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WO2026005090A1 - Appareil et procédé de configuration de système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication - Google Patents

Appareil et procédé de configuration de système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication

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Publication number
WO2026005090A1
WO2026005090A1 PCT/KR2024/009025 KR2024009025W WO2026005090A1 WO 2026005090 A1 WO2026005090 A1 WO 2026005090A1 KR 2024009025 W KR2024009025 W KR 2024009025W WO 2026005090 A1 WO2026005090 A1 WO 2026005090A1
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WO
WIPO (PCT)
Prior art keywords
knowledge
node
partition
information
knowledge base
Prior art date
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Pending
Application number
PCT/KR2024/009025
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English (en)
Korean (ko)
Inventor
이태현
이상림
정익주
권순희
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
LG Electronics Inc
Original Assignee
LG Electronics Inc
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Filing date
Publication date
Application filed by LG Electronics Inc filed Critical LG Electronics Inc
Priority to PCT/KR2024/009025 priority Critical patent/WO2026005090A1/fr
Publication of WO2026005090A1 publication Critical patent/WO2026005090A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Definitions

  • the present disclosure relates to a device and method for constructing a multi-representation transmission system through small-scale knowledge segmentation in a communication system. Specifically, the present disclosure relates to a device and method used for a semantic representation transmission technique in which a destination transmits a semantic representation to more accurately understand the semantic information intended by the source in a system capable of performing semantic communication.
  • the source can perform a knowledge synchronization process that identifies the destination's background knowledge.
  • the source can apply a small-scale knowledge partitioning scenario, increasing the number of knowledge partitions used in the multiple representation transmission scheme.
  • the source In a conventional multiple representation transmission scheme, the source generates multiple representations using each knowledge partition as attention.
  • the destination generates an attention value based on its background knowledge and feeds it back to the source.
  • the source uses the feedbacked attention value to generate a representation through feedback injection encoding and retransmits it. This process is repeated to generate a representation that utilizes a portion of the destination's background knowledge as attention.
  • the destination determines whether the received representation contains its own background knowledge and transmits index information to the source. Based on this information, the source can perform semantic communication using background knowledge that matches the destination's knowledge through a knowledge merging process. While this process allows knowledge synchronization without a feedback injection procedure, it incurs significant communication overhead when the destination performs knowledge matching on multiple representations and feeds this information back to the source. Therefore, this patent proposes a method for performing an efficient knowledge synchronization process by transmitting only whether each knowledge partition contains destination knowledge while minimizing the feedback injection process in a small-scale knowledge partition situation.
  • the present disclosure provides a device and method for constructing a multi-representation transmission system through small-scale knowledge division in a communication system.
  • the present disclosure provides a device and method used for a semantic representation transmission technique in which a destination transmits a semantic representation to more accurately understand semantic information intended by a source in a system capable of performing semantic communication.
  • a method of operating a first node in a communication system comprising: transmitting to a second node information about multiple representations included in a plurality of representations based on a first knowledge partition that is part of a first knowledge base of the first node, the plurality of representations being selected from the plurality of representations based on a quantization level associated with a communication state between the first node and the second node; receiving, from the second node, feedback based on an inclusion state of the first knowledge partition associated with the plurality of representations in a second knowledge base of the second node; and transmitting, to the second node, a plurality of second representations based on a second knowledge partition that is another part of the first knowledge base based on the feedback.
  • a method of operating a second node in a communication system comprising: receiving, from a first node, information about multiple representations included in a plurality of representations based on a first knowledge partition that is part of a first knowledge base of the first node, the plurality of representations being selected from the plurality of representations based on a quantization level associated with a communication state between the first node and the second node; transmitting, to the first node, feedback based on an inclusion state of the first knowledge partition associated with the plurality of representations in a second knowledge base of the second node; and receiving, from the first node, a plurality of second representations based on a second knowledge partition that is another part of the first knowledge base based on the feedback.
  • a first node 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.
  • a second node 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.
  • a control device for controlling a first node in a communication system 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.
  • a control device for controlling a second node in a communication system 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.
  • 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.
  • 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.
  • the present disclosure can provide a device and method for constructing a multi-representation transmission system through small-scale knowledge division in a communication system.
  • the present disclosure can provide a device and method used for a semantic representation transmission technique in which a destination transmits a semantic representation to more accurately understand semantic information intended by a source in a system capable of performing semantic communication.
  • Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.
  • FIG. 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).
  • NG-RAN New Generation Radio Access Network
  • Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.
  • Figure 4 is a diagram illustrating an example of a 5G usage scenario.
  • Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.
  • Figure 6 is a schematic diagram illustrating an example of a perceptron structure.
  • Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.
  • Figure 8 is a schematic diagram illustrating an example of a deep neural network.
  • Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.
  • Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.
  • Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.
  • Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.
  • Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.
  • Figure 14 is a diagram illustrating an example of a THz communication application.
  • Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.
  • FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.
  • Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.
  • Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.
  • Figure 19 is a drawing showing the structure of an optical modulator.
  • Figure 20 is a diagram illustrating an example of a three-level communication model in the present disclosure.
  • FIG. 21 is a diagram illustrating an example of a semantic information source and destination in a system applicable to the present disclosure.
  • FIG. 22 is a diagram illustrating an example of a multiple representation transmission-based semantic communication transmission and reception structure including a feedback injection encoder in a system applicable to the present disclosure.
  • FIG. 23 is a diagram illustrating an example of a knowledge synchronization process in a small-scale knowledge partition situation or utilizing feedback injection in a system applicable to the present disclosure.
  • FIG. 24 is a diagram illustrating an example of a transmission structure utilizing a semantic diversity scheme based on multiple representation transmission in a system applicable to the present disclosure.
  • FIG. 25 is a diagram illustrating an example of a contextualizing encoder structure of a Source in a system applicable to the present disclosure.
  • Figure 26 shows the knowledge partition utilized in the contextualizing encoder of the destination in a system applicable to the present disclosure and the background knowledge it possesses.
  • FIG. 27 is a diagram illustrating an example of a feedback injection encoder structure of a source in a system applicable to the present disclosure.
  • FIG. 28 is a diagram illustrating an example of a transmission structure utilizing a multiple representation transmission-based semantic diversity scheme including a feedback injection encoder in a system applicable to the present disclosure.
  • FIG. 29 is a diagram illustrating an example of a combining ratio control process that takes into account downstream task operations at a destination in a system applicable to the present disclosure.
  • FIG. 30 is a diagram illustrating an example of a process for calculating the attention coefficient of a destination and characteristics of the attention coefficient in a system applicable to the present disclosure.
  • FIG. 31 is a diagram illustrating an example of an attention coefficient distribution according to whether or not a knowledge base of a destination is included in a system applicable to the present disclosure.
  • FIG. 32 is a diagram illustrating an example of a distribution of variance values of an attention coefficient depending on whether a knowledge base of a destination is included in a system applicable to the present disclosure.
  • FIG. 33 is a diagram illustrating an example of a feedback method for whether a destination's knowledge base is included in a received representation in a system applicable to the present disclosure.
  • FIG. 34 is a diagram illustrating an example of a selective transmission and reception process for multiple representation transmission in a system applicable to the present disclosure.
  • FIG. 35 is a diagram illustrating an example of a multiple representation transmission-based semantic communication procedure in a small-scale knowledge partition situation in a system applicable to the present disclosure.
  • FIG. 36 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.
  • FIG. 37 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.
  • FIG. 38 illustrates a communication system (1) applicable to various embodiments of the present disclosure.
  • FIG. 39 illustrates a wireless device that can be applied to various embodiments of the present disclosure.
  • FIG. 40 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.
  • Figure 41 illustrates a signal processing circuit for a transmission signal.
  • FIG. 42 illustrates another example of a wireless device applicable to various embodiments of the present disclosure.
  • FIG. 43 illustrates a mobile device applicable to various embodiments of the present disclosure.
  • FIG. 44 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.
  • FIG. 45 illustrates a vehicle applicable to various embodiments of the present disclosure.
  • FIG. 46 illustrates an XR device applicable to various embodiments of the present disclosure.
  • FIG. 47 illustrates a robot applicable to various embodiments of the present disclosure.
  • FIG. 48 illustrates an AI device applicable to various embodiments of the present disclosure.
  • a or B may mean “only A,” “only B,” or “both A and B.” In other words, in various embodiments of the present disclosure, “A or B” may be interpreted as “A and/or B.” 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.”
  • a slash (/) or a comma may mean “and/or.”
  • A/B may mean “A and/or B.”
  • A/B may mean “only A,” “only B,” or “both A and B.”
  • A, B, C may mean “A, B, or C.”
  • “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” 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.”
  • “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.”
  • 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.”
  • 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
  • E-UMTS Evolved UMTS
  • LTE-A Advanced/LTE-A pro
  • 3GPP NR New Radio or New Radio Access Technology
  • 3GPP 6G may be an evolved version of 3GPP NR.
  • LTE refers to technology after 3GPP TS 36.xxx Release 8.
  • LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A
  • LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro
  • 3GPP NR refers to technology after TS 38.
  • 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.
  • RRC Radio Resource Control
  • RRC Radio Resource Control
  • Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP 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.
  • a terminal 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.
  • PSS Primary Synchronization Signal
  • SSS Secondary Synchronization Signal
  • PBCH Physical Broadcast Channel
  • DL RS Downlink Reference Signal
  • 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).
  • PDCCH physical downlink control channel
  • PDSCH physical downlink shared channel
  • the terminal may perform a random access procedure (RACH) for the base station (S13 to S16).
  • RACH random access procedure
  • 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.
  • PRACH physical random access channel
  • RAR Random Access Response
  • a contention resolution procedure may additionally be performed (S16).
  • 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.
  • the terminal can receive downlink control information (DCI) through the PDCCH.
  • DCI downlink control information
  • 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.
  • 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.
  • 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.
  • PDSCH Physical Downlink Shared Channel
  • 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.
  • DMRS Demodulation Reference Signal
  • the PDCCH carries downlink control information (DCI) and employs modulation methods such as QPSK.
  • DCI downlink control information
  • a PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs), depending on the Aggregation Level (AL).
  • CCEs Control Channel Elements
  • Each CCE is comprised of six Resource Element Groups (REGs). Each REG is defined by one OFDM symbol and one (P)RB.
  • 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.
  • 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.
  • PUSCH Physical Uplink Shared Channel
  • 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.
  • CP-OFDM Cyclic Prefix - Orthogonal Frequency Division Multiplexing
  • DFT-s-OFDM Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing
  • 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.
  • 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.
  • new radio access technology new RAT, NR.
  • next-generation communication 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.
  • new RAT New RAT
  • FIG. 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).
  • NG-RAN New Generation Radio Access Network
  • 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.
  • 5G Core Network: 5GC 5th generation core network
  • the gNB is connected to the access and mobility management function (AMF) via the NG-C interface
  • the gNB is connected to the user plane function (UPF) via the NG-U interface.
  • AMF access and mobility management function
  • UPF user plane function
  • Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.
  • 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
  • Figure 4 is a diagram illustrating an example of a 5G usage scenario.
  • 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.
  • 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).
  • eMBB enhanced mobile broadband
  • mMTC massive machine type communication
  • URLLC ultra-reliable and low latency communications
  • KPI key performance indicator
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 the network operator's edge network servers to minimize latency.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Therefore, the potential to replace 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.
  • 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.
  • next-generation communications e.g., 6G
  • 6G next-generation communications
  • 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.
  • Table 1 is a table showing an example of the requirements of a 6G system.
  • 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.
  • eMBB enhanced mobile broadband
  • URLLC ultra-reliable low latency communications
  • mMTC massive machine-type communication
  • AI integrated communication tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.
  • Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.
  • 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:
  • 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.
  • 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).
  • 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.
  • WIET wireless information and energy transfer
  • Small cell networks The 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • AI 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.
  • 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.
  • M2M machine-to-machine
  • BCIs brain-computer interfaces
  • 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.
  • 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.
  • Deep learning-based AI algorithms require a large amount of training data to optimize training parameters.
  • a large amount of training data is used offline. This 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.
  • 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.
  • 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.
  • Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data.
  • 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.
  • 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.
  • the learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.
  • the neural network cores used in learning methods are mainly divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machines (RNN).
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN recurrent boltzmann machines
  • An artificial neural network is an example of a network of multiple perceptrons.
  • Figure 6 is a schematic diagram illustrating an example of a perceptron structure.
  • 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.
  • 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.
  • Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.
  • the layer where the input vector is located is called the input layer
  • the layer where the final output value is located is called the output layer
  • all 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.
  • 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 machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning.
  • the artificial neural network used for deep learning is called a deep neural network (DNN).
  • Figure 8 is a schematic diagram illustrating an example of a deep neural network.
  • 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.
  • 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.
  • the correlation characteristic can mean the joint probability of inputs and outputs.
  • Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.
  • 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).
  • a weight is added to each connection in the connection process from one input node to the hidden layer, a total of h ⁇ w weights must be considered. Since there are h ⁇ w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.
  • the convolutional neural network of Fig. 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.
  • Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.
  • 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.
  • 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.
  • 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.
  • a neural network with multiple convolutional layers is called a deep convolutional neural network (DCNN).
  • 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.
  • 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.
  • Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.
  • a recurrent neural network 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.
  • Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.
  • the recurrent neural network operates in a predetermined order of time for the input data sequence.
  • 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 point 2, time point 3, ,,, time point T.
  • Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).
  • 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.
  • 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.
  • 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.
  • 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.
  • Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.
  • THz communications Key characteristics 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.
  • 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.
  • FSO can be a promising technology for providing backhaul connectivity in 6G systems, in conjunction with fiber-optic networks.
  • 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.
  • BS base station
  • MIMO technology 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.
  • 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.
  • 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.
  • 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.
  • Unmanned Aerial Vehicles 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.
  • eMBB enhanced mobile broadband
  • URLLC ultra low-access control
  • mMTC massive machine type of networks
  • 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 6
  • 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.
  • 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.
  • Autonomous wireless networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes.
  • sensing will be tightly integrated with communications to support autonomous systems.
  • 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.
  • 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.
  • 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.
  • 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.
  • THz Terahertz
  • 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.
  • 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.
  • Figure 14 is a diagram illustrating an example of a THz communication application.
  • THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks.
  • THz wireless communication can be applied to vehicle-to-vehicle and backhaul/fronthaul connections.
  • 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.
  • Table 2 below shows examples of technologies that can be used in THz waves.
  • Transceiver Device Available immatures UTC-PD, RTD and SBD Modulation and Coding Low order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, Turbo Antenna Omni and Directional, phased array with low number of antenna elements Bandwidth 69GHz (or 23GHz) at 300GHz Channel models Partially Data rate 100Gbps Outdoor deployment No Free space loss High Coverage Low Radio Measurements 300GHz indoor Device size Few micrometers
  • 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.
  • Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.
  • 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.
  • a multiplier doubler, tripler, multiplier
  • a multiplier is essential.
  • 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.
  • beamforming can be implemented by applying an array antenna or the like to the antenna of Fig. 15.
  • IF represents intermediate frequency
  • tripler and multiplexer represent multipliers
  • PA represents power amplifier
  • LNA low noise amplifier
  • PLL phase-locked loop.
  • FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.
  • Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.
  • 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.
  • optical component-based THz signal generation requires a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector.
  • 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
  • a UTC-PD Uni-Travelling Carrier Photo-Detector
  • the UTC-PD is capable of detecting light at 150 GHz or higher.
  • an EDFA Erbium-Doped Fiber Amplifier
  • a PD Photo Detector
  • an OSA optical module (Optical Sub Assembly) that modularizes various optical communication functions (photoelectric conversion, electro-optical conversion, etc.) into a single component
  • a DSO represents a digital storage oscilloscope.
  • Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.
  • Figure 19 is a drawing showing the structure of an optical modulator.
  • 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.
  • 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.
  • 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).
  • O/E converter optical/electrical converter
  • a terahertz transmission and reception system can be implemented using a single optical-to-electrical converter.
  • 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.
  • 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).
  • the present disclosure relates to a device and method used for a semantic representation transmission technique in which a destination transmits a semantic representation to more accurately understand semantic information intended by a source in a system capable of performing semantic communication.
  • Digital signature technology provides a solution that guarantees message integrity, message authentication, and non-repudiation, excluding confidentiality, among the four goals of information protection.
  • Existing authentication techniques cannot respond when trust between the parties exchanging information is broken. Therefore, digital signature technology is necessary, providing third-party verification for dispute resolution, authentication of the origin of message content, and verification of forgery.
  • Figure 20 is a diagram illustrating an example of a three-level communication model in the present disclosure.
  • the semantic features generated from the source and transmitted to the destination must be generated by considering the downstream tasks operating at the destination, thus requiring a task-oriented semantic communication system, which allows for preserving task-relevant information while introducing useful invariances for the downstream tasks.
  • FIG. 21 is a diagram illustrating an example of a semantic information source and destination in a system applicable to the present disclosure.
  • Figure 21 shows the characteristics of semantic communication, which is level B of Figure 20. With respect to message x transmitted from source to destination, the following definition is made.
  • the Shannon entropy H(W) of the world model W is as shown in mathematical formula 1 and is called the model entropy of the semantic source.
  • Equation 2 the logical probability m(x) of message x is as shown in Equation 2.
  • Equations 2 and 3 when considering background knowledge K, the set of possible worlds in Equations 2 and 3 is limited to sets compatible with K. Therefore, it is expressed as a conditional logical probability as in Equations 4 and 5.
  • Logical probabilities are different from a priori statistical probabilities because they have background knowledge, and in the new distribution, A and B are no longer logically independent (as ).
  • model entropies of the source that does not consider background knowledge or that does consider background knowledge are as in Equations 11 and 12.
  • semantic layer can be added, overseeing the overall operation of semantic data and messages.
  • these semantic layers can be located at the source and destination.
  • a protocol which is a set of rules between layers, and a definition of a series of operational processes are required.
  • FIG. 22 is a diagram illustrating an example of a multiple representation transmission-based semantic communication transmission and reception structure including a feedback injection encoder in a system applicable to the present disclosure.
  • the destination's background knowledge partition is utilized as the attention of each representation and transmitted through a feedback injection-based encoding process.
  • the source can perform semantic communication within the scope of the destination's background knowledge, and the destination can set and receive combining weights for multiple representations to improve the performance of the target task.
  • FIG. 23 is a diagram illustrating an example of a knowledge synchronization process in a small-scale knowledge partition situation or utilizing feedback injection in a system applicable to the present disclosure.
  • the source can perform a knowledge synchronization process to identify the destination's background knowledge information.
  • the source can apply a small-scale knowledge partition situation, increasing the number of knowledge partitions used in the multiple representation transmission scheme for more efficient knowledge synchronization.
  • Figure 23 illustrates the differences between the operation of the existing multiple representation transmission scheme and the small-scale knowledge partition situation.
  • the destination In the existing multiple representation transmission scheme, the destination generates an attention value based on its background knowledge for the multiple representations generated by the source using each knowledge partition as attention and feeds it back to the source. The source then uses the feedbacked attention value to generate a representation through a feedback injection encoding process and retransmits it.
  • the source generates and transmits a representation that utilizes a portion of the destination's background knowledge as attention.
  • the destination determines whether the received representation contains its own background knowledge and transmits index information about this to the source. Based on this information, the source can perform semantic communication using background knowledge that matches the destination's knowledge through a knowledge merging process. While the above process allows knowledge synchronization without a feedback injection procedure, significant communication overhead occurs during the process where the destination performs knowledge matching on multiple representations and feeds this information back to the source. Therefore, this patent proposes a method for efficiently performing knowledge synchronization by transmitting only whether each knowledge partition contains destination knowledge while minimizing the feedback injection process in small-scale knowledge partition situations.
  • a source in a system based on multiple representation transmission technology for performing semantic communication, a source generates a representation based on knowledge information identical to the background knowledge of the destination, and the destination determines whether the representation generated by the source through a small-scale knowledge partition includes the knowledge of the destination through an attention coefficient-based parameter and reports the information to the source, and the source identifies the knowledge base information of the destination based on the received knowledge information and requests additional information for fine tuning thereof, and a semantic layer protocol and procedure according to the related procedures for performing the same are proposed.
  • FIG. 24 is a diagram illustrating an example of a transmission structure utilizing a semantic diversity scheme based on multiple representation transmission in a system applicable to the present disclosure.
  • the background knowledge possessed by the source and destination has a structure of a knowledge graph, and the background knowledge of the source can be divided into N knowledge partitions through graph clustering.
  • the source uses multiple contextualizing encoders, each of which uses the knowledge partition generated through background knowledge clustering as attention, to generate multiple representations for each knowledge partition for a single source data and transmits them to the destination.
  • Figure 24 is a diagram illustrating the process of generating and transmitting multiple representations based on the multiple contextualizing encoders described above.
  • the n-th contextualizing encoder uses the n-th knowledge partition to create a contextualized representation by converting the embedding (x i ) corresponding to the i-th graph component of the graph representation of the source data into an embedding (x i ) corresponding to the i-th graph component. Encode it as .
  • FIG. 25 is a diagram illustrating an example of a contextualizing encoder structure of a Source in a system applicable to the present disclosure.
  • the contextualizing encoder described in the above process can use an encoder that generates a representation for input data by using input background knowledge as attention.
  • Fig. 25 shows an embodiment of a graph transformer-based contextualizing encoder that utilizes a knowledge graph as attention for input data having a graph structure.
  • the contextualizing encoder in Fig. 25 first generates a self-attention-based representation z_(i,L,t) by utilizing a graph transformer structure that uses L multi-head self-attention encoders with K heads, as in Equations 13 and 14.
  • Contextualizing encoder is the self-attention based representation
  • the attention value ⁇ i between the embedding vector corresponding to the knowledge partition is calculated as in Equation 15, and an assimilation process including the attention value in the graph representation (x i ) of the source data is performed, and the result is used as the input of the self-attention based encoder.
  • the contextualizing encoder performs assimilation T times to generate a contextualized representation. It is generated as in mathematical expressions 16 and 17.
  • the source generates a multiple representation that utilizes multiple knowledge partitions as attention for a single source data and transmits it to the destination.
  • the destination performs reasoning on the received multiple representation based on its background knowledge.
  • the destination calculates the attention value ( ) is calculated as in mathematical expression 18.
  • FIG. 26 is a diagram illustrating an example of a process for determining whether there is an intersection between the knowledge partition utilized in the contextualizing encoder of the destination and the background knowledge held in the system applicable to the present disclosure and a feedback process therefor.
  • the attention coefficient () produced in the process of calculating the above attention value ) is a value used for classification of the i-th index.
  • the attention value calculated at the destination is expressed as a weight sum for the node embedding of the intersection when there is an intersection between the knowledge partition and the destination's background knowledge, so it has information about the portion that the destination's background knowledge actually has in the n-th knowledge partition.
  • the attention value does not have a meaning for the intersection.
  • the destination has an attention value( as shown in Fig. 26. ) between the received representation and the knowledge embedding vector before transmitting it. ) is first calculated. If the above attention coefficient has low variance with respect to the component index of the destination's background knowledge, the destination determines that there is no intersection between the knowledge used to generate the representation and the destination knowledge and reports this to the source. The source readjusts the knowledge partition used for the encoder that has no intersection with the destination's background knowledge to a different knowledge partition and transmits a multiple representation.
  • FIG. 27 is a diagram illustrating an example of a feedback injection encoder structure of a source in a system applicable to the present disclosure.
  • FIG. 28 is a diagram illustrating an example of a transmission structure utilizing a multiple representation transmission-based semantic diversity scheme including a feedback injection encoder in a system applicable to the present disclosure.
  • the knowledge partitions used in the contextualizing encoder of the source all have an intersection with the background knowledge of the destination.
  • the destination calculates the attention value between the node embeddings of the background knowledge that contains the received multiple representations and feeds it back to the source.
  • the source performs an assimilation process for the contextualized representation based on the attention value received through the feedback injection encoder structure.
  • Figure 27 shows an embodiment of the feedback injection encoder structure.
  • the source can reduce the knowledge partition used as attention when generating multiple representations through the feedback injection encoder to the background knowledge partition of the destination, as shown in Figure 28.
  • the source and destination can set the cycle of the feedback procedure for the attention value, and the cycle of the attention value feedback is determined according to the performance metric resulting from the downstream task execution result at the destination.
  • the source can perform semantic communication based on multiple representation transmission, which divides the background knowledge of the destination into N knowledge partitions and utilizes them as attention.
  • the multiple representation transmission method is a closed-loop semantic diversity scheme in which the source and the destination have each other's background knowledge information, and can improve the performance of the downstream task by setting the combining ratio for each multiple representation.
  • the source performs combining ratio control according to the size of the knowledge partition utilized as attention to generate each multiple representation. The size of each knowledge partition can be determined through the difference in the attention value fed back from the destination.
  • the source determines that the knowledge partition used as attention for a representation with a small difference in each attention value has a high proportion of the destination's background knowledge, and can set the combining ratio (v_n) for the corresponding representation as in Equation 19.
  • FIG. 29 is a diagram illustrating an example of a combining ratio control process that takes into account downstream task operations at a destination in a system applicable to the present disclosure.
  • the destination when performing combining ratio control at the destination, the destination utilizes the background knowledge it possesses in the received multiple representations to generate embeddings for performing the downstream task.
  • the destination calculates the similarity between the task-specific embedding and the weight sum of the multiple representations to measure the importance of each representation, normalizes it, and calculates the combining weight (w_n) for the multiple representations as in Equation 20.
  • the weight sum for the multiple representations is ultimately utilized for the downstream task through the combining weight.
  • the initial value of the weight can be set by receiving the weight calculated by the source as feedforward, and the embedding generation MLP and the combining ratio are updated through learning depending on the performance of the task.
  • FIG. 30 is a diagram illustrating an example of a process for calculating the attention coefficient of a destination and characteristics of the attention coefficient in a system applicable to the present disclosure.
  • the source When performing knowledge synchronization using small-scale knowledge partitions at the source, the source must transmit a larger number of representations to the destination than before.
  • the destination calculates the attention coefficient, as shown in Equation 21, using the representations for each received knowledge partition and the embedding vector of its knowledge base.
  • the above attention coefficient is the received representation ( ) and the cosine similarity function (D c ) between the embedding vector of the destination knowledge base ) is a parameter that indicates the similarity between two vectors. As shown in Figure 30, the attention coefficient increases in size when the destination possesses knowledge utilized in the received representation.
  • FIG. 31 is a diagram illustrating an example of an attention coefficient distribution according to whether or not a knowledge base of a destination is included in a system applicable to the present disclosure.
  • FIG. 32 is a diagram illustrating an example of a distribution of variance values of an attention coefficient depending on whether a knowledge base of a destination is included in a system applicable to the present disclosure.
  • the destination determines whether multiple received representations that utilize small-scale knowledge partitions as attention are included in the destination's knowledge.
  • the distribution of the attention coefficient according to the degree to which the source's knowledge partition is included in the destination's knowledge can be represented as in Figure 31.
  • the variance of the attention coefficient increases as the knowledge partition is included in the destination's knowledge. Therefore, the destination calculates the attention coefficient between the received representation and the knowledge base, and determines whether the knowledge partitions used to generate each representation are fully included, partially included, or not included in the destination's knowledge based on the order of the variance of the attention coefficient.
  • the variance distribution of the attention coefficient for the received representation can be represented as in Figure 32.
  • FIG. 33 is a diagram illustrating an example of a feedback method for whether a destination's knowledge base is included in a received representation in a system applicable to the present disclosure.
  • the destination After the process of measuring whether the destination's received representation includes knowledge as described above, the destination transmits to the source an index for the knowledge partition corresponding to fully included and partially included knowledge for the received multiple representations.
  • Figure 33 shows a method of sending information related to whether each knowledge partition of the destination is included to the source as described above.
  • the destination transmits index information for the representation corresponding to the fully included knowledge partition to the source, and in the case of the partially included knowledge partition, transmits the ordering result according to the variance value of the attention coefficient to the source.
  • the source can perform knowledge merging for the knowledge partition that has been fed back as fully included, thereby performing knowledge synchronization with the destination.
  • the source can perform a process of reducing the knowledge partition corresponding to the partially included knowledge partition to the intersection with the destination knowledge through feedback injection.
  • the number of representations for performing the above operation is determined according to the ordering of the representation corresponding to the partially included representation, and the source uses the attention value ( ) is requested as the destination.
  • FIG. 34 is a diagram illustrating an example of a selective transmission and reception process for multiple representation transmission in a system applicable to the present disclosure.
  • the above-mentioned small-scale knowledge partition-based multiple representation transmission scheme can reduce the feedback injection process for knowledge synchronization.
  • the source divides its knowledge into small-scale knowledge partitions, considering the number of components (nodes and edges) included in each knowledge partition and parameters related to the connectivity between knowledge partitions.
  • the source can utilize a graph neural network or similar for this knowledge partitioning process. For the knowledge partitions divided through this process, the source selects multiple representations based on the quantization level and transmits them to the destination.
  • the quantization level is determined by communication link conditions between the source and destination, such as channel bandwidth and channel quality.
  • the source can determine whether knowledge partitions not transmitted are included in the destination by interpolating the selectively transmitted representations. This interpolation process, similar to the source's knowledge partitioning process, can be performed using a graph neural network, taking into account the connectivity of each knowledge partition.
  • the source can transmit multiple representations for knowledge partitions that require additional inclusion, and by repeating the above process, the source can determine whether all knowledge partitions contain destination knowledge.
  • Figure 34 illustrates the optional multiple representation transmission process described above.
  • FIG. 35 is a diagram illustrating an example of a multiple representation transmission-based semantic communication procedure in a small-scale knowledge partition situation in a system applicable to the present disclosure.
  • the transmission and reception process of the multiple representation transmission technology in the small-scale knowledge partitioning described above can be summarized as shown in Figure 35.
  • the source performs a graph neural network-based partitioning process to consider the number of knowledge components included in each partition and the connectivity of each partition when performing background knowledge partitioning. Furthermore, the source selectively transmits a portion of the representation corresponding to the knowledge partition, considering channel quality and bandwidth with the destination.
  • the multiple representations selectively transmitted through this process are used to calculate the attention coefficient, along with the embedding vector of the knowledge base held by the destination.
  • the destination determines whether the knowledge partition used to generate each representation is included based on the variance value of the calculated attention coefficient.
  • Inclusion can be categorized as fully included if the destination's knowledge is fully included, partially included if it is partially included, or not included if it is not included. These cases can be determined by arranging the distribution of the variances of each attention coefficient in order of magnitude.
  • the destination feeds back index information about the corresponding knowledge partition to the source for representations determined to be fully included. For knowledge partitions that fall under partial inclusion, it feeds back ordering information for each partition.
  • the source can obtain information about the destination's knowledge base through the including information for the received knowledge partitions. If additional including information is needed for knowledge partitions that were not transmitted in the above process, it selects additional knowledge partitions, transmits the representation, and repeats the above process.
  • the source needs more accurate information about the destination knowledge during the knowledge synchronization process with the destination, it can request an attention value for the knowledge partition that falls under partially included.
  • feedback injection encoding can be repeatedly performed to perform a more precise knowledge synchronization process for the destination knowledge base.
  • the present disclosure provides a device and method used for a semantic representation transmission technique in which a destination transmits a semantic representation to more accurately understand semantic information intended by a source in a system capable of performing semantic communication.
  • a method of selectively transmitting representations generated through small-scale knowledge partitions from the source according to a defined quantization level is a method of selectively transmitting representations generated through small-scale knowledge partitions from the source according to a defined quantization level.
  • FIG. 36 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.
  • a method performed by a first node in a communication system is provided.
  • each of the first node, the second node, and the plurality of nodes may correspond to one of a terminal or a base station in a wireless communication system.
  • the embodiment of FIG. 36 may further include, before step S3601, one or more of the following steps: a step in which the first node transmits one or more synchronization signals to the second node; a step in which the first node transmits system information to the second node; a step in which the first node transmits configuration information to the second node; and a step in which the first node transmits control information to the second node.
  • the embodiment of FIG. 36 may further include, before step S3601, one or more of the following steps: a step in which the first node receives a random access preamble from the second node; a step in which the first node transmits a random access response (RAR) to the second node; a step in which the first node receives a random access message 3 from the second node; and a step in which the first node transmits a contention resolution message to the second node.
  • Message 3 is a first PUSCH transmission scheduled by the RAR together with an RAR UL grant.
  • step S3601 the first node transmits to the second node information about multiple representations included in a plurality of representations based on a first knowledge partition, which is part of the first knowledge base of the first node.
  • the first node selects the plurality of representations based on a quantization level associated with a communication status between the first node and the second node.
  • step S3602 feedback is received from the second node based on the inclusion status of the second knowledge base of the second node in the first knowledge partition associated with the plurality of expressions.
  • the first node transmits to the second node a plurality of second representations based on a second knowledge partition, which is another part of the first knowledge base, based on the feedback.
  • the inclusion state may be one of a first state in which the first knowledge partition is fully included in the second knowledge base, a second state in which the first knowledge partition is partially included in the second knowledge base, and a third state in which the first knowledge partition is not included in the second knowledge base.
  • the feedback when the inclusion state is the first state, the feedback may include index information of the first knowledge partition for the second knowledge base.
  • the feedback may include ordering information of the first knowledge partition for the second knowledge base.
  • the embodiment of FIG. 36 may further include, when the inclusion state is a second state, a step of transmitting a request for an attention value of the first knowledge partition that is partially included in the second knowledge base to the second node; a step of receiving the attention value of the first knowledge partition from the second node; and a step of obtaining knowledge base information for the second knowledge base based on the attention value.
  • the plurality of second representations based on the second knowledge partition can be transmitted.
  • the request for the attention value may be transmitted for feedback injection encoding for the first knowledge partition.
  • whether the second knowledge partition is included in the second knowledge base may be based on an interpolation process.
  • the interpolation process may be based on the connectivity of each knowledge partition belonging to the first knowledge base.
  • a first node 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. 36.
  • a device for controlling a first node in a communication system includes at least one processor and at least one memory operably connected to the at least one processor.
  • the at least one memory may be configured to store instructions for performing an operating method of the first node according to FIG. 36 based on instructions executed by the at least one processor.
  • one or more non-transitory computer-readable media storing one or more instructions.
  • 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. 36.
  • FIG. 37 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.
  • a method performed by a second node in a communication system is provided.
  • each of the first node, the second node, and the plurality of nodes may correspond to one of a terminal or a base station in a wireless communication system.
  • the embodiment of FIG. 37 may further include, before step S3701, 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.
  • the embodiment of FIG. 37 may further include, before step S3701, 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.
  • the second node receives information about multiple representations included in a plurality of representations based on a first knowledge partition, which is part of a first knowledge base of the first node, from the first node.
  • the plurality of representations are selected from among the plurality of representations based on a quantization level associated with a communication status between the first node and the second node.
  • step S3702 the second node transmits feedback to the first node based on the inclusion status of the second knowledge base of the first knowledge partition associated with the plurality of expressions.
  • the second node receives from the first node a plurality of second representations based on a second knowledge partition, which is another part of the first knowledge base, based on the feedback.
  • the inclusion state may be one of a first state in which the first knowledge partition is fully included in the second knowledge base, a second state in which the first knowledge partition is partially included in the second knowledge base, and a third state in which the first knowledge partition is not included in the second knowledge base.
  • the feedback when the inclusion state is the first state, the feedback may include index information of the first knowledge partition for the second knowledge base.
  • the feedback may include ordering information of the first knowledge partition for the second knowledge base.
  • the embodiment of FIG. 37 may further include, when the inclusion state is a second state, a step of receiving a request for an attention value of the first knowledge partition that is partially included in the second knowledge base from the second node; and a step of transmitting the attention value of the first knowledge partition to the second node.
  • a plurality of second representations based on the second knowledge partition can be received based on knowledge base information about the second knowledge base.
  • knowledge base information for the second knowledge base may be based on the attention value.
  • the request for the attention value may be received for feedback injection encoding for the first knowledge partition.
  • whether the second knowledge partition is included in the second knowledge base may be based on an interpolation process.
  • the interpolation process may be based on the connectivity of each knowledge partition belonging to the first knowledge base.
  • a second node 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. 37.
  • a device for controlling a second node in a communication system includes at least one processor and at least one memory operably connected to the at least one processor.
  • the at least one memory may be configured to store instructions for performing an operating method of the second node according to FIG. 37 based on instructions executed by the at least one processor.
  • one or more non-transitory computer-readable media storing one or more instructions.
  • 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. 37.
  • FIG. 38 illustrates a communication system (1) applicable to various embodiments of the present disclosure.
  • a communication system (1) applicable to various embodiments of the present disclosure includes a wireless device, a base station, and a network.
  • 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.
  • 5G NR New RAT
  • LTE Long Term Evolution
  • 6G wireless communication e.g., 6G wireless communication
  • 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).
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc.
  • the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone).
  • UAV Unmanned Aerial Vehicle
  • 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.
  • 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.
  • 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.
  • vehicles can communicate directly (e.g., V2V (Vehicle to Vehicle)/V2X (Vehicle to everything) communication).
  • IoT devices e.g., sensors
  • IoT devices can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).
  • Wireless communication/connection can be established between wireless devices (100a ⁇ 100f)/base stations (200), and base stations (200)/base stations (200).
  • 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 base station-to-base station communication (150c) (e.g., relay, IAB (Integrated Access Backhaul).
  • 5G NR wireless access technologies
  • uplink/downlink communication 150a
  • sidelink communication 150b
  • base station-to-base station communication 150c
  • wireless devices and base stations/wireless devices, and base stations and base stations can transmit/receive wireless signals to each other.
  • wireless communication/connection can transmit/receive signals through various physical channels.
  • 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.
  • NR supports multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services.
  • SCS subcarrier spacing
  • 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
  • an SCS of 60 kHz or higher supports a bandwidth greater than 24.25 GHz to overcome phase noise.
  • 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 4 below.
  • FR1 can mean the "sub 6 GHz range”
  • FR2 can mean the "above 6 GHz range” and can be called millimeter wave (mmW).
  • mmW millimeter wave
  • FR1 may include a band from 410 MHz to 7125 MHz, as shown in Table 5 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).
  • vehicular communications e.g., autonomous driving
  • the communication system (1) can support terahertz (THz) wireless communication.
  • 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.
  • FIG. 39 illustrates a wireless device that can be applied to various embodiments of the present disclosure.
  • the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR).
  • ⁇ 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. 38.
  • 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.
  • 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).
  • 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.
  • 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.
  • a wireless device may mean a communication modem/circuit/chip.
  • the second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and/or one or more antennas (208).
  • 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.
  • 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).
  • 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.
  • 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.
  • a wireless device may also mean a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors (102, 202).
  • one or more processors (102, 202) may implement one or more layers (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.
  • PDUs Protocol Data Units
  • SDUs Service Data Units
  • 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.
  • signals e.g., baseband signals
  • 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.
  • ASICs Application Specific Integrated Circuits
  • DSPs Digital Signal Processors
  • DSPDs Digital Signal Processing Devices
  • PLDs Programmable Logic Devices
  • FPGAs Field Programmable Gate Arrays
  • the descriptions, functions, procedures, 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.
  • 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.
  • 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.
  • one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals.
  • 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.
  • 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).
  • one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports).
  • One or more transceivers (106, 206) may convert received user data, control information, wireless signals/channels, etc.
  • One or more transceivers (106, 206) may convert processed user data, control information, wireless signals/channels, etc. from baseband signals to RF band signals using one or more processors (102, 202).
  • one or more transceivers (106, 206) may include an (analog) oscillator and/or a filter.
  • FIG. 40 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.
  • 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).
  • the difference between the example of the wireless device described in FIG. 39 and the example of the wireless device in FIG. 40 is that in FIG. 39, the processor (102, 202) and the memory (104, 204) are separated, but in the example of FIG. 40, the memory (104, 204) is included in the processor (102, 202).
  • processor 102, 202
  • memory 104, 204
  • transceiver 106, 206
  • antennas 108, 208
  • Figure 41 illustrates a signal processing circuit for a transmission signal.
  • the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060).
  • the operations/functions of FIG. 41 may be performed in the processor (102, 202) and/or the transceiver (106, 206) of FIG. 39.
  • the hardware elements of FIG. 41 may be implemented in the processor (102, 202) and/or the transceiver (106, 206) of FIG. 39.
  • blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 39.
  • blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 39
  • block 1060 may be implemented in the transceiver (106, 206) of FIG. 39.
  • the codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 41.
  • 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).
  • 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.
  • N is the number of antenna ports
  • M is the number of transmission layers.
  • the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols.
  • the precoder (1040) can perform precoding without performing transform precoding.
  • 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.
  • 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.
  • IFFT Inverse Fast Fourier Transform
  • CP Cyclic Prefix
  • DAC Digital-to-Analog Converter
  • the signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (1010 to 1060) of FIG. 41.
  • a wireless device e.g., 100, 200 of FIG. 39
  • the received wireless signals can be converted into baseband signals through a signal restorer.
  • the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module.
  • ADC analog-to-digital converter
  • FFT fast Fourier transform
  • the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process.
  • a signal processing circuit for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.
  • Figure 42 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 38).
  • the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 39 and may be composed of various elements, components, units/units, and/or modules.
  • 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).
  • the communication circuit (112) may include one or more processors (102, 202) and/or one or more memories (104, 204) of FIG. 39.
  • the transceiver(s) (114) may include one or more transceivers (106, 206) and/or one or more antennas (108, 208) of FIG. 39.
  • 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.
  • 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).
  • 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).
  • the additional element (140) may be configured in various ways depending on the type of the wireless device.
  • 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.
  • the wireless device may be implemented in the form of a robot (Fig. 38, 100a), a vehicle (Fig. 38, 100b-1, 100b-2), an XR device (Fig. 38, 100c), a portable device (Fig. 38, 100d), a home appliance (Fig. 38, 100e), an IoT device (Fig.
  • Wireless devices may be mobile or stationary depending on the use/service.
  • various elements, components, units/parts, and/or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (110).
  • 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).
  • each element, component, unit/part, and/or module within the wireless device (100, 200) may further include one or more elements.
  • the control unit (120) may be composed of a set of one or more processors.
  • 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.
  • 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.
  • FIG 43 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).
  • MS Mobile Station
  • UT User Terminal
  • MSS Mobile Subscriber Station
  • SS Subscriber Station
  • AMS Advanced Mobile Station
  • WT Wireless Terminal
  • 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. 42, respectively.
  • 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.
  • 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.
  • 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).
  • FIG. 44 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.
  • Vehicles or autonomous vehicles can be implemented as mobile robots, cars, trains, manned or unmanned aerial vehicles (AVs), ships, etc.
  • AVs unmanned aerial vehicles
  • a vehicle or autonomous vehicle 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. 42, respectively.
  • 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.
  • IMU intial measurement unit
  • 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.
  • 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).
  • 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.
  • 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.
  • Figure 45 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.
  • the vehicle (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input/output unit (140a), and a position measurement unit (140b).
  • blocks 110 to 130/140a to 140b correspond to blocks 110 to 130/140 of FIG. 42, respectively.
  • 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.
  • 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).
  • the control unit (120) can determine whether the vehicle (100) is being driven normally within the driving line based on the vehicle location information.
  • 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).
  • FIG. 46 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.
  • HMD head-up display
  • FIG. 46 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.
  • HUD head-up display
  • the XR device (100a) may include a communication unit (110), a control unit (120), a memory unit (130), an input/output unit (140a), a sensor unit (140b), and a power supply unit (140c).
  • blocks 110 to 130/140a to 140c correspond to blocks 110 to 130/140 of FIG. 42, respectively.
  • 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.
  • 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.
  • 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.
  • 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).
  • another device e.g., a mobile device (100b)
  • a media server e.g., a media server
  • 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).
  • 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).
  • the mobile device (100b) can act as a controller for the XR device (100a).
  • 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).
  • Figure 47 illustrates robots applicable to various embodiments of the present disclosure. Robots may be classified into industrial, medical, household, military, and other categories depending on their intended use or field.
  • the robot (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input/output unit (140a), a sensor unit (140b), and a driving unit (140c).
  • blocks 110 to 130/140a to 140c correspond to blocks 110 to 130/140 of FIG. 42, respectively.
  • 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.
  • FIG. 48 illustrates an AI device applicable to various embodiments of the present disclosure.
  • 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.
  • 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.
  • 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. 42, respectively.
  • 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.
  • 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).
  • 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.
  • 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.
  • the memory unit (130) can store data that supports various functions of the AI device (100).
  • 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).
  • the memory unit (130) can store control information and/or software codes necessary for the operation/execution of the control unit (120).
  • the input unit (140a) can obtain various types of data from the outside of the AI device (100).
  • 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.
  • 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).
  • 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).
  • the claims described in the various embodiments of the present disclosure may be combined in various ways.
  • 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.
  • 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.

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Abstract

La présente divulgation concerne un appareil et un procédé de configuration d'un système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication. En particulier, la présente divulgation concerne un appareil et un procédé utilisés pour une technique de transmission de représentation sémantique dans laquelle une destination transmet une représentation sémantique afin d'identifier plus précisément des informations sémantiques destinées à une source dans un système capable de réaliser une communication sémantique.
PCT/KR2024/009025 2024-06-27 2024-06-27 Appareil et procédé de configuration de système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication Pending WO2026005090A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/KR2024/009025 WO2026005090A1 (fr) 2024-06-27 2024-06-27 Appareil et procédé de configuration de système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/KR2024/009025 WO2026005090A1 (fr) 2024-06-27 2024-06-27 Appareil et procédé de configuration de système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication

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WO2026005090A1 true WO2026005090A1 (fr) 2026-01-02

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PCT/KR2024/009025 Pending WO2026005090A1 (fr) 2024-06-27 2024-06-27 Appareil et procédé de configuration de système de transmission multi-représentation par partitionnement de connaissances à petite échelle dans un système de communication

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