Device and method for performing semantic communication in wireless communication system

Abstract

The present disclosure is for performing semantic communication in a wireless communication system. A method performed by a terminal may comprise the steps of: receiving configuration information related to communication from a base station; receiving control information from the base station; receiving a data signal on the basis of the configuration information and the control information from the base station; and transmitting feedback information for the data signal to the base station.

Description

Device and method for performing semantic communication in a wireless communication system

[0001] The following description relates to a wireless communication system, and to a device and method for performing semantic communication in a wireless communication system.

[0002] Wireless access systems are widely deployed to provide various types of communication services, such as voice and data. Typically, wireless access systems are multiple access systems that support communications with multiple users by sharing available system resources (e.g., bandwidth, transmission power). Examples of multiple access systems include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), and single-carrier frequency division multiple access (SC-FDMA).

[0003] In particular, as numerous communication devices demand greater communication capacity, enhanced mobile broadband (eMBB) communication technologies are being proposed, improving upon existing radio access technology (RAT). Furthermore, massive machine type communications (mMTC), which connects multiple devices and objects to provide diverse services anytime and anywhere, as well as communication systems that consider reliability and latency-sensitive services / user equipment (UE), are being proposed. Various technological configurations are being proposed for these solutions.

[0004] The present disclosure can provide a device and method for effectively transmitting and receiving semantic information of a message in a wireless communication system.

[0005] The present disclosure may provide a device and method for estimating background knowledge of a destination in a wireless communication system.

[0006] The present disclosure can provide a device and method for generating and transmitting a semantic feature corresponding to source data based on a portion of the entire background knowledge that is most similar to the background knowledge of the destination in a wireless communication system.

[0007] The present disclosure may provide a device and method for dividing entire background knowledge into a plurality of partial background knowledge in a wireless communication system.

[0008] The present disclosure can provide a device and method for transmitting semantic information based on a semantic diversity technique in a wireless communication system.

[0009] The present disclosure may provide a device and method for generating and transmitting semantic features corresponding to source data based on partial background knowledge in a wireless communication system.

[0010] The present disclosure may provide a device and method for calculating and feeding back attention values ​​corresponding to semantic features received in a wireless communication system.

[0011] The present disclosure may provide a device and method for estimating background knowledge of a destination based on attention values ​​in a wireless communication system.

[0012] The present disclosure may provide a device and method for fine tuning background knowledge of an estimated destination in a wireless communication system.

[0013] The technical objectives to be achieved in the present disclosure are not limited to those mentioned above, and other technical tasks not mentioned can be considered by a person having ordinary skill in the technical field to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure described below.

[0014] As an example of the present disclosure, a method performed by a terminal in a wireless communication system includes the steps of receiving configuration information related to communication from a base station, receiving control information from the base station, receiving a data signal from the base station based on the configuration information and the control information, and transmitting feedback information for the data signal to the base station, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, and the feedback information may include information used to estimate one of the partial background knowledges that is most similar to background knowledge possessed by the terminal.

[0015] As an example of the present disclosure, a method performed by a base station in a wireless communication system includes the steps of transmitting communication-related configuration information to a terminal, transmitting control information to the terminal, transmitting a data signal to the terminal based on the configuration information and the control information, and receiving feedback information on the data signal from the terminal, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, and the feedback information may include information used to estimate one of the partial background knowledges that is most similar to background knowledge possessed by the terminal.

[0016] As an example of the present disclosure, in a wireless communication system, a terminal includes a transceiver and a processor connected to the transceiver, wherein the processor receives configuration information related to communication from a base station, receives control information from the base station, receives a data signal from the base station based on the configuration information and the control information, and controls the transmission of feedback information for the data signal to the base station, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, and the feedback information may include information used to estimate one of the partial background knowledges that is most similar to background knowledge possessed by the terminal.

[0017] As an example of the present disclosure, in a wireless communication system, a base station includes a transceiver and a processor connected to the transceiver, wherein the processor transmits configuration information related to communication to a terminal, transmits control information to the terminal, transmits a data signal to the terminal based on the configuration information and the control information, and controls to receive feedback information on the data signal from the terminal, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, and the feedback information may include information used to estimate one of the partial background knowledges that is most similar to background knowledge possessed by the terminal.

[0018] As an example of the present disclosure, a communication device includes at least one processor, and at least one computer memory connected to the at least one processor and storing instructions that direct operations when executed by the at least one processor, the operations including: receiving configuration information related to communication from a base station, receiving control information from the base station, receiving a data signal from the base station based on the configuration information and the control information, and transmitting feedback information for the data signal to the base station, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, and the feedback information may include information used to estimate one of the partial background knowledge that is most similar to background knowledge possessed by the terminal.

[0019] As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction includes at least one instruction executable by a processor, wherein the at least one instruction controls a device to receive configuration information related to communication from a base station, receive control information from the base station, receive a data signal from the base station based on the configuration information and the control information, and transmit feedback information for the data signal to the base station, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, and the feedback information may include information used to estimate one of the partial background knowledge that is most similar to background knowledge possessed by the terminal.

[0020] The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the present disclosure can be derived and understood by a person having ordinary skill in the art based on the detailed description of the present disclosure to be described below.

[0021] The following effects may be achieved by embodiments based on the present disclosure.

[0022] According to the present disclosure, the performance of semantic communication can be improved.

[0023] The effects that can be obtained from the embodiments of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art to which the technical configuration of the present disclosure is applied, from the description of the embodiments of the present disclosure below. In other words, unintended effects resulting from implementing the configuration described in the present disclosure can also be derived from the embodiments of the present disclosure by those skilled in the art.

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

[0025] Figure 1 illustrates an example of a communication system applicable to the present disclosure.

[0026] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0027] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure.

[0028] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure.

[0029] FIG. 5 illustrates an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.

[0030] Figure 6 illustrates an electromagnetic spectrum applicable to the present disclosure.

[0031] FIG. 7 illustrates a THz wireless communication transceiver applicable to the present disclosure.

[0032] Figure 8 illustrates a THz signal generation method applicable to the present disclosure.

[0033] FIG. 9 illustrates a wireless communication transceiver applicable to the present disclosure.

[0034] Figure 10 illustrates a transmitter structure applicable to the present disclosure.

[0035] Figure 11 illustrates a modulator structure applicable to the present disclosure.

[0036] Figure 12 illustrates the structure of a perceptron included in an artificial neural network applicable to the present disclosure.

[0037] Figure 13 illustrates an artificial neural network structure applicable to the present disclosure.

[0038] Figure 14 illustrates an example of a functional framework for application of artificial intelligence technology applicable to the present disclosure.

[0039] Figure 15 illustrates an example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0040] Figure 16 illustrates another example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0041] Figure 17 illustrates another example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0042] Figure 18 illustrates an AI technology-based communication procedure applicable to the present disclosure.

[0043] Figure 19 illustrates a communication model applicable to the present disclosure.

[0044] Figure 20 illustrates an example of a semantic communication framework applicable to the present disclosure.

[0045] FIG. 21 illustrates an example of an initial connection for semantic communication according to one embodiment of the present disclosure.

[0046] FIG. 22 illustrates an example of a semantic diversity technique based on multi-feature transmission according to one embodiment of the present disclosure.

[0047] FIG. 23 and FIG. 24 illustrate examples of background knowledge segmentation according to one embodiment of the present disclosure.

[0048] FIG. 25 illustrates an example of a partial background knowledge-based semantic diversity technique according to one embodiment of the present disclosure.

[0049] FIG. 26 illustrates an example of a semantic encoder structure according to one embodiment of the present disclosure.

[0050] FIG. 27 illustrates an example of multi-feature based background knowledge estimation according to one embodiment of the present disclosure.

[0051] FIG. 28 illustrates an example of a procedure for receiving a data signal for semantic communication according to one embodiment of the present disclosure.

[0052] FIG. 29 illustrates an example of a procedure for transmitting a data signal for semantic communication according to one embodiment of the present disclosure.

[0053] FIG. 30 illustrates an example of a procedure for performing a task based on a semantic feature according to one embodiment of the present disclosure.

[0054] FIG. 31 illustrates an example of a procedure for estimating background knowledge of a destination according to one embodiment of the present disclosure.

[0055] FIG. 32 illustrates a signaling example for estimating background knowledge of a destination according to one embodiment of the present disclosure.

[0056] FIG. 33 illustrates a signaling example for estimating background knowledge of a destination according to one embodiment of the present disclosure.

[0057] The following embodiments combine the components and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, some components and / or features may be combined to form embodiments of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0058] In the description of the drawings, procedures or steps that may obscure the gist of the present disclosure are not described, and procedures or steps that can be understood by a person skilled in the art are also not described.

[0059] Throughout the specification, when a part is said to "comprising" or "including" a component, this does not mean that other components are excluded, but rather that other components can be included, unless specifically stated otherwise. In addition, terms such as "part," "unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the singular and plural sense in the context of describing the present disclosure (especially in the context of the claims below) unless otherwise indicated herein or clearly contradicted by context.

[0060] Embodiments of the present disclosure described herein focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station is understood as a terminal node of a network that directly communicates with the mobile station. Certain operations described herein as being performed by the base station may, in some cases, be performed by an upper node of the base station.

[0061] That is, in a network consisting of multiple network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or other network nodes other than the base station. In this case, the term 'base station' may be replaced by terms such as fixed station, Node B, eNB (eNode B), gNB (gNode B), ng-eNB, advanced base station (ABS), or access point.

[0062] Additionally, in the embodiments of the present disclosure, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0063] Additionally, a transmitter refers to a fixed and / or mobile node that provides data or voice services, and a receiver refers to a fixed and / or mobile node that receives data or voice services. Therefore, for uplink, a mobile station can be the transmitter, and a base station can be the receiver. Similarly, for downlink, a mobile station can be the receiver, and a base station can be the transmitter.

[0064] Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5th generation (5G) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present disclosure may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0065] Furthermore, the embodiments of the present disclosure can be applied to other wireless access systems and are not limited to the systems described above. For example, they can be applied to systems implemented after the 3GPP 5G NR system and are not limited to a specific system.

[0066] That is, obvious steps or parts not described in the embodiments of the present disclosure can be explained by referring to the above documents. In addition, all terms disclosed in this document can be explained by the above standard documents.

[0067] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present disclosure and is not intended to represent the only embodiments in which the technical configurations of the present disclosure may be implemented.

[0068] Additionally, specific terms used in the embodiments of the present disclosure are provided to aid in understanding of the present disclosure, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the present disclosure.

[0069] The following technology can be applied to various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access).

[0070] For clarity, the following 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 may refer to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer 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.

[0071] For background information, terms, abbreviations, etc. used in this disclosure, reference may be made to standard documents published prior to this disclosure. For example, reference may be made to standard documents 36.xxx and 38.xxx.

[0072] Communication system applicable to the present disclosure

[0073] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts of the present disclosure disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0074] Hereinafter, more specific examples will be provided with reference to the drawings. In the drawings / descriptions below, the same drawing reference numerals may represent identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise described.

[0075] Figure 1 illustrates an example of a communication system applied to the present disclosure.

[0076] Referring to FIG. 1, a communication system (100) applied to 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., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G) and may be referred to as a communication / wireless / 5G 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 (artificial intelligence) device / server (100g). 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 vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones). The XR devices (100c) include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of head-mounted devices (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The portable devices (100d) may include smartphones, smart pads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.), etc. The home appliances (100e) may include TVs, refrigerators, washing machines, etc. The IoT devices (100f) may include sensors, smart meters, etc.For example, the base station (120) and the network (130) may also be implemented as wireless devices, and a specific wireless device (120a) may act as a base station / network node to other wireless devices.

[0077] Wireless devices (100a to 100f) can be connected to a network (130) via a base station (120). 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 (100g) via a network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR), or a 6G network. The wireless devices (100a to 100f) can communicate with each other via the base station (120) / network (130), but can also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). Additionally, an IoT device (100f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or another wireless device (100a to 100f).

[0078] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base stations (120), and base stations (120) / base stations (120). Here, the wireless communication / connection can be established through various wireless access technologies such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and base station-to-base station communication (150c) (e.g., relay, IAB (integrated access backhaul)). Through the wireless communication / connection (150a, 150b, 150c), the wireless device and base station / wireless device, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, the wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present disclosure, 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.), resource allocation processes, etc. may be performed.

[0079] Devices applicable to the present disclosure

[0080] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0081] Referring to FIG. 2, the wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G). The wireless device (200) includes at least one processor (202) and at least one memory (204), and may additionally include at least one transceiver (206) and / or at least one antenna (208).

[0082] The processor (202) controls the memory (204) and / or the transceiver (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 first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (206). In addition, the processor (202) may receive a wireless signal including second information / signal via the transceiver (206), and then store information obtained from signal processing of the second information / signal in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may store software code including instructions for performing some or all of the processes controlled by the processor (202), or for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology. The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via at least one antenna (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF (radio frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0083] Hereinafter, the hardware elements of the wireless device (200) will be described in more detail. Although not limited thereto, at least one protocol layer may be implemented by at least one processor (202). For example, at least one processor (202) may implement at least one layer (e.g., a functional layer such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), and service data adaptation protocol (SDAP)). At least one processor (202) may generate at least one Protocol Data Unit (PDU) and / or at least one Service Data Unit (SDU) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. At least one processor (202) may generate a message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. At least one processor (202) can generate a signal (e.g., a baseband signal) including a PDU, an SDU, a message, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in this document, and provide the signal to at least one transceiver (206). At least one processor (202) can receive a signal (e.g., a baseband signal) from at least one transceiver (206) and obtain the PDU, SDU, message, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed in this document.

[0084] At least one processor (202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. The at least one processor (202) may be implemented by hardware, firmware, software, or a combination thereof. For example, at least one application specific integrated circuit (ASIC), at least one digital signal processor (DSP), at least one digital signal processing device (DSPD), at least one programmable logic device (PLD), or at least one field programmable gate array (FPGA) may be included in the at least one processor (202). The descriptions, functions, procedures, proposals, methods, and / or operation 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, proposals, methods, and / or operation flowcharts disclosed in this document may be included in the at least one processor (202), or may be stored in at least one memory (204) and driven by the at least one processor (202). The descriptions, functions, procedures, suggestions, methods and / or flowcharts disclosed in this document may be implemented using firmware or software in the form of code, instructions and / or sets of instructions.

[0085] At least one memory (204) can be connected to at least one processor (202) and can store various forms of data, signals, messages, information, programs, codes, instructions and / or commands. The at least one memory (204) can be configured as a read only memory (ROM), a random access memory (RAM), an erasable programmable read only memory (EPROM), a flash memory, a hard drive, a register, a cache memory, a computer readable storage medium and / or a combination thereof. The at least one memory (204) can be located internally and / or externally to the at least one processor (202). In addition, the at least one memory (204) can be connected to the at least one processor (202) via various technologies such as a wired or wireless connection.

[0086] At least one transceiver (206) can transmit user data, control information, wireless signals / channels, etc., mentioned in the methods and / or flowcharts of this document to at least one other device. At least one transceiver (206) can receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts disclosed in this document from at least one other device. For example, at least one transceiver (206) can be connected to at least one processor (202) and can transmit and receive wireless signals. For example, at least one processor (202) can control at least one transceiver (206) to transmit user data, control information, or wireless signals to at least one other device. Furthermore, at least one processor (202) can control at least one transceiver (206) to receive user data, control information, or wireless signals from at least one other device. In addition, at least one transceiver (206) may be connected to at least one antenna (208), and at least one transceiver (206) may be configured to transmit and receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts disclosed in this document through at least one antenna (208). In this document, at least one antenna may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). At least one transceiver (206) may convert the received 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 at least one processor (202). At least one transceiver (206) may convert the processed user data, control information, wireless signals / channels, etc. from baseband signals to RF band signals using at least one processor (202).For this purpose, at least one transceiver (206) may include an (analog) oscillator and / or filter.

[0087] The components of the wireless device described with reference to FIG. 2 may be referred to by different terms in terms of functionality. For example, the processor (202) may be referred to as a control unit, the transceiver (206) as a communication unit, and the memory (204) as a storage unit. In some cases, the communication unit may be used to mean at least a portion of the processor (202) and the transceiver (206).

[0088] The structure of the wireless device described with reference to FIG. 2 can be understood as the structure of at least a portion of various devices. For example, the structure of the wireless device illustrated in FIG. 2 can be at least a portion of various devices described with reference to FIG. 1 (e.g., a robot (100a), a vehicle (100b-1, 100b-2), an XR device (100c), a portable device (100d), a home appliance (100e), an IoT device (100f), an AI device / server (100g)). Furthermore, according to various embodiments, in addition to the components illustrated in FIG. 2, the device may further include other components.

[0089] For example, the device may be a portable device such as a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), or a portable computer (e.g., a laptop, etc.). In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an interface unit that includes at least one port for connection with another device (e.g., an audio input / output port, a video input / output port), and an input / output unit for inputting and outputting image information / signals, audio information / signals, data, and / or information input from a user.

[0090] For example, the device may be a mobile device such as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, etc. In this case, the device may further include at least one of a driving unit including at least one of an engine, a motor, a power train, wheels, brakes, and a steering unit of the device, a power supply unit including a wired / wireless charging circuit, a battery, etc. that supplies power, a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, an autonomous driving unit that performs functions such as path maintenance, speed control, and destination setting, and a position measurement unit that obtains location information of the mobile device through a global positioning system (GPS) and various sensors.

[0091] For example, the device may be an XR device such as an HMD, a head-up display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc. In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an input / output unit that obtains control information, data, etc. from the outside and outputs the generated XR object, and a sensor unit that senses status information, environmental information, and user information of the device or the surroundings of the device.

[0092] For example, the device may be a robot that can be classified into industrial, medical, household, military, etc. types depending on the purpose or field of use. In this case, the device may further include at least one of a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, and a driving unit that performs various physical actions, such as moving the robot joints.

[0093] For example, the device may be an AI device such as a TV, a projector, a smartphone, a PC, a laptop, a digital broadcasting terminal, a tablet PC, a wearable device, a set-top box (STB), a radio, a washing machine, a refrigerator, digital signage, a robot, a vehicle, etc. In this case, the device may further include at least one of an input unit that acquires various types of data from the outside, an output unit that generates output related to sight, hearing, or touch, a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, and a training unit that trains a model composed of an artificial neural network using learning data.

[0094] The structure of the wireless device illustrated in FIG. 2 may be understood as a part of a RAN node (e.g., a base station, DU, RU, RRH, etc.). That is, the device illustrated in FIG. 2 may be a RAN node. In this case, the device may further include a wired transceiver for front haul and / or back haul communications. However, if the front haul and / or back haul communications are based on wireless communications, at least one transceiver (206) illustrated in FIG. 2 may be used for front haul and / or back haul communications, and a wired transceiver may not be included.

[0095] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure. For example, the transmission signal may be processed by a signal processing circuit. At this time, the signal processing circuit (300) may include a scrambler (310), a modulator (320), a layer mapper (330), a precoder (340), a resource mapper (350), and a signal generator (360). At this time, as an example, the operations / functions of FIG. 3 may be performed in the processor (202) and / or the transceiver (206) of FIG. 2. Furthermore, as an example, the hardware elements of FIG. 3 may be implemented in the processor (202) and / or the transceiver (206) of FIG. 2. As an example, blocks 310 to 360 may be implemented in the processor (202) of FIG. 2. Additionally, blocks 310 to 350 may be implemented in the processor (202) of FIG. 2, and block 360 may be implemented in the transceiver (206) of FIG. 2, and are not limited to the above-described embodiment.

[0096] The codeword can be converted into a wireless signal through the signal processing circuit (300) of FIG. 3. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., a UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH, a PDSCH). Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (310). 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 (320). The modulation scheme may include pi / 2-binary phase shift keying (pi / 2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), etc.

[0097] A complex modulation symbol sequence can be mapped to at least one transmission layer by a layer mapper (330). The modulation symbols of each transmission layer can be mapped to corresponding antenna port(s) by a precoder (340). The output z of the precoder (340) can be obtained by multiplying the output y of the layer mapper (330) by a precoding matrix W of NХM, where N is the number of antenna ports and M is the number of transmission layers. Here, the precoder (340) can perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on the complex modulation symbols. Additionally, the precoder (340) can perform precoding without performing transform precoding.

[0098] The resource mapper (350) 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 (360) 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 (360) can include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like.

[0099] The signal processing process for a received signal in a wireless device may be configured in reverse order of the signal processing process (310 to 360) of FIG. 3. For example, a wireless device (e.g., 200 of FIG. 2) may receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal may be converted into a baseband signal through a signal restorer. For this purpose, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal may be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codeword may be restored to the original information block through decoding. Therefore, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.

[0100] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure. Figure 4 illustrates operations of a terminal (410) and a base station (420) transmitting and / or receiving data and operations performed prior thereto.

[0101] Referring to FIG. 4, in step 401, the terminal (410) and the base station (420) perform synchronization. For example, the terminal (410) performs an initial cell search operation. Specifically, the terminal (410) can detect at least one synchronization signal transmitted from the base station (420) according to a predefined rule. Here, the synchronization signal can include multiple synchronization signals classified according to structure or purpose (e.g., primary synchronization signal, secondary synchronization signal). Through this, the terminal (410) can check the boundary of the frame, subframe, slot, and / or symbol of the base station (420) and obtain information about the base station (420) (e.g., cell identifier).

[0102] In step 403, the terminal (410) obtains system information transmitted from the base station (420). The system information is information related to the properties, characteristics, and / or capabilities of the base station (420) required to access the base station (420) and use the service, and may be classified according to the content (e.g., whether it is essential for access), transmission structure (e.g., channel used, whether provided on-demand), etc., and may be classified into, for example, a master information block (MIB) and a system information block (SIB). If necessary, the terminal (410) may transmit a signal requesting system information before receiving the system information. However, the request and provision of the system information may be performed after the random access procedure described below.

[0103] In step 405, the terminal (410) and the base station (420) perform a random access procedure. The terminal (410) may transmit and / or receive at least one message (e.g., a random access preamble, a random access response (RAR) message, etc.) for the random access procedure based on information related to the random access channel of the base station (420) obtained through system information (e.g., channel position, channel structure, supported preamble structure, etc.). For example, the terminal (410) may transmit a preamble (e.g., MSG1) through the random access channel, receive an RAR message (e.g., MSG2), transmit a message (e.g., MSG3) including information related to the terminal (410) (e.g., identification information) to the base station (420) using scheduling information included in the RAR message, and receive a message (e.g., MSG4) for contention resolution and / or connection establishment. As another example, MSG1 and MSG3 may be sent and received as one message, or MSG2 and MSG4 may be sent and received as one message.

[0104] In step 407, the terminal (410) and the base station (420) perform signaling of control information. Here, the control information may be defined in various layers, such as a layer that controls a connection (e.g., a radio resource control (RRC) layer), a layer that handles mapping between logical channels and transport channels (e.g., a media access control (MAC) layer), and a layer that handles physical channels (e.g., a physical (PHY) layer). For example, the terminal (410) and the base station (420) may perform at least one of signaling for establishing a connection, signaling for determining settings related to communication, and signaling for indicating allocated resources.

[0105] In step 409, the terminal (410) and the base station (420) transmit and / or receive data. In other words, the terminal (410) and the base station (420) can process, transmit, and / or receive data based on the signaling of the control information. For example, when transmitting data, the terminal (410) or the base station (420) can perform at least one of channel encoding, rate matching, scrambling, constellation mapping, layer mapping, waveform modulation, antenna mapping, and resource mapping on the information bits. Conversely, when receiving data, the terminal (410) or the base station (420) can perform at least one of signal extraction from resources, waveform demodulation for each antenna, signal arrangement considering layer mapping, constellation demapping, descrambling, and channel decoding.

[0106] 6G communication systems and core implementation technologies of 6G systems

[0107] The 5G system defines various operating bands within FR1 (frequency range 1), which covers 410 MHz to 7125 MHz, and FR2 (frequency range 2), which covers 24,250 MHz to 71,000 MHz. Various frequencies are being discussed as operating bands for the subsequent 6G system, and the use of higher frequencies than 5G systems is also being considered for wider bandwidth and higher transmission speeds. One such band is the THz (terahertz) frequency band, which covers approximately 100 GHz to 10 THz. The THz frequency band is a band that has both the transparency of radio waves and the straightness of light waves, and communications using the THz frequency band are expected to play a transitional role from existing radio-centered communications to lightwave-based communications.

[0108] 6G systems utilizing the THz frequency band have the following goals: i) very high data rates per device, ii) a very large number of connected devices, iii) global connectivity, iv) very low latency, v) reduced energy consumption of battery-free IoT devices, vi) ultra-reliable connectivity, and vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: “intelligent connectivity,” “deep connectivity,” “holographic connectivity,” and “ubiquitous connectivity,” and the 6G system can be designed to satisfy the requirements as shown in [Table 1] below.

[0109] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0110] At this time, the 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security. FIG. 5 illustrates an example of a communication structure that can be provided in a 6G system applicable to the present disclosure. Referring to FIG. 5, the 6G system is expected to have simultaneous wireless communication connectivity that is 50 times higher than that of a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become even more crucial in 6G communications by providing end-to-end latency of less than 1 ms. Furthermore, 6G systems will boast significantly higher volumetric spectral efficiency than the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, potentially eliminating the need for separate charging for mobile devices in 6G systems.

[0111] As core implementation technologies of the 6G system, technologies such as artificial intelligence (AI), THz (terahertz) communication, optical wireless technology, FSO backhaul network, massive MIMO technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS) can be adopted.

[0112] For example, THz communication is a communication that utilizes a spectrum in a frequency band between 0.1 THz and 10 THz with a corresponding wavelength in the range of 0.03 mm to 3 mm as shown in Fig. 6, and can be implemented using circuit elements having a structure as shown in Fig. 7. In addition, optical wireless technology is a technology that generates and modulates THz signals using optical elements, and can be implemented based on devices having structures as shown in Figs. 8, 9, 10, and 11.

[0113] In addition, AI can be implemented based on various models such as neural networks and machine learning (machine models). For example, an AI model of a neural network structure can be based on the structure of a perceptron as shown in Fig. 12. Referring to Fig. 12, an artificial neural network can be composed of multiple perceptrons. According to the structure of the perceptron, when an input vector x={x1, x2, …, xd} is input, each component is multiplied by a weight {W1, W2, …, Wd}, and all the results are added, and then an activation function σ(·) is applied. A large artificial neural network structure can be formed by extending the simplified perceptron structure illustrated in Fig. 12, and the input vector can be applied to perceptrons of different dimensions. When perceptrons are stacked, a neural network having an input layer, a hidden layer, and an output layer as shown in Fig. 13 can be configured.

[0114] AI technology utilizing the structure of the neural network as described above can be operated based on a functional framework as shown in FIG. 14 below. FIG. 14 illustrates an example of a functional framework for application of AI technology applicable to the present disclosure. First, the data collection block (1410) performs data preparation on input data collected from objects (e.g., UE, RAN node, network node, etc.) to generate training data (1411) and / or inference data (1411) including processed input data. The model training block (1420) performs training on an AI model using the training data (1411) and provides information on the trained model to the model inference block (1430). The model inference block (1430) generates an output (1416) by performing inference and / or prediction using the inference data (1411). Additionally, the model inference block (1430) can provide model performance feedback (1414) to the model training block (1420). Here, the output (1416) refers to the inference output of the AI ​​model generated by the model inference block (1430), and the details of the inference output may vary depending on the use case. The actor block (1440) triggers or performs a specified task / action based on the output (1416). The actor block (1440) can trigger a task / action for another object (e.g., at least one UE, at least one RAN node, at least one network node, etc.) or for itself. Any one of the functions illustrated in FIG. 14 described above may be performed by two or more entities including the RAN, the network node, the network operator's OAM, or the UE in collaboration. This may be referred to as a split AI operation.

[0115] FIG. 15 illustrates an example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 15 illustrates a case where a model training function (e.g., a function of a model training block (1420)) is included in a network node, and a model inference function (e.g., a function of a model inference block (1430)) is included in a RAN node. Referring to FIG. 15 , in step 1, RAN node 1 and RAN node 2 transmit input data (e.g., training data) for training an AI model to the network node. Here, RAN node 1 and RAN node 2 may also transmit data collected from the UE (e.g., measurements of the UE related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, the UE's position, velocity, etc.) to the network node. In step 2, the network node trains the AI ​​model using the received training data. In step 3, the network node distributes / updates the AI ​​model to RAN node 1 and / or RAN node 2. RAN node 1 and / or RAN node 2 may continue model training based on the received AI model. In this procedure, it is assumed that the AI ​​model is deployed / updated only to RAN node 1. In step 4, RAN node 1 receives input data (e.g., inference data) for AI model inference from the UE and RAN node 2. In step 5, RAN node 1 performs inference based on the AI ​​model using the received inference data to generate output data (e.g., a prediction or a decision). In step 6, if applicable, RAN node 1 may send model performance feedback to network nodes. In step 7, RAN node 1, RAN node 2, and UE (or 'RAN node 1 and UE', or 'RAN node 1 and RAN node 2') perform actions based on the output data. For example, in case of load balancing operation, the UE may move from RAN node 1 to RAN node 2.In step 8, RAN node 1 and RAN node 2 transmit feedback information to the network nodes.

[0116] FIG. 16 illustrates another example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 16 illustrates a case where a model training function (e.g., a function of a model training block (1420)) and a model inference function (e.g., a function of a model inference block (1430)) are included in a RAN node. Referring to FIG. 16, in step 1, a UE and a RAN node 2 transmit input data (e.g., training data) for training an AI model to a RAN node 1. In step 2, RAN node 1 trains an AI model using the received training data. In step 3, RAN node 1 receives input data (e.g., inference data) for AI model-based inference from the UE and RAN node 2. In step 4, RAN node 1 performs AI model-based inference using the received inference data to generate output data (e.g., a prediction or decision). In step 5, RAN node 1, RAN node 2, and UE (or 'RAN node 1 and UE', or 'RAN node 1 and RAN node 2') perform actions based on the output data. For example, in case of load balancing operation, the UE may move from RAN node 1 to RAN node 2. In step 6, RAN node 2 transmits feedback information to RAN node 1.

[0117] FIG. 17 illustrates another example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 17 illustrates a case where a model training function (e.g., a function of a model training block (1420)) is included in a RAN node, and a model inference function (e.g., a function of a model inference block (1430)) is included in a UE. Referring to FIG. 17, in step 1, a UE transmits input data (e.g., training data) for training an AI model to a RAN node. Here, the RAN node may collect data from various UEs and / or other RAN nodes. In step 2, the RAN node trains an AI model using the received training data. In step 3, the RAN node distributes / updates the AI ​​model to the UE. The UE may continue model training based on the received AI model. In step 4, input data (e.g., inference data) for inference based on the AI ​​model is received from the UE, the RAN node, and / or other UEs. In step 5, the UE generates output data (e.g., predictions or decisions) by performing AI model-based inference using the received inference data. In step 6, if applicable, the UE may transmit model performance feedback to the RAN node. In step 7, the UE and the RAN node perform actions based on the output data. In step 8, the UE transmits feedback information to the RAN node.

[0118] According to the aforementioned framework and procedures, an AI model can be trained and utilized in a wireless communication system. In the aforementioned framework and procedures, various data, such as input data, training data, and inference data, are introduced. The specific content of the aforementioned data may vary depending on the task for which the AI ​​model is utilized. For example, information used in various embodiments of the present disclosure described below may be included in the aforementioned data.

[0119] Figure 18 illustrates an AI technology-based communication procedure applicable to the present disclosure. The detailed procedures illustrated in Figure 18 can be combined with various embodiments of the present disclosure described below. For example, data generated according to various embodiments of the present disclosure can be used for operations (e.g., configuration, training, inference, and / or data transmission / reception) in at least one of the detailed procedures illustrated in Figure 18. As another example, the results of the inference illustrated in Figure 18 can be used to transmit and / or receive data according to various embodiments of the present disclosure.

[0120] Referring to FIG. 18, in step 1801, at least one of a UE (1810), a RAN node (1820), and a network node (1830) performs an initial access procedure. For example, in this step, at least one of an initial cell search operation, a system information acquisition operation, a random access operation, and a registration operation may be performed. In step 1803, at least one of the UE (1810), the RAN node (1820), and the network node (1830) performs a configuration procedure. Through the configuration procedure, parameters, resources, connections, and / or entities necessary for performing subsequent procedures in layers between the UE (1810) and the RAN node (1820) and / or in at least one layer between the UE (1810) and the network node (1830) may be determined and / or created. In this case, the configuration procedure may be performed based on information, status, and / or characteristics of an AI model used for subsequent training and inference.

[0121] At step 1805, at least one of the UE (1810), the RAN node (1820), and the network node (1830) performs a model training procedure. At least one of the UE (1810), the RAN node (1820), and the network node (1830) may collect training data and perform learning using the training data. For example, the model training procedure may be performed as described with reference to FIG. 15, FIG. 16, or FIG. 17. If an offline-trained model is used, this step may be omitted.

[0122] At step 1807, at least one of the UE (1810), the RAN node (1820), and the network node (1830) performs a task using the trained model. That is, the task may be performed based on the results of inference and / or prediction using the trained model. For example, the task may be a procedure belonging to a communication protocol, and may be a preparatory operation for subsequent data transmission and / or reception, or may be related to data transmission and / or reception, or may be related to data processing (e.g., encoding, decoding, etc.).

[0123] In step 1809, at least one of the UE (1810), the RAN node (1820), and the network node (1830) transmits and / or receives data. At this time, the result of the task performed in step 1807 may be used. In some cases, the task performed in step 1807 may include transmitting and / or receiving data, in which case this step may be omitted as it is part of step 1807.

[0124] Specific embodiments of the present disclosure

[0125] The present disclosure relates to a technology for transmitting and receiving semantic features in a wireless communication system supporting semantic communication. Specifically, the present disclosure relates to a technology for estimating background knowledge possessed by a source to a destination, and generating and transmitting semantic features corresponding to source data based on the estimated background knowledge. Here, the semantic feature may be referred to as a semantic representation. Furthermore, the background knowledge refers to data sets required for generating and interpreting semantic representations for semantic communication. The background knowledge applicable to the present disclosure may be defined in various forms, and for example, may be defined as at least one data set in the form of a graph representing relationships between nodes.

[0126] Problems related to communication can be divided into three levels, as illustrated in Figure 19, according to the philosophy of Shannon and Weaver. Figure 19 illustrates a communication model applicable to the present disclosure. The problem at Level A (1910) is a technical problem, which concerns how accurately symbols in communication can be conveyed. The problem at Level B (1920) is a semantic problem, which concerns how accurately the conveyed symbols convey the desired meaning. The problem at Level C (1930) is an effectiveness problem, which concerns how effectively the received meaning influences the operation in the desired manner.

[0127] Shannon's information theory focuses only on technical problems at Level A (1910) and does not consider communication from a semantic perspective. However, Weaver argues that Shannon's information theory is sufficiently general to consider problems at Level B (1920) and Level C (1930) by adding semantic transmitters, semantic receivers, and semantic noise to Shannon's communication model.

[0128] Meanwhile, one of the various goals of 6G communications is to enable a variety of new services that interconnect people and machines. Therefore, there is a need to provide a semantic communication method that goes beyond simply considering the technical issues of Level A (1910) in communication systems and considers the semantic issues of Level B (1920).

[0129] Typically, when communicating information between people, each word is associated with its corresponding "meaning." Considering this in relation to the communication model in Figure 19, if the concept associated with the message sent from the source is accurately interpreted by the destination, then proper semantic communication can be considered to have occurred.

[0130] For semantic communication, a source can generate semantic features based on given or collected raw data and transmit the generated semantic features to a destination. The destination interprets and infers the received semantic features according to the source's intent. At this time, semantic communication is not primarily about reducing reconstruction errors that occur during the process of restoring received semantic representations to their original raw data. Rather, it requires an approach that assesses whether downstream tasks performed by the destination utilize the received semantic features to operate according to the source's intent, i.e., whether the interpretation and / or inference of semantic features is successful. Therefore, the destination utilizes its own background knowledge when performing inference operations, and it is desirable for the background knowledge contained in the data transmitted from the source to be reflected in the destination's background knowledge to ensure correct interpretation results.

[0131] As mentioned above, semantic features generated at the source and delivered to the destination must be created with consideration for the downstream tasks that will be performed at the destination. Therefore, a task-oriented semantic communication system is needed to facilitate semantic communication. This system preserves task-relevant information while introducing useful invariances for downstream tasks.

[0132] The semantic communication characteristics of Level B of FIG. 19 can be expressed as in FIG. 20. FIG. 20 illustrates an example of a semantic communication framework applicable to the present disclosure. Referring to FIG. 20, the following definition can be applied to a message x transmitted from a source (2020) to a destination (2010).

[0133] world model The Shannon entropy H(W) is expressed as [Mathematical Formula 1] below.

[0134]

[0135] In [Mathematical Formula 1], H(W) is the world model As Shannon entropy for , it can be referred to as model entropy of semantic source. Also, refers to the probability distribution for the model.

[0136] World Model The probability distribution is It is called a set of interpretations, is the probability distribution for the model, The corresponding model for which x is "true" When a set of its models is called, the logical probability m(x) of message x is expressed as in mathematical expression 2.

[0137]

[0138] In [Mathematical Equation 2], m(x) is the logical probability of message x, is a world model, is the corresponding model for which x is "true" is a set for, refers to the probability distribution for the model. Here, stands for a general propositional satisfaction relation. is 'entailed'. It is also called 'model', which semantically means 'entails the following result' or 'is a stronger condition'. That is, reveals relevance from a semantic point of view.

[0139] Semantic entropy of message x is defined as in [Mathematical Formula 3].

[0140]

[0141] In [Equation 3], is the semantic entropy of message x, and m(x) is the logical probability of message x.

[0142] At this time, when considering background knowledge K, the set of possible worlds in [Equation 2] and [Equation 3] is limited to a set compatible with K. Therefore, the set of possible worlds in [Equation 2] and [Equation 3] is expressed as a conditional logical probability as in [Equation 4] and [Equation 5].

[0143]

[0144] In [Equation 4], denotes the conditional logical probability of message x given that background knowledge is K, is a world model, is the corresponding model for which x is "true" is a set for, refers to the probability distribution for the model. Here, refers to a general propositional satisfaction relation.

[0145]

[0146] In [Equation 5], is the semantic entropy of message x when the background knowledge is K, means the conditional logical probability of message x given that the background knowledge is K.

[0147] For example, assume that the statistical probabilities are p and the background knowledge is K, and the truth table is given as in [Table 2] below.

[0148] [Table 2] is the truth table for p(A)=p(B)=0.5 and K={A→B}.

[0149] #ABA→Bprobability10010.2520110.2531000.2541110.25

[0150] At this point, the possible worlds are reduced to a series of truth assignments where A → B is true. That is, the possible worlds can be reduced to Case 1, Case 2, and Case 4 where A → B is true.

[0151] Therefore, the conditional logical probability can be expressed as in [Equation 6], [Equation 7], and [Equation 8].

[0152]

[0153]

[0154]

[0155] Here, since background knowledge K exists, the conditional logical probability is different from the a priori statistical probability, and in the new distribution, A and B are no longer logically independent. That is, am.

[0156] When background knowledge K exists, the new distribution for the model set can be expressed as in [Equation 9] and [Equation 10].

[0157]

[0158] In [Equation 9], is the new distribution of the model set, is the probability distribution for the model. Also, refers to a general propositional satisfaction relation.

[0159]

[0160] In [Equation 10], is the model entropy of the world model W when the background knowledge is K, is a new distribution for that model.

[0161] When background knowledge is not considered, the model entropy of the source is expressed as in [Mathematical Formula 11], and when background knowledge is considered, the model entropy of the source is expressed as in [Mathematical Formula 12].

[0162]

[0163] In [Equation 11], is the model entropy for the world model W when background knowledge is not taken into account.

[0164]

[0165] In [Equation 12], is background knowledge World model in case of is the model entropy for .

[0166] As shown in [Equations 11] and

[12] , shared background knowledge helps prevent information loss during the compression of messages intended to be conveyed from the source, and allows the destination to obtain maximum information from the source by transmitting and receiving only short messages. Thus, communication at the semantic level can provide performance improvements compared to existing technical levels because it takes background knowledge into account. In other words, as described above, when generating and conveying semantic features from the source to the destination while considering the downstream tasks of the destination, utilizing background knowledge can be seen as consistent with the purpose of performing semantic communication.

[0167] To implement semantic communication encompassing all of the aforementioned components, a new layer called the semantic layer can be added, which governs the overall operation of semantic data and messages. When building a task-oriented semantic communication system, the semantic layer can be included in both the source and destination. For communication between the semantic layers of the source and destination, a protocol, which is a convention between the layers, and a definition of a series of operations are required.

[0168] That is, to perform accurate representation and reasoning in semantic communication within the newly defined semantic layer, a process of reaching consensus on background knowledge information between the source and destination is required. In particular, in situations where background knowledge-related information is not shared between the source and destination, a process of estimating background knowledge is necessary to perform semantic communication. Therefore, this disclosure proposes a method for estimating background knowledge of the destination from the source.

[0169] Figure 21 illustrates an example of an initial connection for semantic communication according to one embodiment of the present disclosure. Referring to Figure 21, a source (2120) and a destination (2110) each possess background knowledge. In this case, the source (2120) may possess a large amount of background knowledge, and the destination (2110) may possess a small amount of background knowledge. In particular, the background knowledge of the destination (2110) may correspond to a portion of the background knowledge of the source (2120). Therefore, the source (2120) can obtain information about the background knowledge of the destination (2110) by estimating which portion of the background knowledge of the source (2120) corresponds to the background knowledge of the destination (2110).

[0170] This disclosure proposes a semantic diversity scheme based on multiple feature transmission to acquire background knowledge information about a destination from a source. The semantic diversity scheme applies the channel diversity scheme used in existing wireless communication systems to semantic communication. Existing channel diversity schemes are technologies that improve the reliability of received signals by transmitting and receiving the same signal using different wireless channels. Existing channel diversity schemes can obtain diversity gains by combining corresponding signals based on channel information at the transmitter or receiver. On the other hand, the diversity scheme in semantic communication differs from existing channel diversity schemes in that it treats background knowledge (2222a, 2222b, 2222c) as channels between the source (2220) and the destination (2210), as illustrated in FIG. 22. FIG. 22 illustrates an example of a semantic diversity technique based on multi-feature transmission according to an embodiment of the present disclosure. Referring to FIG. 22 , a source (2220) can generate multiple semantic features for source data using multiple background knowledges (2222a, 2222b, 2222c) according to the semantic diversity technique, and transmit the generated multiple semantic features to a destination (2210). The multiple semantic features can be referred to as multiple semantic features.

[0171] The behavior of the source and destination according to semantic diversity techniques may vary depending on whether the source and destination share information about background knowledge. For example, if the source and destination share information about background knowledge, a closed-loop semantic diversity technique can be used to set a combination ratio for multiple semantic features and obtain semantic diversity gains for downstream tasks. Conversely, if the source and destination do not share information about background knowledge, an open-loop semantic diversity technique can be used to select a single feature from multiple semantic features and perform partial operations on the downstream task using the single feature.

[0172] The present disclosure assumes a situation where the source and destination do not share background knowledge information. Therefore, during the initial connection for semantic communication, the source and destination of the present disclosure share background knowledge information based on an open-loop semantic diversity technique. In other words, the source can estimate and obtain information about the destination's background knowledge based on the open-loop semantic diversity technique.

[0173] Specifically, the initial connection process for semantic communication according to various embodiments of the present disclosure includes a process of dividing background knowledge held by a source into a plurality of partial background knowledges, setting the divided partial background knowledges as a candidate set of background knowledge for the destination, a process of generating and transmitting multiple semantic features using the partial background knowledges as attention, and a process of acquiring information about the background knowledge of the destination through selection of the multiple semantic features. Hereinafter, the present disclosure proposes a semantic layer protocol and procedure for performing the initial connection for semantic communication as described above.

[0174] The background knowledge of the source and destination may have a knowledge graph structure. The background knowledge of the source may be divided into N knowledge partitions based on graph clustering. The destination may retain partial background knowledge of one of the N knowledge partitions held by the source as its own background knowledge. In this disclosure, a knowledge partition may be referred to as "partial knowledge," "partial background knowledge," or other terms having equivalent technical meanings.

[0175] Figures 23 and 24 illustrate examples of background knowledge segmentation according to one embodiment of the present disclosure. Figures 23 and 24 illustrate graph clustering for background knowledge, exemplifying a spectral clustering method based on an adjacency matrix of a knowledge graph.

[0176] Referring to FIG. 23, the source can generate a Laplacian matrix (L=DA) (2340) based on an adjacency matrix (A) (2320) for a knowledge graph (G) (2310) and a diagonal matrix (D) (2330) representing degrees of each node for the knowledge graph (G) (2310), and perform spectral clustering based on the Laplacian matrix (2340). That is, the source can perform clustering for each node of the knowledge graph according to the sign of the second minimum eigenvector (x2) (2350) of the Laplacian matrix (2340). For example, nodes 1, 2, and 6, whose second minimum eigenvector (x2) (2350) has a positive sign, can be organized into one group (x2) (2360), and nodes 3, 4, and 5, whose second minimum eigenvector has a negative sign, can be organized into one group (2370). That is, background knowledge having a knowledge graph (G) including nodes 1-6 can be divided into partial background knowledge having a knowledge graph including nodes 1, 2, and 6, and partial background knowledge having a knowledge graph including nodes 3, 4, and 5.

[0177] In addition, the source can perform spectral clustering to form more clusters for the nodes of the background knowledge based on the interval of the component value of the second minimum eigenvector (x2) or the third minimum eigenvector (x3), as illustrated in FIG. 24. That is, the nodes included in the background knowledge can be divided to form two clusters (2411, 2412) according to the values ​​of the components of the second minimum eigenvector (x2) (2410), or can be divided to form four clusters (2421, 2422, 2423, 2424) according to the component value of the third minimum eigenvector (x3) (2420). Alternatively, the nodes of the background knowledge can be divided to form multiple clusters based on different eigenvectors. Here, one cluster can correspond to one partial background knowledge.

[0178] The spectral clustering of FIGS. 23 and 24 described above is an example of a method for dividing background knowledge into multiple partial background knowledge pieces, and the various embodiments of the present disclosure are not limited thereto. That is, the various embodiments of the present disclosure can also be applied to background knowledge divided into different structures and in different ways.

[0179] FIG. 25 illustrates an example of a semantic diversity technique based on partial background knowledge according to one embodiment of the present disclosure. FIG. 25 illustrates a semantic diversity technique using N pieces of partial background knowledge.

[0180] Referring to FIG. 25, a source (2520) generates multiple semantic features for source data (2502) using a plurality of contextualizing encoders (2522-1, 2522-2, …, 2522-N) and transmits the generated multiple semantic features to a destination (2510).

[0181] Each of the plurality of contextual encoders (2522-1, 2522-2, …, 2522-N) generates a semantic feature corresponding to the input source data (2502) using partial background knowledge as attention. The partial background knowledge can be obtained by dividing the background knowledge of the source into multiple parts as described above. That is, the n-th contextual encoder generates an embedding (x) corresponding to the i-th graph component for the graph representation of the source data (2502). i ) using the nth partial background knowledge to contextualize the feature. Encodes as. Contextualized features may be referred to as semantic features or contextualized semantic features. For example, contextualized encoder #1 (2522-1) uses partial background knowledge #1 as attention to generate semantic features corresponding to source data (2502). (2504-1) generates, and contextual encoder #2 (2522-2) uses partial background knowledge #2 as attention to generate semantic features corresponding to the source data (2502). (2504-2) generates, and contextual encoder #N (2522-N) uses partial background knowledge #N as attention to generate semantic features corresponding to the source data (2502). Create (2504-N).

[0182] Multiple semantic features generated using multiple contextual encoders (2522-1, 2522-2, …, 2522-N) may be transmitted to the destination (2510) at the same time or at different times. For example, each of the multiple semantic features may be transmitted to the destination (2510) sequentially.

[0183] FIG. 26 illustrates an example of a semantic encoder structure according to one embodiment of the present disclosure.

[0184] Referring to FIG. 26, a semantic encoder (2600) includes a graph representation block (2610) and at least one contextualizing encoder (2620).

[0185] The graph representation block (2610) transforms source data into input data having a graph structure and provides the transformed input data to a contextualization encoder (2620). The contextualization encoder (2620) is a graph transformer-based encoder that utilizes background knowledge of the graph structure as attention, and in particular, generates contextualized features for the input data by utilizing partial background knowledge as attention. To this end, the contextualization encoder (2620) may include L self-attention encoders (2622) and additional attention encoders (2624).

[0186] Each of the L self-attention encoders (2622) includes a multi-head self-attention block (2622a), an addition and normalization block (2622b), a multi-layer perceptron (MLP) block (2622c), and an addition and normalization block (2622d). The multi-head self-attention block (2622a) includes K attention heads, and uses multiple query vectors, key vectors, and value vectors to represent each input vector in a different representation space for each purpose, and obtains a self-attention value for each input vector. The addition and normalization block (2622b) is a hierarchical normalization block that adds and normalizes the input and output of the multi-head self-attention block (2622a). The MLP block (2622c) is a fully connected layer that performs detailed learning on each input value. The summation and normalization block (2622d) is a layer normalization block that adds and normalizes the input and output of the MLP block (2622c) and outputs the result.

[0187] The additional attention encoder (2624) includes a multi-head attention block (2624a), an add & normalization (2624b), a multi-layer perceptron (MLP) block (2624c), and an add & normalization block (2622d). The multi-head attention block (2624a) receives the output of the self-attention encoder (2622) and the graph representation (2604) of partial background knowledge as input, performs multi-head attention, and outputs the result. The add & normalization block (2624b) is a layer normalization block that adds and normalizes the output of the graph representation block (2610) and the output of the multi-head attention block (2624a) and outputs the result. The MLP block (2624c) is a fully connected layer that performs detailed learning for each input value. The summation and normalization block (2624d) is a hierarchical normalization block that adds and normalizes the input and output of the MLP block (2624c) and outputs the result.

[0188] As described above, the contextual encoder (2620) utilizes a graph transformer structure that uses L self-attention encoders (2622) with K heads to generate self-attention-based semantic features. Generates self-attention-based semantic features. Self-attention-based semantic features can be determined through operations such as determining multi-head attention-based features based on the similarity to past self-attention-based features, normalizing the sum of multi-head attention-based features and past self-attention-based features, and aggregating multi-head attention. For example, self-attention-based semantic features can be generated as in [Equation 13] and [Equation 14].

[0189]

[0190] In [Equation 13], is the output feature of the multi-head attention block in the lth self-attention encoder in the tth assimilation process for the ith graph component, and K is the head of the self-attention encoder (2622). It is a function that normalizes all input values ​​to values ​​between 0 and 1 and outputs them, and has the characteristic that the sum of the output values ​​is always 1. is the attention function used in the kth attention head, which calculates the similarity between input factors. is the final output feature of the lth self-attention encoder in the tth assimilation process for the ith graph element, is the final output feature of the lth self-attention encoder in the tth assimilation process for the jth graph component, is the weight matrix used in the kth attention head.

[0191]

[0192] In [Equation 14], is the final output feature of the (l+1)th self-attention encoder in the t-th assimilation process for the j-th graph component, is the layer normalization function, is the output feature of the multi-head attention block in the lth self-attention encoder in the tth assimilation process for the ith graph element, is the final output feature of the lth self-attention encoder in the tth assimilation process for the jth graph component, is a function that aggregates the results of multi-head attention. can be implemented as a multi-layered feed forward neural network.

[0193] Contextual encoder (2620) is a self-attention based semantic feature and attention values ​​between the embedding vectors corresponding to partial background knowledge. Determine the attention value can be determined based on the similarity between semantic features and embedding vectors based on self-attention. For example, the attention value can be calculated as shown in [Mathematical Formula 15] below. The contextual encoder (2620) uses an additional attention encoder (2624) to represent the graph of the source data. For , an assimilation process including attention values ​​is performed, and the result is used as input to a self-attention encoder (2622). The contextualization encoder (2620) repeats the assimilation process using an additional attention encoder (2624) T times, and the contextualized feature Creates.

[0194]

[0195] In [Equation 15], is the i-th attention value, is a function that normalizes all input values ​​to values ​​between 0 and 1 and outputs them. is an inner product-based attention function that can measure the similarity between input factors, is the self-attention feature for the i-th graph component that has passed through a total of L self-attention encoder layers in the t-th assimilation process, refers to an embedding vector whose index is c among the graph components of partial background knowledge.

[0196]

[0197] In [Equation 16], is a semantic feature that goes back to the initial input of the self-attention encoder after going through the tth assimilation, is the layer normalization function, is the i-th attention value, is the ith graph representation of the source data, is a function that aggregates the results of multi-head attention. can be implemented as a multi-layered feed forward neural network.

[0198]

[0199] In [Equation 17], is a contextualized feature for the i-th source data, is a semantic feature generated through L self-attention encoding and T assimilation processes for the i-th graph component, and is the feature finally output from the contextual encoder.

[0200] Through the aforementioned process, the source generates multiple semantic features using multiple partial background knowledge as attention for a single source data and transmits the generated multiple semantic features to the destination. The multiple semantic features may be transmitted simultaneously or sequentially.

[0201] FIG. 27 illustrates an example of multi-feature based background knowledge estimation according to one embodiment of the present disclosure.

[0202] Referring to FIG. 27, the destination (2710) receives multiple semantic features from the source (2720) and performs reasoning on the multiple semantic features using a multi-head attention mechanism based on the partial background knowledge it possesses. Specifically, the destination (2710) determines an attention value between the embedding vector of the background knowledge it possesses and the received multiple semantic features based on the similarity between the multiple semantic features and the embedding vector. For example, the attention value is calculated as in [Mathematical Formula 18].

[0203]

[0204] In [Equation 18], denotes the attention value for the nth multi-semantic feature of the i-th index, is a function that normalizes all input values ​​to values ​​between 0 and 1 and outputs them. is an inner product-based attention function that can measure the similarity between input factors, means the nth multi-semantic feature of the i-th index, refers to a component whose index is c among the node embeddings of background knowledge held by the destination.

[0205] In [Equation 18], the attention value Attention coefficient produced in the process of calculating is used for classification of the i-th index, and the attention value can be interpreted as a weight sum that utilizes the classification results in the embedding of background knowledge. Therefore, the destination (2710) is the attention value calculated at the destination (2710). By feeding back the source, information about partial background knowledge held by the destination can be transmitted to the source. At this time, the source (2720) can transmit the attention value used to generate multiple semantic features. and the attention value received from the destination (2710). The difference is calculated, and the partial background knowledge of the encoder with the smallest calculated difference is determined as the background knowledge possessed by the destination (1710).

[0206] As described above, the source can estimate and / or determine the background knowledge of the destination based on multiple semantic features. Specifically, the source can divide the entire background knowledge based on the knowledge graph it possesses into N partial background knowledge based on graph clustering, and set the divided partial background knowledge as background knowledge candidates of the destination. At this time, the destination can retain one of the partial background knowledge candidates of the source as background knowledge. The source can encode the source data using N contextual encoders, thereby generating multiple semantic features that have the divided partial background knowledge as attention. can be generated. The source is multiple semantic features. The destination receives multiple semantic features from the source and calculates the attention values ​​between the received multiple semantic features and the embedding vector based on the background knowledge it has. It produces the destination. The destination is the produced attention values. Feeds back to the source. The source receives the attention values Attention values ​​generated during the multi-semantic feature encoding process and, based on the comparison results, information about the background knowledge possessed by the destination can be obtained. In the following description, the attention values ​​generated from the source To distinguish between the attention values ​​generated at the source and the attention values ​​generated at the destination, the attention values ​​generated at the source may be referred to as first attention values, and the attention values ​​generated at the destination may be referred to as second attention values.

[0207] According to one embodiment, the source is the first attention values ​​of the source generated during the encoding process. can be transmitted to the destination. The source can transmit the first attention values ​​of the source to the destination. If the destination transmits the first attention value and the second attention value The destination can compare multiple semantic features and, based on the comparison result, select a semantic feature having partial background knowledge with the highest similarity to the background knowledge held by the destination as attention. The destination can perform a downstream task using the selected semantic feature and calculate a performance metric according to the result of performing the downstream task. The destination can request the source to fine-tune the partial background knowledge corresponding to the semantic feature by transmitting the selected semantic feature, information about the selected semantic feature, and / or the performance metric to the source. However, this is only an embodiment, and the present disclosure is not limited thereto. That is, the source may not transmit the first attention value to the destination.

[0208] If the source does not transmit the first attention value to the destination, the source may estimate and / or select one partial background knowledge among the partial background knowledge of the source, which is identical to or most similar to the background knowledge of the destination, and then generate at least one semantic feature for the source data using the partial background knowledge, and transmit the generated at least one semantic feature to the destination. Accordingly, the destination may perform a downstream task using the at least one semantic feature received from the source, and may calculate a performance metric according to the result of performing the downstream task. The destination may request the source to fine-tune the partial background knowledge corresponding to the semantic feature by transmitting the selected semantic feature, information about the selected semantic feature, and / or the performance metric to the source.

[0209] FIG. 28 illustrates an example of a procedure for receiving a data signal for semantic communication according to one embodiment of the present disclosure. FIG. 28 illustrates a method performed by a terminal operating as a destination.

[0210] Referring to FIG. 28, in step S2801, the terminal receives configuration information related to communication from the base station. For example, the terminal may receive synchronization information and / or system information for communication. Specifically, the terminal may perform an initial cell search operation to detect at least one synchronization signal and obtain information about the base station based on the synchronization signal. In addition, the terminal may obtain system information including information related to the attributes, characteristics, and / or capabilities of the base station required for connecting to the base station and using the service. Information related to the capabilities of the base station may include information related to the capabilities of semantic communication. In addition, according to one embodiment, the configuration information may include information or parameters necessary for performing a procedure for identifying partial background knowledge.

[0211] In step S2803, the terminal receives control information from the base station. The control information may include at least one of control information related to establishing a connection between the terminal and the base station, control information for determining settings related to communication, and control information for indicating allocated resources. For example, the terminal may receive control information for establishing a connection for semantic communication, control information for determining settings related to semantic communication, or control information for indicating resources for semantic communication. Alternatively, the terminal may receive scheduling information for receiving at least one semantic feature.

[0212] In step S2805, the terminal receives a data signal from the base station. That is, the terminal receives the data signal from the base station based on configuration information and control information. At this time, the data signal includes a plurality of semantic features generated based on partial background knowledge derived from the background knowledge information of the base station. Each of the plurality of semantic features may be generated based on a plurality of contextual encoders that utilize at least a portion of different partial background knowledge for the source data as attention.

[0213] In step S2807, the terminal transmits feedback information to the base station. That is, the terminal generates feedback information for the data signal and transmits the generated feedback information to the base station. For example, the feedback information may include ACK (acknowledge) / NACK (negative-ACK) information indicating whether decoding was successful. Alternatively, according to one embodiment, the feedback information includes information used by the base station to estimate one of the partial background knowledge of the base station that is most similar to the background knowledge held by the terminal. Specifically, the feedback information includes second attention values ​​between a plurality of semantic features received from the base station and an embedding vector of the background knowledge held by the terminal.

[0214] FIG. 29 illustrates an example of a procedure for transmitting a data signal for semantic communication according to one embodiment of the present disclosure. FIG. 29 illustrates a method performed by a base station operating as a source.

[0215] Referring to FIG. 29, in step S2901, the base station transmits configuration information related to communication. That is, the base station transmits synchronization information and / or system information for communication. Specifically, the base station may transmit at least one synchronization signal including information about the base station, and system information including information related to the attributes, characteristics, and / or capabilities of the base station required for the terminal to use the service. Information related to the capabilities of the base station may include information related to the capabilities of semantic communication. Furthermore, according to one embodiment, the configuration information may include information or parameters necessary for performing a procedure for identifying partial background knowledge.

[0216] In step S2903, the base station transmits control information to the terminal. The control information may include at least one of control information related to establishing a connection between the terminal and the base station, control information for determining settings related to communication, and control information for indicating allocated resources. For example, the base station may transmit control information for establishing a semantic communication connection, control information for determining settings related to semantic communication, or control information for indicating resources for semantic communication to the terminal. Alternatively, the base station may transmit scheduling information for receiving at least one semantic feature.

[0217] In step S2905, the base station transmits a data signal to the terminal. That is, the base station transmits the data signal to the terminal based on configuration information and control information. At this time, the data signal includes a plurality of semantic features generated based on partial background knowledge derived from the base station's background knowledge information. Each of the plurality of semantic features may be generated based on a plurality of contextual encoders that utilize at least a portion of different partial background knowledge for the source data as attention.

[0218] In step S2907, the base station receives feedback information from the terminal. That is, the base station receives feedback information from the terminal in response to the data signal transmission. For example, the feedback information may include ACK / NACK information indicating whether decoding was successful. Alternatively, according to one embodiment, the feedback information may include second attention values ​​between a plurality of semantic features received from the base station and an embedding vector based on background knowledge held by the terminal. The base station may estimate a partial background knowledge most similar to the background knowledge held by the terminal among the partial background knowledge of the base station using the second attention values ​​received from the terminal. The base station may generate a semantic feature corresponding to the source data using the estimated partial background knowledge, and transmit the generated semantic feature to the terminal, thereby performing communication with the terminal.

[0219] FIG. 30 illustrates an example of a procedure for performing a task based on semantic features according to one embodiment of the present disclosure. FIG. 30 illustrates a method performed by a terminal operating as a destination.

[0220] Referring to FIG. 30, in step S3001, the terminal receives a plurality of semantic features. That is, the terminal receives a plurality of semantic features corresponding to specific source data from the base station. Each of the plurality of semantic features may be generated using each of the contextual encoders that utilize the segmented partial background knowledge of the base station as attention. The partial background knowledge used for attention in each of the contextual encoders may differ from each other in at least part. For example, the first partial background knowledge corresponding to the first contextual encoder and the second partial background knowledge corresponding to the second contextual encoder may have at least one different graph node.

[0221] In step S3003, the terminal transmits second attention values. The terminal may determine second attention values ​​based on the received plurality of semantic features and background knowledge possessed by the terminal, and then transmit the generated second attention values ​​to the base station. That is, the terminal may transmit the second attention values ​​as information used to estimate one of the partial background knowledge generated by the base station that is most similar to the background knowledge possessed by the terminal. According to one embodiment, the terminal may determine the second attention values ​​by performing a multi-head attention mechanism based on the received plurality of semantic features and background knowledge possessed by the terminal. In addition, the terminal may transmit the second attention values ​​to the base station as feedback information for the plurality of semantic features received from the base station.

[0222] In step S3005, the terminal determines whether first attention values ​​are received from the base station. For example, the first attention values ​​may be received prior to transmission of the second attention values ​​or after transmission of the second attention values. According to one embodiment, the first attention values ​​may be generated during a process of generating a plurality of semantic features at the base station and may be received together with the plurality of semantic features, or may be received before the terminal transmits the second attention values.

[0223] When the first attention values ​​are received, in step S3007, the terminal determines a single semantic feature using the first attention values. That is, based on the first attention values ​​and the second attention values, the terminal can select, among the multiple semantic features received in step S3001, a single semantic feature having partial background knowledge as attention that is identical to or most similar to the background knowledge possessed by the terminal. Specifically, the terminal can calculate the difference between the first attention values ​​and the second attention values, and select and determine the semantic feature corresponding to the index with the smallest difference value.

[0224] In step S3009, the terminal performs a task using the determined semantic feature. That is, the terminal can perform a downstream task using one of the determined semantic features among multiple semantic features.

[0225] If the first attention values ​​are not received, in step S3011, the terminal performs a task using the semantic features received from the base station. That is, the terminal may transmit the second attention values ​​to the base station and then receive at least one semantic feature from the base station. At this time, the received semantic feature may be generated based on partial background knowledge estimated to be identical to or most similar to the background knowledge of the terminal among the partial background knowledge possessed by the base station.

[0226] After performing step S3009 or step S3011, the terminal may calculate performance metrics based on the results of downstream task execution. The calculated performance metrics may be used by the base station to maintain or fine-tune partial background knowledge estimated to be identical to or most similar to the terminal's background knowledge. For example, if the terminal transmits a performance metric to the base station, the base station may maintain or fine-tune the partial background knowledge estimated based on the performance metric.

[0227] In the embodiment described with reference to FIG. 30, the terminal performs an operation to determine whether the first attention values ​​are received. However, whether the first attention values ​​are received may be determined by a predefined protocol. In this case, the operation to determine whether the first attention values ​​are received is not performed, and the procedure may be defined such that steps S3007 and S3009 are performed according to the protocol, or the procedure may be defined such that step S3011 is performed.

[0228] Figure 31 illustrates an example of a procedure for estimating background knowledge of a destination according to one embodiment of the present disclosure. Figure 31 illustrates signaling between a base station and a terminal when the base station operates as a source and the terminal operates as a destination.

[0229] Referring to Figure 31, in step S3101, the base station sets partial background knowledge. The base station divides the entire background knowledge it possesses into multiple partial background knowledge pieces and sets the divided partial background knowledge pieces as candidates for the background knowledge held by the terminal. For example, if the entire background knowledge held by the base station is defined in a graph format, the base station can divide the background knowledge into multiple partial background knowledge pieces based on graph clustering.

[0230] In step S3103, the base station generates semantic features corresponding to each partial background knowledge. That is, the base station generates multiple semantic features by encoding the source data using multiple encoders that utilize the partial background knowledge as attention.

[0231] In step S3105, the base station transmits semantic features to the terminal. That is, the base station transmits multiple semantic features for the source data to the terminal. According to one embodiment, the multiple semantic features may be transmitted sequentially. According to one embodiment, the base station may transmit first attention values ​​generated during the process of generating semantic features to the terminal. However, according to another embodiment, this step of transmitting the first attention values ​​may be omitted.

[0232] In step S3107, the base station receives second attention values ​​from the terminal. The second attention values ​​include attention values ​​between a plurality of semantic features and an embedding vector of the background knowledge of the terminal. The base station receives the second attention values ​​generated by the terminal as feedback information for the plurality of semantic features. That is, the base station can receive the second attention values ​​as information used to estimate one of the partial background knowledge generated by the base station that is most similar to the background knowledge possessed by the terminal.

[0233] In step S3109, the base station estimates the background knowledge of the terminal based on the first attention values ​​and the second attention values. That is, the base station selects one partial background knowledge that is estimated to be identical to or most similar to the background knowledge of the terminal from among the partial background knowledge set in step S3101 based on the difference value between the first attention values ​​and the second attention values. Specifically, the base station may calculate the difference value between the first attention values ​​and the second attention values ​​for each index, and estimate the partial background knowledge of the encoder corresponding to the index with the smallest difference value as the partial background knowledge that is identical to or most similar to the background knowledge of the terminal.

[0234] In step S3111, the base station generates and transmits semantic features using an encoder corresponding to the estimated partial background knowledge. That is, the base station generates semantic features for the source data using an encoder that has attention to partial background knowledge that is identical to or most similar to the terminal's background knowledge among the partial background knowledge held by the base station, and transmits the generated semantic features to the terminal.

[0235] After performing step S3101 or step S3111, the base station may receive performance metrics from the terminal based on the results of downstream task execution. The base station may maintain or fine-tune the estimated partial background knowledge based on the received performance metrics. The base station may generate semantic features for source data using an encoder that has the fine-tuned partial background knowledge as attention, and transmit the generated semantic features to the terminal, thereby performing semantic communication with the terminal.

[0236] Figure 32 illustrates an example of signaling for estimating background knowledge of a destination according to one embodiment of the present disclosure. Figure 32 illustrates signaling between a base station and a terminal when the base station acts as a source and the terminal acts as a destination.

[0237] Referring to FIG. 32, in step S3201, the base station (3220) divides the background knowledge. That is, the base station (3220) divides the background knowledge into multiple partial background knowledge based on graph clustering.

[0238] In step S3203, the base station (3220) generates semantic features using encoders corresponding to each partial background knowledge. That is, the base station (3220) performs encoding on the i-th source data using N encoders that utilize different partial background knowledge as attention, thereby generating multiple semantic features for the source data. Creates.

[0239] At step S3205, the base station (3220) provides a plurality of semantic features is transmitted to the terminal (3210). At this time, multiple semantic features can be sequentially transmitted to the terminal (3210).

[0240] In step S3207, the terminal (3210) calculates second attention values ​​based on the received plurality of semantic features. That is, the terminal (3210) performs a multi-head attention mechanism based on the received plurality of semantic features and the background knowledge possessed by the terminal (3210), thereby calculating the second attention values. Calculate.

[0241] At step S3209, the terminal (3210) sets the second attention values transmits to the base station. That is, the terminal (3210) receives a plurality of semantic features from the base station (3220). Second attention values ​​as feedback information for can be transmitted to the base station.

[0242] In step S3211, the base station (3220) estimates the background knowledge of the terminal based on the second attention values. The background knowledge estimation of the terminal (3210) includes multiple semantic features. The first attention values ​​generated in the process of generating This can be further utilized. That is, the base station (3220) has the first attention values and second attention values The difference between the first attention value and the second attention value is calculated for each index, the encoder corresponding to the index with the smallest calculated difference value is selected, and the partial background knowledge utilized as the attention of the selected encoder can be estimated to be the same or most similar partial background knowledge as the background knowledge of the terminal (3210).

[0243] In step S2313, the base station (3320) determines the estimated partial background knowledge as the background knowledge of the terminal. That is, the base station (3320) determines the partial background knowledge that is identical or similar to the background knowledge of the terminal (3310) among the multiple partial background knowledges possessed by the base station (3320) as the background knowledge of the terminal (3310), and can then utilize the partial background knowledge in semantic communication with the terminal (3310).

[0244] In step S3215, the base station (3220) generates a semantic feature using an encoder corresponding to the background knowledge of the terminal (3210). That is, the base station (3220) performs encoding on the source data using an encoder that has attention to a specific partial background knowledge estimated to be identical to or most similar to the background knowledge of the terminal among the partial background knowledge of the base station, thereby generating a semantic feature. can be created. Here, means the semantic feature encoded by the i-th source data using the n-th encoder.

[0245] At step S3217, the base station (3220) uses semantic features is transmitted to the terminal (3210). This means that the terminal (3210) transmits the semantic feature This is to enable downstream tasks to be performed using the base station (3220). The base station (3220) uses an encoder that has partial background knowledge that is identical to or most similar to the background knowledge held by the terminal (3210) as attention to generate semantic features. By transmitting to the terminal (3210), the performance of the downstream task of the terminal (3210) can be guaranteed to a certain level or higher.

[0246] Figure 33 illustrates an example of signaling for estimating background knowledge of a destination according to one embodiment of the present disclosure. Figure 33 illustrates signaling between a base station and a terminal when the base station operates as a source and the terminal operates as a destination.

[0247] Referring to FIG. 33, in step S3301, the base station (3320) divides the background knowledge. That is, the base station (3320) divides the background knowledge into multiple partial background knowledge based on graph clustering.

[0248] In step S3303, the base station (3320) generates semantic features using encoders corresponding to each partial background knowledge. That is, the base station (3320) performs encoding on the i-th source data using N encoders that utilize different partial background knowledge as attention, thereby generating multiple semantic features for the source data. Creates.

[0249] At step S3305, the base station (3320) provides a plurality of semantic features and first attention values is transmitted to the terminal (3310). At this time, multiple semantic features and / or first attention values can be sequentially transmitted to the terminal (3310). The first attention values ​​are a plurality of semantic features. It can be created during the process of creating.

[0250] In step S3307, the terminal (3310) calculates second attention values ​​based on the received plurality of semantic features. That is, the terminal (3310) performs a multi-head attention mechanism based on the received plurality of semantic features and the background knowledge possessed by the terminal (3310), thereby calculating the second attention values. Calculate.

[0251] At step S3309, the terminal (3310) sets the second attention values transmits to the base station. That is, the terminal (3310) receives a plurality of semantic features from the base station (3320). Second attention values ​​as feedback information for can be transmitted to the base station.

[0252] In step S3311, the base station (3320) estimates the background knowledge of the terminal based on the first attention values ​​and the second attention values. First attention values are multiple semantic features can be generated in the process of generating the first attention values. That is, the base station (3320) and second attention values The difference between the first attention value and the second attention value is calculated for each index, the encoder corresponding to the index with the smallest calculated difference value is selected, and the partial background knowledge utilized as the attention of the selected encoder can be estimated to be the same or most similar partial background knowledge as the background knowledge of the terminal (3310).

[0253] In step S3313, the base station (3320) determines the estimated partial background knowledge as the background knowledge of the terminal. That is, the base station (3320) determines the partial background knowledge that is identical or similar to the background knowledge of the terminal (3310) among the multiple partial background knowledges possessed by the base station (#320) as the background knowledge of the terminal (3310), and can then utilize the partial background knowledge in semantic communication with the terminal (3310).

[0254] In step S3315, the terminal (3310) determines the semantic feature using the first attention values ​​and the second attention values. That is, the terminal (3310) determines the semantic feature using the first attention values. and second attention values The difference between the first attention value and the second attention value is calculated for each index, and the semantic feature corresponding to the index with the smallest calculated difference value is selected. The selected semantic feature may be treated as having been generated by an encoder among multiple encoders of the base station (3320) that utilizes partial background knowledge that is identical to or most similar to the background knowledge of the terminal (3310) as attention. Here, step S3315 may be performed after step S3309, or at substantially the same time as step S3309. For example, step S3315 may be performed before step S3309 or step S3311, or may be performed simultaneously with or after steps S3311 and S3313.

[0255] In step S3317, the terminal (3310) performs a task using the determined semantic feature. That is, the terminal (3310) can perform a downstream task using a semantic feature generated by an encoder that utilizes partial background knowledge that is identical to or most similar to the background knowledge of the terminal (3310) among the multiple semantic features received from the base station (3320) as attention.

[0256] Referring to Figures 32 and 33, the terminal can calculate performance metrics based on the performance results of downstream tasks and report the calculated performance metrics to the base station. This is to enable the base station to maintain or fine-tune its estimated partial background knowledge based on the received performance metrics. In other words, the base station can fine-tune the terminal's estimated background knowledge based on the performance metrics.

[0257] In the above-described disclosure, the base station operates as a source and the terminal operates as a destination. However, embodiments of the present disclosure are not limited to this. For example, a terminal may operate as a source and another terminal communicating with that terminal may operate as a destination. Alternatively, a terminal may operate as a source and the base station may operate as a destination.

[0258] The proposed methods described above can be implemented independently, but they can also be implemented as a combination (or merge) of some of the proposed methods. Rules can be defined so that the base station notifies the terminal of the applicability of the proposed methods (or information about the rules of the proposed methods) through a predefined signal (e.g., a physical layer signal or a higher layer signal).

[0259] The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential features described herein. Therefore, the above detailed description should not be construed as limiting in all respects but rather as illustrative. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are intended to be included within the scope of the present disclosure. Furthermore, claims that are not explicitly cited in the claims may be combined to form an embodiment or incorporated into a new claim through a post-filing amendment.

[0260] Embodiments of the present disclosure can be applied to various wireless access systems. Examples of various wireless access systems include the 3rd Generation Partnership Project (3GPP) or 3GPP2 systems.

[0261] The embodiments of the present disclosure can be applied not only to the various wireless access systems described above, but also to all technical fields that utilize these various wireless access systems. Furthermore, the proposed method can also be applied to mmWave and THz communication systems utilizing ultra-high frequency bands.

[0262] Additionally, embodiments of the present disclosure can be applied to various applications such as autonomous vehicles and drones.

Claims

1. A method performed by a terminal in a wireless communication system, A step of receiving communication-related setting information from a base station; A step of receiving control information from the base station; A step of receiving a data signal based on the setting information and control information from the base station; and A step of transmitting feedback information for the above data signal to the base station, The above data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, A method wherein the above feedback information includes information used to estimate one of the partial background knowledge most similar to the background knowledge possessed by the terminal.

2. In claim 1, The above multiple semantic features are generated by multiple encoders that encode source data using the partial background knowledge as attention. The partial background knowledge utilized in each of the above plurality of encoders is at least partially different from each other.

3. In claim 1, A method in which information used to estimate the one most similar to the background knowledge possessed by the terminal includes attention values ​​between the background knowledge possessed by the terminal and the plurality of semantic features.

4. In claim 3, A step of receiving attention values ​​related to the plurality of generated semantic features from the base station; A step of selecting one semantic feature among the plurality of semantic features based on the background knowledge possessed by the terminal and the difference between the attention values ​​among the plurality of semantic features and the received attention values; and A method further comprising the step of performing a downstream task using the selected semantic features.

5. In claim 4, A method further comprising the step of transmitting a performance metric for a result of performing the above downstream task to the base station.

6. A method performed by a base station in a wireless communication system, A step of transmitting communication-related setting information to a terminal; A step of transmitting control information to the terminal; A step of transmitting a data signal to the terminal based on the setting information and control information; and A step of receiving feedback information on a data signal from the terminal, wherein the data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, A method wherein the above feedback information includes information used to estimate one of the partial background knowledge most similar to the background knowledge possessed by the terminal.

7. In claim 6, A method further comprising a step of dividing the background knowledge information of the base station into the partial background knowledge based on graph clustering.

8. In claim 6, The information used to estimate the one most similar to the background knowledge possessed by the terminal includes the background knowledge possessed by the terminal and the attention values ​​between the plurality of semantic features, A method further comprising a step of estimating one of the partial background knowledges that is most similar to the background knowledge possessed by the terminal based on the difference between the background knowledge possessed by the terminal and the attention values ​​between the plurality of semantic features and the attention values ​​obtained when generating the plurality of semantic features.

9. In claim 8, A step of generating one semantic feature for the source data based on the most similar one to the background knowledge possessed by the terminal; and A method further comprising the step of transmitting the generated semantic feature to the terminal.

10. In claim 8, A step of receiving performance metrics for the results of performing a downstream task from the terminal; and A method comprising the step of fine-tuning said estimated partial background knowledge based on said performance metric.

11. In a terminal in a wireless communication system, Transmitter and receiver; and comprising a processor connected to the above transceiver, The above processor, Receives communication-related setting information from the base station, Receive control information from the above base station, Receive a data signal based on the setting information and control information from the above base station, Control to transmit feedback information for the above data signal to the base station, The above data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, A terminal including the feedback information, which is used to estimate one of the partial background knowledge most similar to the background knowledge possessed by the terminal.

12. In a base station in a wireless communication system, Transmitter and receiver; and comprising a processor connected to the above transceiver, The above processor, Transmits communication-related setting information to the terminal, Transmit control information to the above terminal, Transmitting a data signal to the terminal based on the above setting information and control information, Controls to receive feedback information on data signals from the above terminal, The above data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, A base station, wherein the above feedback information includes information used to estimate one of the partial background knowledge most similar to the background knowledge possessed by the terminal.

13. In communication devices, At least one processor; At least one computer memory coupled to said at least one processor and storing instructions that direct operations when executed by said at least one processor, The above actions are, A step of receiving communication-related setting information from a base station; A step of receiving control information from the base station; A step of receiving a data signal based on the setting information and control information from the base station; and A step of transmitting feedback information for the above data signal to the base station, The above data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, A communication device wherein the feedback information includes information used to estimate one of the partial background knowledge most similar to the background knowledge possessed by the terminal.

14. In a non-transitory computer-readable medium storing at least one instruction, comprising at least one instruction executable by the processor; At least one of the above commands causes the device to: Receives communication-related setting information from the base station, Receive control information from the above base station, Receive a data signal based on the setting information and control information from the above base station, Control to transmit feedback information for the above data signal to the base station, The above data signal includes a plurality of semantic features generated based on partial background knowledge derived from background knowledge information of the base station, The above feedback information is a non-transitory computer-readable medium including information used to estimate one of the partial background knowledge most similar to the background knowledge possessed by the terminal.

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