCH carries uplink control information, HARQ-ACK and / or scheduling request (SR), and can be divided into multiple PUCCHs depending on the PUCCH transmission length.

[0127]

[0128] Below, we describe new radio access technology (new RAT, NR).

[0129] As more and more communication devices demand greater communication capacity, the need for improved mobile broadband communication compared to existing radio access technology (RAT) is emerging. Furthermore, massive Machine Type Communications (MTC), which connects numerous devices and objects to provide various services anytime, anywhere, is also a key issue to be considered in next-generation communication. Furthermore, communication system design that considers reliability and latency-sensitive services / terminals is being discussed. The introduction of next-generation radio access technologies that take into account enhanced mobile broadband communication, massive MTC, and URLLC (Ultra-Reliable and Low Latency Communication) is being discussed, and in various embodiments of the present disclosure, these technologies are conveniently referred to as new RAT or NR.

[0130]

[0131] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0132] Referring to FIG. 2, the NG-RAN may include a gNB and / or an eNB that provides user plane and control plane protocol termination to the UE. FIG. 1 illustrates a case where only a gNB is included. The gNB and eNB are connected to each other via an Xn interface. The gNB and eNB are connected to the 5th generation core network (5G Core Network: 5GC) via the NG interface. More specifically, the gNB is connected to the access and mobility management function (AMF) via the NG-C interface, and the gNB is connected to the user plane function (UPF) via the NG-U interface.

[0133]

[0134] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0135] Referring to FIG. 3, the gNB can provide functions such as 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 configuration and provision, and dynamic resource allocation. The AMF can provide functions such as NAS security and idle state mobility processing. The UPF can provide functions such as mobility anchoring and PDU processing. The SMF (Session Management Function) can provide functions such as terminal IP address allocation and PDU session control.

[0136]

[0137] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0138] The 5G usage scenario illustrated in FIG. 4 is merely exemplary, and the technical features of various embodiments of the present disclosure can also be applied to other 5G usage scenarios not illustrated in FIG. 4.

[0139] Referring to Figure 4, the three key requirement areas for 5G include (1) enhanced mobile broadband (eMBB), (2) massive machine type communication (mMTC), and (3) ultra-reliable and low latency communications (URLLC). Some use cases may require optimization across multiple areas, while others may focus on just one key performance indicator (KPI). 5G supports these diverse use cases in a flexible and reliable manner.

[0140] eMBB focuses on improving data speeds, latency, user density, and overall capacity and coverage of mobile broadband connections. It targets throughputs of around 10 Gbps. eMBB significantly exceeds basic mobile internet access, enabling rich interactive experiences, media and entertainment applications in the cloud, and augmented reality. Data is a key driver of 5G, and for the first time, dedicated voice services may not be available in the 5G era. In 5G, voice is expected to be handled as an application, simply using the data connection provided by the communication system. The increased traffic volume is primarily due to the increasing content size and the growing number of applications that require high data rates. Streaming services (audio and video), interactive video, and mobile internet connectivity will become more prevalent as more devices connect to the internet. Many of these applications require always-on connectivity to push real-time information and notifications to users. Cloud storage and applications are rapidly growing on mobile communication platforms, and this 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 significantly lower end-to-end latency to maintain a superior user experience when tactile interfaces are used. In entertainment, for example, cloud gaming and video streaming are other key factors driving the demand for mobile broadband. Entertainment is essential on smartphones and tablets, regardless of location, including in highly mobile environments like trains, cars, and airplanes. Another use case is augmented reality and information retrieval for entertainment, where augmented reality requires extremely low latency and instantaneous data volumes.

[0141] mMTC is designed to enable communication between a large number of low-cost, battery-powered devices, supporting applications such as smart metering, logistics, field, and body sensors. mMTC targets a battery life of approximately 10 years and / or a population of approximately 1 million devices per square kilometer. mMTC enables seamless connectivity of embedded sensors across all sectors and is one of the most anticipated 5G use cases. The number of IoT devices is projected to reach 20.4 billion by 2020. Industrial IoT is one area where 5G will play a key role, enabling smart cities, asset tracking, smart utilities, agriculture, and security infrastructure.

[0142] URLLC is ideal for vehicle communications, industrial control, factory automation, remote surgery, smart grids, and public safety applications by enabling devices and machines to communicate with high reliability, very low latency, and high availability. URLLC targets latency on the order of 1 ms. URLLC encompasses 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.

[0143] Next, we will look more specifically at a number of usage examples included within the triangle in Fig. 4.

[0144] 5G can complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) by delivering streams rated at hundreds of megabits per second to gigabits per second. These high speeds may be required to deliver TV at resolutions beyond 4K (6K, 8K, and beyond), as well as virtual reality (VR) and augmented reality (AR). VR and AR applications include near-immersive sports events. Certain applications may require specialized network configurations. For example, for VR gaming, a gaming company may need to integrate its core servers with a network operator's edge network servers to minimize latency.

[0145] Automotive is expected to be a significant new driver for 5G, with numerous use cases for in-vehicle mobile communications. For example, passenger entertainment demands both high capacity and high mobile broadband, as future users will consistently expect high-quality connectivity regardless of their location and speed. Another automotive application is augmented reality dashboards. An AR dashboard allows drivers to identify objects in the dark on top of what they see through the windshield. The AR dashboard overlays information to inform the driver about the distance and movement of objects. In the future, wireless modules will enable vehicle-to-vehicle communication, information exchange between vehicles and supporting infrastructure, and information exchange between vehicles and other connected devices (e.g., devices accompanying pedestrians). Safety systems can guide drivers to safer driving behaviors, reducing the risk of accidents. The next step will be remotely controlled or autonomous vehicles, which require highly reliable and fast communication between different autonomous vehicles and / or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving tasks, leaving drivers to focus solely on traffic anomalies that the vehicle itself cannot detect. The technological requirements for autonomous vehicles will require ultra-low latency and ultra-high-speed reliability, increasing traffic safety to levels unattainable by humans.

[0146] Smart cities and smart homes, often referred to as smart societies, will be embedded with dense wireless sensor networks. A distributed network of intelligent sensors will identify conditions for cost- and energy-efficient maintenance of cities or homes. Similar setups can be implemented for individual homes. Temperature sensors, window and heating controllers, burglar alarms, and appliances will all be wirelessly connected. Many of these sensors typically require low data rates, low power, and low cost. However, for example, real-time HD video may be required from certain types of devices for surveillance purposes.

[0147] The consumption and distribution of energy, including heat and gas, are becoming increasingly decentralized, requiring automated control of distributed sensor networks. Smart grids interconnect these sensors using digital information and communication technologies to collect and act on information. This information can include the behavior of suppliers and consumers, enabling smart grids to improve efficiency, reliability, economic efficiency, sustainable production, and the automated distribution of fuels like electricity. Smart grids can also be viewed as another low-latency sensor network.

[0148] The health sector has numerous applications that can benefit from mobile communications. Telecommunications systems can support telemedicine, which provides clinical care in remote locations. This can help reduce distance barriers and improve access to health services that are otherwise unavailable in remote rural areas. It can also be used to save lives in critical care and emergency situations. Mobile-based wireless sensor networks can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

[0149] Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Therefore, the potential replacement of cables with reconfigurable wireless links presents an attractive opportunity for many industries. However, achieving this requires wireless connections to operate with similar latency, reliability, and capacity to cables, while simplifying their management. Low latency and extremely low error rates are new requirements for 5G connectivity.

[0150] Logistics and freight tracking are important use cases for mobile communications, enabling the tracking of inventory and packages anywhere using location-based information systems. Logistics and freight tracking typically require low data rates but may require wide-range and reliable location information.

[0151] Hereinafter, examples of next-generation communications (e.g., 6G) that can be applied to various embodiments of the present disclosure will be described.

[0152]

[0153] 6G system in general

[0154] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements as shown in Table 1 below. In other words, Table 1 is a table showing an example of the requirements of a 6G system.

[0155]

[0156] Per device peak data rate1TbpsE2E latency1msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0157]

[0158] 6G systems can have key factors such as 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 access network congestion, and enhanced data security.

[0159]

[0160] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0161] 6G systems are expected to have 50 times the simultaneous wireless connectivity of 5G systems. URLLC, a key feature of 5G, will become even more crucial in 6G communications by providing end-to-end latency of less than 1 ms. 6G systems will have significantly higher volumetric spectral efficiency, compared to the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, eliminating the need for separate charging for mobile devices in 6G systems. New network characteristics in 6G may include:

[0162] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system is crucial for 6G.

[0163] Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary, upgrading the wireless evolution from "connected objects" to "connected intelligence." AI can be applied at every stage of the communication process (or at every signal processing step, as described below).

[0164] - Seamless integration of wireless information and energy transfer: 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.

[0165] - Ubiquitous super 3D connectivity: Access to networks and core network functions of drones and very low Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

[0166] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0167] - Small cell networks: The concept of small cell networks was introduced to improve received signal quality in cellular systems by increasing throughput, energy efficiency, and spectral efficiency. Consequently, small cell networks are essential for 5G and beyond-5G (5GB) communication systems. Accordingly, 6G communication systems also adopt the characteristics of small cell networks.

[0168] Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another key feature of 6G communication systems. Multi-tier networks comprised of heterogeneous networks improve overall QoS and reduce costs.

[0169] High-capacity backhaul: Backhaul connections are characterized by high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems may be potential solutions to this problem.

[0170] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communications is a key feature of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0171] - Softwarization and virtualization: Softwarization and virtualization are two critical features that form the foundation of the design process for 5GB networks to ensure flexibility, reconfigurability, and programmability. Furthermore, billions of devices can be shared on a shared physical infrastructure.

[0172]

[0173] Core implementation technology of 6G systems

[0174] Artificial Intelligence

[0175] The most crucial and newly introduced technology for 6G systems is AI. 4G systems did not involve AI. 5G systems will support partial or very limited AI. However, 6G systems will fully support AI for automation. Advances in machine learning will create more intelligent networks for real-time communications in 6G. Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analyses to determine how complex target tasks should be performed. In other words, AI can increase efficiency and reduce processing delays.

[0176] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). 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.

[0177] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0178] Machine learning can be used for channel estimation and channel tracking, as well as for power allocation and interference cancellation in the physical layer of the downlink (DL). Furthermore, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0179] However, the application of DNN for transmission at the physical layer may have the following problems.

[0180] Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in obtaining training data from specific channel environments, a large amount of training data is used offline. This means that static training on training data in specific channel environments can lead to conflicts with the dynamic characteristics and diversity of the wireless channel.

[0181] Furthermore, current deep learning primarily targets real-world signals. However, signals at the physical layer of wireless communications are complex signals. Further research is needed on neural networks that detect complex-domain signals to match the characteristics of wireless communication signals.

[0182] Below, we will look at machine learning in more detail.

[0183] Machine learning refers to a series of operations that train machines to perform tasks that humans can or cannot perform. Machine learning requires data and a learning model. Data learning methods in machine learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0184] Neural network training aims to minimize output errors. It involves repeatedly inputting training data into a neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error.

[0185] Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. Labeled training data is input to a neural network, and the error can be calculated by comparing the output (categories) of the neural network with the training data labels. The calculated error is backpropagated through the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated through backpropagation. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, in the early stages of training a neural network, a high learning rate can be used to quickly allow the network to achieve a certain level of performance, thereby increasing efficiency. In the later stages of training, a low learning rate can be used to increase accuracy.

[0186] Learning methods may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted by a transmitter in a communication system, supervised learning is preferable to unsupervised learning or reinforcement learning.

[0187] The learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0188] The neural network cores used in learning methods are mainly divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machines (RNN).

[0189] An artificial neural network is an example of a network of multiple perceptrons.

[0190]

[0191] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0192] Referring to Fig. 6, when an input vector x=(x1,x2,...,xd) is input, the entire process of multiplying each component by a weight (W1,W2,...,Wd), adding up all the results, and then applying the activation function σ(·) is called a perceptron. A large-scale artificial neural network structure can extend the simplified perceptron structure illustrated in Fig. 6 to apply the input vector to perceptrons of different dimensions. For convenience of explanation, input values ​​or output values ​​are called nodes.

[0193] Meanwhile, the perceptron structure illustrated in Fig. 6 can be explained as consisting of a total of three layers based on input and output values. An artificial neural network in which there are H perceptrons of (d+1) dimensions between the 1st layer and the 2nd layer, and K perceptrons of (H+1) dimensions between the 2nd layer and the 3rd layer can be expressed as in Fig. 7.

[0194]

[0195] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0196] 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, and all layers located between the input layer and the output layer are called hidden layers. The example in Fig. 7 shows three layers, but when counting the number of layers in an actual artificial neural network, the input layer is excluded, so it can be viewed as a total of two layers. An artificial neural network is composed of perceptrons, which are basic blocks, connected in two dimensions.

[0197] The aforementioned input, hidden, and output layers can be applied jointly not only to multilayer perceptrons but also to various artificial neural network structures, such as CNNs and RNNs, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network. The machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning. Furthermore, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0198]

[0199] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0200] The deep neural network illustrated in Figure 8 is a multilayer perceptron consisting of eight hidden layers and eight output layers. The multilayer perceptron structure is referred to as a fully connected neural network. In a fully connected neural network, there is no connection between nodes located in the same layer, and there is a connection only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, and can be usefully applied to identify correlation characteristics between inputs and outputs. Here, the correlation characteristic can mean the joint probability of inputs and outputs.

[0201] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0202]

[0203] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0204] In DNN, nodes within a single layer are arranged vertically in a one-dimensional manner. However, Fig. 9 can assume a case where nodes are arranged two-dimensionally, with w nodes in width and h nodes in height (the convolutional neural network structure of Fig. 9). In this case, since a weight is added to each connection in the connection process from one input node to the hidden layer, a total of hΥw weights must be considered. Since there are hΥw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0205] The convolutional neural network of Fig. 9 has a problem in that the number of weights increases exponentially according to the number of connections. Therefore, instead of considering the connections of all modes between adjacent layers, it assumes that there are small filters, and performs weighted sum and activation function operations on the overlapping portions of the filters, as in Fig. 10.

[0206]

[0207] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0208] Each filter has a weight corresponding to the number of its size, and weight learning can be performed so that a specific feature on the image can be extracted as a factor and output. In Fig. 10, a filter of size 3Y3 is applied to the upper left 3Y3 region of the input layer, and the output value resulting from performing weighted sum and activation function operations on the corresponding node is stored in z22.

[0209] The above filter performs weighted sum and activation function operations while moving at a certain horizontal and vertical interval while scanning the input layer, and places the output value at the current filter position. This operation method is similar to the convolution operation for images in the field of computer vision, so a deep neural network with 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. In addition, a neural network with multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0210] In the convolutional layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located in the area covered by the filter, starting from the node where the current filter is located. This allows a single filter to focus on features within a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a two-dimensional area is an important criterion for judgment. Meanwhile, CNNs can apply multiple filters immediately before the convolutional layer, and can generate multiple output results through the convolution operation of each filter.

[0211] Meanwhile, depending on the data properties, there may be data for which sequence characteristics are important. Considering the length variability and chronological relationship of such sequence data, a structure that applies a method of inputting one element of the data sequence at each timestep and inputting the output vector (hidden vector) of the hidden layer output at a specific timestep together with the immediately following element in the sequence is called a recurrent neural network structure.

[0212]

[0213] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0214] Referring to Figure 11, a recurrent neural network (RNN) is a structure that inputs elements (x1(t), x2(t), ,..., xd(t)) of a data sequence at a time point t into a fully connected neural network, and then inputs the hidden vectors (z1(t-1), z2(t-1),..., zH(t-1)) of the immediately preceding time point t-1 together and applies a weighted sum and activation function. The reason for transmitting the hidden vector to the next time point in this way is because the information in the input vectors of the preceding time points is considered to be accumulated in the hidden vector of the current time point.

[0215]

[0216] Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.

[0217] Referring to Figure 12, the recurrent neural network operates in a predetermined order of time for the input data sequence.

[0218] When the input vector (x1(t), x2(t), ,..., xd(t)) at time point 1 is input to the recurrent neural network, the hidden vector (z1(1), z2(1),..., zH(1)) is input together with the input vector (x1(2), x2(2),..., xd(2)) at time point 2, and the vector (z1(2), z2(2),..., zH(2)) of the hidden layer is determined through a weighted sum and an activation function. This process is repeatedly performed until time points 2, 3, ,,, T.

[0219] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).

[0220] It is a neural network core used in a learning manner, and includes various deep learning techniques such as DNN, CNN, RNN, Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0221] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0222] THz (Terahertz) communication

[0223] Data rates can be increased by increasing bandwidth. This can be achieved by utilizing sub-THz communications with wide bandwidths and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, typically refer to the frequency range between 0.1 THz and 10 THz, with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (sub-THz band) is considered a key part of the THz band for cellular communications. Adding the sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz lies in the far infrared (IR) frequency band. While part of the optical band, the 300 GHz to 3 THz band lies at the boundary of the optical band, immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF.

[0224]

[0225] Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.

[0226] Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0227] Optical wireless technology

[0228] OWC technology is designed for 6G communications, in addition to RF-based communications for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul networks. OWC technology has already been used in 4G communication systems, but it will be used more widely to meet the demands of 6G communication systems. OWC technologies such as light fidelity, visible light communication, optical camera communication, and wideband-based FSO communication are already well-known. 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 4D mapping in 6G communications based on wideband.

[0229] FSO backhaul network

[0230] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber-optic network. Therefore, data transmission in an FSO system is similar to that of a fiber-optic system. Therefore, FSO can be a promising technology for providing backhaul connectivity in 6G systems, in conjunction with fiber-optic networks. Using FSO, ultra-long-distance communications are possible, even over distances exceeding 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas, such as the ocean, space, underwater, and isolated islands. FSO also supports cellular base station (BS) connections.

[0231] Massive MIMO technology

[0232] One of the key technologies for improving spectral efficiency is the application of MIMO technology. As MIMO technology improves, spectral efficiency also improves. Therefore, massive MIMO technology will be crucial in 6G systems. Because MIMO technology utilizes multiple paths, multiplexing technology must be considered to ensure that data signals can be transmitted along more than one path, as well as beam generation and operation technologies suitable for the THz band.

[0233] Blockchain

[0234] Blockchain will become a crucial technology for managing massive amounts of data in future communication systems. Blockchain is a form of distributed ledger technology. A distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchains are managed by a peer-to-peer network and can exist without being managed by a central authority or server. Data on a blockchain is collected and organized into blocks. Blocks are linked together and protected using cryptography. Blockchain perfectly complements large-scale IoT with its inherently enhanced interoperability, security, privacy, reliability, and scalability. Therefore, blockchain technology offers several features, such as interoperability between devices, traceability of large amounts of data, autonomous interaction with other IoT systems, and the massive connectivity stability of 6G communication systems.

[0235] 3D networking

[0236] 6G systems integrate terrestrial and airborne networks to support vertically expanded user communications. 3D BS will be provided via low-orbit satellites and UAVs. Adding a new dimension in altitude and associated degrees of freedom, 3D connections differ significantly from existing 2D networks.

[0237] Quantum communication

[0238] Unsupervised reinforcement learning holds promise in the context of 6G networks. Supervised learning approaches cannot label the massive amounts of data generated by 6G networks. Unsupervised learning does not require labeling. Therefore, this technology can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows for truly autonomous network operation.

[0239] drone

[0240] Unmanned Aerial Vehicles (UAVs), or drones, will be a key element in 6G wireless communications. In most cases, high-speed wireless connections will be provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs offer specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled mobility. During emergencies such as natural disasters, deploying terrestrial communication infrastructure is not economically feasible, and sometimes, volatile environments make it impossible to provide services. UAVs can easily handle these situations. UAVs will become a new paradigm in wireless communications. This technology facilitates three fundamental requirements for wireless networks: enhanced mobile broadband (eMBB), URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most important technologies for 6G communications.

[0241] Cell-free Communication

[0242] Tight integration of multiple frequencies and heterogeneous communication technologies is crucial in 6G systems. As a result, users will be able to seamlessly move from one network to another without requiring any manual configuration on their devices. The best network will be automatically selected from available communication technologies. This will break the limitations of the cell concept in wireless communications. Currently, user movement from one cell to another in dense networks results in excessive handovers, resulting in handover failures, handover delays, data loss, and a ping-pong effect. 6G cell-free communications will overcome all of these challenges and provide better QoS. Cell-free communications will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as heterogeneous radios on devices.

[0243] Integration of wireless information and energy transmission

[0244] WIET uses the same fields and waves as wireless communication systems. Specifically, sensors and smartphones will be charged using wireless power transfer during communication. WIET is a promising technology for extending the life of battery-powered wireless systems. Therefore, battery-less devices will be supported by 6G communications.

[0245] Integration of sensing and communication

[0246] Autonomous wireless networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

[0247] Integration of Access Backhaul Networks

[0248] In 6G, the density of access networks will be enormous. Each access network will be connected to backhaul connections, such as fiber optics and FSO networks. To accommodate the massive number of access networks, there will be tight integration between access and backhaul networks.

[0249] Holographic beam forming

[0250] Beamforming is a signal processing procedure that adjusts an antenna array to transmit a wireless signal in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, including high signal-to-noise ratio, interference avoidance and rejection, and high network efficiency. Holographic beamforming (HBF) is a novel beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a highly effective approach for efficient and flexible signal transmission and reception in multi-antenna communication devices in 6G.

[0251] Big data analysis

[0252] Big data analytics is a complex process for analyzing diverse, large-scale data sets, or "big data." This process uncovers hidden data, unknown correlations, and customer trends, ensuring complete data management. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process massive amounts of data in 6G systems.

[0253] Large Intelligent Surface (LIS)

[0254] THz-band signals have strong linearity, which can create many shadow areas due to obstacles. LIS technology, which enables expanded communication coverage, enhanced communication stability, and additional value-added services by installing LIS near these shadow areas, is becoming increasingly important. LIS is an artificial surface made of electromagnetic materials that can alter the propagation of incoming and outgoing radio waves. While LIS can be viewed as an extension of massive MIMO, it differs from massive MIMO in its array structure and operating mechanism. Furthermore, LIS operates as a reconfigurable reflector with passive elements, passively reflecting signals without using active RF chains, which offers the advantage of low power consumption. Furthermore, because each passive reflector in LIS must independently adjust the phase shift of the incoming signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift via the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0255]

[0256] Terahertz (THz) wireless communications in general

[0257]

[0258] THz wireless communication uses THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and can refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared light, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, so they have high linearity and can focus beams. In addition, since the photon energy of THz waves is only a few meV, they have the characteristic of being harmless to the human body. The frequency bands expected to be used for THz wireless communication may be the D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz), which have low propagation loss due to molecular absorption in the air. Discussions on standardization of THz wireless communication are being centered around the IEEE 802.15 THz working group in addition to 3GPP, and standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) may specify or supplement the contents described in various embodiments of the present disclosure. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0259]

[0260] Figure 14 is a diagram illustrating an example of a THz communication application.

[0261] As illustrated in Figure 14, THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections, such as indoor small cells, wireless connections in data centers, and near-field communications, such as kiosk downloads.

[0262] Table 2 below shows examples of technologies that can be used in THz waves.

[0263] Transceivers DeviceAvailable immature: UTC-PD, RTD and SBDModulation and CodingLow order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, TurboAntennaOmni and Directional, phased array with low number of antenna elementsBandwidth69GHz (or 23 GHz) at 300GHzChannel modelsPartiallyData rate100GbpsOutdoor deploymentNoFree space lossHighCoverageLowRadio Measurements300GHz indoorDevice sizeFew micrometers

[0264]

[0265] THz wireless communications can be categorized based on the methods used to generate and receive THz waves. THz generation methods can be categorized as either optical or electronic-based.

[0266]

[0267] Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.

[0268] Methods for generating THz using electronic components include a method using semiconductor components such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a MMIC (Monolithic Microwave Integrated Circuits) method using an integrated circuit based on a compound semiconductor HEMT (High Electron Mobility Transistor), and a method using a Si-CMOS-based integrated circuit. In the case of Fig. 15, a multiplier (doubler, tripler, multiplier) is applied to increase the frequency, and it passes through a subharmonic mixer and is radiated by an antenna. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, and matches it to the desired harmonic frequency and filters out all remaining frequencies. In addition, beamforming can be implemented by applying an array antenna or the like to the antenna of Fig. 15. In Fig. 15, IF represents intermediate frequency, tripler and multiplexer represent multipliers, PA represents power amplifier, LNA represents low noise amplifier, and PLL represents phase-locked loop.

[0269]

[0270] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0271] Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

[0272] Optical component-based THz wireless communication technology refers to a method of generating and modulating THz signals using optical components. Optical component-based THz signal generation technology 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 technologies that use only electronic components, this technology can easily increase the frequency, generate high-power signals, and obtain flat response characteristics over a wide frequency band. As illustrated in Figure 16, optical component-based THz signal generation requires a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector. In the case of Figure 16, the light signals of two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In Fig. 16, an optical coupler refers to a semiconductor device that transmits an electrical signal using optical waves to provide electrical isolation and coupling between circuits or systems, and a UTC-PD (Uni-Travelling Carrier Photo-Detector) is a type of photodetector that uses electrons as active carriers and reduces the travel time of electrons with bandgap grading. The UTC-PD is capable of detecting light at 150 GHz or higher. In Fig. 17, an EDFA (Erbium-Doped Fiber Amplifier) ​​represents an erbium-doped fiber amplifier, a PD (Photo Detector) represents a semiconductor device that can convert an optical signal into an electrical signal, an OSA represents an optical module (Optical Sub Assembly) that modularizes various optical communication functions (photoelectric conversion, electro-optical conversion, etc.) into a single component, and a DSO represents a digital storage oscilloscope.

[0273]

[0274] The structure of a photoelectric converter (or photoelectric converter) is described with reference to FIGS. 18 and 19.

[0275] Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.

[0276] Figure 19 is a drawing showing the structure of an optical modulator.

[0277] In general, the phase of a signal can be changed by passing the optical source of a laser through an optical wave guide. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An opto-electrical modulator (O / E converter) can generate THz pulses by optical rectification operation by a nonlinear crystal, photoelectric conversion by a photoconductive antenna, emission from a bunch of relativistic electrons, etc. Terahertz pulses generated in the above manner can have a length in units of femtoseconds to picoseconds. An optical / electronic converter (O / E converter) performs down conversion by utilizing the non-linearity of the device.

[0278] Considering the THz spectrum usage, it is likely that THz systems will use multiple contiguous gigahertz bands for fixed or mobile service purposes. Based on the outdoor scenario criteria, the available bandwidth can be classified based on the oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the THz pulse length for one carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0279] Effective down-conversion from the infrared band (IR band) to the terahertz band (THz band) depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired terahertz band (THz band), it is necessary to design an optical / electrical converter (O / E converter) with the most ideal non-linearity for transferring to the corresponding terahertz band (THz band). If an optical / electrical converter (O / E converter) that is not suitable for the target frequency band is used, errors are likely to occur in the amplitude and phase of the corresponding pulse.

[0280] In a single-carrier system, a terahertz transmission and reception system can be implemented using a single optical-to-electrical converter. Depending on the channel environment, in a multi-carrier system, the number of optical-to-electrical converters may be equal to the number of carriers. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-converted using an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency region of the specific resource region may include multiple chunks. Each chunk may be composed of at least one component carrier (CC).

[0281]

[0282] Specific descriptions of various embodiments of the present disclosure

[0283] Hereinafter, various embodiments of the present disclosure will be described in more detail.

[0284]

[0285] The present disclosure relates to a method for selecting an optimal path in terms of network resource efficiency when searching for a path for entanglement routing between any two source nodes and destination nodes connected through a multi-hop path in a quantum communication system. More specifically, the present invention proposes a method for searching for a resource-efficient path for performing multi-hop entanglement exchange between a source node and a destination node based on network topology information including information such as the link entanglement rate of each direct quantum channel link, the Bell state measurement (BSM) efficiency of each node, and the node degree of freedom.

[0286]

[0287] Background to various embodiments of the present disclosure

[0288] Bell state and Bell basis

[0289] 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 for the four-dimensional Hilbert space for the two qubits, and is called the Bell basis.

[0290]

[0291] Creation of a bell state

[0292] FIG. 20 is a diagram illustrating an example of a quantum circuit for generating a bell state in a system applicable to the present disclosure.

[0293] The Bell state can be generated through a two-qubit quantum circuit consisting of a Hadamard gate and a CNOT gate (controlled not gate), as shown in Fig. 20. Four two-qubit inputs It has a bell state output as shown in Table 3. Table 3 shows the input / output states of the bell state generation circuit.

[0294]

[0295]

[0296]

[0297] Bell state measurement / Bell state analysis

[0298] FIG. 21 is a diagram illustrating an example of a bell state measurement circuit in a system applicable to the present disclosure.

[0299] As previously explained, since the Bell states form an orthonormal basis, an appropriate measurement can be defined to identify the four Bell states, which is called Bell state measurement or Bell state analysis. In Bell state measurement, the goal is to determine which of the four quantum entanglement states defined by the Bell states the states of two qubits belong to. If the order of the CNOT gate and the Hadamard gate in the Bell state generation circuit of Fig. 20 is reversed, a Bell state measurement circuit as shown in Fig. 21 is obtained. Measurement results as shown in Table 4 can be obtained for the four quantum entanglement states corresponding to the Bell states. Table 4 shows the input and output states of the Bell state measurement circuit.

[0300]

[0301]

[0302] quantum teleportation

[0303] Quantum teleportation is a technology that transmits quantum information from a sender at a specific location to a receiver a certain distance away. Contrary to the original meaning of the word "teleport," in quantum teleportation, the carriers on both sides are fixed, and the actual transfer of carriers is not done, but rather the transfer of quantum information between carriers. This teleportation of information requires an entangled quantum state, or Bell state, which provides statistical correlation between separate physical systems. Since any change experienced by one of the entangled particles causes the other particle to undergo the same change, the two particles behave as if they were in a single quantum state.

[0304]

[0305] FIG. 22 is a diagram illustrating an example of a quantum teleportation system applicable to the present disclosure.

[0306] Specifically, Figure 22 schematically illustrates a quantum teleportation protocol using photons. Quantum teleportation requires the resources of a classical channel capable of transmitting two classical bits, a Bell state (entanglement state) generation device, a quantum channel for transporting two particles in a Bell state to different locations of the transmitting and receiving ends, a Bell state measurement device at the transmitting end, and a unitary operation device at the receiving end. The quantum information to be transmitted The operation of the protocol is as follows:

[0307] (1) Entanglement generation: An entangled state of two qubits is created using a Bell state generation device.

[0308] (2) Entanglement distribution: The generated entangled state is transferred through a quantum channel, with one qubit being transferred to the location of the sender Alice (A) and the other qubit being transferred to the location of the receiver Bob (B).

[0309] (3) Quantum pre-processing: Alice wants to transmit the quantum state And performs Bell state measurement on one qubit in the Bell state that he has, and obtains a result corresponding to one of the four Bell states. At this time, the state of the qubit that Bob has changes with respect to the result of Alice's Bell state measurement, as shown in Table 5. Table 5 shows the change in Bob's qubit according to the result of Alice's Bell state measurement.

[0310]

[0311]

[0312] (4) Classical transmission: Alice encodes the Bell state measurement result of step 3 into two classical bits and transmits it to Bob through the classical channel.

[0313] (5) Quantum post-processing: Bob performs a unitary operation on the remaining qubit in the Bell state based on the two bits of information received from Alice to obtain the quantum information that Alice wanted to transmit. Obtain the same quantum state as .

[0314]

[0315] Entanglement generation and distribution

[0316] Entanglement generation and distribution are key elements of quantum teleportation. Since Alice and Bob are nodes located far apart, the generation of entanglement at any location must be complemented by an entanglement distribution function that "transfers" one entangled particle to the other. In this context, there is already widespread academic consensus on the adoption of photons as flying qubits, or entanglement carriers. Due to their relatively low interaction with the environment, photons exhibit moderate decoherence, enabling high-speed, low-loss transmission, and are easily controlled using standard optical components.

[0317]

[0318] FIG. 23 is a diagram illustrating an example of spontaneous parametric down-conversion in a system applicable to the present disclosure.

[0319] FIG. 24 is a diagram illustrating an example of an atomic excitation method in an optical cavity using a laser pulse in a system applicable to the present disclosure.

[0320] FIG. 25 is a diagram illustrating an example of a method for simultaneously exciting two atoms using laser pulses in a system applicable to the present disclosure.

[0321] Figures 23 to 25 illustrate practical design schemes for entanglement generation and distribution. The spontaneous parametric downconversion scheme of Figure 23 utilizes the characteristic that when a laser beam is projected onto a nonlinear crystal, the photon beam is sometimes split into polarization-entangled photon pairs. Since this method creates entangled pairs between photons, Alice and Bob each convert the photons they receive into matter qubits using a flying-matter transducer.

[0322] Figure 24 illustrates how entanglement is created between two remote atoms by exciting atoms in an optical cavity using a laser pulse on Alice's side, and the resulting photons are then incident on Bob's side through a quantum channel into the optical cavity. This method demonstrates that entanglement is initially created between the atoms and the photons, and then transformed into entanglement between atoms via the photons.

[0323] Figure 25 illustrates how Alice and Bob simultaneously excite atoms within their respective optical resonators using laser pulses, and then perform Bell-state measurements on the two photons emitted from both sides at a third node, which can be called a repeater, thereby creating entanglement between the two atoms. Entanglement swapping demonstrates that the entanglement between the atoms and the photons is converted to entanglement between the atoms.

[0324]

[0325] From the perspective of where entanglement is generated, there are differences in that Fig. 23 generates entanglement at the midpoint, Fig. 24 at the transmitter, and Fig. 25 at both ends. However, all three methods have in common that they require a quantum channel because the entanglement state is transmitted via photons, which are flying qubits, and that the form of the entanglement ultimately distributed is entanglement between atoms, that is, entanglement between material qubits that are easy to process and store information.

[0326]

[0327] Incompleteness involved in the quantum teleportation process

[0328] FIG. 26 is a diagram illustrating an example of an imperfection that degrades the quantum teleportation process in a system applicable to the present disclosure.

[0329] Similar to classical communication, quantum communication processes can also be affected by imperfections in real-world environments, affecting the quality of transmitted information. While Figure 26 depicts the quantum teleportation process in an idealized environment as a closed physical system, practical quantum teleportation processes must be represented as open physical systems because they are affected by unwanted interactions with the surrounding environment. These interactions with the environment cause irreversible changes in the quantum state, a process called decoherence. This decoherence affects not only the transfer of the unknown quantum state but also the entanglement generation and distribution processes that must precede quantum teleportation. Another source of imperfection in quantum teleportation is the series of quantum operations performed on the quantum state. Contamination of the quantum operations can exacerbate the imperfection of quantum teleportation.

[0330] Specifically, Figure 26 schematically illustrates the relationships among various imperfections that affect the fidelity of qubits transmitted via quantum teleportation. Regardless of the specific cause of performance degradation, inherent imperfections in quantum systems result in the transformation of pure quantum states into mixed quantum states. Addressing these quantum imperfections is a critical challenge in quantum information science. However, modeling imperfections in the quantum domain to accurately capture the effects of the various imperfections involved in the quantum teleportation process remains an open problem.

[0331]

[0332] Quantum decoherence and quantum channel models

[0333] FIG. 27 is a diagram illustrating an example of a quantum channel model in a system applicable to the present disclosure.

[0334] As previously discussed, environmental coherence constitutes a major source of quantum state corruption, which can occur not only in quantum memory but also during quantum transmission or quantum processing. Figure 27 illustrates the relationship between quantum channel models widely used to model environmental coherence.

[0335] Environmental decoherence can be described as unwanted interactions between a qubit and its environment, more specifically, entanglement, which disrupts the coherent superposition of the underlying quantum state. For example, in such cases, a qubit (or quantum system) loses energy due to its interaction with the environment, such as when a qubit's excited state decays due to spontaneous emission of a photon, or when a photon is lost or absorbed during transmission through an optical fiber. This type of decoherence process can be modeled using an amplitude damping channel. Another example of environmental decoherence is known as dephasing or phase damping, which characterizes the loss of quantum information without energy loss. This can occur, for example, in cases such as scattering of a photon or perturbation of electronic states due to stray charges.

[0336]

[0337] FIG. 28 is a diagram illustrating an example of Pauli-I, Pauli-Z, Pauli-X, and Pauli-Y gates in a system applicable to the present disclosure.

[0338] However, the amplitude damping channel or phase damping channel model results in a system with 2 N-qubits. N It is not feasible to simulate these channels classically, because they have a Hilbert space of dimension N. For efficient classical simulation, the amplitude and phase decay channels are Pauli channels N P It can be approximated by mapping the input state with density operator ρ to the state as shown in mathematical expression 2 below.

[0339]

[0340] Here, I, X, Y, and Z correspond to the single-qubit Pauli operators of Fig. 28, and p x , py , p z is the probability that Pauli X, Pauli Y, and Pauli Z errors will occur. Bit-flip errors corresponding to the Pauli X channel and bit-phase-flip errors corresponding to the Pauli Y channel are related to amplitude attenuation, while phase-flip errors corresponding to the Pauli Z channel are caused by phase attenuation. Most practical quantum systems are asymmetric channels, which are channels in which bit-flip, phase-flip, or bit-phase-flip errors predominate. Bit-flip, phase-flip, and bit-phase-flip errors occur with equal probability (p x =p y =p z ) A special case of the Pauli channel is called a depolarizing channel and can be mathematically expressed as in Equation 3 below.

[0341]

[0342]

[0343] Quantum Internet

[0344] The quantum internet is a broader network encompassing both bits and qubits, connecting both information represented as bits and information represented as qubits. Based on the concept of the quantum internet linking bits and qubits, quantum information processing can be understood as follows.

[0345] 1) Quantum computing can be understood as the process of storing bit information in qubits, converting the qubit state according to the laws of quantum physics, and then obtaining bits from the qubits through measurement.

[0346] 2) Quantum teleportation is the process of transferring the state of a qubit to another qubit.

[0347] 3) Quantum memory is the process of storing qubit states and restoring the same qubit states.

[0348] 4) Quantum key distribution generates bits, stores them in qubits, transmits them, and then reconstructs them through measurement. Quantum key distribution ensures informational security because if the state stored in the qubits is attacked by an eavesdropper, errors in the information shared between the sender and receiver increase.

[0349] In a quantum internet environment, cloud quantum computing services can be used as follows: Users design quantum circuits and transmit them to the cloud quantum computing service. The transmitted quantum circuits are described using bit information. The cloud quantum computing service then maps the received quantum circuits to qubits, implementing quantum dynamics. The service then collects information expressed as bits by measuring the qubits and transmits it to the user. The user then interprets the collected measurement results.

[0350] The quantum internet can interconnect distant cloud quantum computers. If a user uses two cloud quantum computing services, the qubits in the quantum computers are linked through the bits that describe the user. If the physical qubits of the two cloud quantum computing services share entanglement, or if teleportation is applied to the qubit states, the two quantum computers can be linked through the qubits to perform distributed quantum computing.

[0351] Quantum computers based on NISQ technology, while inherently noisy, can be connected via a quantum internet, allowing them to handle a larger number of qubits and utilize them for information processing. A quantum internet could further enhance the capabilities of NISQ technology. A key technology enabling a quantum internet is the ability to interconnect qubits that are far apart. Atoms can be used as qubits stationary in a single location, while photons can be used to connect qubits.

[0352]

[0353] quantum networks

[0354] FIG. 29 is a diagram illustrating an example of a quantum communication network model in a system applicable to the present disclosure.

[0355] In the future, long-distance transmission of quantum information through quantum networks will emerge as an important research area. The need for multi-hop quantum teleportation research is emerging to transmit quantum information between two distant nodes. Quantum channels can be established through the entanglement shared between adjacent nodes. Intermediate nodes, such as repeaters or trusted nodes, are introduced to transmit quantum information between nodes that do not directly share entanglement. Figure 29 shows a quantum network model based on a mesh structure. Each node contains multiple quantum memories, and adjacent nodes are connected through both classical and quantum channels.

[0356] In these quantum network environments, there is generally no direct quantum channel between any source node and destination node that can share entanglement through direct transmission. Therefore, technologies for multi-hop quantum information transmission are required. There are two main approaches to research related to this multi-hop quantum information transmission. The first is a multi-hop quantum teleportation technique that performs quantum teleportation on a hop-by-hop basis, similar to routing in classical communication, so that information moves to the destination node through hop-by-hop transmission. The other method is a multi-hop entanglement exchange method. It first forms the entanglement that will be utilized for information transmission between the source and destination nodes by performing multiple entanglement exchanges on the links of the multi-hop and then performs quantum teleportation using this entanglement, so that the actual data can be transmitted to the destination node without directly traveling the entire multi-hop path.

[0357] Performing multi-hop quantum teleportation or multi-hop entanglement exchange requires an algorithm that finds the optimal path among multiple multi-hop paths connecting the source and destination nodes. Generally, the shortest path has the highest entanglement rate and the fewest hops. Therefore, when performing entanglement routing via a single path, using a shortest path algorithm can maximize network resource efficiency. However, considering that entanglement routing is a resource allocation process for quantum information transmission, the entanglement rate achievable via a single path may not be sufficient depending on the traffic characteristics of the data to be transmitted. In this case, network resources consisting of multiple paths must be allocated to entanglement routing. Conventional entanglement routing techniques individually select each path based on metrics such as entanglement success rate. This approach inevitably reduces resource efficiency compared to using a single shortest path, as it includes suboptimal paths that have more hops or lower entanglement rates than the shortest path. Therefore, when performing entanglement routing based on multiple paths, a new metric is needed for organic utilization between them, and an algorithm for selecting the shortest path with the potential to improve network resource efficiency can be proposed based on this metric.

[0358] Conventional metrics, such as the end-to-end entanglement ratio, are distance-dependent factors between the source and destination nodes, and thus share similar characteristics to distance vectors or evaluation functions in classical routing. However, metrics applied to organically utilize multiple paths are intended to account for correlations between different paths, and thus may consist of factors not directly related to the distance between the source and destination nodes. If the evaluation function of a shortest path algorithm simultaneously considers distance-dependent and non-distance-dependent factors, the absolute magnitude of the distance-dependent factor may vary significantly depending on the network size or the distance between the source and destination nodes, leading to unintended changes in the contribution of the two factors to the evaluation function. Therefore, there is a need to propose an optimal path algorithm based on an evaluation function that can adapt to network size and the distance between the source and destination nodes, and an entanglement routing protocol utilizing such an optimal path algorithm.

[0359]

[0360] Composition of various embodiments of the present disclosure

[0361] The present disclosure defines an adaptive operation of an optimal path algorithm according to the network scale when searching for a path for entanglement routing between any two source nodes and destination nodes connected through a multi-hop path in a quantum network. More specifically, the present disclosure proposes an operation method of an optimal path algorithm and an entanglement routing method based thereon, which distinguishes between factors considered in the optimal path algorithm those that depend on the distance between the source node and the destination node and those that do not, and adaptively adjusts the distance-dependent factors according to the distance between the source node and the destination node or the network scale, thereby maintaining the contributions of the distance-dependent and non-distance-dependent factors in the evaluation function constant.

[0362]

[0363] In this disclosure, we consider an optimal path search algorithm that operates based on an evaluation function in searching for a path for entanglement routing (ER) between any two source nodes and destination nodes connected through a multi-hop path in a quantum network. The evaluation function provides an evaluation value for the optimal path from the source node to the corresponding node for all nodes searched so far, and is used as a criterion for selecting an intermediate node to be searched first in the process of searching for an optimal path to reach the destination node. In the optimal path search algorithm considered in this disclosure, node v x The evaluation function f(x) for can be in the form of the following mathematical expression 4.

[0364]

[0365] In the evaluation function above, if the distance-dependent factor is g(x)×h(x) and the non-distance-dependent factor is k(x), then ω e Wow ω d ω is a coefficient that determines the contribution of factors that depend on the distance between the source node and the destination node and factors that do not, respectively. e corresponds to the contribution of g(x)×h(x) to f(x) and ω d corresponds to the contribution of k(x) to f(x), 0≤ω e ,ω d ≤1, ω e +ω d =1 condition is satisfied.

[0366] g(x) is the source node v s From node v x It can be an evaluation function for the optimal path explored so far between nodes, and h(x) is the node v x From destination node v d It may be an estimate of the evaluation function for the expected path to node v. x From destination node v dSince the path to node v has not been explored yet, h(x) is x and destination node v d It can be an estimate derived based on information such as the straight-line distance between g(x) and h(x) and the source node v. s and node v x , node v x and destination node v d Since it is related to the path between source node v s and destination node v d It may be a factor influenced by the distance between them.

[0367] For example, g(x) is the source node v s and node v x It can be related to the entanglement formation rate of the optimal path explored so far between nodes, and h(x) is x and destination node v d It can be related to an estimate of the entanglement formation rate between, and in this case, it can be defined as in the following mathematical expressions 5 and 6, respectively.

[0368]

[0369]

[0370] r in the formula for g(x) m is the source node v s and node v x The optimal path explored so far between R and v s Represents a node m hops away from r0 = v s , r M =v x am. is node r m and r m+1 is the link entanglement ratio for the link between is node r m is the efficiency of atomic Bell state measurement (atomic BSM). In the formula of h(x), is node vx is the interatomic BSM efficiency, is the average link entanglement formation rate for all links in the network, is the average of the inter-atomic BSM efficiency for all nodes in the network. D x,d is node v x From destination node v d It is the straight-line distance to is the average length of direct quantum channel links across all links in the network. Therefore, is v x Wow v d It corresponds to the average number of hops between them.

[0371] k(x) is node v x Node v derived based on the unique characteristics of x can be an evaluation function of k(x) for node v. x Since it is related to the unique characteristics of the node v, it may not be affected by the distance between the source node and the destination node. For example, k(x) is the distance between the node v x It can be a value representing the degree of freedom of a node. The degree of freedom of a node is a unique characteristic of a node and is not directly related to the quality of the path between the source node and the destination node that includes the node. However, it can be a factor that indirectly increases the efficiency of the path because organic utilization with other paths is possible through a node with a high degree of freedom. In this case, k(x) is expressed as the following mathematical expression 7 for node v. x Nodal degrees of freedom δ x It can be obtained by normalizing the degree of freedom of the node with the largest degree of freedom in the network.

[0372]

[0373] Since g(x)×h(x) and k(x) can represent different physical quantities, they can be defined on different scales depending on the characteristics of the physical quantities they represent. As in the previous example, g(x) is the entanglement formation rate between the source node and h(x) is the entanglement formation rate between the source node and node v.x represents an estimate of the entanglement formation rate between the destination node and k(x), and k(x) represents the entanglement formation rate between the node v and the destination node. x If the value representing the degree of freedom of the node is g(x)×h(x), the absolute value of g(x)×h(x) itself may decrease as the distance between the source node and the destination node increases. On the other hand, k(x) is the value representing the degree of freedom of the node v. x Since it corresponds to the unique characteristic of , it is independent of the distance between the source node and the destination node. Therefore, in this case, as the value of g(x)×h(x) decreases, the original ω for the evaluation function f(x) e Wow ω d Through the value of , the contribution of g(x)×h(x) may gradually decrease and the contribution of k(x) may increase, contrary to the intended contribution of g(x)×h(x) and k(x).

[0374] In this disclosure, we propose a method to prevent changes in the actual contribution due to differences in the scales of g(x)×h(x) and k(x). In this disclosure, we propose an enhanced DESPA (eDESPA) algorithm that applies adaptive parameter management to the evaluation function of the DESPA algorithm, and we present a method for performing entanglement routing by utilizing one or more optimal paths searched based on this algorithm.

[0375] An optimal path algorithm adaptable to various network environments and an entanglement routing technique based on it.

[0376] The network's management node transmits information to the network nodes that determines whether to activate adaptive mode, taking into account network topology characteristics. Each network node determines whether to activate adaptive mode based on this information. If adaptive mode is activated, it sets the evaluation function for the eDESPA (enhanced DESPA) algorithm based on the following mathematical expression (8).

[0377]

[0378] Here C eis a scaling factor derived to predict the ratio of the scales of g(x)×h(x) and k(x) based on the average value for the network topology and the distance between the source node and the destination node, and thereby offset the scale change according to the distance of the g(x)×h(x) term. Each node is C through the following mathematical expression 9. e You can determine the value.

[0379]

[0380] corresponds to the average of the node degrees of freedom for all nodes in the network. D s,d represents the straight-line distance between the source node and the destination node, is the average length of direct quantum channel links across all links in the network. is the average link entanglement formation rate for all links in the network, is the average of the inter-atomic BSM efficiency for all nodes in the network.

[0381]

[0382] FIG. 30 is a diagram illustrating an example of a process for applying adaptive parameters of an optimal path algorithm in a system applicable to the present disclosure.

[0383] FIG. 31 is a diagram illustrating an example of a process for applying adaptive parameters of an optimal path algorithm in a system applicable to the present disclosure.

[0384] FIG. 30 and FIG. 31 are embodiments of an entanglement routing process based on the eDESPA (enhanced DESPA) algorithm proposed in the present disclosure. FIG. 30 is an example in which a manager node provides a reference value for determining whether to activate the adaptive mode by considering the network environment, and each node performs entanglement routing by determining whether to activate the adaptive mode based on the reference value when performing individual entanglement routing requests. FIG. 31 is an example in which a manager node determines whether to constantly activate or deactivate the adaptive mode based on the network environment and transmits this to each node in the network, and each node then continuously applies or deactivates the adaptive mode to all entanglement routing requests received thereafter to perform entanglement routing.

[0385] Fig. 30 is an example in which a manager node provides a reference value that can determine whether to activate the adaptive mode by considering the network environment, and each node of the network performs entanglement routing by determining whether to apply the adaptive mode based on this reference value. This example can be utilized in an environment in which a source node-destination node pair located relatively close to each other and a source node-destination node pair located relatively far apart coexist in a relatively large network, in which the adaptive mode can be selectively activated accordingly. For this operation, the manager node can provide each node of the network with network environment information and a reference value D0 for the distance between the source node and the destination node. Depending on the network environment and the distance between the source node and the destination node, the scale of the evaluation function term g(x)×h(x), which corresponds to the end-to-end entanglement rate, may drop sharply beyond a certain distance. The manager node considers this and determines D0 and propagates it to each node of the network through signaling.

[0386] When an entanglement routing request (ER request) is transmitted from an arbitrary source node, the manager node broadcasts the first entanglement routing command (ER command) to the network along with timing information (time_info) and the number of optimal paths (K). The nodes receiving this transmit the ER command to the network, which is the distance D between the source node and the destination node. s,d Compare with D0, and D s,d >If D0, C e Recall The eDESPA algorithm can be performed in adaptive mode by deriving it according to the formula and applying it to the evaluation function. D s,d If ≤D0, C e By setting it to =1, it can operate in normal mode without distance adaptation. At this time, the network manager node can use the message to permanently enable or disable the adaptive mode by setting D0 to a specific value. For example, D0=0 can indicate permanent activation of the adaptive mode, and D0=MAX can indicate permanent deactivation of the adaptive mode (MAX is the maximum value allowed in the D0 field).

[0387] Each node attempts to form link entanglement based on the optimal path derived through the eDESPA algorithm and transmits the first entanglement routing report (ER report) to the manager node, including whether it succeeded or failed. The manager node then searches for a path between the source node and the destination node that can be formed by a combination of successful links and transmits a second entanglement routing command including inter-atomic Bell state measurement (BSM) information for this. Each node performs inter-atomic BSM according to the second entanglement routing command and transmits the second entanglement routing report including the result. The manager node then confirms the successfully formed path and transmits it to the source node, thereby ending the entanglement routing process.

[0388]

[0389] Figure 31 is an example in which a manager node determines whether to activate adaptive mode by considering the network environment and instructs each node in the network to operate in that mode, and each node in the network continuously activates or deactivates adaptive mode accordingly to perform entangled routing. This example can be utilized in cases where adaptive mode is constantly deactivated in a relatively small network and constantly activated in a relatively large network, regardless of the distance between the source and destination nodes. The manager node determines whether to operate the eDESPA algorithm in adaptive mode in the network based on network environment information and propagates the selected mode information to each node in the network through signaling.

[0390] When an entanglement routing request (ER request) is transmitted from an arbitrary source node, the manager node broadcasts the first entanglement routing command (ER command) to the network along with timing information (time_info) and the number of optimal paths (K). Nodes receiving this select the operating mode of the eDESPA algorithm based on whether the previously shared mode information is the normal mode or the adaptive mode. When the eDESPA algorithm operates in the normal mode, C e = is set to 1, and operates in adaptive mode. is set to . Unlike in Fig. 30, if the adaptive mode is indicated from the network, the scaling factor is always applied regardless of the distance between the source node and the destination node.

[0391] Each node attempts to form link entanglement based on the optimal path derived through the eDESPA algorithm and transmits the first entanglement routing report (ER report) to the manager node, including whether it succeeded or failed. The manager node then searches for a path between the source node and the destination node that can be formed by a combination of successful links and transmits a second entanglement routing command including inter-atomic Bell state measurement (BSM) information for this. Each node performs inter-atomic BSM according to the second entanglement routing command and transmits the second entanglement routing report including the result. The manager node then confirms the successfully formed path and transmits it to the source node, thereby ending the entanglement routing process.

[0392]

[0393] In FIGS. 30 and 31, a reference value that can determine whether to activate the adaptive mode or an indicator that indicates whether to activate the adaptive mode can be determined from a manager node in a network topology information update procedure and propagated to all nodes, or when a source node generates an entanglement routing request, the source node can determine information related to the adaptive mode for the request and transmit it by including it in an entanglement routing request message (ER_request in FIGS. 30 and 31).

[0394]

[0395] Effects of various embodiments of the present disclosure

[0396] The expected effects of various embodiments of the present disclosure are as follows.

[0397] The present disclosure proposes an optimal path algorithm that adaptively operates according to the distance between a source node and a destination node, and an entanglement routing method based thereon, in searching a path for entanglement routing between any two source nodes and destination nodes connected through a multi-hop path in a quantum network.

[0398] The present disclosure distinguishes between factors that are dependent on the distance between a source node and a destination node and factors that are not dependent on the distance between the source node and the destination node when the evaluation function used in the optimal path algorithm coexists, and adaptively adjusts the distance-dependent factor according to the distance between the source node and the destination node or the network size, thereby obtaining the effect of maintaining the contribution of the distance-dependent factor and the non-distance-dependent factor in the evaluation function constant.

[0399] This ultimately achieves the effect of significantly improving the entanglement formation rate and resource efficiency in networks of various sizes compared to entanglement routing techniques based on conventional optimal path algorithms.

[0400]

[0401] Characteristic configurations of various embodiments of the present disclosure are as follows.

[0402] (1) In a quantum communication network, a method for performing entanglement routing between a source node and a destination node,

[0403] Receive information on the link entanglement formation rate of each quantum channel link constituting the network, information on the efficiency of Bell state measurement between quantum memories of each node, information on the degree of freedom of the nodes, information on the contribution of distance-dependent and distance-independent factors, and information on the operation mode of the optimal path algorithm.

[0404] Based on the above information, derive evaluation function values ​​for multiple paths starting from the source node to the destination node,

[0405] Deriving one or more optimal paths based on the above evaluation function values,

[0406] A method and procedure for performing multi-hop entanglement exchange based on the above-described optimal path.

[0407]

[0408] [Description of the first node claim]

[0409] The embodiments described below are specifically described with reference to FIG. 32 in terms of the operation of the first node. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for or combined with some components of another method, as long as they are not mutually exclusive.

[0410] FIG. 32 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.

[0411] According to various embodiments of the present disclosure, a method performed by a first node in a communication system is provided.

[0412] According to various embodiments of the present disclosure, each of the first node, the second node, the third node, and the plurality of nodes may correspond to one of a terminal or a base station in a wireless communication system.

[0413] The embodiment of FIG. 32 may further include, before step S3201, one or more of the following steps: a step in which the first node transmits one or more synchronization signals to one or more of the second node, the third node, and the plurality of nodes; a step in which the first node transmits system information to one or more of the second node, the third node, and the plurality of nodes; a step in which the first node transmits configuration information to one or more of the second node, the third node, and the plurality of nodes; and a step in which the first node transmits control information to one or more of the second node, the third node, and the plurality of nodes.

[0414] The embodiment of FIG. 32 may further include, before step S3201, one or more of the following steps: a first node receiving a random access preamble from one or more of a second node, a third node, or a plurality of nodes; a first node transmitting a random access response (RAR) to one or more of the second node, the third node, or a plurality of nodes; a first node receiving a random access message 3 from one or more of the second node, the third node, or a plurality of nodes; and a first node transmitting a contention resolution message to one or more of the second node, the third node, or a plurality of nodes. Message 3 is a first PUSCH transmission scheduled by RAR together with an RAR UL grant.

[0415] In step S3201, the first node receives network information from the plurality of nodes.

[0416] At step S3202, the first node receives a request for entanglement routing (ER) between the second node and the third node from the second node.

[0417] In step S3203, based on the network information, the first node performs the ER to determine a specific path between the second node and the third node based on first factor information that depends on a distance value between the second node and the third node, second factor information that does not depend on the distance value, and a scaling factor for the first factor information or the second factor information.

[0418]

[0419] According to various embodiments of the present disclosure, the network information may further include entanglement formation rate information of quantum channel links constituting the plurality of paths, Bell state measurement efficiency information of each node constituting the channel links, node degree of freedom information of each node, and location information of each node.

[0420] According to various embodiments of the present disclosure, the embodiment of FIG. 32 may further include a step of transmitting a first weight, a second weight, and a scaling activity value to the plurality of nodes based on the network information. The scaling factor may be configured to be adjusted based on the scaling activity value.

[0421] According to various embodiments of the present disclosure, the step S3203 of performing the ER includes: transmitting a first ER command to the plurality of nodes; receiving a first ER report related to whether first link entanglement creation between the second node and the third node is successful for first paths derived based on the path evaluation function from the plurality of nodes; wherein the first paths are derived based on a path evaluation function including the first factor information, the first weight for the first factor information, the second factor information, the second weight for the second factor information, and the scaling factor; determining second paths among the first paths in which the first link entanglement creation is successful; transmitting a second ER command including related information of a BSM (bell state measurement) necessary for entanglement exchange in the second paths to the plurality of nodes; A step of receiving a second ER report on whether the second link entanglement formation for the second paths is successful based on the performance of the BSM from the plurality of nodes; A step of transmitting an ER completion message including information on the specific path among the second paths in which the second link entanglement formation is successful to the second node may be included.

[0422] According to various embodiments of the present disclosure, the scaling activation value may be a reference distance value determined based on the network information. When the distance value between the second node and the third node is less than or equal to the reference distance value, the scaling factor may be 1. When the distance value between the second node and the third node is greater than the reference distance value, the scaling factor may be a value based on the network information.

[0423] According to various embodiments of the present disclosure, the scaling activation value may be a value of a 1-bit indicator determined based on the network information. When the 1-bit indicator is a first value, the scaling factor may be 1. When the 1-bit indicator is a second value, the scaling factor may be a value based on the network information.

[0424] According to various embodiments of the present disclosure, multi-hop entanglement exchange can be performed based on the specific path.

[0425] According to various embodiments of the present disclosure, the BSM may correspond to Bell state measurement (BSM) between quantum memories.

[0426]

[0427] According to various embodiments of the present disclosure, a first node is provided in a communication system. The first node includes a transceiver and at least one processor, wherein the at least one processor may be configured to perform the operating method of the first node according to FIG. 32.

[0428]

[0429] According to various embodiments of the present disclosure, a device for controlling a first node in a communication system is provided. The device 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 an operating method of the first node according to FIG. 32 based on instructions executed by the at least one processor.

[0430]

[0431] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRM) storing one or more instructions are provided. The one or more instructions, when executed by one or more processors, perform operations, and the operations may include the operating method of the first node according to FIG. 32.

[0432]

[0433] [Description of the second node claim]

[0434] The embodiments described below are specifically described with reference to FIG. 33 in terms of the operation of the second node. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for or combined with some components of another method, as long as they are not mutually exclusive.

[0435] FIG. 33 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0436] According to various embodiments of the present disclosure, a method performed by a second node in a communication system is provided.

[0437] According to various embodiments of the present disclosure, each of the first node, the second node, the third node, and the plurality of nodes may correspond to one of a terminal or a base station in a wireless communication system.

[0438] The embodiment of FIG. 33 may further include, before step S3301, one or more of the following steps: a step in which the second node receives one or more synchronization signals from the first node; a step in which the second node receives system information from the first node; a step in which the second node receives configuration information from the first node; and a step in which the second node receives control information from the first node.

[0439] The embodiment of FIG. 33 may further include, before step S3301, one or more of the following steps: a step in which the second node transmits a random access preamble to the first node; a step in which the second node receives a random access response (RAR) from the first node; a step in which the second node transmits a random access message 3 to the first node; and a step in which the second node receives a contention resolution message from the first node. Message 3 is a first PUSCH transmission scheduled by RAR together with an RAR UL grant.

[0440] In step S3301, the second node receives network information from multiple nodes. The network information is also transmitted to the first node.

[0441] In step S3302, the second node transmits a request for entanglement routing (ER) between the second node and the third node to the first node.

[0442] In step S3304, based on the network information, the second node performs the ER to determine a specific path between the second node and the third node based on first factor information that depends on a distance value between the second node and the third node, second factor information that does not depend on the distance value, and a scaling factor for the first factor information or the second factor information.

[0443]

[0444] According to various embodiments of the present disclosure, the network information may further include entanglement formation rate information of quantum channel links constituting the plurality of paths, Bell state measurement efficiency information of each node constituting the channel links, and node degree of freedom information of each node.

[0445] According to various embodiments of the present disclosure, the embodiment of FIG. 33 may further include a step of receiving a first weight, a second weight, and a scaling activation value based on the network information from the first node. The scaling factor may be configured to be adjusted based on the scaling activation value.

[0446] According to various embodiments of the present disclosure, the step S3304 of performing the ER may include: receiving a first ER command from the first node; and receiving an ER completion message including information of the specific path from the first node. The specific path may be a path in which a second link entanglement creation based on a bell state measurement (BSM) is successful among second paths. The second paths may be paths in which a first link entanglement creation is successful among first paths between the second node and the third node. The first paths may be derived based on a path evaluation function comprising the first factor information, the first weight for the first factor information, the second factor information, the second weight for the second factor information, and the scaling factor.

[0447] According to various embodiments of the present disclosure, the scaling activation value may be a reference distance value determined based on the network information. When the distance value between the second node and the third node is less than or equal to the reference distance value, the scaling factor may be 1. When the distance value between the second node and the third node is greater than the reference distance value, the scaling factor may be a value based on the network information.

[0448] According to various embodiments of the present disclosure, the scaling activation value may be a value of a 1-bit indicator determined based on the network information. When the 1-bit indicator is a first value, the scaling factor may be 1. When the 1-bit indicator is a second value, the scaling factor may be a value based on the network information.

[0449] According to various embodiments of the present disclosure, multi-hop entanglement exchange can be performed based on the specific path.

[0450] According to various embodiments of the present disclosure, the BSM may correspond to Bell state measurement (BSM) between quantum memories.

[0451]

[0452] According to various embodiments of the present disclosure, a second node is provided in a communication system. The second node includes a transceiver and at least one processor, wherein the at least one processor may be configured to perform the operating method of the second node according to FIG. 33.

[0453]

[0454] According to various embodiments of the present disclosure, a device for controlling a first node in a communication system is provided. The device 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 an operating method of a second node according to FIG. 33 based on instructions executed by the at least one processor.

[0455]

[0456] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRM) storing one or more instructions are provided. The one or more instructions, when executed by one or more processors, perform operations, and the operations may include the operating method of a second node according to FIG. 33.

[0457]

[0458] Communication system applicable to the present disclosure

[0459] FIG. 34 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0460] Referring to FIG. 34, a communication system (1) applicable to various embodiments of the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution), 6G wireless communication), and may be referred to as a communication / wireless / 5G device / 6G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices, and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. Mobile devices may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.), etc. Home appliances may include a TV, a refrigerator, a washing machine, etc. IoT devices may include a sensor, a smart meter, etc. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device (200a) may act as a base station / network node to other wireless devices.

[0461] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (AI) technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, or a 6G network. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to Everything) communication). In addition, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0462] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (Integrated Access Backhaul). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of various embodiments of the present disclosure.

[0463] Meanwhile, NR supports multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, an SCS of 15 kHz supports a wide area in traditional cellular bands; an SCS of 30 kHz / 60 kHz supports dense urban areas, lower latency, and wider carrier bandwidth; and an SCS of 60 kHz or higher supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0464] The NR frequency band can be defined by two types of frequency ranges (FR1, FR2). The numerical values ​​of the frequency ranges can be changed, and for example, the frequency ranges of the two types (FR1, FR2) can be as shown in Table 6 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 can mean the "sub 6 GHz range", and FR2 can mean the "above 6 GHz range" and can be called millimeter wave (mmW).

[0465]

[0466] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz-6000MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0467]

[0468] As described above, the numerical value of the frequency range of the NR system can be changed. For example, FR1 may include a band from 410 MHz to 7125 MHz, as shown in Table 7 below. That is, FR1 may include a frequency band above 6 GHz (or 5850, 5900, 5925 MHz, etc.). For example, the frequency band above 6 GHz (or 5850, 5900, 5925 MHz, etc.) included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, such as for vehicular communications (e.g., autonomous driving).

[0469] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR141MHz-7125MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0470]

[0471] According to various embodiments of the present disclosure, the communication system (1) can support terahertz (THz) wireless communication. THz wireless communication is a wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. The frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band where propagation loss due to absorption of molecules in the air is small.

[0472]

[0473] Wireless devices applicable to the present disclosure

[0474] Below, examples of wireless devices to which various embodiments of the present disclosure are applied are described.

[0475] FIG. 35 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0476] Referring to FIG. 35, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 34.

[0477] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). In addition, the processor (102) may receive a wireless signal including second information / signal via 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). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected 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 an RF (Radio Frequency) unit. In various embodiments of the present disclosure, a wireless device may mean a communication modem / circuit / chip.

[0478] 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). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). Furthermore, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals 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). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected 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. In various embodiments of the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0479] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., 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) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0480] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0481] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0482] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless 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 wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0483] FIG. 36 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0484] According to FIG. 36, the 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).

[0485] The difference between the example of the wireless device described in FIG. 35 and the example of the wireless device in FIG. 36 is that in FIG. 35, the processor (102, 202) and the memory (104, 204) are separated, but in the example of FIG. 36, the memory (104, 204) is included in the processor (102, 202).

[0486] Here, the specific description of the processor (102, 202), memory (104, 204), transceiver (106, 206), and one or more antennas (108, 208) is as described above, so in order to avoid unnecessary repetition of description, the description of the repeated description is omitted.

[0487] Below, examples of signal processing circuits to which various embodiments of the present disclosure are applied are described.

[0488] Figure 37 illustrates a signal processing circuit for a transmission signal.

[0489] Referring to FIG. 37, 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). Although not limited thereto, the operations / functions of FIG. 37 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 35. The hardware elements of FIG. 37 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 35. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 35. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 35, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 35.

[0490] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 37. Here, the codeword is an encoded bit sequence of an information block. The information block can include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal can be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0491] Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (1010). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by a precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols. In addition, the precoder (1040) can perform precoding without performing transform precoding.

[0492] The resource mapper (1050) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (1060) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) can include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0493] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (1010 to 1060) of FIG. 37. For example, a wireless device (e.g., 100, 200 of FIG. 35) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0494] Below, examples of wireless device utilization to which various embodiments of the present disclosure are applied are described.

[0495] Figure 38 illustrates another example of a wireless device applicable to various embodiments of the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 34).

[0496] Referring to FIG. 38, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 35 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 35. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 35. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0497] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 34, 100a), a vehicle (Fig. 34, 100b-1, 100b-2), an XR device (Fig. 34, 100c), a portable device (Fig. 34, 100d), a home appliance (Fig. 34, 100e), an IoT device (Fig. 34, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 34, 400), a base station (Fig. 34, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0498] In FIG. 38, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and a first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of a set of one or more processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0499] Below, the implementation example of Fig. 38 is described in more detail with reference to the drawings.

[0500] Figure 39 illustrates a mobile device applicable to various embodiments of the present disclosure. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smartwatch, smartglasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a Mobile Station (MS), a User Terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT).

[0501] Referring to FIG. 39, the portable device (100) may include 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). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 38, respectively.

[0502] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (120) can control components of the mobile device (100) to perform various operations. The control unit (120) can include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / codes / commands required for operating the mobile device (100). In addition, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the mobile device (100) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (140b) can support connection between the mobile device (100) and other external devices. The interface unit (140b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can input or output video information / signals, audio information / signals, 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.

[0503] For example, in the case of data communication, the input / output unit (140c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (110) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0504] FIG. 40 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0505] Vehicles or autonomous vehicles can be implemented as mobile robots, cars, trains, manned or unmanned aerial vehicles (AVs), ships, etc.

[0506] Referring to FIG. 40, a vehicle or autonomous vehicle (100) may include 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 (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 38, respectively.

[0507] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0508] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0509] Figure 41 illustrates a vehicle applicable to various embodiments of the present disclosure. The vehicle may also be implemented as a means of transportation, a train, an aircraft, a ship, or the like.

[0510] Referring to FIG. 41, 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). Here, blocks 110 to 130 / 140a to 140b correspond to blocks 110 to 130 / 140 of FIG. 38, respectively.

[0511] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other vehicles or external devices such as base stations. The control unit (120) can control components of the vehicle (100) to perform various operations. The memory unit (130) can store data / parameters / programs / codes / commands that support various functions of the vehicle (100). The input / output unit (140a) can output AR / VR objects based on information in the memory unit (130). The input / output unit (140a) can include a HUD. The position measurement unit (140b) can obtain position information of the vehicle (100). The position information can include absolute position information of the vehicle (100), position information within a driving line, acceleration information, position information with respect to surrounding vehicles, etc. The position measurement unit (140b) can include GPS and various sensors.

[0512] For example, the communication unit (110) of the vehicle (100) can receive map information, traffic information, etc. from an external server and store them in the memory unit (130). The location measurement unit (140b) can obtain vehicle location information through GPS and various sensors and store the information in the memory unit (130). The control unit (120) can create a virtual object based on the map information, traffic information, and vehicle location information, and the input / output unit (140a) can display the created virtual object on the vehicle window (1410, 1420). In addition, the control unit (120) can determine whether the vehicle (100) is being driven normally within the driving line based on the vehicle location information. If the vehicle (100) abnormally deviates from the driving line, the control unit (120) can display a warning on the vehicle window through the input / output unit (140a). Additionally, the control unit (120) can broadcast a warning message regarding driving abnormalities to surrounding vehicles through the communication unit (110). Depending on the situation, the control unit (120) can transmit vehicle location information and information regarding driving / vehicle abnormalities to relevant authorities through the communication unit (110).

[0513] Figure 42 illustrates an XR device applicable to various embodiments of the present disclosure. The XR device may be implemented as an HMD, a head-up display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, and the like.

[0514] Referring to FIG. 42, 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). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 38, respectively.

[0515] The communication unit (110) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. The media data can include videos, images, sounds, etc. The control unit (120) can control components of the XR device (100a) to perform various operations. For example, the control unit (120) can be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation and processing, etc. The memory unit (130) can store data / parameters / programs / codes / commands required for driving the XR device (100a) / generating XR objects. The input / output unit (140a) can obtain control information, data, etc. from the outside, and output the generated XR objects. The input / output unit (140a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module, etc. The sensor unit (140b) can obtain the XR device status, surrounding environment information, user information, etc. 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, a light sensor, a microphone, and / or a radar. The power supply unit (140c) supplies power to the XR device (100a) and may include a wired / wireless charging circuit, a battery, etc.

[0516] For example, the memory unit (130) of the XR device (100a) may include information (e.g., data, etc.) required for creating an XR object (e.g., AR / VR / MR object). The input / output unit (140a) may obtain a command to operate the XR device (100a) from the user, and the control unit (120) may operate the XR device (100a) according to the user's operating command. For example, when a user attempts to watch a movie, news, etc. through the XR device (100a), the control unit (120) may transmit content request information to another device (e.g., a mobile device (100b)) or a media server through the communication unit (130). The communication unit (130) may download / stream content such as movies and news from another device (e.g., a mobile 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, and metadata generation / processing for content, and can generate / output an XR object based on information about surrounding space or real objects acquired through the input / output unit (140a) / sensor unit (140b).

[0517] In addition, the XR device (100a) is wirelessly connected to the mobile device (100b) through the communication unit (110), and the operation of the XR device (100a) can be controlled by the mobile device (100b). For example, the mobile device (100b) can act as a controller for the XR device (100a). To this end, the XR device (100a) can obtain three-dimensional position information of the mobile device (100b), and then generate and output an XR object corresponding to the mobile device (100b).

[0518] Figure 43 illustrates robots applicable to various embodiments of the present disclosure. Robots may be classified into industrial, medical, household, and military applications, depending on their intended use or field.

[0519] Referring to FIG. 43, 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). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 38, respectively.

[0520] The communication unit (110) can transmit and receive signals (e.g., driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The control unit (120) can control components of the robot (100) to perform various operations. The memory unit (130) can store data / parameters / programs / codes / commands that support various functions of the robot (100). The input / output unit (140a) can obtain information from the outside of the robot (100) and output information to the outside of the robot (100). The input / output unit (140a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module. The sensor unit (140b) can obtain internal information of the robot (100), surrounding environment information, user information, etc. 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, a light sensor, a microphone, a radar, etc. The driving unit (140c) may perform various physical operations such as moving the robot joints. In addition, the driving unit (140c) may enable the robot (100) to drive on the ground or fly in the air. The driving unit (140c) may include an actuator, a motor, wheels, brakes, propellers, etc.

[0521] FIG. 44 illustrates an AI device applicable to various embodiments of the present disclosure.

[0522] AI devices can be implemented as 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.

[0523] Referring to FIG. 44, the AI ​​device (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a / 140b), a learning processor unit (140c), and a sensor unit (140d). Blocks 110 to 130 / 140a to 140d correspond to blocks 110 to 130 / 140 of FIG. 38, respectively.

[0524] The communication unit (110) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices (e.g., FIG. W1, 100x, 200, 400) or AI servers (200) using wired and wireless communication technology. To this end, the communication unit (110) can transmit information within the memory unit (130) to the external device or transfer a signal received from the external device to the memory unit (130).

[0525] The control unit (120) may determine at least one executable 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 control unit (120) may control components of the AI ​​device (100) to perform the determined operation. For example, the control unit (120) may request, search, receive, or utilize data from the learning processor unit (140c) or the memory unit (130), and may control components of the AI ​​device (100) to perform at least one executable operation, a predicted operation, or an operation determined to be desirable. In addition, the control unit (120) may collect history information including the operation contents of the AI ​​device (100) or user feedback on the operation, and store the collected history information in the memory unit (130) or the learning processor unit (140c), or transmit the collected history information to an external device such as an AI server (FIG. W1, 400). The collected history information may be used to update a learning model.

[0526] The memory unit (130) can store data that supports various functions of the AI ​​device (100). For example, the memory unit (130) can store data obtained from the input unit (140a), data obtained from the communication unit (110), output data of the learning processor unit (140c), and data obtained from the sensing unit (140). In addition, the memory unit (130) can store control information and / or software codes necessary for the operation / execution of the control unit (120).

[0527] The input unit (140a) can obtain various types of data from the outside of the AI ​​device (100). For example, the input unit (120) can obtain learning data for model learning, input data to which the learning model will be applied, etc. The input unit (140a) may include a camera, a microphone, and / or a user input unit. The output unit (140b) may generate output related to sight, hearing, or touch. The output unit (140b) may include a display unit, a speaker, and / or a haptic module, etc. The sensing unit (140) can obtain at least one of internal information of the AI ​​device (100), information about the surrounding environment of the AI ​​device (100), and user information 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, a light sensor, a microphone, and / or a radar, etc.

[0528] The learning processor unit (140c) can train a model composed of an artificial neural network using learning data. The learning processor unit (140c) can perform AI processing together with the learning processor unit of the AI ​​server (Figure W1, 400). The learning processor unit (140c) can process information received from an external device via the communication unit (110) and / or information stored in the memory unit (130). In addition, the output value of the learning processor unit (140c) can be transmitted to an external device via the communication unit (110) and / or stored in the memory unit (130).

[0529] The claims described in the various embodiments of the present disclosure may be combined in various ways. For example, the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a device, and the technical features of the device claims of the various embodiments of the present disclosure may be combined and implemented as a method. Furthermore, the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a device, and the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a method.

Claims

1. In the operation method of the first node in the communication system, A step of transmitting at least one synchronization signal to a plurality of nodes constituting a plurality of paths in a network; A step of transmitting control information to the plurality of nodes; A step of receiving network information from the plurality of nodes; A step of receiving a request for entanglement routing (ER) between a second node and a third node from the second node; A step of performing the ER to determine a specific path between the second node and the third node based on the network information, the first factor information depending on the distance value between the second node and the third node, the second factor information not depending on the distance value, and the scaling factor for the first factor information or the second factor information, method.

2. In paragraph 1, The above network information is, Further comprising information on the entanglement formation rate of quantum channel links constituting the plurality of paths, information on the efficiency of Bell state measurement of each node constituting the channel links, information on the degree of freedom of each node, and information on the location of each node. method.

3. In paragraph 1, Further comprising a step of transmitting a first weight, a second weight, and a scaling activation value to the plurality of nodes based on the network information, The above scaling factor is configured to be adjusted based on the above scaling activity value, The steps of performing the above ER are: A step of transmitting a first ER command to the plurality of nodes; A step of receiving a first ER report related to the success or failure of first link entanglement creation for first paths between the second node and the third node derived based on the path evaluation function from the plurality of nodes? The first paths are derived based on a path evaluation function comprising the first factor information, the first weight for the first factor information, the second factor information, the second weight for the second factor information, and the scaling factor; A step of determining second paths among the first paths in which the first link entanglement formation is successful; A step of transmitting a second ER command including relevant information of BSM (bell state measurement) required for entanglement exchange in the second paths to the plurality of nodes; A step of receiving a second ER report on whether the second link entanglement formation for the second paths is successful based on the performance of the BSM from the plurality of nodes; A step of transmitting an ER completion message including information on the specific path in which the second link entanglement formation is successful among the second paths to the second node, method.

4. In paragraph 3, The above scaling activity value is a reference distance value determined based on the above network information, If the distance value between the second node and the third node is less than or equal to the reference distance value, the scaling factor is 1, If the distance value between the second node and the third node is greater than the reference distance value, the scaling factor is a value based on the network information. method.

5. In paragraph 3, The above scaling activation value is the value of a 1-bit indicator determined based on the above network information, If the above 1-bit indicator is the first value, the scaling factor is 1, If the above 1-bit indicator is a second value, the scaling factor is a value based on the network information. method.

6. In paragraph 3, Multi-hop entanglement exchange is performed based on the above specific path, method.

7. In paragraph 3, The above BSM corresponds to the Bell state measurement (BSM) between quantum memories. method.

8. In the method of operation of the second node in the communication system, A step of receiving at least one synchronization signal from a first node; A step of receiving control information from the first node; A step of receiving network information from a plurality of nodes, wherein the network information is also transmitted to the first node; A step of transmitting a request for entanglement routing (ER) between the second node and the third node to the first node; A step of performing the ER to determine a specific path between the second node and the third node based on the network information, the first factor information depending on the distance value between the second node and the third node, the second factor information not depending on the distance value, and the scaling factor for the first factor information or the second factor information, method.

9. In paragraph 8, The above network information is, Further comprising information on the entanglement formation rate of quantum channel links constituting the plurality of paths, information on the efficiency of Bell state measurement of each node constituting the channel links, and information on the node degree of freedom of each node. method.

10. In paragraph 8, Further comprising a step of receiving a first weight, a second weight, and a scaling activation value based on the network information from the first node, The above scaling factor is configured to be adjusted based on the above scaling activity value, The steps of performing the above ER are: A step of receiving a first ER command from the first node; A step of receiving an ER completion message including information of the specific path from the first node, The above specific path is a path in which the second link entanglement creation based on BSM (bell state measurement) among the second paths is successful, The above second paths are paths in which the first link entanglement formation is successful among the first paths between the second node and the third node, The first paths are derived based on a path evaluation function consisting of the first factor information, the first weight for the first factor information, the second factor information, the second weight for the second factor information, and the scaling factor. method.

11. In paragraph 10, The above scaling activity value is a reference distance value determined based on the above network information, If the distance value between the second node and the third node is less than or equal to the reference distance value, the scaling factor is 1, If the distance value between the second node and the third node is greater than the reference distance value, the scaling factor is a value based on the network information. method.

12. In paragraph 10, The above scaling activation value is the value of a 1-bit indicator determined based on the above network information, If the above 1-bit indicator is the first value, the scaling factor is 1, If the above 1-bit indicator is a second value, the scaling factor is a value based on the network information. method.

13. In paragraph 10, Multi-hop entanglement exchange is performed based on the above specific path, method.

14. In paragraph 10, The above BSM corresponds to atomic Bell state measurement (BSM) between quantum memories. method.

15. In the first node of the communication system, Transmitter and receiver; at least one processor; and At least one memory operably connectable to said at least one processor and storing instructions that, when executed by said at least one processor, perform operations; The above actions are, Comprising all steps of the method according to one of claims 1 to 7, Node 1.

16. In the second node of the communication system, Transmitter and receiver; at least one processor; and At least one memory operably connectable to said at least one processor and storing instructions that, when executed by said at least one processor, perform operations; The above actions are, Comprising all steps of a method according to one of claims 8 to 14, Second node.

17. In a control device that controls a first node in a communication system, at least one processor; and comprising at least one memory operably connected to at least one of the processors; The at least one memory stores instructions for performing operations based on being executed by the at least one processor, The above actions are, Comprising all steps of the method according to one of claims 1 to 7, controller.

18. In a control device that controls a second node in a communication system, at least one processor; and comprising at least one memory operably connected to at least one of the processors; The at least one memory stores instructions for performing operations based on being executed by the at least one processor, The above actions are, Comprising all steps of a method according to one of claims 8 to 14, controller.

19. In one or more non-transitory computer-readable media storing one or more instructions, The one or more instructions perform operations based on being executed by one or more processors, The above actions are, Comprising all steps of the method according to one of claims 1 to 7, Computer readable medium.

20. In one or more non-transitory computer-readable media storing one or more instructions, The one or more instructions perform operations based on being executed by one or more processors, The above actions are, Comprising all steps of a method according to one of claims 8 to 14, Computer readable medium.

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