Apparatus and method for supporting multi-representation transmission scheme including knowledge merging procedure in communication system

Abstract

The present disclosure relates to an apparatus and a method for supporting a multi-representation transmission scheme including a knowledge merging procedure. Specifically, the present disclosure relates to an apparatus and a method used for a semantic representation transmission technique in which a destination transmits a semantic representation in order to more accurately identify semantic information intended by a source in a system capable of performing semantic communication.

Description

Device and method for supporting a multi-representation transmission scheme including a knowledge merging procedure in a communication system

[0001] The present disclosure relates to a device and method for supporting a multi-representation transmission method including a knowledge merging procedure. Specifically, the present disclosure relates to a device and method used for a semantic representation transmission technique in a system capable of performing semantic communication, in which a destination transmits a semantic representation to more accurately understand the semantic information intended by the source.

[0002]

[0003] In order to perform the merging process for the destination's knowledge partition, a process is required to identify the components (nodes and edges) of the knowledge partition used in each representation. While the existing feedback injection process could utilize the knowledge partition as attention to generate a representation, the destination must transmit index information for each component to obtain information about which components the knowledge partition used to generate the representation contains. The destination can obtain the component index through each attention coefficient. Attention coefficients ( ) is a parameter that can be used for classification based on the destination's knowledge for the i-th component. The attention coefficient value calculated by the destination has a higher value for components that are highly related to the received i-th component, and components that are not used for representation generation among the destination's knowledge form a uniform distribution. The destination sets a cut-off parameter (a) according to the distribution of the attention coefficient. cutoff ) can be set to set the scope of the knowledge component for the received representation. The destination can identify the knowledge component used in the received representation, but in order to transmit the result to the source, information about all corresponding indices must be fed back to the source. Since the number of attention coefficients to be transmitted to the source is ultimately equal to the number of components, the size of the coefficients to be transmitted also increases as the knowledge base size increases. Therefore, this patent proposes a method for more efficiently performing the knowledge merging technology that combines the knowledge partitions used in each representation into one in the multiple representation transmission technology so that the representation transmitted from the source matches the destination.

[0004]

[0005] To address the above-described problems, the present disclosure provides a device and method for supporting a multi-representation transmission scheme including a knowledge merging procedure.

[0006] 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.

[0007] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0008]

[0009] According to various embodiments of the present disclosure, a method of operating a first node in a communication system is provided, the method comprising: transmitting information of multiple representations based on a first knowledge partition of single source data to a second node; receiving feedback from the second node, the feedback including a second similarity value based on the multiple representations and background knowledge of the second node; receiving a scaling value for the second similarity value from the second node; determining a difference value between a first similarity value of a contextualizing encoder for the first knowledge partition and the second similarity value using the scaling value; generating a single representation by performing knowledge merging based on a knowledge component produced for each of the multiple representations based on the difference value; and transmitting the single representation to the second node.

[0010] According to various embodiments of the present disclosure, a method of operating a second node in a communication system is provided, the method comprising: receiving information of multiple representations based on a first knowledge partition of single source data from a first node; transmitting feedback to the first node, the feedback including a second similarity value based on the multiple representations and background knowledge of the second node; transmitting a scaling value for the second similarity value to the first node; and receiving a single representation from the first node, wherein the single representation is based on knowledge merging for knowledge components produced for each of the multiple representations, the knowledge merging being based on a difference value between a first similarity value of a contextualizing encoder for the first knowledge partition and the second similarity value, the difference value being based on the scaling value.

[0011] According to various embodiments of the present disclosure, in a communication system, a first node is provided, comprising: a transceiver; at least one processor; and at least one memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, wherein the operations include all steps of a method of operating the first node according to various embodiments of the present disclosure.

[0012] According to various embodiments of the present disclosure, in a communication system, a second node is provided, comprising: a transceiver; at least one processor; and at least one memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, wherein the operations include all steps of a method of operating the second node according to various embodiments of the present disclosure.

[0013] According to various embodiments of the present disclosure, a control device for controlling a first node in a communication system is provided, comprising: at least one processor; and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on being executed by the at least one processor, wherein the operations include all steps of an operating method of the first node according to various embodiments of the present disclosure.

[0014] According to various embodiments of the present disclosure, a control device for controlling a second node in a communication system is provided, comprising: at least one processor; and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on being executed by the at least one processor, wherein the operations include all steps of an operating method of the second node according to various embodiments of the present disclosure.

[0015] 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.

[0016] According to various embodiments of the present disclosure, there is provided one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions, when executed by one or more processors, perform operations, the operations comprising all steps of a method of operating a second node according to various embodiments of the present disclosure.

[0017]

[0018] To solve the above-described problems, the present disclosure can provide a device and method for supporting a multi-representation transmission scheme including a knowledge merging procedure.

[0019] 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.

[0020]

[0021] The accompanying drawings are intended to aid in understanding the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0022] Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.

[0023] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0024] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0025] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0026] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0027] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0028] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0029] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0030] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0031] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0032] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0033] Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.

[0034] Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.

[0035] Figure 14 is a diagram illustrating an example of a THz communication application.

[0036] Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.

[0037] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0038] Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

[0039] Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.

[0040] Figure 19 is a drawing showing the structure of an optical modulator.

[0041] Figure 20 is a diagram illustrating an example of a three-level communication model in the present disclosure.

[0042] FIG. 21 is a diagram illustrating an example of a semantic information source and destination in a system applicable to the present disclosure.

[0043] 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.

[0044] Figure 23 shows the attention coefficient (a) of the destination according to the background knowledge of the transmission representation in a system applicable to the present disclosure. ic) is a diagram showing an example of distribution.

[0045] 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.

[0046] FIG. 25 is a diagram illustrating an example of a contextualizing encoder structure of a Source in a system applicable to the present disclosure.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] FIG. 30 is a diagram illustrating an example of a scaling value feedback process of a destination and an attention value difference calculation process of a source in a system applicable to the present disclosure.

[0052] FIG. 31 is a diagram illustrating an example of a semantic communication transmission / reception system structure based on multiple representation transmission including knowledge merging in a system applicable to the present disclosure.

[0053] FIG. 32 is a diagram illustrating an example of a multiple representation transmission-based semantic communication procedure including a knowledge merging process in a system applicable to the present disclosure.

[0054] FIG. 33 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.

[0055] FIG. 34 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0056] FIG. 35 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0057] FIG. 36 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0058] FIG. 37 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0059] Figure 38 illustrates a signal processing circuit for a transmission signal.

[0060] FIG. 39 illustrates another example of a wireless device applicable to various embodiments of the present disclosure.

[0061] FIG. 40 illustrates a mobile device applicable to various embodiments of the present disclosure.

[0062] FIG. 41 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0063] FIG. 42 illustrates a vehicle applicable to various embodiments of the present disclosure.

[0064] FIG. 43 illustrates an XR device applicable to various embodiments of the present disclosure.

[0065] FIG. 44 illustrates a robot applicable to various embodiments of the present disclosure.

[0066] FIG. 45 illustrates an AI device applicable to various embodiments of the present disclosure.

[0067]

[0068] In various embodiments of the present disclosure, “A or B” may mean “only A,” “only B,” or “both A and B.” In other words, in various embodiments of the present disclosure, “A or B” may be interpreted as “A and / or B.” For example, in various embodiments of the present disclosure, “A, B or C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.”

[0069] In various embodiments of the present disclosure, a slash ( / ) or a comma may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B, or C."

[0070] In various embodiments of the present disclosure, “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” Furthermore, in various embodiments of the present disclosure, the expressions “at least one of A or B” or “at least one of A and / or B” may be interpreted as equivalent to “at least one of A and B.”

[0071] Additionally, in various embodiments of the present disclosure, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0072] Additionally, parentheses used in various embodiments of the present disclosure may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in various embodiments of the present disclosure is not limited to "PDCCH", and "PDDCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0073] Technical features individually described in a single drawing in various embodiments of the present disclosure may be implemented individually or simultaneously.

[0074]

[0075] The following technologies can be used in various wireless access systems, such as CDMA, FDMA, TDMA, OFDMA, and SC-FDMA. CDMA can be implemented using wireless technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (Global System for Mobile communications) / GPRS (General Packet Radio Service) / EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented using wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is a part of UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is a part of E-UMTS (Evolved UMTS) that uses E-UTRA, and LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A / LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

[0076]

[0077] For clarity, the description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical spirit of the present disclosure is not limited thereto. LTE refers to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR refers to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. “xxx” refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system. For background technology, terms, abbreviations, etc. used in the description of the present disclosure, reference may be made to matters described in standard documents published prior to the present disclosure. For example, reference may be made to the following documents.

[0078]

[0079] 3GPP LTE

[0080] - 36.211: Physical channels and modulation

[0081] - 36.212: Multiplexing and channel coding

[0082] - 36.213: Physical layer procedures

[0083] - 36.300: Overall description

[0084] - 36.331: Radio Resource Control (RRC)

[0085] 3GPP NR

[0086] - 38.211: Physical channels and modulation

[0087] - 38.212: Multiplexing and channel coding

[0088] - 38.213: Physical layer procedures for control

[0089] - 38.214: Physical layer procedures for data

[0090] - 38.300: NR and NG-RAN Overall Description

[0091] - 38.331: Radio Resource Control (RRC) protocol specification

[0092] 3GPP NR for system architecture

[0093] - 23.501: System architecture for the 5G System (5GS)

[0094] - 23.502: Procedures for the 5G System (5GS)

[0095] - 23.503: Policy and charging control framework for the 5G System (5GS)

[0096]

[0097] Physical Channel and Frame Structure

[0098] Physical channels and general signal transmission

[0099] Figure 1 is a diagram illustrating an example of physical channels and general signal transmission used in a 3GPP system.

[0100] In a wireless communication system, a terminal receives information from a base station via the downlink (DL) and transmits it to the base station via the uplink (UL). The information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0101]

[0102] 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.

[0103]

[0104] 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).

[0105]

[0106] Meanwhile, when accessing a base station for the first time or when there are no radio resources for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (S13 to S16). To this end, the terminal may transmit a specific sequence as a preamble via a physical random access channel (PRACH) (S13 and S15) and receive a response message (RAR (Random Access Response) message) to the preamble via a PDCCH and a corresponding PDSCH. In the case of a contention-based RACH, a contention resolution procedure may additionally be performed (S16).

[0107]

[0108] The terminal that has performed the procedure described above can then perform PDCCH / PDSCH reception (S17) and physical uplink shared channel (PUSCH) / physical uplink control channel (PUCCH) transmission (S18) as general uplink / downlink signal transmission procedures. In particular, the terminal can receive downlink control information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and different formats can be applied depending on the purpose of use.

[0109]

[0110] Meanwhile, the control information that the terminal transmits to the base station via the uplink or that the terminal receives from the base station may include downlink / uplink ACK / NACK signals, CQI (Channel Quality Indicator), PMI (Precoding Matrix Index), RI (Rank Indicator), etc. The terminal may transmit the above-described control information such as CQI / PMI / RI via PUSCH and / or PUCCH.

[0111]

[0112] Structure of uplink and downlink channels

[0113] Downlink channel structure

[0114] 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.

[0115]

[0116] (1) Physical Downlink Shared Channel (PDSCH)

[0117] PDSCH carries downlink data (e.g., DL-shared channel transport block, DL-SCH TB) and modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM are applied. The TB is encoded to generate a codeword. PDSCH can carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and the 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.

[0118]

[0119] (2) Physical downlink control channel (PDCCH)

[0120] The PDCCH carries downlink control information (DCI) and employs modulation methods such as QPSK. A PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs), depending on the Aggregation Level (AL). Each CCE is comprised of six Resource Element Groups (REGs). A REG is defined by one OFDM symbol and one (P)RB.

[0121] 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.

[0122]

[0123] Uplink channel structure

[0124] 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.

[0125] (1) Physical Uplink Shared Channel (PUSCH)

[0126] PUSCH carries uplink data (e.g., UL-shared channel transport block, UL-SCH TB) and / or uplink control information (UCI), and is transmitted based on a CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform, a DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform, etc. When the PUSCH is transmitted based on a DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. For example, when transform precoding is disabled (e.g., transform precoding is disabled), the UE transmits the PUSCH based on the CP-OFDM waveform, and when transform precoding is enabled (e.g., transform precoding is enabled), the UE can transmit the PUSCH based on the CP-OFDM waveform or the DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants in DCI, or semi-statically scheduled (configured grant) based on higher layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)). PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0127] (2) Physical Uplink Control Channel (PUCCH)

[0128] 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.

[0129]

[0130] Below, we describe new radio access technology (new RAT, NR).

[0131] 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.

[0132]

[0133] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0134] Referring to FIG. 2, the NG-RAN may include a gNB and / or an eNB that provides user plane and control plane protocol termination to the UE. FIG. 1 illustrates a case where only a gNB is included. The gNB and eNB are connected to each other via an Xn interface. The gNB and eNB are connected to the 5th generation core network (5G Core Network: 5GC) via the NG interface. More specifically, the gNB is connected to the access and mobility management function (AMF) via the NG-C interface, and the gNB is connected to the user plane function (UPF) via the NG-U interface.

[0135]

[0136] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0137] Referring to FIG. 3, the gNB can provide functions such as inter-cell radio resource management (Inter Cell RRM), radio bearer management (RB control), connection mobility control (Connection Mobility Control), radio admission control (Radio Admission Control), measurement configuration and provision, and dynamic resource allocation. The AMF can provide functions such as NAS security and idle state mobility processing. The UPF can provide functions such as mobility anchoring and PDU processing. The SMF (Session Management Function) can provide functions such as terminal IP address allocation and PDU session control.

[0138]

[0139] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0140] 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.

[0141] Referring to Figure 4, the three key requirements areas for 5G include (1) enhanced mobile broadband (eMBB), (2) massive machine type communication (mMTC), and (3) ultra-reliable and low latency communications (URLLC). Some use cases may require optimization across multiple areas, while others may focus on just one key performance indicator (KPI). 5G supports these diverse use cases in a flexible and reliable manner.

[0142] eMBB focuses on improving data speeds, latency, user density, and overall capacity and coverage of mobile broadband connections. It targets throughputs of around 10 Gbps. eMBB significantly exceeds basic mobile internet access, enabling rich interactive experiences, media and entertainment applications in the cloud, and augmented reality. Data is a key driver of 5G, and for the first time, dedicated voice services may not be available in the 5G era. In 5G, voice is expected to be handled as an application, simply using the data connection provided by the communication system. The increased traffic volume is primarily due to the increasing content size and the growing number of applications that require high data rates. Streaming services (audio and video), interactive video, and mobile internet connectivity will become more prevalent as more devices connect to the internet. Many of these applications require always-on connectivity to push real-time information and notifications to users. Cloud storage and applications are rapidly growing on mobile communication platforms, and this can be applied to both work and entertainment. Cloud storage is a particular use case driving the growth of uplink data rates. 5G is also used for remote work in the cloud, requiring significantly lower end-to-end latency to maintain a superior user experience when tactile interfaces are used. In entertainment, for example, cloud gaming and video streaming are other key factors driving the demand for mobile broadband. Entertainment is essential on smartphones and tablets, regardless of location, including in highly mobile environments like trains, cars, and airplanes. Another use case is augmented reality and information retrieval for entertainment, where augmented reality requires extremely low latency and instantaneous data volumes.

[0143] 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.

[0144] 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.

[0145] Next, we will look more specifically at a number of usage examples included within the triangle in Fig. 4.

[0146] 5G can complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) by delivering streams rated at hundreds of megabits per second to gigabits per second. These high speeds may be required to deliver TV at resolutions beyond 4K (6K, 8K, and beyond), as well as virtual reality (VR) and augmented reality (AR). VR and AR applications include near-immersive sports events. Certain applications may require specialized network configurations. For example, for VR gaming, a gaming company may need to integrate its core servers with a network operator's edge network servers to minimize latency.

[0147] Automotive is expected to be a significant new driver for 5G, with numerous use cases for in-vehicle mobile communications. For example, passenger entertainment demands both high capacity and high mobile broadband, as future users will consistently expect high-quality connectivity regardless of their location and speed. Another automotive application is augmented reality dashboards. An AR dashboard allows drivers to identify objects in the dark on top of what they see through the windshield. The AR dashboard overlays information to inform the driver about the distance and movement of objects. In the future, wireless modules will enable vehicle-to-vehicle communication, information exchange between vehicles and supporting infrastructure, and information exchange between vehicles and other connected devices (e.g., devices accompanying pedestrians). Safety systems can guide drivers to safer driving behaviors, reducing the risk of accidents. The next step will be remotely controlled or autonomous vehicles, which require highly reliable and fast communication between different autonomous vehicles and / or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving tasks, leaving drivers to focus solely on traffic anomalies that the vehicle itself cannot detect. The technological requirements for autonomous vehicles will require ultra-low latency and ultra-high-speed reliability, increasing traffic safety to levels unattainable by humans.

[0148] 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.

[0149] The consumption and distribution of energy, including heat and gas, are becoming increasingly decentralized, requiring automated control of distributed sensor networks. Smart grids interconnect these sensors using digital information and communication technologies to collect and act on information. This information can include the behavior of suppliers and consumers, enabling smart grids to improve efficiency, reliability, economic efficiency, sustainable production, and the automated distribution of fuels like electricity. Smart grids can also be viewed as another low-latency sensor network.

[0150] The health sector has numerous applications that can benefit from mobile communications. Telecommunications systems can support telemedicine, which provides clinical care in remote locations. This can help reduce distance barriers and improve access to health services that are otherwise unavailable in remote rural areas. It can also be used to save lives in critical care and emergency situations. Mobile-based wireless sensor networks can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

[0151] 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.

[0152] 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.

[0153] Hereinafter, examples of next-generation communications (e.g., 6G) that can be applied to various embodiments of the present disclosure will be described.

[0154]

[0155] 6G system in general

[0156] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements as shown in Table 1 below. In other words, Table 1 is a table showing an example of the requirements of a 6G system.

[0157]

[0158] Per device peak data rate1TbpsE2E latency1msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0159] 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.

[0160]

[0161] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0162] 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:

[0163] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system is crucial for 6G.

[0164] 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).

[0165] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0166] - Ubiquitous super 3D connectivity: Access to networks and core network functions of drones and very low Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

[0167] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0168] - 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.

[0169] Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another key feature of 6G communication systems. Multi-tier networks comprised of heterogeneous networks improve overall QoS and reduce costs.

[0170] 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.

[0171] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communications is a key feature of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0172] - Softwarization and virtualization: Softwarization and virtualization are two critical features that form the foundation of the design process for 5GB networks to ensure flexibility, reconfigurability, and programmability. Furthermore, billions of devices can be shared on a shared physical infrastructure.

[0173]

[0174] Core implementation technology of 6G systems

[0175]

[0176] Artificial Intelligence

[0177] 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.

[0178] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0179] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0180] 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.

[0181] However, the application of DNN for transmission at the physical layer may have the following problems.

[0182] Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in obtaining training data from specific channel environments, a large amount of training data is used offline. This means that static training on training data in specific channel environments can lead to conflicts with the dynamic characteristics and diversity of the wireless channel.

[0183] Furthermore, current deep learning primarily targets real-world signals. However, signals at the physical layer of wireless communications are complex signals. Further research is needed on neural networks that detect complex-domain signals to match the characteristics of wireless communication signals.

[0184] Below, we will look at machine learning in more detail.

[0185] 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.

[0186] 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.

[0187] Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. The labeled training data is input to a neural network, and the error is 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 reach a certain level of performance, thereby improving efficiency. In the later stages of training, a low learning rate can be used to improve accuracy.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] An artificial neural network is an example of a network of multiple perceptrons.

[0192]

[0193] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0194] Referring to Fig. 6, when an input vector x=(x1,x2,...,xd) is input, the entire process of multiplying each component by a weight (W1,W2,...,Wd), adding up all the results, and then applying the activation function σ(·) is called a perceptron. A large-scale artificial neural network structure can extend the simplified perceptron structure illustrated in Fig. 6 to apply the input vector to perceptrons of different dimensions. For convenience of explanation, input values ​​or output values ​​are called nodes.

[0195] Meanwhile, the perceptron structure illustrated in Fig. 6 can be explained as consisting of a total of three layers based on input and output values. An artificial neural network in which there are H perceptrons of (d+1) dimensions between the 1st layer and the 2nd layer, and K perceptrons of (H+1) dimensions between the 2nd layer and the 3rd layer can be expressed as in Fig. 7.

[0196]

[0197] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0198] The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. The example in Fig. 7 shows three layers, but when counting the number of layers in an actual artificial neural network, the input layer is excluded, so it can be viewed as a total of two layers. An artificial neural network is composed of perceptrons, which are basic blocks, connected in two dimensions.

[0199] The aforementioned input, hidden, and output layers can be applied jointly not only to multilayer perceptrons but also to various artificial neural network structures, such as CNNs and RNNs, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network. The machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning. Furthermore, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0200]

[0201] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0202] The deep neural network illustrated in Figure 8 is a multilayer perceptron consisting of eight hidden layers and eight output layers. The multilayer perceptron structure is referred to as a fully connected neural network. In a fully connected neural network, there is no connection between nodes located in the same layer, and there is a connection only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, and can be usefully applied to identify correlation characteristics between inputs and outputs. Here, the correlation characteristic can mean the joint probability of inputs and outputs.

[0203] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0204]

[0205] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0206] In DNN, nodes within a single layer are arranged vertically in a one-dimensional manner. However, Fig. 9 can assume a case where nodes are arranged two-dimensionally, with w nodes in width and h nodes in height (the convolutional neural network structure of Fig. 9). In this case, since a weight is added to each connection in the connection process from one input node to the hidden layer, a total of hΥw weights must be considered. Since there are hΥw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0207] 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.

[0208]

[0209] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0210] Each filter has a weight corresponding to the number of its size, and weight learning can be performed so that a specific feature on the image can be extracted as a factor and output. In Fig. 10, a filter of size 3Y3 is applied to the upper left 3Y3 region of the input layer, and the output value resulting from performing weighted sum and activation function operations on the corresponding node is stored in z22.

[0211] The above filter performs weighted sum and activation function operations while moving at a certain horizontal and vertical interval while scanning the input layer, and places the output value at the current filter position. This operation method is similar to the convolution operation for images in the field of computer vision, so a deep neural network with this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. In addition, a neural network with multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0212] In the convolutional layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located in the area covered by the filter, starting from the node where the current filter is located. This allows a single filter to focus on features within a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a two-dimensional area is an important criterion for judgment. Meanwhile, CNNs can apply multiple filters immediately before the convolutional layer, and can generate multiple output results through the convolution operation of each filter.

[0213] Meanwhile, depending on the data properties, there may be data for which sequence characteristics are important. Considering the length variability and chronological relationship of such sequence data, a structure that applies a method of inputting one element of the data sequence at each timestep and inputting the output vector (hidden vector) of the hidden layer output at a specific timestep together with the immediately following element in the sequence is called a recurrent neural network structure.

[0214]

[0215] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0216] Referring to Figure 11, a recurrent neural network (RNN) is a structure that inputs elements (x1(t), x2(t), ,..., xd(t)) of a data sequence at a time point t into a fully connected neural network, and then inputs the hidden vectors (z1(t-1), z2(t-1),..., zH(t-1)) of the immediately preceding time point t-1 together and applies a weighted sum and activation function. The reason for transmitting the hidden vector to the next time point in this way is because the information in the input vectors of the preceding time points is considered to be accumulated in the hidden vector of the current time point.

[0217]

[0218] Figure 12 is a diagram schematically illustrating an example of the operating structure of a recurrent neural network.

[0219] Referring to Figure 12, the recurrent neural network operates in a predetermined order of time for the input data sequence.

[0220] When the input vector (x1(t), x2(t), ,..., xd(t)) at time point 1 is input to the recurrent neural network, the hidden vector (z1(1), z2(1),..., zH(1)) is input together with the input vector (x1(2), x2(2),..., xd(2)) at time point 2, and the vector (z1(2), z2(2),..., zH(2)) of the hidden layer is determined through a weighted sum and an activation function. This process is repeatedly performed until time points 2, 3, ,,, T.

[0221] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).

[0222] It is a neural network core used in a learning manner, and includes various deep learning techniques such as DNN, CNN, RNN, Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0223] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0224] THz (Terahertz) communication

[0225] Data rates can be increased by increasing bandwidth. This can be achieved by utilizing sub-THz communications with wide bandwidths and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, typically refer to the frequency range between 0.1 THz and 10 THz, with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (sub-THz band) is considered a key part of the THz band for cellular communications. Adding the sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz lies in the far infrared (IR) frequency band. While part of the optical band, the 300 GHz to 3 THz band lies at the boundary of the optical band, immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF.

[0226]

[0227] Figure 13 is a diagram illustrating an example of the electromagnetic spectrum.

[0228] Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0229] Optical wireless technology

[0230] 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.

[0231] FSO backhaul network

[0232] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber-optic network. Therefore, data transmission in an FSO system is similar to that of a fiber-optic system. Therefore, FSO can be a promising technology for providing backhaul connectivity in 6G systems, in conjunction with fiber-optic networks. Using FSO, ultra-long-distance communications are possible, even over distances exceeding 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas, such as the ocean, space, underwater, and isolated islands. FSO also supports cellular base station (BS) connections.

[0233] Massive MIMO technology

[0234] 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.

[0235] Blockchain

[0236] 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.

[0237] 3D networking

[0238] 6G systems integrate terrestrial and airborne networks to support vertically expanded user communications. 3D BS will be provided via low-orbit satellites and UAVs. Adding a new dimension in altitude and associated degrees of freedom, 3D connections differ significantly from existing 2D networks.

[0239] Quantum communication

[0240] 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.

[0241] drone

[0242] Unmanned Aerial Vehicles (UAVs), or drones, will be a key element in 6G wireless communications. In most cases, high-speed wireless connections will be provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs offer specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled mobility. During emergencies such as natural disasters, deploying terrestrial communication infrastructure is not economically feasible, and sometimes, volatile environments make it impossible to provide services. UAVs can easily handle these situations. UAVs will become a new paradigm in wireless communications. This technology facilitates three fundamental requirements for wireless networks: enhanced mobile broadband (eMBB), URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most important technologies for 6G communications.

[0243] Cell-free Communication

[0244] 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.

[0245] Integration of wireless information and energy transmission

[0246] 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.

[0247] Integration of sensing and communication

[0248] Autonomous wireless networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

[0249] Integration of Access Backhaul Networks

[0250] In 6G, the density of access networks will be enormous. Each access network will be connected to backhaul connections, such as fiber optics and FSO networks. To cope with the enormous number of access networks, there will be tight integration between access and backhaul networks.

[0251] Holographic beam forming

[0252] 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.

[0253] Big data analysis

[0254] 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.

[0255] Large Intelligent Surface (LIS)

[0256] 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.

[0257]

[0258] Terahertz (THz) wireless communications in general

[0259] THz wireless communication uses THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and can refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared light, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, so they have high linearity and can focus beams. In addition, since the photon energy of THz waves is only a few meV, they have the characteristic of being harmless to the human body. The frequency bands expected to be used for THz wireless communication may be the D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz), which have low propagation loss due to molecular absorption in the air. Discussions on standardization of THz wireless communication are being centered around the IEEE 802.15 THz working group in addition to 3GPP, and standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) may specify or supplement the contents described in various embodiments of the present disclosure. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0260]

[0261] Figure 14 is a diagram illustrating an example of a THz communication application.

[0262] As illustrated in Figure 14, THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections, such as indoor small cells, wireless connections in data centers, and near-field communications, such as kiosk downloads.

[0263] Table 2 below shows examples of technologies that can be used in THz waves.

[0264] Transceivers DeviceAvailable immature: UTC-PD, RTD and SBDModulation and CodingLow order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, TurboAntennaOmni and Directional, phased array with low number of antenna elementsBandwidth69GHz (or 23 GHz) at 300GHzChannel modelsPartiallyData rate100GbpsOutdoor deploymentNoFree space lossHighCoverageLowRadio Measurements300GHz indoorDevice sizeFew micrometers

[0265]

[0266] 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.

[0267]

[0268] Fig. 15 is a diagram illustrating an example of an electronic component-based THz wireless communication transmitter and receiver.

[0269] Methods for generating THz using electronic components include a method using semiconductor components such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a MMIC (Monolithic Microwave Integrated Circuits) method using an integrated circuit based on a compound semiconductor HEMT (High Electron Mobility Transistor), and a method using a Si-CMOS-based integrated circuit. In the case of Fig. 15, a multiplier (doubler, tripler, multiplier) is applied to increase the frequency, and it passes through a subharmonic mixer and is radiated by an antenna. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, and matches it to the desired harmonic frequency and filters out all remaining frequencies. In addition, beamforming can be implemented by applying an array antenna or the like to the antenna of Fig. 15. In Fig. 15, IF represents intermediate frequency, tripler and multiplexer represent multipliers, PA represents power amplifier, LNA represents low noise amplifier, and PLL represents phase-locked loop.

[0270]

[0271] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0272] Fig. 17 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

[0273] Optical component-based THz wireless communication technology refers to a method of generating and modulating THz signals using optical components. Optical component-based THz signal generation technology generates an ultra-high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. Compared to technologies that use only electronic components, this technology can easily increase the frequency, generate high-power signals, and obtain flat response characteristics over a wide frequency band. As illustrated in Figure 16, optical component-based THz signal generation requires a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector. In the case of Figure 16, the light signals of two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In Fig. 16, an optical coupler refers to a semiconductor device that transmits an electrical signal using optical waves to provide electrical isolation and coupling between circuits or systems, and a UTC-PD (Uni-Travelling Carrier Photo-Detector) is a type of photodetector that uses electrons as active carriers and reduces the travel time of electrons with bandgap grading. The UTC-PD is capable of detecting light at 150 GHz or higher. In Fig. 17, an EDFA (Erbium-Doped Fiber Amplifier) ​​represents an erbium-doped fiber amplifier, a PD (Photo Detector) represents a semiconductor device that can convert an optical signal into an electrical signal, an OSA represents an optical module (Optical Sub Assembly) that modularizes various optical communication functions (photoelectric conversion, electro-optical conversion, etc.) into a single component, and a DSO represents a digital storage oscilloscope.

[0274]

[0275] The structure of a photoelectric converter (or photoelectric converter) is described with reference to FIGS. 18 and 19.

[0276] Fig. 18 is a diagram illustrating the structure of a photon source-based transmitter.

[0277] Figure 19 is a drawing showing the structure of an optical modulator.

[0278] In general, the phase of a signal can be changed by passing the optical source of a laser through an optical wave guide. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An opto-electrical modulator (O / E converter) can generate THz pulses by optical rectification operation by a nonlinear crystal, photoelectric conversion by a photoconductive antenna, emission from a bunch of relativistic electrons, etc. Terahertz pulses generated in the above manner can have a length in units of femtoseconds to picoseconds. An optical / electronic converter (O / E converter) performs down conversion by utilizing the non-linearity of the device.

[0279] Considering the THz spectrum usage, it is likely that THz systems will use multiple contiguous gigahertz bands for fixed or mobile service purposes. Based on the outdoor scenario criteria, the available bandwidth can be classified based on the oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the THz pulse length for one carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0280] Effective down-conversion from the infrared band (IR band) to the terahertz band (THz band) depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired terahertz band (THz band), it is necessary to design an optical / electrical converter (O / E converter) with the most ideal non-linearity for transferring to the corresponding terahertz band (THz band). If an optical / electrical converter (O / E converter) that is not suitable for the target frequency band is used, errors are likely to occur in the amplitude and phase of the corresponding pulse.

[0281] In a single-carrier system, a terahertz transmission and reception system can be implemented using a single optical-to-electrical converter. Depending on the channel environment, in a multi-carrier system, the number of optical-to-electrical converters may be equal to the number of carriers. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-converted using an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency region of the specific resource region may include multiple chunks. Each chunk may be composed of at least one component carrier (CC).

[0282]

[0283] Specific descriptions of various embodiments of the present disclosure

[0284] Hereinafter, various embodiments of the present disclosure will be described in more detail.

[0285]

[0286] 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.

[0287]

[0288] The symbols / abbreviations / terms used in this disclosure are as follows.

[0289] - AI: Artificial Intelligence

[0290] - ML: Machine Learning

[0291] - NN: Neural Network

[0292] - DNN: Deep Neural Network

[0293] - GNN: Graph Neural Network

[0294] -MLP: Multi-Layer Perceptron

[0295] - NCE: Noise Contrastive Estimation

[0296]

[0297] Technical problems to be solved by various embodiments of the present disclosure

[0298] Digital signature technology provides a solution that guarantees message integrity, message authentication, and non-repudiation, excluding confidentiality, among the four goals of information security. 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.

[0299]

[0300] Figure 20 is a diagram illustrating an example of a three-level communication model in the present disclosure.

[0301] Shannon and Weaver proposed that there are three levels of communication problems.

[0302] ① Level A: How accurately can symbols be transmitted in communication? (Technical issue)

[0303] ② Level B: How accurately do the transmitted symbols convey the desired meaning? (Semantic problem)

[0304] ③ Level C: How effectively does the received meaning influence behavior in the desired way? (Effectiveness issue)

[0305] Shannon's information theory focuses only on level A, and thus does not consider communication from a semantic perspective. However, Weaver explained that Shannon's information theory is general enough to be extended to consider levels B and C, by adding a "semantic transmitter," a "semantic receiver," and "semantic noise" to Shannon's communication model. Figure 20 is a full diagram illustrating this.

[0306] One of the many goals of 6G communications is to enable a variety of new services that interconnect people and machines with varying levels of intelligence. Therefore, it is necessary to move beyond the traditional technical issues and consider semantic issues.

[0307] When looking at human communication, when exchanging information, word information is related to the corresponding “meaning.” If we relate this to the diagram in Figure 1, we can see that correct semantic communication occurs when the concept related to the message sent by the source is correctly interpreted by the destination.

[0308] This is not the original purpose of reducing the reconstruction error that occurs in the process of restoring the semantic features received by the destination back to the original raw data when the source generates and transmits the semantic features given to the source or using the collected raw data, but rather, it requires an approach to whether the downstream task, which is the task performed by the destination, operates properly (i.e., whether the interpretation / reasoning is good) according to the intention transmitted by the source using the transmitted semantic features, and when the destination performs the inference operation, the background knowledge contained in the data transmitted from the source must be able to be reflected in the background knowledge of the destination so that the destination can operate using the background knowledge it possesses and obtain the interpretation result for it.

[0309] In this way, 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.

[0310]

[0311] FIG. 21 is a diagram illustrating an example of a semantic information source and destination in a system applicable to the present disclosure.

[0312] 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.

[0313] 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.

[0314]

[0315] World model W s Let be the set of interpretations with probability distribution μ, and μ(w) be the model distribution, W x The corresponding model W for which x is “true” s When a set of its models is called, the logical probability m(x) of message x is as shown in mathematical expression 2. ( is the usual propositional satisfaction relationship / The symbol is also called "entails" or "is a model," which semantically means "entails the following result" or "is a stronger condition." This symbol reveals a connection from a semantic point of view.

[0316]

[0317] Semantic entropy H of x s (x) is as shown in mathematical formula 3.

[0318]

[0319] At this time, 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.

[0320]

[0321]

[0322] For example, let p be a statistical probability and let the truth table with background knowledge K be given as in Table 3. Table 3 shows the truth table with p(A)=p(B)=0.5 and K={A→B}.

[0323] #ABA→Bprobability10010.2520110.2531000.2541110.25

[0324]

[0325] Then, the possible worlds are “reduced” to a series of truth assignments (i.e., Cases 1, 2, and 4) where A→B is true. Therefore, conditional logical probabilities can be obtained as in Equations 6, 7, and 8.

[0326]

[0327]

[0328]

[0329] 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 ).

[0330] When background knowledge K exists, if μ' is a new distribution of the set of models, it is expressed as in Equations 9 and 10.

[0331]

[0332]

[0333]

[0334] In this example, the model entropies of the source that does not consider background knowledge or that does consider background knowledge are as in Equations 11 and 12.

[0335]

[0336]

[0337] As shown in Equations 11 and 12, the presence of shared background knowledge allows for the compression of the message intended to be conveyed from the source without loss of information, and with the help of shared background knowledge, communication can be performed with shorter messages to maximize the source's information. Thus, one of the key reasons why semantic-level communication can offer performance improvements compared to existing technical-level communication is the consideration of background knowledge. Thus, utilizing background knowledge when generating and conveying semantic features considering the downstream task at the destination, as mentioned above, is consistent with the purpose of performing semantic communication.

[0338] To implement semantic communication that encompasses all of the components described above, a new layer called the semantic layer can be added, overseeing the overall operation of semantic data and messages. Reflecting a task-oriented semantic communication system, these semantic layers can be located at the source and destination. To facilitate communication between these source and destination semantic layers, a protocol, which is a set of rules between layers, and a definition of a series of operational processes are required.

[0339]

[0340] 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.

[0341] In order to perform accurate representation and reasoning in semantic communication constructed on a newly definable semantic layer, a process of agreement on background knowledge information between the source and destination is required. In particular, in a semantic communication system based on a multiple representation transmission scheme, which generates and transmits multiple representations by utilizing multiple knowledge for a single source data, as shown in Figure 22, the destination's background knowledge partition is utilized as the attention of each representation and transmitted through a feedback injection-based encoding process. Through this 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.

[0342] However, in a task-oriented communication system, in order to improve the performance of the target task, the system must operate based on the entropy of the query for the task's operation. Knowledge partition( ) query of target task ) for entropy( ) can be expressed by mathematical expressions 13 and 14.

[0343]

[0344]

[0345] Semantic communication systems can perform tasks with higher performance when the entropy value of the target task query is lower. From this perspective, to minimize the entropy of the target query required for task operation, the background knowledge of the source and destination must be identical. Since the multiple representations received by the destination through multiple representation transmission technology are generated by utilizing the destination's knowledge partition as attention, to minimize the entropy value, the knowledge partitions used to generate each representation must be combined to create and transmit a single representation with the same background knowledge as the destination.

[0346]

[0347] Figure 23 shows the attention coefficient of the destination according to the background knowledge of the transmission representation in a system applicable to the present disclosure. ) is a diagram showing an example of distribution.

[0348] In order to perform the merging process for the destination's knowledge partition, a process is required to identify the components (nodes and edges) of the knowledge partition used in each representation. While the existing feedback injection process could utilize the knowledge partition as attention to generate a representation, the destination must transmit index information for each component to obtain information about which components the knowledge partition used to generate the representation contains. The destination can obtain the component index through each attention coefficient. Attention coefficients ( ) is a parameter that can be used for classification based on the destination's knowledge for the i-th component. Figure 23 shows the distribution of the destination's attention coefficient according to the knowledge partition of the representation transmitted by the source. The attention coefficient value calculated by the destination has a higher value as the component is highly related to the received i-th component, and the components that are not used for representation generation among the destination's knowledge form a uniform distribution. The destination sets the cut-off parameter ( ) can be set to set the scope of the knowledge component for the received representation. Through a process such as Fig. 23, the destination can identify the components of the knowledge used in the received representation, but in order to transmit the result to the source, information about all corresponding indices must be fed back to the source. Since the number of attention coefficients to be transmitted to the source is ultimately equal to the number of components, the size of the coefficients to be transmitted also increases as the knowledge base size increases. Therefore, this patent proposes a method for more efficiently performing the knowledge merging technology that combines the knowledge partitions used in each representation into one in the multiple representation transmission technology so that the representation transmitted from the source matches the destination.

[0349]

[0350] Composition of various embodiments of the present disclosure

[0351] In the present disclosure, in a system based on multiple representation transmission technology for performing semantic communication, the source generates a representation based on knowledge information identical to the background knowledge of the destination, the process of finding a knowledge component through the difference between the attention value used by the source in the contextualizing encoding process and the attention value fed back to the destination, the process of transmitting a scaling parameter from the destination to the source to calculate the difference between the attention values, the process of additionally transmitting a scaling parameter when there are multiple cases for the knowledge component, the process of transmitting a single representation by performing knowledge merging based on the calculated component index, and the semantic layer protocol and procedure according to the related procedures for performing the same are proposed.

[0352]

[0353] Multiple representation transmission scheme with feedback injection procedure

[0354] 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.

[0355] Below, we discuss the process by which each representation is generated by utilizing the destination's knowledge partition as attention through multiple representation transmission technology and feedback injection process.

[0356] The background knowledge possessed by the source and destination has a knowledge graph structure, 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 single source data and transmit 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 generates an embedding (x) corresponding to the i-th graph component for the graph representation of the source data. i ) contextualized representation using the n-th knowledge partition Encode it as .

[0357]

[0358] FIG. 25 is a diagram illustrating an example of a contextualizing encoder structure of a Source in a system applicable to the present disclosure.

[0359] 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 example 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 utilizes a graph transformer structure that uses L multi-head self-attention encoders with K heads to generate a self-attention-based representation. It is generated as in mathematical expressions 15 and 16.

[0360]

[0361]

[0362]

[0363] Contextualizing encoder is the self-attention based representation Attention value between the embedding vector corresponding to the knowledge partition is calculated as in mathematical expression 17, and the graph representation of the source data ( ) performs an assimilation process including the attention value and uses the result as input to the self-attention based encoder. Finally, the contextualizing encoder performs assimilation T times to create a contextualized representation. It is generated as in mathematical expressions 18 and 19.

[0364]

[0365]

[0366]

[0367] Through the above process, 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 based on its background knowledge for the received multiple representation. At this time, the destination uses the embedding vector (D) of the background knowledge it possesses. c ) and the attention value between the received representation ( ) is calculated as in mathematical formula 20.

[0368]

[0369]

[0370] 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.

[0371] The attention coefficient () produced in the process of calculating the above attention value ) is a value used for classification of the i-th index. When the destination has a background knowledge c-th component index that overlaps with the knowledge partition, it has a large value, but when there is no overlap, it has a small value. Therefore, 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. On the other hand, when there is an intersection between the knowledge partition and the destination's background knowledge, the attention value does not have a meaning for the intersection. Therefore, 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.

[0372]

[0373] 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.

[0374] 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.

[0375] Through the above process, all knowledge partitions used in the contextualizing encoder of the source 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. In addition, 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.

[0376] Through the transmission structure including the above feedback injection encoder, 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. At each attention value feedback cycle 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 21.

[0377]

[0378] 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.

[0379] Meanwhile, when performing combining ratio control at the destination, the destination utilizes the background knowledge it has in the received multiple representations to generate embeddings for performing downstream tasks. The destination measures the importance of each representation by calculating the similarity between the task-specific embedding and the weight sum of the multiple representations, and normalizes it to generate the combining weight (w) for the multiple representations. n ) is calculated as in mathematical expression 22, and the weight sum for multiple representations is finally utilized in the downstream task through the combining weight as in Fig. 29. In the process of calculating the combining ratio of the destination, the initial value of the weight can be set by receiving the weight calculated from the source as feedforward, and the embedding generation MLP and the combining ratio are updated through learning according to the performance of the task.

[0380]

[0381]

[0382] Knowledge merging procedure

[0383] Below, we explain how to perform the knowledge merging process in a situation where each representation is transmitted and received using the destination's knowledge partition as attention through the feedback injection process.

[0384] In the above feedback injection-based multiple representation transmission technology, if sufficient feedback is provided, each representation is generated by utilizing the destination's knowledge partition as attention. In a situation where the representation generated through the feedback injection process has converged, the source is the contextualizing encoder's attention value ( ) and feedback attention value( ) are compared. At this time, the attention value of the contextualizing encoder ( ) is the embedding vector (S) corresponding to the knowledge partition of the source c ) and representation using it as attention( ) is calculated as in mathematical formula 23.

[0385]

[0386] Attention value (feedback received from Destination) ) is the embedding vector (D) corresponding to the knowledge partition of the destination c ) and the final generated representation( ) is calculated as in mathematical formula 24.

[0387]

[0388]

[0389] The representation finally generated through the above feedback injection is the knowledge partition of the source ( ) and the destination's knowledge partition formed by the intersection between the destination's knowledge ) is used as attention, and it is assumed that the knowledge embedding vectors of the source and destination are formed in the same dimension (S c =D c ).

[0390] Source is two attention values and In order to calculate the difference between the two attention values, the scaling between the two attention values ​​must be adjusted. To this end, the destination calculates a scaling value ( ) is additionally fed back as a source as in mathematical expression 25.

[0391]

[0392]

[0393] FIG. 30 is a diagram illustrating an example of a scaling value feedback process of a destination and an attention value difference calculation process of a source in a system applicable to the present disclosure.

[0394] Scaling value( ) to find the area of ​​knowledge partition that has been reduced in the feedback process. and is a scaling adjustment parameter for calculating the difference. As shown in Fig. 30, the source calculates the difference between the two attention values ​​through the received scaling value and based on this, it can search for the component reduced in the feedback process through the embedding vector and attention coefficient it has. In addition, if there are multiple sets of components obtained in the search process, the source sends the scaling value ( corresponding to a different index to the destination. ) to narrow down the candidate range of the component set.

[0395]

[0396] FIG. 31 is a diagram illustrating an example of a semantic communication transmission / reception system structure based on multiple representation transmission including knowledge merging in a system applicable to the present disclosure.

[0397] Through the above process, the source is partitioned into the source's knowledge ( ) is formed by the intersection between the destination's knowledge partitions. ) is the index of the component corresponding to the remaining knowledge. ) can be produced. When the above process is applied to each representation in the multiple representation transmission technology, the source can identify components among the source's background knowledge that are not included in the destination's background knowledge, and through this, the source can obtain component level information about the destination's background knowledge. Figure 31 illustrates a process of transmitting a single representation to the destination by performing knowledge merging using the knowledge component information obtained through the attention value difference.

[0398]

[0399] FIG. 32 is a diagram illustrating an example of a multiple representation transmission-based semantic communication procedure including a knowledge merging process in a system applicable to the present disclosure.

[0400] In the multiple representation transmission technology described above, the knowledge merging process can be summarized as shown in Fig. 32. The area indicated by the dotted line at the bottom in Fig. 32 represents the knowledge merging process proposed in this patent, and the previous step represents the process for the multiple representation transmission technology. The source performs a search for a knowledge component through the difference between the attention value used in the contextualizing encoder and the attention value fed back to the destination to perform the knowledge merging process. At this time, the source provides a scaling value ( ) requests, and the destination feeds back the scaling value. The source uses the scaling value to calculate the difference in attention value and based on this, the source's knowledge partition ( ) is formed by the intersection between the destination's knowledge partitions. ) is the index of the component corresponding to the remaining knowledge. ) is produced. In the above production process, If there are multiple solutions, the source provides a scaling value ( ) to perform component search. Through the above process, the source performs knowledge merging based on the knowledge component produced for each representation, and transmits a single representation to the destination using a contextualizing encoder that utilizes the same knowledge as the destination's background knowledge as attention.

[0401]

[0402] Effects of various embodiments of the present disclosure

[0403] 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.

[0404] Characteristic configurations of various embodiments of the present disclosure are as follows.

[0405] (1) As an operation to perform knowledge merging in a multiple representation transmission technique that is effective for semantic communication operations.

[0406] The process in which the destination transmits a scaling value for the attention value to compare the attention value corresponding to the knowledge partition held by the source with the attention value corresponding to the knowledge partition of the destination received as feedback.

[0407] The process of performing a search for the knowledge component reduced in the feedback injection process through the difference between the two attention values ​​in the source and requesting an additional scaling value from the destination in the process.

[0408] Through the above process, we propose a process of identifying the knowledge information of the destination by component unit, combining the information obtained from each precoding block, and forming the same knowledge between the source and destination to transmit and receive a single representation.

[0409]

[0410] [Description of the first node (source) claim]

[0411] The embodiments described below are specifically described with reference to FIG. 33 in terms of the operation of the first node. The methods described below are distinguished for convenience of explanation, and it is understood that, unless mutually exclusive, some components of one method may be substituted for or combined with some components of another method.

[0412] FIG. 33 is a diagram illustrating an example of the operation process of the first node in a system applicable to the present disclosure.

[0413] According to various embodiments of the present disclosure, a method performed by a first node in a communication system is provided.

[0414] According to various embodiments of the present disclosure, 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.

[0415] The embodiment of FIG. 33 may further include, before step S3301, 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.

[0416] The embodiment of FIG. 33 may further include, before step S3301, 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.

[0417] In step S3301, the first node transmits information of multiple representations based on a first knowledge partition of single source data to the second node.

[0418] In step S3302, the first node receives feedback from the second node including the plurality of expressions and a second similarity value based on background knowledge of the second node.

[0419] In step S3303, the first node receives a scaling value for the second similarity value from the second node.

[0420] In step S3304, the first node determines a difference value between the first similarity value and the second similarity value of the contextualizing encoder for the first knowledge segmentation using the scaling value.

[0421] In step S3305, the first node performs knowledge merging based on the knowledge component produced for each of the plurality of expressions based on the difference value, thereby generating a single representation.

[0422] At step S3306, the first node transmits the single expression to the second node.

[0423]

[0424] According to various embodiments of the present disclosure, the embodiment of FIG. 33 may further include the step of calculating an index of the remaining knowledge components excluding the second knowledge partition of the second node in the first knowledge partition; and, if there are multiple indices, the step of requesting the second node for the scaling value of an index different from the index.

[0425] According to various embodiments of the present disclosure, the second knowledge segmentation may be composed of an intersection of the background knowledge of the second node.

[0426] According to various embodiments of the present disclosure, the single representation can be generated using the contextualization encoder based on the result of the knowledge merging for the knowledge component.

[0427] According to various embodiments of the present disclosure, the contextualizing encoder may be configured to generate a representation related to the same knowledge as the background knowledge of the second node.

[0428] According to various embodiments of the present disclosure, the first similarity value may be based on a similarity between a representation associated with a first embedding vector corresponding to the first knowledge segmentation and the first embedding vector.

[0429] According to various embodiments of the present disclosure, the second similarity value may be based on a similarity between a representation associated with a first embedding vector corresponding to the first knowledge division and a second embedding vector corresponding to the second knowledge division of the second node.

[0430]

[0431] According to various embodiments of the present disclosure, a first node is provided in a communication system. The first node includes a transceiver and at least one processor, wherein the at least one processor may be configured to perform the operating method of the first node according to FIG. 33.

[0432]

[0433] According to various embodiments of the present disclosure, a device for controlling a first node in a communication system is provided. The device includes at least one processor and at least one memory operably connected to the at least one processor. The at least one memory may be configured to store instructions for performing an operating method of the first node according to FIG. 33 based on instructions executed by the at least one processor.

[0434]

[0435] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRM) storing one or more instructions are provided. The one or more instructions, when executed by one or more processors, perform operations, and the operations may include the operating method of the first node according to FIG. 33.

[0436]

[0437] [Description of the second node (destination) claim]

[0438] The embodiments described below are specifically described with reference to FIG. 34 in terms of the operation of the second node. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for some components of another method, or may be applied in combination with each other, as long as they are not mutually exclusive.

[0439] FIG. 34 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0440] According to various embodiments of the present disclosure, a method performed by a second node in a communication system is provided.

[0441] According to various embodiments of the present disclosure, 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.

[0442] The embodiment of FIG. 34 may further include, before step S3401, 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.

[0443] The embodiment of FIG. 34 may further include, before step S3401, 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.

[0444] At step S3401, the second node receives information of multiple representations based on a first knowledge partition of single source data from the first node.

[0445] In step S3402, the second node transmits feedback to the first node including the plurality of expressions and a second similarity value based on background knowledge of the second node.

[0446] In step S3403, the second node transmits a scaling value for the second similarity value to the first node.

[0447] At step S3404, the second node receives a single representation from the first node.

[0448] The single representation is based on knowledge merging of the knowledge components produced for each of the multiple representations. The knowledge merging is based on the difference value between the first similarity value of the contextualizing encoder for the first knowledge segment and the second similarity value. The difference value is based on the scaling value.

[0449]

[0450] According to various embodiments of the present disclosure, the embodiment of FIG. 34 may further include, when there are multiple indices of knowledge components other than the second knowledge partition of the second node in the first knowledge partition, a step of receiving a request for the scaling value of an index different from the index from the first node.

[0451] According to various embodiments of the present disclosure, the second knowledge segmentation may be composed of an intersection of the background knowledge of the second node.

[0452] According to various embodiments of the present disclosure, the single representation can be generated using the contextualization encoder based on the result of the knowledge merging for the knowledge component.

[0453] According to various embodiments of the present disclosure, the contextualizing encoder may be configured to generate a representation related to the same knowledge as the background knowledge of the second node.

[0454] According to various embodiments of the present disclosure, the first similarity value may be based on a similarity between a representation associated with a first embedding vector corresponding to the first knowledge segmentation and the first embedding vector.

[0455] According to various embodiments of the present disclosure, the second similarity value may be based on a similarity between a representation associated with a first embedding vector corresponding to the first knowledge division and a second embedding vector corresponding to the second knowledge division of the second node.

[0456]

[0457] According to various embodiments of the present disclosure, a second node is provided in a communication system. The second node includes a transceiver and at least one processor, wherein the at least one processor may be configured to perform the operating method of the second node according to FIG. 34.

[0458]

[0459] According to various embodiments of the present disclosure, a device for controlling a second node in a communication system is provided. The device includes at least one processor and at least one memory operably connected to the at least one processor. The at least one memory may be configured to store instructions for performing an operating method of the second node according to FIG. 34 based on instructions executed by the at least one processor.

[0460]

[0461] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRM) storing one or more instructions are provided. The one or more instructions, when executed by one or more processors, perform operations, and the operations may include the operating method of a second node according to FIG. 34.

[0462]

[0463] Communication system applicable to the present disclosure

[0464] FIG. 35 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0465] Referring to FIG. 35, a communication system (1) applicable to various embodiments of the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution), 6G wireless communication), and may be referred to as a communication / wireless / 5G device / 6G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices, and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. Mobile devices may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.), etc. Home appliances may include a TV, a refrigerator, a washing machine, etc. IoT devices may include a sensor, a smart meter, etc. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device (200a) may act as a base station / network node to other wireless devices.

[0466] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (AI) technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, or a 6G network. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to Everything) communication). In addition, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0467] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (Integrated Access Backhaul). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of various embodiments of the present disclosure.

[0468] Meanwhile, NR supports multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, an SCS of 15 kHz supports a wide area in traditional cellular bands; an SCS of 30 kHz / 60 kHz supports dense urban areas, lower latency, and wider carrier bandwidth; and an SCS of 60 kHz or higher supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0469] 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. For convenience of explanation, among the frequency ranges used in the NR system, FR1 can mean the "sub 6 GHz range", and FR2 can mean the "above 6 GHz range" and can be called millimeter wave (mmW).

[0470]

[0471] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz-6000MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0472]

[0473] As described above, the numerical value of the frequency range of the NR system can be changed. For example, FR1 may include a band from 410 MHz to 7125 MHz, as shown in Table 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).

[0474] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR141MHz-7125MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0475] According to various embodiments of the present disclosure, the communication system (1) can support terahertz (THz) wireless communication. THz wireless communication is a wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. The frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band where propagation loss due to absorption of molecules in the air is small.

[0476]

[0477] Wireless devices applicable to the present disclosure

[0478] Below, examples of wireless devices to which various embodiments of the present disclosure are applied are described.

[0479] FIG. 36 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0480] Referring to FIG. 36, 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). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 35.

[0481] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). In addition, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In various embodiments of the present disclosure, a wireless device may mean a communication modem / circuit / chip.

[0482] The second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). Furthermore, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In various embodiments of the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0483] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0484] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0485] 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.

[0486] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0487] FIG. 37 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0488] According to FIG. 37, 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).

[0489] The difference between the example of the wireless device described in FIG. 36 and the example of the wireless device in FIG. 37 is that in FIG. 36, the processor (102, 202) and the memory (104, 204) are separated, but in the example of FIG. 37, the memory (104, 204) is included in the processor (102, 202).

[0490] Here, the specific description of the processor (102, 202), memory (104, 204), transceiver (106, 206), and one or more antennas (108, 208) is as described above, so in order to avoid unnecessary repetition of description, the description of the repeated description is omitted.

[0491] Below, examples of signal processing circuits to which various embodiments of the present disclosure are applied are described.

[0492] Figure 38 illustrates a signal processing circuit for a transmission signal.

[0493] Referring to FIG. 38, the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060). Although not limited thereto, the operations / functions of FIG. 38 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 36. The hardware elements of FIG. 38 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 36. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 36. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 36, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 36.

[0494] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 38. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0495] Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (1010). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by a precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols. In addition, the precoder (1040) can perform precoding without performing transform precoding.

[0496] The resource mapper (1050) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (1060) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) can include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0497] 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. 38. For example, a wireless device (e.g., 100, 200 of FIG. 36) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0498] Below, examples of wireless device utilization to which various embodiments of the present disclosure are applied are described.

[0499] Figure 39 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 35).

[0500] Referring to FIG. 39, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 36 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 36. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 36. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0501] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 35, 100a), a vehicle (Fig. 35, 100b-1, 100b-2), an XR device (Fig. 35, 100c), a portable device (Fig. 35, 100d), a home appliance (Fig. 35, 100e), an IoT device (Fig. 35, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 35, 400), a base station (Fig. 35, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0502] In FIG. 39, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and a first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of a set of one or more processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0503] Below, the implementation example of Fig. 39 is described in more detail with reference to the drawings.

[0504] Figure 40 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).

[0505] Referring to FIG. 40, 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. 39, respectively.

[0506] 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.

[0507] For example, in the case of data communication, the input / output unit (140c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (110) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0508] FIG. 41 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0509] Vehicles or autonomous vehicles can be implemented as mobile robots, cars, trains, manned or unmanned aerial vehicles (AVs), ships, etc.

[0510] Referring to FIG. 41, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 39, respectively.

[0511] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0512] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0513] Figure 42 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.

[0514] Referring to FIG. 42, the vehicle (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), and a position measurement unit (140b). Here, blocks 110 to 130 / 140a to 140b correspond to blocks 110 to 130 / 140 of FIG. 39, respectively.

[0515] 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.

[0516] For example, the communication unit (110) of the vehicle (100) can receive map information, traffic information, etc. from an external server and store them in the memory unit (130). The location measurement unit (140b) can obtain vehicle location information through GPS and various sensors and store the information in the memory unit (130). The control unit (120) can create a virtual object based on the map information, traffic information, and vehicle location information, and the input / output unit (140a) can display the created virtual object on the vehicle window (1410, 1420). In addition, the control unit (120) can determine whether the vehicle (100) is being driven normally within the driving line based on the vehicle location information. If the vehicle (100) abnormally deviates from the driving line, the control unit (120) can display a warning on the vehicle window through the input / output unit (140a). Additionally, the control unit (120) can broadcast a warning message regarding driving abnormalities to surrounding vehicles via 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 via the communication unit (110).

[0517] Figure 43 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.

[0518] Referring to FIG. 43, the XR device (100a) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), a sensor unit (140b), and a power supply unit (140c). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 39, respectively.

[0519] The communication unit (110) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. The media data can include videos, images, sounds, etc. The control unit (120) can control components of the XR device (100a) to perform various operations. For example, the control unit (120) can be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation and processing, etc. The memory unit (130) can store data / parameters / programs / codes / commands required for driving the XR device (100a) / generating XR objects. The input / output unit (140a) can obtain control information, data, etc. from the outside, and output the generated XR objects. The input / output unit (140a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module, etc. The sensor unit (140b) can obtain the XR device status, surrounding environment information, user information, etc. The sensor unit (140b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar. The power supply unit (140c) supplies power to the XR device (100a) and may include a wired / wireless charging circuit, a battery, etc.

[0520] For example, the memory unit (130) of the XR device (100a) may include information (e.g., data, etc.) required for creating an XR object (e.g., AR / VR / MR object). The input / output unit (140a) may obtain a command to operate the XR device (100a) from the user, and the control unit (120) may operate the XR device (100a) according to the user's operating command. For example, when a user attempts to watch a movie, news, etc. through the XR device (100a), the control unit (120) may transmit content request information to another device (e.g., a mobile device (100b)) or a media server through the communication unit (130). The communication unit (130) may download / stream content such as movies and news from another device (e.g., a mobile device (100b)) or a media server to the memory unit (130). The control unit (120) controls and / or performs procedures such as video / image acquisition, (video / image) encoding, and metadata generation / processing for content, and can generate / output an XR object based on information about surrounding space or real objects acquired through the input / output unit (140a) / sensor unit (140b).

[0521] In addition, the XR device (100a) is wirelessly connected to the mobile device (100b) through the communication unit (110), and the operation of the XR device (100a) can be controlled by the mobile device (100b). For example, the mobile device (100b) can act as a controller for the XR device (100a). To this end, the XR device (100a) can obtain three-dimensional position information of the mobile device (100b), and then generate and output an XR object corresponding to the mobile device (100b).

[0522] Figure 44 illustrates robots applicable to various embodiments of the present disclosure. Robots may be classified into industrial, medical, household, and military applications, depending on their intended use or field.

[0523] Referring to FIG. 44, the robot (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), a sensor unit (140b), and a driving unit (140c). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 39, respectively.

[0524] 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.

[0525] FIG. 45 illustrates an AI device applicable to various embodiments of the present disclosure.

[0526] 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.

[0527] Referring to FIG. 45, 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. 39, respectively.

[0528] The communication unit (110) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices (e.g., FIG. W1, 100x, 200, 400) or AI servers (200) using wired and wireless communication technology. To this end, the communication unit (110) can transmit information within the memory unit (130) to the external device or transfer a signal received from the external device to the memory unit (130).

[0529] The control unit (120) may determine at least one executable operation of the AI ​​device (100) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the control unit (120) may control components of the AI ​​device (100) to perform the determined operation. For example, the control unit (120) may request, search, receive, or utilize data from the learning processor unit (140c) or the memory unit (130), and may control components of the AI ​​device (100) to perform at least one executable operation, a predicted operation, or an operation determined to be desirable. In addition, the control unit (120) may collect history information including the operation contents of the AI ​​device (100) or user feedback on the operation, and store the collected history information in the memory unit (130) or the learning processor unit (140c), or transmit the collected history information to an external device such as an AI server (FIG. W1, 400). The collected history information may be used to update a learning model.

[0530] The memory unit (130) can store data that supports various functions of the AI ​​device (100). For example, the memory unit (130) can store data obtained from the input unit (140a), data obtained from the communication unit (110), output data of the learning processor unit (140c), and data obtained from the sensing unit (140). In addition, the memory unit (130) can store control information and / or software codes necessary for the operation / execution of the control unit (120).

[0531] The input unit (140a) can obtain various types of data from the outside of the AI ​​device (100). For example, the input unit (120) can obtain learning data for model learning, input data to which the learning model will be applied, etc. The input unit (140a) may include a camera, a microphone, and / or a user input unit. The output unit (140b) may generate output related to sight, hearing, or touch. The output unit (140b) may include a display unit, a speaker, and / or a haptic module, etc. The sensing unit (140) can obtain at least one of internal information of the AI ​​device (100), information about the surrounding environment of the AI ​​device (100), and user information using various sensors. The sensing unit (140) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar, etc.

[0532] The learning processor unit (140c) can train a model composed of an artificial neural network using learning data. The learning processor unit (140c) can perform AI processing together with the learning processor unit of the AI ​​server (Figure W1, 400). The learning processor unit (140c) can process information received from an external device via the communication unit (110) and / or information stored in the memory unit (130). In addition, the output value of the learning processor unit (140c) can be transmitted to an external device via the communication unit (110) and / or stored in the memory unit (130).

[0533] The claims described in the various embodiments of the present disclosure may be combined in various ways. For example, the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a device, and the technical features of the device claims of the various embodiments of the present disclosure may be combined and implemented as a method. Furthermore, the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a device, and the technical features of the method claims of the various embodiments of the present disclosure may be combined and implemented as a method.

Claims

1. In the operation method of the first node in the communication system, A step of transmitting information of multiple representations based on a first knowledge partition of single source data to a second node; A step of receiving feedback from the second node, the feedback including a second similarity value based on the plurality of expressions and background knowledge of the second node; A step of receiving a scaling value for the second similarity value from the second node; A step of determining a difference value between a first similarity value of a contextualizing encoder for the first knowledge segmentation and the second similarity value using the scaling value; A step of generating a single representation by performing knowledge merging based on the knowledge component produced for each of the plurality of expressions based on the above difference value; and comprising the step of transmitting the single expression to the second node; method.

2. In paragraph 1, A step of calculating an index of the remaining knowledge components excluding the second knowledge partition of the second node in the first knowledge partition; If there are multiple indices, further comprising a step of requesting the second node for the scaling value of an index different from the index, method.

3. In paragraph 1, The above second knowledge division is composed of the intersection of the background knowledge of the second node, method.

4. In paragraph 1, The above single representation is generated using the contextual encoder based on the result of the knowledge merging for the above knowledge component. method.

5. In paragraph 4, The above contextualization encoder is configured to generate a representation related to the same knowledge as the background knowledge of the second node. method.

6. In paragraph 1, The above first similarity value is based on the similarity between the representation associated with the first embedding vector corresponding to the first knowledge division and the first embedding vector. method.

7. In paragraph 1, The second similarity value is based on the similarity between the representation associated with the first embedding vector corresponding to the first knowledge division and the second embedding vector corresponding to the second knowledge division of the second node. method.

8. In the method of operation of the second node in the communication system, A step of receiving information of multiple representations based on a first knowledge partition of single source data from a first node; A step of transmitting feedback to the first node including a second similarity value based on the plurality of expressions and background knowledge of the second node; A step of transmitting a scaling value for the second similarity value to the first node; comprising a step of receiving a single representation from the first node, The above single expression is based on knowledge merging of knowledge components produced for each of the above multiple expressions, The above knowledge merging is based on the difference value between the first similarity value of the contextualizing encoder for the first knowledge division and the second similarity value, The above difference value is based on the above scaling value, method.

9. In paragraph 8, In the case where there are multiple indices of the remaining knowledge components excluding the second knowledge partition of the second node in the first knowledge partition, the method further includes receiving a request for the scaling value of an index different from the index from the first node. method.

10. In paragraph 8, The above second knowledge division is composed of the intersection of the background knowledge of the second node, method.

11. In paragraph 8, The above single representation is generated using the contextual encoder based on the result of the knowledge merging for the above knowledge component. method.

12. In paragraph 11, The above contextualization encoder is configured to generate a representation related to the same knowledge as the background knowledge of the second node. method.

13. In paragraph 8, The above first similarity value is based on the similarity between the representation associated with the first embedding vector corresponding to the first knowledge division and the first embedding vector. method.

14. In paragraph 8, The second similarity value is based on the similarity between the representation associated with the first embedding vector corresponding to the first knowledge division and the second embedding vector corresponding to the second knowledge division of the second node. method.

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

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

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

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

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

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

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