Apparatus and method for background knowledge matching based on codebook alignment for semantic communication in wireless communication system

Abstract

The present disclosure relates to carrying out background knowledge matching based on codebook alignment for semantic communication in a wireless communication system. A method carried out by a first apparatus comprises the steps of: carrying out an initial access procedure; transmitting capability information; carrying out signaling for configuration related to communication; transmitting a reference signal to a second apparatus; generating a semantic representation on the basis of the reference signal; and matching background knowledge of the first apparatus with background knowledge of a second apparatus on the basis of the semantic representation.

Description

Device and method for background knowledge matching based on codebook alignment for semantic communication in a wireless communication system

[0001] The present disclosure relates to a wireless communication system, and more specifically to an apparatus and method for background knowledge matching based on codebook alignment for semantic communication in a wireless communication system.

[0002] Wireless access systems are being widely deployed to provide various types of communication services, such as voice and data. Generally, a wireless access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.). Examples of multiple access systems include 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) systems.

[0003] In particular, as many communication devices require large communication capacities, enhanced mobile broadband (eMBB) communication technology is being proposed as an improvement over existing radio access technology (RAT). Furthermore, communication systems are being proposed that consider not only massive machine type communications (mmTC), which connects multiple devices and objects to provide various services anytime and anywhere, but also services and user equipment (UE) that are sensitive to reliability and latency. Various technical configurations are being proposed to achieve this.

[0004] The present disclosure relates to an apparatus and method for semantic communication in a wireless communication system.

[0005] The present disclosure relates to a message compression device and method for semantic communication in a wireless communication system.

[0006] The present disclosure relates to an apparatus and method for reducing entropy for semantic communication in a wireless communication system.

[0007] The present disclosure relates to an apparatus and method for establishing relationships between labels for semantic communication in a wireless communication system.

[0008] The present disclosure relates to an apparatus and method for codebook alignment based on a reference signal in a wireless communication system.

[0009] The present disclosure relates to an apparatus and method for background knowledge matching based on codebook alignment in a wireless communication system.

[0010] The present disclosure relates to an apparatus and method for generating a reference point for codebook alignment using a reference signal in a wireless communication system.

[0011] The present disclosure relates to an apparatus and method for sharing background knowledge between devices in semantic communication in a wireless communication system.

[0012] The present disclosure relates to an apparatus and method for providing a protocol of a layer for semantic communication in a wireless communication system.

[0013] The present disclosure relates to an apparatus and method for grouping for background knowledge matching in a wireless communication system.

[0014] The technical objectives to be achieved in this disclosure are not limited to those mentioned above, and other unmentioned technical problems may be considered by those skilled in the art to which the technical configuration of this disclosure applies, based on the embodiments of this disclosure described below.

[0015] As an example of the present disclosure, a method performed by a first device in a wireless communication system comprises the steps of performing an initial connection procedure, transmitting capability information, performing signaling for a setting related to communication, transmitting a reference signal to a second device, generating a semantic expression based on the reference signal, and matching background knowledge of the first device with background knowledge of the second device based on the semantic expression.

[0016] As an example of the present disclosure, a method performed by a second device in a wireless communication system comprises the steps of performing an initial connection procedure, transmitting capability information, performing signaling for a setting related to communication, generating a semantic representation based on a reference signal, and matching codebook vectors of the second device and codebook vectors of the first device based on the semantic representation, wherein the reference signal is received from the first device.

[0017] As an example of the present disclosure, in a wireless communication system, a first device comprises a transceiver and a processor coupled to the transceiver, wherein the processor performs an initial connection procedure, transmits capability information, performs signaling for settings related to communication, transmits a reference signal to a second device, generates a semantic expression based on the reference signal, and is configured to match background knowledge of the first device with background knowledge of the second device based on the semantic expression.

[0018] As an example of the present disclosure, in a wireless communication system, a second device comprises a transceiver and a processor connected to the transceiver, wherein the processor is configured to perform an initial connection procedure, transmit capability information, perform signaling for a setting related to communication, generate a semantic representation based on a reference signal, and match codebook vectors of the second device and codebook vectors of the first device based on the semantic representation, wherein the reference signal is received from the first device.

[0019] As an example of the present disclosure, a communication device comprises at least one processor, at least one computer memory connected to the at least one processor and storing instructions that direct operations as executed by the at least one processor, wherein the operations include performing an initial connection procedure, transmitting capability information, performing signaling for a setting related to communication, transmitting a reference signal to a second device, generating a semantic expression based on the reference signal, and matching background knowledge of the first device with background knowledge of the second device based on the semantic expression.

[0020] As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction comprises said at least one instruction executable by a processor, said at least one instruction controlling the device to perform the steps of performing an initial connection procedure, transmitting capability information, performing signaling for a setting related to communication, transmitting a reference signal to a second device, generating a semantic representation based on said reference signal, and matching background knowledge of the first device with background knowledge of the second device based on said semantic representation.

[0021] The embodiments of the present disclosure described above are merely 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 those skilled in the art based on the detailed description of the present disclosure set forth below.

[0022] The following effects may be achieved by embodiments based on the present disclosure.

[0023] According to the present disclosure, the amount of data required to transmit a message in semantic communication can be reduced through background knowledge matching.

[0024] The effects obtainable from the embodiments of the present disclosure are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by those skilled in the art to which the technical configuration of the present disclosure applies from the description of the embodiments of the present disclosure below. That is, unintended effects resulting from implementing the configuration described in the present disclosure can also be derived by those skilled in the art from the embodiments of the present disclosure.

[0025] The drawings attached below are intended to aid in understanding the present disclosure and may provide embodiments of the present disclosure together with the detailed description. 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 one another to form new embodiments. Reference numerals in each drawing may denote structural elements.

[0026] FIG. 1 illustrates an example of a communication system applicable to the present disclosure.

[0027] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0028] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure.

[0029] FIG. 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure.

[0030] FIG. 5 illustrates an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.

[0031] FIG. 6 illustrates an electromagnetic spectrum applicable to the present disclosure.

[0032] FIG. 7 illustrates a transmitter structure applicable to the present disclosure.

[0033] FIG. 8 illustrates an example of a functional framework for the application of artificial intelligence technology applicable to the present disclosure.

[0034] FIG. 9 illustrates an example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0035] FIG. 10 illustrates a communication procedure based on artificial intelligence (AI) technology applicable to the present disclosure.

[0036] Figure 11 illustrates a three-level problem that constitutes wireless communication.

[0037] Figure 12 conceptually illustrates the structure of semantic communication.

[0038] FIG. 13 illustrates an example of semantic communication between devices having different background knowledge.

[0039] Figure 14 illustrates an example of semantic channel equalization according to a ball radius parameter.

[0040] Figure 15a illustrates the label relationship between the task of distinguishing numbers from 0 to 9 and the task of distinguishing even or odd numbers.

[0041] Figure 15b illustrates the label relationship between the task of determining modulus 3 and the task of distinguishing numbers 0 through 9.

[0042] FIG. 16 illustrates an example where a source representation is matched to a destination representation space.

[0043] FIG. 17a illustrates a representation codebook point according to one embodiment of the present disclosure.

[0044] FIG. 17b illustrates a codebook sequence generated as a result of a space-filling vector quantization technique according to one embodiment of the present disclosure.

[0045] FIG. 18 illustrates an example of codebook sequence alignment according to one embodiment of the present disclosure.

[0046] FIG. 19 illustrates an example of codebook alignment for a plurality of labels according to one embodiment of the present disclosure.

[0047] FIG. 20 illustrates another example of codebook alignment for a plurality of labels according to one embodiment of the present disclosure.

[0048] FIG. 21 illustrates the procedure of a background knowledge matching protocol based on semantic representation codebook alignment according to one embodiment of the present disclosure.

[0049] FIG. 22 illustrates an example of a procedure for performing semantic communication of a terminal according to one embodiment of the present disclosure.

[0050] FIG. 23 illustrates an example of a communication procedure based on background knowledge matching according to one embodiment of the present disclosure.

[0051] FIG. 24 illustrates an example of a background knowledge matching procedure according to one embodiment of the present disclosure.

[0052] FIG. 25 illustrates an example of a background knowledge matching procedure for a plurality of label representation spaces according to one embodiment of the present disclosure.

[0053] FIG. 26 illustrates an example of a wireless device applicable to the present disclosure.

[0054] FIG. 27 illustrates an example of a portable device applicable to the present disclosure.

[0055] FIG. 28 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure.

[0056] FIG. 29 illustrates an example of a vehicle applicable to the present disclosure.

[0057] FIG. 30 illustrates an example of an extended reality (XR) device applicable to the present disclosure.

[0058] FIG. 31 illustrates an example of a robot applicable to the present disclosure.

[0059] FIG. 32 illustrates an example of an AI device applicable to the present disclosure.

[0060] The following embodiments are combinations of the components and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, 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 any embodiment may be included in other embodiments, or may be replaced with corresponding components or features of other embodiments.

[0061] In the description of the drawings, procedures or steps that could obscure the gist of the present disclosure have not been described, nor have procedures or steps that are understandable to those skilled in the art been described.

[0062] Throughout the specification, when a part is described as "comprising" or "including" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "...part," "...unit," and "module" as used in the specification refer to a unit that performs at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software. Additionally, "one (a or an)," "one," "the," and similar related terms may be used in the context describing the present disclosure (particularly in the context of the following claims) in both singular and plural forms, unless otherwise indicated in the specification or clearly contradicted by the context.

[0063] In this specification, the embodiments of the present disclosure are described with a focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station refers to a terminal node of a network that communicates directly with a mobile station. Specific operations described in this document as being performed by a base station may, in some cases, be performed by an upper node of the base station.

[0064] 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, '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.

[0065] 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).

[0066] Furthermore, the transmitting end refers to a fixed and / or mobile node that provides data or voice services, and the receiving end refers to a fixed and / or mobile node that receives data or voice services. Therefore, in the case of the uplink, a mobile station can be the transmitting end and a base station can be the receiving end. Similarly, in the case of the downlink, a mobile station can be the receiving end and a base station can be the transmitting end.

[0067] Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of the wireless access systems, such as IEEE 802.xx systems, 3GPP (3rd Generation Partnership Project) systems, 3GPP LTE (Long Term Evolution) systems, 3GPP 5G (5th generation) NR (New Radio) systems and 3GPP2 systems, 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.

[0068] In addition, the embodiments of the present disclosure may be applied to other wireless access systems and are not limited to the systems described above. For example, they may be applicable to systems applied after the 3GPP 5G NR system and are not limited to specific systems.

[0069] That is, obvious steps or parts not described in the embodiments of the present disclosure may be described by referring to the aforementioned documents. Additionally, all terms disclosed in this document may be explained by the aforementioned standard documents.

[0070] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present disclosure and is not intended to represent the only embodiment in which the technical configuration of the present disclosure can be implemented.

[0071] Additionally, specific terms used in the embodiments of the present disclosure are provided to aid in understanding the present disclosure, and the use of such specific terms may be modified in other forms without departing from the technical spirit of the present disclosure.

[0072] 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).

[0073]

[0074] For the sake of clarity, the following description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical scope of this disclosure is not limited thereto. LTE may refer to technology from 3GPP TS 36.xxx Release 8 onwards. Specifically, LTE technology from 3GPP TS 36.xxx Release 10 onwards is referred to as LTE-A, and LTE technology from 3GPP TS 36.xxx Release 13 onwards may be referred to as LTE-A pro. 3GPP NR may refer to technology from TS 38.xxx Release 15 onwards. 3GPP 6G may refer to technology from TS Release 17 and / or Release 18 onwards. "xxx" indicates a specific standard document number. LTE / NR / 6G may be collectively referred to as 3GPP systems.

[0075] Regarding the background technology, 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.

[0076]

[0077] Communication systems applicable to the present disclosure

[0078] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods, and / or flowcharts of the disclosure disclosed in this document may be applied to various fields requiring wireless communication / connection (e.g., 5G) between devices.

[0079] Examples are provided in more detail below with reference to the drawings. In the following drawings and descriptions, the same reference numerals may represent the same or corresponding hardware blocks, software blocks, or function blocks unless otherwise described.

[0080] FIG. 1 illustrates an example of a communication system to which the present disclosure applies.

[0081] Referring to FIG. 1, the communication system (100) to which the present disclosure applies includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using 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 Thing) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with wireless communication capabilities, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, etc. Here, the vehicle (100b-1, 100b-2) may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device (100c) includes an augmented reality (AR) / virtual reality (VR) / mixed reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. The portable device (100d) may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch, smart glasses), a computer (e.g., a laptop, etc.). The home appliance (100e) may include a TV, a refrigerator, a washing machine, etc. The IoT device (100f) may include a sensor, a smart meter, etc.For example, the base station (120) and 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.

[0082] Wireless devices (100a to 100f) can be connected to a network (130) through a base station (120). AI technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) through the network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, or a 6G network. The wireless devices (100a to 100f) may communicate with each other through the base station (120) / network (130), but may 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). Also, an IoT device (100f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or other wireless devices (100a to 100f).

[0083] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base station (120) and between base station (120) / base station (120). Here, wireless communication / connection can be established through various wireless access technologies such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (integrated access backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on the various proposals of the present disclosure, at least some of the following may be performed: a process for setting various configuration information for transmitting / receiving wireless signals, a process for various signal processing (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), a resource allocation process, etc.

[0084]

[0085] Devices applicable to the present disclosure

[0086] FIG. 2 illustrates an example of a wireless device that can be applied to the present disclosure.

[0087] Referring to FIG. 2, the wireless device (200) can transmit and receive wireless signals through 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).

[0088] 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 sequences of operation disclosed in this document. For example, the processor (202) may process information within the memory (204) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a second information / signal through the transceiver (206) and then store information obtained from the 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, memory (204) may store software code containing 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 sequences of operations disclosed in this document. Here, the processor (202) and memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology. A transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals through at least one antenna (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with a radio frequency (RF) unit. In this disclosure, a wireless device may mean a communication modem / circuit / chip.

[0089] Hereinafter, 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., functional layers such as PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource control), and SDAP (service data adaptation protocol). At least one processor (202) may generate at least one PDU (Protocol Data Unit) and / or at least one SDU (service data unit) according to the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. At least one processor (202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. At least one processor (202) may generate a signal (e.g., baseband signal) including a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this document and provide it to at least one transceiver (206). At least one processor (202) may receive a signal (e.g., baseband signal) from at least one transceiver (206) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document.

[0090] At least one processor (202) may be referred to as a controller, microcontroller, microprocessor, or microcomputer. 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 at least one processor (202). The descriptions, functions, procedures, proposals, methods, and / or operation sequences 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. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be included in at least one processor (202) or stored in at least one memory (204) and driven by at least one processor (202). The descriptions, functions, procedures, proposals, 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.

[0091] At least one memory (204) may be connected to at least one processor (202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. At least one memory (204) may be composed of ROM (read-only memory), RAM (random access memory), EPROM (erasable programmable read-only memory), flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. At least one memory (204) may be located inside and / or outside of at least one processor (202). Additionally, at least one memory (204) may be connected to at least one processor (202) via various technologies, such as wired or wireless connections.

[0092] At least one transceiver (206) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc. of this document to at least one other device. At least one transceiver (206) may receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc. disclosed in this document from at least one other device. For example, at least one transceiver (206) may be connected to at least one processor (202) and may transmit and receive wireless signals. For example, at least one processor (202) may control at least one transceiver (206) to transmit user data, control information, or wireless signals to at least one other device. Additionally, at least one processor (202) may control at least one transceiver (206) to receive user data, control information, or wireless signals from at least one other device. Additionally, 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., as described in the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams 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).To this end, at least one transceiver (206) may include an (analog) oscillator and / or filter.

[0093] The components of the wireless device described with reference to FIG. 2 may be referred to by other terms in terms of their function. For example, the processor (202) may be referred to as the control unit, the transceiver (206) as the communication unit, and the memory (204) as the storage unit. In some cases, the communication unit may be used to mean at least a part of the processor (202) and the transceiver (206).

[0094] The structure of the wireless device described with reference to FIG. 2 can be understood as the structure of at least part of various devices. For example, the structure of the wireless device exemplified in FIG. 2 may be at least part of the various devices described with reference to FIG. 1 (e.g., robot (100a), vehicle (100b-1, 100b-2), XR device (100c), portable device (100d), home appliance (100e), IoT device (100f), AI device / server (100g)). Furthermore, according to various embodiments, the device may include other components in addition to the components exemplified in FIG. 2.

[0095] For example, the device may be a portable device such as a smartphone, smartpad, wearable device (e.g., smart watch, smart glasses), or portable computer (e.g., 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., audio input / output port, video input / output port), and an input / output unit for inputting and outputting video information / signals, audio information / signals, data, and / or information input by a user.

[0096] For example, the device may be a mobile device such as a mobile robot, vehicle, train, manned / unmanned aerial vehicle (AV), or ship. In this case, the device may further include at least one of a drive unit comprising at least one of an engine, motor, power train, wheel, brake, and steering device of the device; a power supply unit that supplies power and includes a wired / wireless charging circuit, battery, etc.; a sensor unit that senses state 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 acquires position information of the moving body through a GPS (global positioning system) and various sensors.

[0097] For example, the device may be an XR device such as an HMD, a HUD (head-up display) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, 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 acquires control information, data, etc. from the outside and outputs a generated XR object, and a sensor unit that senses state information, environment information, and user information of the device or the surroundings of the device.

[0098] For example, the device may be a robot that can be classified into industrial, medical, household, military, etc., 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 state information, environmental information, and user information of the device or its surroundings, and a drive unit that performs various physical actions, such as moving robot joints.

[0099] For example, the device may be an AI device such as a TV, projector, smartphone, PC, laptop, digital broadcasting terminal, tablet PC, wearable device, set-top box (STB), radio, washing machine, refrigerator, digital signage, robot, 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 state information, environmental information, and user information of the device or its surroundings, and a training unit that learns a model composed of an artificial neural network using training data.

[0100] The structure of the wireless device illustrated in FIG. 2 can be understood as part of a RAN node (e.g., 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 communication. However, if the front haul and / or back haul communication is based on wireless communication, at least one transceiver (206) illustrated in FIG. 2 is used for front haul and / or back haul communication, and the wired transceiver may not be included.

[0101]

[0102] 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. In this case, the signal processing circuit (300) may include scramblers (310), modulators (320), a layer mapper (330), a precoder (340), resource mappers (350), and signal generators (360). In this case, for example, the operation / function of FIG. 3 may be performed in the processor (202) and / or transceiver (206) of FIG. 2. Also, for example, the hardware elements of FIG. 3 may be implemented in the processor (202) and / or transceiver (206) of FIG. 2. For 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 embodiments described above.

[0103] A 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 transmission block (e.g., UL-SCH transmission block, DL-SCH transmission block). Here, the information block may include AI-related data (e.g., training data, AI model data, input data, output data, etc.), and the codeword may be an encoded bit sequence corresponding to the AI-related data. The wireless signal may be transmitted through various physical channels (e.g., PUSCH, PDSCH). Specifically, the codeword may be converted into a scrambled bit sequence by scramblers (310). The scrambled sequence used for scrambling is generated based on an initialization value, which may include ID information of the wireless device, etc. The scrambled bit sequence may be modulated into a modulation symbol sequence by modulators (320). Modulation methods may include pi / 2-BPSK (pi / 2-binary phase shift keying), m-PSK (m-phase shift keying), m-QAM (m-quadrature amplitude modulation), etc.

[0104] A complex modulation symbol sequence can be mapped to at least one transmission layer by a layer mapper (330). Here, a transmission layer is a logical resource unit for mapping signals or data transmitted through spatial resources to antenna ports, and one transmission layer can correspond to one stream or one antenna port. Each complex modulation symbol included in the complex modulation symbol sequence is mapped to at least one transmission layer, thereby determining which antenna port it will be transmitted through. The modulation symbols of each transmission layer can be mapped to the 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 an N-XM precoding matrix W, 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., a discrete Fourier transform (DFT)) on the complex modulation symbols. Additionally, the precoder (340) can perform precoding without performing transform precoding.

[0105] Resource mappers (350) can map the modulation symbols of each antenna port to time-frequency resources. The time-frequency resources may include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. Signal generators (360) generate radio signals from the mapped modulation symbols, and the generated radio signals can be transmitted to other devices through each antenna. To this end, each of the signal generators (360) may include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, etc.

[0106] The signal processing process for a received signal in a wireless device can be configured as the inverse of the signal processing process (310 to 360) of FIG. 3. For example, a wireless device (e.g., 200 in FIG. 2) can receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal can be converted into a baseband signal through a signal restorer. To this end, 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. Subsequently, the baseband signal can be restored into a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scrambling process. The codeword can be restored into the original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0107] The signal processing circuit (300) described with reference to FIG. 3 is illustrated as including a plurality of scramblers (310), modulators (320), a plurality of resource mappers (350), and a plurality of signal generators (360). However, at least one of the scramblers, modulators, resource mappers, and signal generators may be implemented as a single integrated structure. That is, the number of at least one of the scramblers, modulators, resource mappers, and signal generators may be less than the number of layers. Furthermore, at least one of the components illustrated in FIG. 3 may be omitted.

[0108]

[0109] FIG. 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure. FIG. 4 illustrates the operation of a terminal (410) and a base station (420) transmitting and / or receiving data, and the operation performed prior to this.

[0110] 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 may include a plurality of synchronization signals (e.g., primary synchronization signal, secondary synchronization signal) classified according to structure or use. Through this, the terminal (410) can identify the boundaries 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).

[0111] In step 403, the terminal (410) obtains system information transmitted from the base station (420). The system information is information related to the attributes, characteristics, and / or capabilities of the base station (420) required to connect to the base station (420) and use the service, and can be classified according to content (e.g., whether it is essential for connection), transmission structure (e.g., channel used, whether it is provided on-demand), etc., and can be classified, for example, into a master information block (MIB) and a system information block (SIB). If necessary, the terminal (410) may transmit a signal requesting the system information prior to receiving the system information. The system information may include information related to AI functions. For example, the system information is information required for operations performed based on AI, and may include at least one of information related to an AI model, information related to training, and information related to inference / prediction. However, the request and provision of the system information may be performed after the random access procedure described later.

[0112] 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 for the random access procedure (e.g., random access preamble, RAR (random access response) message, etc.) based on information related to the random access channel of the base station (420) obtained through system information (e.g., channel location, channel structure, structure of supported preamble, etc.). For example, the terminal (410) may transmit a preamble (e.g., MSG1) through the random access channel, receive a RAR message (e.g., MSG2), transmit a message (e.g., MSG3) containing 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 can be transmitted and received as a single message, or MSG2 and MSG4 can be transmitted and received as a single message.

[0113] 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 the connection (e.g., a radio resource control (RRC) layer), a layer that handles mapping between logical channels and transmission 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 to establish a connection, signaling to determine settings related to communication, and signaling to indicate allocated resources. Additionally, the signaling of control information may be performed to convey information related to AI functions. For example, information related to AI functions is information necessary for operations performed based on AI, and may include at least one of information related to an AI model, information related to training, and information related to inference / prediction. More specifically, the information related to the AI ​​function signaled in step 407 can be combined and / or combined with the information related to the AI ​​function signaled in step 403, and both can be defined as having a hierarchical, mutually complementary, or substitute structure.

[0114] In step 409, the terminal (410) and the base station (420) transmit and / or receive data. That is, the terminal (410) and the base station (420) can process, transmit and / or receive data based on the signaling of control information. For example, when transmitting data, the terminal (410) or the base station (420) may 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) may perform at least one of signal extraction from resources, antenna-specific waveform demodulation, signal placement considering layer mapping, constellation demapping, descrambling, and channel decoding. Here, the transmitted data is AI-related data, and may include, for example, data for AI-based operations or data generated by AI-based operations.

[0115] Steps 401 through 409 described with reference to FIG. 4 must not necessarily be performed in the order exemplified in FIG. 4, and the order of at least some of the steps may vary. Additionally, at least some of steps 401 through 409 may be combined into a single step or omitted. That is, the steps exemplified in FIG. 4 may be performed in various modified forms.

[0116]

[0117] 6G communication systems and core implementation technologies of 6G systems

[0118] 5G systems define various operating bands within FR1 (frequency range 1), which includes 410 MHz to 7125 MHz, and FR2 (frequency range 2), which includes 24,250 MHz to 71,000 MHz. Various frequencies are being discussed as operating bands for subsequent 6G systems, and the use of frequencies higher than those of 5G systems is also being considered for wider bandwidth and higher transmission speeds. As one example, the use of the THz (Terahertz) frequency band, which includes approximately 100 GHz to 10 THz, is being discussed. The THz frequency band is a band that possesses both the penetrability of radio waves and the directivity of optical waves, and communication using the THz frequency band is expected to play a transitional role from existing radio-based communication to optical-based communication.

[0119] 6G systems utilizing the THz frequency band as described above aim for: 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 6G systems can be four aspects, such as "intelligent connectivity," "deep connectivity," "holographic connectivity," and "ubiquitous connectivity," and 6G systems can be designed to satisfy requirements such as those shown in [Table 1] below.

[0120] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0121] At this time, the 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLC), 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 by a 6G system applicable to the present disclosure. Referring to FIG. 5, the 6G system is expected to have simultaneous wireless communication connectivity 50 times higher than that of a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become an even more dominant technology in 6G communication by providing end-to-end latency of less than 1ms. In this case, 6G systems will exhibit significantly superior volumetric spectral efficiency, unlike the frequently used area-spectral efficiency. Since 6G systems can provide advanced battery technology for very long battery life and energy harvesting, mobile devices in 6G systems may not require separate charging. New network characteristics in 6G could be as follows: - Satellite integrated network: To provide a global mobile population, 6G is expected to be integrated with satellites. Integrating terrestrial, satellite, and airborne networks into a single wireless communication system is critical for 6G.

[0122] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and will update wireless evolution from "connected things" to "connected intelligence." AI can be applied at each stage of the communication process (or at each step of the signal processing described below).

[0123] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0124] - Ubiquitous Super 3D Connectivity: Connectivity to the network and core network functions of drones and very low Earth orbit satellites will create Super 3D connectivity in 6G ubiquitous.

[0125] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0126] - Small cell networks: The idea of ​​small cell networks was introduced to improve the quality of received signals in cellular systems as a result of increased throughput, energy efficiency, and spectrum efficiency. Consequently, small cell networks are an essential feature of communication systems for 5G and beyond 5G (5GB). Therefore, 6G communication systems also adopt the characteristics of small cell networks.

[0127] - Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.

[0128] - High-capacity backhaul: Backhaul connections are characterized as high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems can be possible solutions to this problem.

[0129] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communication is one of the functions of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0130] - Softwarization and virtualization: Softwarization and virtualization are two important features that form the basis of the design process in 5GB networks to ensure flexibility, reconfigurability, and programmability. Additionally, billions of devices can be shared across a shared physical infrastructure.

[0131] To satisfy the aforementioned characteristics, 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) may be adopted as core implementation technologies of the 6G system.

[0132] For example, THz communication can be utilized in 6G systems. THz communication is a communication that uses a spectrum in the frequency band between 0.3 THz and 3 THz with a corresponding wavelength in the range of 0.1 mm to 1 mm as shown in Fig. 6. Referring to Fig. 6, the frequency band of the THz wave is located in the intermediate region between the infrared band and the millimeter wave band; accordingly, the THz wave can be understood as a radio wave with the shortest wavelength and, at the same time, a light wave with the longest wavelength. As a result, the THz wave shares some characteristics of infrared and microwave waves, and specifically, can simultaneously possess the penetrability of electromagnetic waves and the directivity of light waves.

[0133]

[0134] FIG. 7 illustrates a transmitter structure applicable to the present disclosure.

[0135] Referring to Fig. 7, in order to modulate data onto an optical signal, the optical source of a laser can be passed through an optical wave guide to change the phase of the signal. At this time, data is loaded by changing electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform.

[0136] Data may be provided by a data signal generator. Here, the data may include various user data, configuration information, control information, etc. transmitted through a channel. Furthermore, the data may include data related to AI-based operations, for example, information for configuring an AI model, input / output data for tasks of an AI model, etc. To this end, components related to AI functions (e.g., an AI processing unit) may be included in the data signal generator or may interact with the data signal generator.

[0137] An O / E converter can generate THz pulses based on optical rectification by a nonlinear crystal, O / E conversion by a photoconductive antenna, emission from a bundle of relativistic electrons, etc. THz pulses generated in such a manner can have a length ranging from femtoseconds to picoseconds. The O / E converter performs down-conversion by utilizing the non-linearity of the device.

[0138] When considering the usage of the THz spectrum, it is highly likely that multiple contiguous GHz bands will be used for fixed or mobile service applications for THz systems. According to outdoor scenario criteria, available bandwidth can be classified based on an oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the length of the THz pulse for a single carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0139] Effective down-conversion from the infrared band to the THz band depends on how the nonlinearity of the photoelectric converter (O / E converter) is utilized. In other words, to achieve down-conversion to the desired THz band, it is required to design an O / E converter with the most ideal nonlinearity for transferring to that specific band. If an O / E converter that does not match the target frequency band is used, there is a high probability of errors occurring regarding the amplitude and phase of the corresponding pulse.

[0140] In a single-carrier system, a THz transceiver system can be implemented using a single photoelectric converter. Depending on the channel environment, in a multi-carrier system, as many photoelectric converters as there are carriers may be required. This phenomenon will be particularly pronounced in multi-carrier systems utilizing multiple broadbands according to the plans related to the aforementioned spectrum applications. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on a photoelectric converter can be transmitted in a specific resource region (e.g., a specific frame). The frequency domain of the specific resource region may include multiple chunks. Each chunk may consist of at least one component carrier (CC).

[0141]

[0142] AI technology can be introduced in 6G systems. Efficient resource management and optimization are required to maintain connectivity between various services and devices. AI technology may include techniques capable of performing data analysis, pattern recognition, and predictive modeling using AI / ML (artificial intelligence / machine learning) models. Here, an AI / ML model can be understood as a set of parameter values ​​and / or weight values ​​related to mathematical formulas or algorithms generated through learning, designed to discover patterns in input data or perform predictions. To create such an AI / ML model, an AI / ML model training procedure is required to build the model by learning the relationship between input and output in a data-driven manner. Various learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, can be utilized as training algorithms. Users can input specific data into a trained AI / ML model to generate output, and the procedure of obtaining output data by inputting input data into the AI / ML model can be referred to as AI / ML 'inference' or 'prediction'.

[0143] Network control parameters can be obtained as output through AI / ML inference using trained AI / ML models. Users can improve network efficiency by utilizing these output parameter values. For example, AI technology can be applied in various fields, such as wireless network resource allocation, traffic management, fault prediction, and Quality of Service (QoS) management. In particular, machine learning can efficiently allocate resources even in dynamically changing network environments based on real-time data. Therefore, AI technology can be utilized to provide hyper-connectivity and ultra-low latency.

[0144] In this case, AI / ML inference can be performed based on a combination of various devices. For example, the UE and the network can jointly perform AI / ML inference, and such an AI / ML model may be referred to as a two-sided AI / ML model or a two-sided model. In this case, the UE may perform the first part of the inference first, and the base station may perform the remaining inference, and vice versa. As another example, all inference may be performed by the UE, and such an AI / ML model may be referred to as a UE-side AI / ML model or a UE-side model.

[0145] In addition, life cycle management (LCM) for AI / ML models can be performed. Life cycle management may include model training, model deployment, model inference, model monitoring, and model updating. To this end, support may be required for data collection, model training, functionality / model identification, model delivery / transfer, model inference operations, functionality / model selection / enable / disable / fallback, functionality / model monitoring, model updating, and UE capabilities.

[0146] For example, AI / ML technology can be operated based on a functional framework such as FIG. 8. FIG. 8 illustrates an example of a functional framework for the application of AI / ML technology applicable to the present disclosure. First, a data collection function (810) generates training data (801), monitoring data (803), and / or inference data (805) containing processed input data by performing data preparation on input data collected from objects (e.g., UE, RAN node, network node, etc.). A model training function (820), having received the training data (801) from the data collection function (810), performs training on an AI / ML model using the training data (801) and provides the trained / updated model (813) to a model repository (840). The model repository (840) can store and retain the received trained / updated model (813).

[0147] A management function (830) may be used to control AI / ML model training. The management function (830) may control the operation of AI / ML models or AI / ML functions, or supervise their performance. To this end, the management function (830) may receive monitoring data (830) from the data collection function (810) and receive inference output (809) from the inference function (840). The management function (830) performs the role of managing the inference operation so that it can be performed efficiently based on the data received from the data collection function (810) and the inference function (840). That is, the management function (830) may transmit performance feedback or a retraining request (807) to the model training function (820) to improve the inference operation. Here, the performance feedback may be used to direct the learning goal or as a reward for reinforcement learning. Additionally, the management function (830) can provide management instructions (811) that instruct the inference function (840) to select AI / ML models or AI / ML-based functions to use, enable / disable them, or switch to non-AI / ML operations.

[0148] The inference function (840) generates an inference output (809) by performing inference and / or prediction using inference data (805) received by the data collection function (810). Here, the inference output (809) refers to the inference output of the AI / ML model used by the inference function (840), and the details of the inference output may vary depending on the use case. The AI / ML model used by the inference function (840) can be controlled by the management function (830). That is, the management function (830) can transmit a model transmission / delivery request signal (815) to the model repository function (850) to request the necessary AI / ML model, and the model repository function (850) can transmit the corresponding AI / ML model to the inference function (840) via a model transmission / delivery signal (817). Thus, the inference function (840) can perform inference using the AI / ML model (817) according to the received management instructions (811).

[0149] Additionally, the management function (830) can trigger or perform a specified task / action based on the inference output (809). Thus, the management function (830) can trigger a task / action on other objects (e.g., at least one UE, at least one RAN node, at least one network node, etc.) or on itself. Any one of the functions exemplified in FIG. 8 described above may be performed by two or more entities among the RAN, network node, network operator's OAM, or UE in collaboration. This may be referred to as a split AI operation.

[0150] To use an AI / ML model, not all of the functions (810 to 850) illustrated in FIG. 8 must be used, and the method of combining them is not limited to a specific method. Therefore, the functions (810 to 850) may be operated in an integrated manner, or some functions may be omitted. Furthermore, the functions (810 to 850) illustrated in FIG. 8 are not necessarily limited to being implemented as separate devices or apparatuses. For example, some or all of the functions (810 to 850) may be included in the processor (202) of FIG. 2. Additionally, the model storage function (850) may be included in the memory (204) of FIG. 2.

[0151]

[0152] FIG. 9 illustrates an example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 9 illustrates a case where a model training function (820) is included in a network node and a model inference function (840) is included in a RAN node. Referring to FIG. 9, in step 1, RAN node 1 and RAN node 2 transmit input data (e.g., training data) for training an AI model to a network node. Here, RAN node 1 and RAN node 2 may also transmit data collected from a UE (e.g., UE measurements related to RSRP, RSRQ, SINR of a serving cell and neighboring cells, UE location, 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 to perform 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 generates output data (e.g., prediction or decision) by performing AI model-based inference using the received inference data. In Step 6, if applicable, RAN Node 1 may transmit model performance feedback to the network nodes. In Step 7, RAN Node 1, RAN Node 2, and the UE (or 'RAN Node 1 and UE', or 'RAN Node 1 and RAN Node 2') perform an action based on the output data. For example, in the case of a load balancing action, 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.

[0153] Network nodes can manage AI models based on feedback information regarding the inference results of AI models. For example, network nodes can perform additional training on AI models or generate additional information about AI models (e.g., performance information, accuracy information, etc.). If additional training is performed on AI models, network nodes can distribute the updated AI models to RAN node 1.

[0154] As explained with reference to Fig. 9, model training can be performed by network nodes, and inference using the model can be performed by RAN node 1. In other words, the model training and inference functions can be distributed. Generally, model training requires a large amount of computational resources because it involves optimization using large amounts of data and complex algorithms. In contrast, inference is a process of drawing conclusions about new data using an already trained model, and therefore requires relatively fewer computational resources compared to model training. Therefore, by using the procedure of Fig. 9, model training can be performed through network nodes if the computational resources of the UE or RAN nodes are insufficient. Additionally, security regarding the AI ​​model can be ensured because the AI ​​model is not exposed to the UE.

[0155] FIG. 9 illustrates a case where the model training function (820) is included in a network node and the model inference function (840) is included in a RAN node, but the present disclosure is not limited thereto. For example, if the computational resources of the RAN node are sufficient, both the model training function (820) and the model inference function (840) may be included in RAN node 1. In this case, RAN node 1 receives training data for training an AI model from the UE and RAN node 2. RAN node 1 trains the AI ​​model using the received training data. Subsequently, RAN node 1 receives inference data for AI model inference from the UE and RAN node 2. RAN node 1 generates output data by performing AI model-based inference using the received inference data. Based on the output data, the UE, RAN node 1, and RAN node 2 can perform communication-related operations (e.g., handover, cell change). Subsequently, the UE and RAN node 2 can transmit feedback regarding the operations to RAN node 1. Therefore, RAN Node 1 can train the AI ​​model and update the AI ​​model it will use through feedback information regarding the inference results of the AI ​​model. According to the aforementioned method, since signaling with the network is not required for AI model training and inference, the network load or the latency to train the AI ​​model or receive inference results can be reduced. Additionally, since the UE performs inference using the AI ​​model, the UE's personal information is not transmitted to network nodes, etc. Consequently, security regarding personal information can be enhanced.

[0156] As another example, the model training function (820) may be included in the RAN node, and the model inference function (840) may be included in the UE. The RAN node receives training data for training an AI model from the UE and trains the AI ​​model using the received training data. The RAN node distributes the trained AI model to the UE. The UE can generate output data by performing inference based on the received AI model. At this time, the data for inference can be received from the RAN node or data acquired by the UE itself can be used. The UE and the RAN node can perform communication-related operations based on the output data generated by inference. Subsequently, the UE can send feedback regarding the operation to the RAN node. Thus, through feedback information regarding the inference results of the AI ​​model, the RAN node can train the AI ​​model and distribute the updated AI model to the UE. According to the method described above, the load on the RAN node can be reduced by having the model training function (820) in the RAN node and the model inference function (840) in the UE perform inference. In addition, if the UE performs inference using data acquired independently, even if the UE loses its connection to the RAN node after receiving the AI ​​model, the UE can continue to perform inference using the received AI model and operate based on the inference results.

[0157] According to the aforementioned framework and procedure, an AI model can be trained and utilized in a wireless communication system. The model training function (820) and the model inference function (840) can be combined in various ways and are not necessarily limited to the case of FIG. 9. In the aforementioned framework and procedure, various data such as input data, training data, and inference data are introduced, and the specific details of the aforementioned data may vary depending on the task in which the AI ​​model is utilized. For example, information used in the various embodiments of the present disclosure described below may be included in the aforementioned data.

[0158]

[0159] FIG. 10 illustrates an AI technology-based communication procedure applicable to the present disclosure. The detailed procedure illustrated in FIG. 10 may be combined with various embodiments of the present disclosure described below. For example, data generated according to various embodiments of the present disclosure may be used for operations (e.g., setup, training, inference, and / or data transmission / reception) in at least one of the detailed procedure illustrated in FIG. 10. As another example, the result of the inference illustrated in FIG. 10 may be used to transmit and / or receive data according to various embodiments of the present disclosure.

[0160] Referring to FIG. 10, in step S1001, at least one of the UE (1010), RAN node (1020), and network node (1030) performs an initial connection 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 S1003, at least one of the UE (1010), RAN node (1020), and network node (1030) performs a configuration procedure. Through the configuration procedure, parameters, resources, connections, and / or entities necessary to perform subsequent procedures at layers between the UE (1010) and the RAN node (1020) and / or at least one layer between the UE (1010) and the network node (1030) may be determined and / or created. At this time, the configuration procedure may be performed based on information, status, and / or characteristics of the AI ​​model used for subsequent training and inference.

[0161] In step S1005, at least one of the UE (1010), RAN node (1020), and network node (1030) performs a model training procedure. At least one of the UE (1010), RAN node (1020), and network node (1030) may collect training data and perform training using the training data. For example, the model training procedure may be performed as described with reference to FIG. 9. If an offline trained model is used, this step may be omitted.

[0162] In step S1007, at least one of the UE (1010), RAN node (1020), and network node (1030) performs a task using a trained model. That is, the task may be performed by the result of inference and / or prediction using the trained model. For example, the task may be a procedure belonging to a communication protocol, a preliminary operation for subsequent data transmission and / or reception, related to data transmission and / or reception, or related to data processing (e.g., encoding, decoding, etc.).

[0163] In step S1009, at least one of the UE (1010), RAN node (1020), and network node (1030) transmits and / or receives data. At this time, the result of the task performed in step 1007 may be used. In some cases, the task performed in step 1007 may include the transmission and / or reception of data, in which case this step may be omitted as it is part of step 1007.

[0164]

[0165] Specific embodiments of the present disclosure

[0166] The present disclosure is intended to prevent mismatches in the interpretation of semantic expressions between a source and a destination in semantic communication. Semantic communication refers to a communication technique in which a source and a destination transmit and receive semantic expressions and perform communication through the interpretation of semantic expressions. The source and the destination each possess background knowledge. Through background knowledge, the entropy of semantic communication can be reduced. However, because the background knowledge of the source and the destination differs, the source and the destination may interpret a single semantic expression differently, and a mismatch may occur. A matching method through the alignment of semantic expressions between a source and a destination to prevent such mismatches is disclosed below. The alignment of semantic expressions can be performed based on the transmission of a reference signal and the semantic expression of the reference signal. For the alignment of semantic expressions, semantic expressions can be connected to each other. For example, semantic expressions can be connected in a sequence. Semantic expressions connected in a sequence can be matched by aligning them based on at least one semantic expression or a codebook vector.

[0167]

[0168] Problems related to communication can be divided into three levels as illustrated in FIG. 11, according to the philosophy of Shannon and Weaver. FIG. 11 illustrates a communication model applicable to the present disclosure. The problem of Level A (1110) is a technical problem related to how accurately symbols in communication can be transmitted, and the problem of Level B (1120) is a semantic problem related to how accurately the transmitted symbols convey the desired meaning. The problem of Level C (1130) is an effectiveness problem related to how effectively the received meaning influences the operation in the desired manner.

[0169] Shannon's information theory focuses only on technical problems at Level A (1110). However, Weaver explains that if semantic transmitters, semantic receivers, and semantic noise are added to Shannon's communication model, Shannon's information theory is general enough to account for problems at Level B (1120) and Level C (1130).

[0170] Meanwhile, one of the various goals of 6G communication is to enable various new services that interconnect people and machines. Therefore, in addition to considering only the technical problems of Level A (1110) in the communication system, it is necessary to provide a semantic communication method that considers the semantic problems of Level B (1120). Semantic communication means that a first device and a second device corresponding to the source and destination, respectively, efficiently transmit and receive semantic information using background knowledge, which is common information. Referring to the communication model of FIG. 11, if the meaning of the intended message sent by the first device corresponding to the source is accurately interpreted by the second device corresponding to the destination, it can be said that correct semantic communication has been performed.

[0171] For semantic communication, a source can generate a semantic representation based on given or collected raw data and transmit the generated semantic representation to a destination. The destination interprets and reasons the received semantic representation according to the source's intent. In this context, semantic communication requires an approach that considers not the reduction of reconstruction errors occurring during the process of restoring the received semantic representation to the original raw data, but rather whether the downstream task—the task performed at the destination—can operate according to the source's intent using the received semantic representation. Therefore, the destination utilizes its own background knowledge when performing reasoning operations, and it is desirable that the background knowledge contained in the data transmitted from the source be reflected in the destination's background knowledge to obtain correct interpretation results. In other words, since the destination performs interpretation and reasoning operations on the semantic representation using background knowledge, it is necessary for the source and the destination to share the same background knowledge.

[0172] As mentioned above, semantic features generated at a source and transmitted to a destination must be created with downstream operations running at the destination in mind. Therefore, a task-oriented semantic communication system is required, and such a system enables the preservation of task-relevant information while introducing invariances useful for downstream operations.

[0173] Figure 12 conceptually illustrates the structure of semantic communication. The semantic communication in Figure 12 corresponds to the semantic communication at level B of Figure 11. Referring to Figure 12, a message transmitted from a source to a destination In relation to, it is defined as follows.

[0174] World model The Shannon entropy of is given by Equation 1. The Shannon entropy of the world model is called the model entropy of the semantic source.

[0175]

[0176] In mathematical formula 1, is the World model Shannon entropy, is a probability distribution, represents the model distribution.

[0177] World Model The probability distribution It is a set of interpretations, and is the model distribution, cast The model where is "true" When referred to as the set of its models, the message The logical probability of is given by Equation 2.

[0178]

[0179] In mathematical formula 2, is a message The logical probability of, is a probability distribution, is the model distribution, means world model. A symbol is a general propositional satisfaction relation ( is the usual propositional satisfaction relation), semantically meaning that it "entails the following result" or "is a stronger condition." This sign reveals an association from a semantic perspective.

[0180] semantic entropy It is expressed as in mathematical formula 3.

[0181]

[0182] In mathematical formula 3, Is semantic entropy, is a message It means the logical probability of.

[0183] Background Knowledge When considering , the set of possible worlds of Equations 2 and 3 is background knowledge It is restricted to a set compatible with . Therefore, semantic entropy is expressed as a conditional logical probability as shown in Equations 4 and 5.

[0184]

[0185] In mathematical formula 4, background knowledge Message considering The logical probability of, represents the model distribution.

[0186]

[0187] In mathematical formula 5, background knowledge Message considering The logical probability of, background knowledge Message considering It refers to the semantic entropy of.

[0188] for example, is statistical probabilities, and background knowledge Let us assume that the truth table is as shown in Table 2. Table 2 is and This is a truth table.

[0189] # probability10010.2520110.2531000.2541110.25

[0190] In Table 2, the possible worlds are It is "reduced" to a series of true assignments, namely Cases 1, 2, and 4. Thus, a conditional logical probability as in Equation 6 is obtained.

[0191]

[0192] In mathematical formula 6, background knowledge Message considering The logical probability that is true, background knowledge Message considering The logical probability that is true, Is and It refers to the logical probability that all are true.

[0193] Due to the influence of background knowledge, logical probabilities differ from priori statistical probabilities, and in a new distribution and is no longer logically independent. In other words, am.

[0194] Background Knowledge If exists, If this is a new distribution of the set of models, the new model distribution and Shannon entropy are expressed as Equation 7 and Equation 8, respectively.

[0195]

[0196] In mathematical formula 7, represents the model distribution.

[0197]

[0198] In mathematical formula 8, background knowledge A model considering It refers to the semantic entropy of.

[0199] In the example of Table 2, the model entropy of the source without considering background knowledge is given by Equation 9.

[0200]

[0201] In mathematical formula 9, is a model It refers to the semantic entropy of.

[0202] In the example of Table 2, the model entropy of the source considering background knowledge is given by Equation 10.

[0203]

[0204] In mathematical formula 10, background knowledge A model considering It refers to the semantic entropy of.

[0205] Equations 9 and 10 demonstrate that the presence of shared background knowledge allows for the compression of the message intended to be transmitted from the source without information loss, and enables the maximum extraction of information from the source using shorter messages. In other words, one of the main reasons why communication at the semantic level can provide performance improvements over communication at the conventional technical level is that background knowledge is taken into account. As previously mentioned, when semantic features are generated and transmitted considering downstream tasks located at the destination, the utilization of background knowledge can be seen as consistent with the purpose of performing semantic communication.

[0206] In order for semantic communication incorporating all the previously described components to be performed, a new layer called a semantic layer, which governs the overall operation of semantic data and messages, may be added to the communication device. This semantic layer can be located at the source and destination based on a task-oriented semantic communication system. To perform communication between the semantic layers located at the source and destination, it is necessary to define a protocol—a set of rules between layers—and a series of operational processes.

[0207] In semantic communication configured in a newly defined semantic layer, the source and destination require a consensus process regarding background knowledge information to perform accurate reasoning. Fig. 13 illustrates an example of semantic communication between devices having different background knowledge. Fig. 13 shows a device that distinguishes digits from 0 to 9 for the MNIST dataset. and a device that distinguishes between even and odd numbers The process of performing semantic communication between them is illustrated. Referring to Fig. 13, since the two devices have different background knowledge, they generate representations in different spaces for the same message signal. Therefore, the device The RX of the device Inference cannot be performed on the expression generated by .

[0208] To overcome the background knowledge mismatch problem in such semantic communication, a knowledge matching technique based on semantic channel equalization has been proposed. Semantic channel equalization is a method of performing preprocessing or postprocessing so that a representation generated from a source can be interpreted as an appropriate intent at the destination. The semantic channel equalizer is designed based on optimal transport theory, which finds a joint distribution that minimizes a distance-based cost function for two different data distributions. The source and destination are trained using a coupling matrix such as Equation 11, which represents the empirical distribution between the two through their respective representation samples.

[0209]

[0210] In mathematical formula 11, is the cost matrix, is the coupling matrix, is an empirical distribution, is the number of training samples corresponding to the i-th index of the source, is the number of training samples corresponding to the j-th index of the destination, and represents the probability mass in the discrete domain.

[0211] Transformation function used in semantic channel equalizers Optimization is performed as shown in Equation 12 so that it can be close to the coupling matrix.

[0212]

[0213] In mathematical formula 12, is a conversion function, is the coupling matrix, represents the number of training samples corresponding to the i-th index of the source.

[0214] In semantic communication, finding a transformation that prevents semantic mismatch is more important than finding an exact transformation function between two distributions. Therefore, when designing a semantic channel equalizer, a process is added as shown in Equation 13 to ensure that the source representation is adjacent to the center of the destination representation space by setting the ball radius parameter. Figure 14 illustrates an example of semantic channel equalization according to the ball radius parameter. Figure 14 shows the ball radius in the semantic channel equalizer design process The matching process between the source representation space and the destination representation space is illustrated accordingly. Referring to FIG. 14, the source representation is matched to the destination representation space according to the ball radius parameter. For example, when the ball radius parameter is 1, the source representation can be matched to the entire representation space of the destination, and when the ball radius parameter is 0, the source representation can be matched to the center point of the representation space of the destination.

[0215]

[0216] In mathematical formula 13, is a function to reduce the distribution of the destination, is the ball radius parameter, represents the center point of the destination distribution.

[0217] An object function that includes all the processes from Equation 11 to Equation 13 above can be represented as Equation 14, and a semantic channel equalizer can be designed through joint optimization.

[0218]

[0219] In mathematical formula 14, is a conversion function, is the coupling matrix, is a function to reduce the distribution of the destination, is the number of training samples corresponding to the i-th index of the source, represents the cost matrix. This is the part for regularization.

[0220] In order to execute the aforementioned semantic channel equalization-based knowledge matching protocol, the relationships between labels existing in different knowledge ( ) must be defined first. Figures 15a and 15b illustrate label relationships between two devices performing different tasks on the MNIST dataset. Figure 15a illustrates the label relationship between the task of distinguishing digits from 0 to 9 and the task of distinguishing even or odd numbers. Figure 15b illustrates the label relationship between the task of determining modulus 3 and the task of distinguishing digits from 0 to 9. Referring to Figures 15a and 15b, for the label relationships between the tasks to be defined, the source and destination must perform the process of sharing the location of the representation space for the reference signal by transmitting and receiving a reference signal based on the original data.

[0221] Furthermore, for semantic channel equalization to be designed, a coupling function must be calculated for the source and destination label representation spaces regarding the label relationship. To calculate the coupling function, a process in which the source and destination transmit and receive multiple representation samples is required. Additionally, the size of the coupling matrix increases with the number of data samples, and the computational complexity of the calculation process utilizing the coupling matrix will also increase with the number of data samples. To address this, the diameter of the ball in the semantic channel equalization process If you set to 0, you can design an equalizer without calculating the coupling matrix and requesting a destination representation sample as in Equation 15.

[0222]

[0223] In mathematical formula 15, is a conversion function, is the coupling matrix, is a function to reduce the distribution of the destination, is the number of training samples corresponding to the i-th index of the source, E is the center point of the destination distribution, represents the probability mass function of the distribution.

[0224] FIG. 16 illustrates an example in which a source representation is matched to a destination representation space. Referring to FIG. 16, a semantic channel equalization process designed based on Equation 15 can be described as a process in which the destination reports the central point of the representation space for each label index and maps the transmitted signal from the source to converge the representation to the central point. In the aforementioned process, inference is performed at the destination by mapping the received representation to the target label index of the destination. However, a problem arises in which information other than label information is lost from the message signal transmitted by the source. Therefore, the present disclosure defines a quantized codebook for the semantic representation space and proposes a background knowledge matching protocol based on a codebook alignment process between the source and the destination, as well as a signaling process for this purpose.

[0225]

[0226] The present disclosure proposes a plurality of processes for performing knowledge matching on different background knowledge at a source and a destination capable of performing semantic communication. The plurality of processes include a process for generating a codebook sequence by the source and the destination establishing an index order of codebook vectors through a space-filling vector quantization process, a process for setting an initial point to perform codebook sequence alignment by the source and the destination sharing a reference signal based on original data, an alignment process according to the codebook index order, a codebook vector grouping and selection process to mitigate ambiguity issues in the alignment process, and a semantic layer protocol and procedure according to the associated procedure in performing the aforementioned processes.

[0227] The semantic representation codebook alignment protocol proposed in the present disclosure is one in which a source and a destination have their own representation spaces ( It is assumed that a situation exists where vector quantization is performed on ) and a representation codebook is possessed. For example, it can be assumed that the source and destination possess a vector quantized representation space through a model that utilizes representation space quantization, such as a VQ-VAE (vector quantized variational auto-encoder). In other words, the representation space described below may be a vector quantized representation space. Furthermore, the source and destination have a label relationship ( It is assumed that ) is not yet defined.

[0228] The source and destination perform ordering for each codebook vector by performing space-fill vector quantization based on the semantic representation codebook they possess. The space-fill vector quantization method minimizes the sum of the minimum distance between each representation point and the interpolation line between the representation codebook vectors when performing vector quantization. Fig. 17a illustrates the representation codebook points. Fig. 17b illustrates the codebook sequence generated as a result of the space-fill vector quantization technique. Interpretability of the representation space is ensured by forming lines between the codebook points as shown in Fig. 17b for the representation codebook points possessed as shown in Fig. 17a.

[0229] Sources and destinations holding codebook sequences based on space-fill vector quantization generate representations based on their respective representation space codebooks by transmitting and receiving reference signals based on the original data. The source and destination align codebook sequences in the same label representation space according to the codebook index order.

[0230] FIG. 18 illustrates an example of codebook sequence alignment. FIG. 18 shows a background knowledge matching protocol for a representation space corresponding to a single label index.

[0231] Referring to Fig. 18, the representation space corresponding to the i-th label index of the source (1800) and the representation space corresponding to the j-th label index of the destination Codebook sequence alignment for background knowledge matching between (1810) is performed. The source and destination acquire a representation point of the reference signal in the representation space. Here, the reference signal contains the same original data. The acquired representation of the reference signal is matched to the line closest to the representation of the reference signal among the interpolation lines generated through space-fill vector quantization. Among the two codebook vectors corresponding to the matched line, the codebook vector closer to the representation of the reference signal is set as the initial index. The source and destination match codebook vectors based on the initial index and perform alignment according to the codebook sequence. For example, the initial index is codebook vector p m and q n In cases corresponding to, p m and q n The codebook sequences are sorted by matching them with each other. After the initial index matching, representation codebook sorting is performed according to the order of the codebook sequences corresponding to each label representation space.

[0232] FIG. 19 illustrates an example of codebook alignment for a plurality of labels according to one embodiment of the present disclosure. FIG. 19 illustrates an example of codebook alignment where the source and destination include the same number of labels.

[0233] Generally, the background knowledge possessed by the source and destination includes multiple labels. Therefore, in order for a knowledge matching protocol for multiple labels to be performed, a representation codebook alignment is performed using reference signals equal to the number of label indices for which knowledge matching is to be performed, as shown in FIG. 19. Referring to FIG. 19, the source and destination can perform representation codebook alignment by matching representation spaces in which representations obtained by the same reference signal exist. For example, P1 (1901) and Q1 (1911) are representation spaces containing representations obtained by the same reference signal and are matched with each other. In FIG. 19, P2 (1903) is matched with Q2 (1913), P4 (1905) is matched with Q4 (1915), and P3 (1907) is matched with Q3 (1917). For example, the matching of each representation space can be performed in the same manner as shown in FIG. 18.

[0234] FIG. 20 illustrates another example of codebook alignment for a plurality of labels according to one embodiment of the present disclosure. FIG. 20 illustrates codebook alignment in the case where the source and destination include different numbers of labels.

[0235] Referring to FIG. 20, regarding the representation codebooks held by the source and destination, ambiguity may arise during the codebook alignment process if the number of codebook vectors belonging to the label representation space for which knowledge matching is to be performed differs. To resolve this, the source and destination perform grouping and selection procedures for the representation codebook vectors. In FIG. 20 (2001) (2011), (2007) (2017) A knowledge matching case regarding the representation space represents a case where the number of codebook vectors included in the source's label representation space is greater than that of the destination. In this case, the source performs codebook alignment by grouping codebook vector pairs with the minimum distance from each other in the label representation space so that a one-to-one mapping is formed between the source and the destination. Also, in FIG. 20 (2003) (2013), (2005) (2015) A knowledge matching case regarding the representation space represents a case where the number of codebook vectors included in the source's label representation space is smaller than that of the destination. In such cases, the destination performs matching with the source's codebook sequence by selecting the codebook vector closest to the center point of the label representation space from among the pairs of codebook vectors that have the minimum distance from each other in the destination's label representation space. In other words, devices in which the number of codebook vectors in the representation space is greater than the number of codebook vectors in the matched representation space perform grouping.

[0236]

[0237] FIG. 21 illustrates a procedure for matching background knowledge based on semantic expression codebook alignment according to one embodiment of the present disclosure. FIG. 21 illustrates a signal exchange between a first device (2100) and a second device (2110). Here, the first device (2100) refers to a device that transmits a reference signal, and the second device (2110) refers to a device that receives a reference signal.

[0238] Referring to FIG. 21, in steps S2101 and S2103, the first device (2100) and the second device (2110) generate a representation codebook sequence. For example, the representation codebook sequence can be generated by performing a space-fill vector quantization operation on the semantic representation space held by the first device (2100) and the second device (2110), respectively. The representation codebook sequence refers to a sequence in which codebook vectors of the semantic representation space are connected using an interpolation line. Based on the representation codebook sequence, the codebook vectors included in the representation codebook sequence may have an order.

[0239] In step S2105, the first device (2100) and the second device (2110) report the number of labels for knowledge matching. In other words, the first device (2100) and the second device (2110) define and report the number of label representation spaces for performing knowledge matching.

[0240] In step S2107, the first device (2100) transmits a reference signal to the second device (2110). The first device (2100) may transmit the reference signal as many times as there are label representation spaces. For example, the reference signal may include original data before the semantic representation is acquired.

[0241] In step S2109, the first device (2100) and the second device (2110) align the expression codebook sequence. That is, the first device (2100) and the second device (2110) generate an expression for a reference signal and align the expression codebook sequence based on the semantic expression of the reference signal and adjacent codebook vectors and codebook sequences. The alignment of the expression codebook sequence can be performed based on an initial index based on the semantic expression of the reference signal and the order of the codebook sequences.

[0242] In steps S2111 and S2113, the first device (2100) and the second device (2110) group codebook vector pairs. For example, at least one of the first device (2100) and the second device (2110), where the number of codebook vectors included in the representation space is greater than the number of codebook vectors in the matched representation space, can group the codebook vectors into codebook vector pairs. Codebook vector pairs can be grouped based on the distance between the codebook vectors.

[0243] In step S2115, the first device (2100) and the second device (2110) terminate background knowledge matching. Background knowledge matching is performed by aligning the codebook sequences of each of the representation spaces. In other words, background knowledge matching is performed based on codebook sequence alignment.

[0244] Through the aforementioned procedure, the source and destination determine the initial codebook point and alignment direction for performing semantic representation codebook alignment, and execute the background knowledge matching protocol. In the above process, if the number of codebook vectors existing in each label representation space differs, the source and destination perform a grouping and selection process for the closest pair of codebook vectors, thereby establishing a one-to-one relationship for the codebook alignment of the source and destination.

[0245]

[0246] Hereinafter, a detailed embodiment of the aforementioned background knowledge matching protocol is described. The background knowledge matching protocol is performed by a first device and a second device. The first device may be a source or a destination, and the second device may be a destination or a source. In other words, the first device and the second device may each be a source and a destination, or a destination and a source. The first device and the second device are devices that already possess a semantic representation codebook and can represent a reference signal as a semantic representation by the semantic representation codebook.

[0247]

[0248] FIG. 22 illustrates an example of a procedure for performing semantic communication of a terminal according to one embodiment of the present disclosure. FIG. 22 illustrates a method performed by a terminal (e.g., the first device (2100) of FIG. 21) that generates and transmits a semantic representation.

[0249] Referring to FIG. 22, in step S2201, the terminal performs an initial connection procedure. The terminal detects a synchronization signal from a base station, receives system information, and can perform a random access procedure. Specifically, the terminal obtains information related to a random access channel included in the system information and can transmit a random access preamble based on the obtained information. At this time, the terminal can select a random access channel occasion (RO) for transmitting the random access preamble based on the detected synchronization signal.

[0250] In step S2203, the terminal transmits capability information. The capability information includes information indicating the hardware or software capabilities of the terminal. Although not illustrated in FIG. 22, the terminal may receive a request message for capability information from a base station and transmit a message containing capability information in response to the request message. According to one embodiment, the capability information may be information related to semantic communication and may include at least one of information related to whether task-oriented semantic communication is supported, whether background knowledge mapping is supported, or whether a codebook vector is possessed. According to another embodiment, information related to semantic communication may be transmitted through a procedure other than capability information. For example, information related to semantic communication may be transmitted through the signaling of step S2205 below.

[0251] In step S2205, the terminal performs signaling for configuration related to communication. For example, the terminal may perform control signaling with a base station for configuration necessary to perform communication. According to one embodiment, the terminal may transmit and / or receive configuration information for background knowledge matching of semantic communication. Specifically, the terminal may receive from the base station configuration information related to at least one of the following: the number of labels for knowledge matching, the number of codebook vectors included in each label, a ball radius parameter for changing the size of the representation space, a matrix for preprocessing or postprocessing (e.g., a transformation matrix or a coupling matrix), and a parameter for vector quantization (e.g., space-fill vector quantization). That is, the terminal may receive configuration information necessary for semantic communication according to various embodiments of the present disclosure.

[0252] In step S2207, the terminal transmits a reference signal. The reference signal may be transmitted to a base station or another terminal. For example, the base station or another terminal may be the destination of the semantic communication. To transmit the reference signal, the terminal may determine the number of reference signals. For example, the terminal may negotiate the number of reference signals to be transmitted with the base station or another terminal, receive the number of reference signals from the base station or another terminal, or transmit a number of reference signals defined by the terminal. The terminal may generate a semantic representation based on the transmitted reference signals.

[0253] In step S2209, the terminal performs background knowledge matching based on the semantic representation of the reference signal. The terminal can perform background knowledge matching by aligning codebook sequences based on the semantic representation of the reference signal. For example, the terminal can perform background knowledge matching by setting an initial index based on the position of the semantic representation of the reference signal in the representation space, and aligning the codebook sequences of the representation spaces based on the initial index.

[0254] Although not illustrated in FIG. 22, after background knowledge matching is performed, the terminal can generate and transmit a semantic expression based on the matched background knowledge. Additionally, the terminal can receive and interpret the semantic expression based on the matched background knowledge.

[0255]

[0256] FIG. 23 illustrates an example of a communication procedure based on background knowledge matching according to one embodiment of the present disclosure. FIG. 23 illustrates a procedure performed by a first device (e.g., the first device (2100) of FIG. 21). For example, the first device may be a terminal or a base station.

[0257] Referring to FIG. 23, in step S2301, the first device transmits a reference signal. The reference signal is a signal for aligning a codebook sequence. Here, the reference signal may be transmitted a predetermined number of times. For example, the predetermined number may be defined by the first device and the second device prior to the procedure of FIG. 23. As another example, the predetermined number may be equal to the number of label representation spaces of the first device and the second device.

[0258] In step S2303, the first device generates a semantic representation of a reference signal. The first device can generate a semantic representation from the reference signal based on a semantic representation codebook it possesses. The first device can generate a semantic representation of at least one reference signal. One semantic representation can be generated for each reference signal. A semantic representation generated from one reference signal can be located in a single label representation space. In other words, semantic representations can be generated in multiple label representation spaces based on multiple reference signals.

[0259] In step S2305, the first device performs background knowledge matching based on the semantic representation of the reference signal. To perform background knowledge matching, the first device may align codebook sequences based on the semantic representation of the reference signal. For example, the first device determines a line between the semantic representation of the reference signal and the codebook vector closest to it, and sets the codebook vector located on that line that is closest to the semantic representation of the reference signal as an initial index. The first device may align codebook sequences by mapping the initial index of the first device and the initial index of the second device. The first device may perform background knowledge matching by performing codebook sequence alignment for a plurality of label representation spaces.

[0260] In step S2307, the first device performs communication based on the matching result. The first device may generate a signal based on the matching result and transmit the signal. For example, the signal transmitted to the second device may be a semantic representation generated based on the matched background knowledge. Additionally, the first device may interpret the signal received from the second device based on the matching result. Here, the transmitted or received signal may include a semantic representation.

[0261]

[0262] FIG. 24 illustrates an example of a background knowledge matching procedure according to one embodiment of the present disclosure. FIG. 24 may be an example of a procedure corresponding to step S2305 of FIG. 23. FIG. 24 illustrates a procedure performed by a first device (e.g., the first device (2100) of FIG. 21). For example, the first device may be a terminal or a base station.

[0263] Referring to FIG. 24, in step S2401, the first device generates a codebook sequence. For example, the codebook sequence may be generated by performing space-fill vector quantization on the codebook vectors held by the first device. The codebook sequence may be a sequence of codebook vectors connected in a row. For example, the codebook sequence may be generated using a space-fill vector quantization technique.

[0264] In step S2403, the first device generates a semantic representation of a reference signal. The semantic representation of the reference signal may be generated in a label representation space. The label representation space may be a space containing at least one codebook vector. A semantic representation of a single reference signal may be generated in a single label representation space. The reference signal may be a signal transmitted or received equally by the first device and the second device. The reference signal may be original data.

[0265] In step S2405, the first device performs matching based on the semantic representation of the reference signal. To perform matching, the first device sets an initial index. For example, the initial index may be set to a codebook vector that is closest to the semantic representation of the reference signal among the codebook vectors located on the line between the codebook vectors closest to the semantic representation of the reference signal. As an example, to perform matching, the first device may obtain information related to the initial index of the second device. The first device may sort the codebook sequences based on the initial indices of the first device and the second device, and match the sorted codebook vectors with each other.

[0266]

[0267] FIG. 25 illustrates an example of a background knowledge matching procedure for a plurality of label representation spaces according to one embodiment of the present disclosure. FIG. 25 illustrates a procedure performed by a first device (e.g., the first device (2100) of FIG. 21). For example, the first device may be a terminal or a base station.

[0268] Referring to FIG. 25, in step S2501, the first device defines the number of reference signals. The number of reference signals may be defined by at least one of the first device and the second device. For example, the number of reference signals may be defined by the first device and reported to the second device. The number of reference signals may be defined based on the number of label representation spaces.

[0269] In step S2503, the first device can generate a semantic representation of at least one reference signal. For example, the semantic representation of the reference signal can be generated based on a codebook held by the first device. The semantic representation of the reference signal can be located in a label representation space. A single semantic representation of a reference signal can be located in a single label representation space. The label representation spaces of the first device and the second device, where semantic representations of the same reference signal are respectively located, can be mapped to each other.

[0270] In step S2505, the first device may perform codebook-based grouping. Grouping may be performed according to the number of codebook vectors included in the label representation space. For example, grouping may be performed according to the number of codebook vectors in the label representation spaces of the first device and the second device that are mapped to each other. For example, grouping may be performed only in the label representation space that contains a greater number of codebook vectors than the other device. The first device may group the pairs of codebook vectors that are closest to each other within a label representation space into a single group. If there are multiple pairs of codebook vectors that are closest to each other, the pair of codebook vectors closest to the center point of the label representation space may be grouped together.

[0271] In step S2507, the first device performs matching based on the grouping result and the semantic representation of the reference signal. The first device may align the codebook sequence based on an initial index based on the semantic representation of the reference signal and perform background knowledge matching. One group may be matched with one codebook vector. For example, if there are five codebook vectors in the label representation space of the first device and four codebook vectors in the corresponding label representation space of the second device, the first device may perform matching with the codebook vector of the second device by grouping the two codebook vectors.

[0272]

[0273] According to one embodiment of the present disclosure, the procedures of FIGS. 23 through 25 may be performed by a second device (e.g., the second device (2110) of FIG. 21). However, if the procedure of FIG. 23 is performed by the second device, the second device may receive a reference signal in step S2301.

[0274]

[0275] Hereinafter, examples of wireless device applications to which various embodiments of the present disclosure are applied will be described.

[0276] FIG. 26 illustrates an example of a wireless device applicable to the present disclosure. The wireless device may be implemented in various forms depending on the use—example / service (see FIG. 1).

[0277] Referring to FIG. 26, the wireless device (200) corresponds to the wireless device (200) of FIG. 2 and may be composed of various elements, components, units / parts, and / or modules. For example, the wireless device (200) may include a communication unit (210), a control unit (220), a memory unit (230), and additional elements (240). The communication unit may include a communication circuit (212) and transceiver(s) (214). For example, the communication circuit (212) may include one or more processors (202) and / or one or more memories (204) of FIG. 2. For example, the transceiver(s) (214) may include one or more transceivers (206) and / or one or more antennas (208) of FIG. 2. The control unit (220) is electrically connected to the communication unit (210), the memory unit (230), and additional elements (240) and controls the overall operation of the wireless device. For example, the control unit (220) can control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (230). Additionally, the control unit (220) can transmit information stored in the memory unit (230) to an external entity (e.g., another communication device) via a wireless / wired interface through the communication unit (210), or store information received from an external entity (e.g., another communication device) via a wireless / wired interface through the communication unit (210) in the memory unit (230).

[0278] The additional element (240) may be configured in various ways depending on the type of wireless device. For example, the additional element (240) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a holographic device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 1, 400), a base station (Fig. 1, 200), a network node, etc. Depending on the use—e.g., service—the wireless device may be movable or used in a fixed location.

[0279] In FIG. 26, various elements, components, units / parts, and / or modules within the wireless device (200) may be entirely interconnected via a wired interface, or at least partially connected via a communication unit (210). For example, within the wireless device (200), the control unit (220) and the communication unit (210) may be connected via a wire, and the control unit (220) and the first unit (e.g., 230, 240) may be connected wirelessly via the communication unit (210). Additionally, each element, component, unit / part, and / or module within the wireless device (200) may include one or more additional elements. For example, the control unit (220) may be composed of one or more sets of processors. For example, the control unit (220) may be composed of a set of a communication control processor, an application processor, an Electronic Control Unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory and / or a combination thereof.

[0280] Hereinafter, an implementation example of FIG. 26 will be described in more detail with reference to the drawings.

[0281] FIG. 27 illustrates an example of a portable device applicable to the present disclosure. The portable device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch, smart glasses), or a portable computer (e.g., a laptop). The portable device may be referred to as a Mobile Station (MS), a User Terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT).

[0282] Referring to FIG. 27, the portable device (200) may include an antenna unit (208), a communication unit (210), a control unit (220), a memory unit (230), a power supply unit (240a), an interface unit (240b), and an input / output unit (240c). The antenna unit (208) may be configured as part of the communication unit (210). Blocks 210 to 230 / 240a to 240c of FIG. 27 correspond to blocks 210 to 230 / 240 of FIG. 26, respectively.

[0283] The communication unit (210) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (220) can control the components of the portable device (200) to perform various operations. The control unit (220) may include an AP (Application Processor). The memory unit (230) can store data / parameters / programs / code / commands required for the operation of the portable device (200). Additionally, the memory unit (230) can store input / output data / information, etc. The power supply unit (240a) supplies power to the portable device (200) and may include wired / wireless charging circuits, batteries, etc. The interface unit (240b) can support the connection between the portable device (200) and other external devices. The interface unit (240b) may include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (240c) can receive or output video information / signals, audio information / signals, data, and / or information input by a user. The input / output unit (240c) may include a camera, a microphone, a user input unit, a display unit (240d), a speaker and / or a haptic module, etc.

[0284] For example, in the case of data communication, the input / output unit (240c) acquires information / signals (e.g., touch, text, voice, image, video) input by the user, and the acquired information / signals can be stored in the memory unit (230). The communication unit (210) converts the information / signals stored in the memory into wireless signals and can directly transmit the converted wireless signals to another wireless device or to a base station. Additionally, the communication unit (210) can receive wireless signals from another wireless device or base station and then restore the received wireless signals to their original information / signals. The restored information / signals are stored in the memory unit (230) and then can be output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (240c).

[0285] FIG. 28 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure. The vehicle or autonomous vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, etc.

[0286] Referring to FIG. 28, a vehicle or autonomous vehicle (200-1) may include an antenna unit (208-1), a communication unit (210-1), a control unit (220-1), a driving unit (240a-1), a power supply unit (240b-1), a sensor unit (240c-1), and an autonomous driving unit (240d-1). The antenna unit (208-1) may be configured as part of the communication unit (210-1). Blocks 210-1 / 230-1 / 240a-1 to 240d-1 of FIG. 28 correspond to blocks 210 / 230 / 240 of FIG. 26, respectively.

[0287] The communication unit (210-1) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (Road Side Unit), etc.), and servers. The control unit (220-1) can perform various operations by controlling elements of the vehicle or autonomous vehicle (200-1). The control unit (220-1) may include an Electronic Control Unit (ECU). The driving unit (240a-1) can drive the vehicle or autonomous vehicle (200-1) on the ground. The driving unit (240a-1) may include an engine, motor, power train, wheels, brakes, steering device, etc. The power supply unit (240b-1) supplies power to the vehicle or autonomous vehicle (200-1) and may include wired / wireless charging circuits, batteries, etc. The sensor unit (240c-1) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (240c-1) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (240d-1) may implement technologies such as maintaining the driving lane, technologies for automatically adjusting speed such as adaptive cruise control, technologies for automatically driving along a predetermined path, and technologies for automatically setting a path and driving when a destination is set.

[0288] For example, the communication unit (210-1) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (240d-1) can generate an autonomous driving path and a driving plan based on the acquired data. The control unit (220-1) can control the drive unit (240a-1) so that the vehicle or the autonomous vehicle (200-1) moves along the autonomous driving path according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (210-1) can acquire the latest traffic information data from an external server non-periodically and can acquire surrounding traffic information data from surrounding vehicles. Additionally, during autonomous driving, the sensor unit (240c-1) can acquire vehicle status and surrounding environment information. The autonomous driving unit (240d-1) can update the autonomous driving path and the driving plan based on the newly acquired data / information. The communication unit (210-1) can transmit information regarding the vehicle location, autonomous driving path, driving plan, etc. to an external server. An external server can predict traffic information data in advance using AI technology, etc., based on information collected from vehicles or autonomous vehicles, and can provide the predicted traffic information data to vehicles or autonomous vehicles. If the device (220-2) is an autonomous vehicle, it can perform the same procedure as the vehicle or autonomous vehicle (200-1). In addition, if the device (220-2) is a base station or a roadside base station, the device (220-2) can transmit data and control signals to the vehicle or autonomous vehicle (200-1) through the communication unit (210-2).

[0289] FIG. 29 illustrates an example of a vehicle applicable to the present disclosure. The vehicle may be implemented as a means of transport, a train, an aircraft, a ship, etc. Referring to FIG. 29, the vehicle (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), and a position measurement unit (240b). Here, blocks 210 to 230 / 240a to 240b correspond to blocks 210 to 230 / 240 of FIG. 26, respectively.

[0290] The communication unit (210) can transmit and receive signals (e.g., data, control signals, etc.) with other vehicles or external devices such as base stations. The control unit (220) can control the components of the vehicle (200) to perform various operations. The memory unit (230) can store data / parameters / programs / codes / commands that support various functions of the vehicle (100). The input / output unit (240a) can output AR / VR objects based on information within the memory unit (230). The input / output unit (240a) may include a HUD. The position measurement unit (240b) can acquire position information of the vehicle (200). The position information may include absolute position information of the vehicle (200), position information within the driving line, acceleration information, position information relative to surrounding vehicles, etc. The position measurement unit (240b) may include GPS and various sensors.

[0291] For example, the communication unit (210) of the vehicle (200) can receive map information, traffic information, etc. from an external server and store it in the memory unit (230). The location measurement unit (240b) can acquire vehicle location information through GPS and various sensors and store it in the memory unit (230). The control unit (220) creates a virtual object based on map information, traffic information, and vehicle location information, etc., and the input / output unit (240a) can display the created virtual object on the glass window inside the vehicle (240a-1, 240a-2). In addition, the control unit (220) can determine whether the vehicle (200) is operating normally within the driving line based on the vehicle location information. If the vehicle (200) deviates abnormally from the driving line, the control unit (220) can display a warning on the glass window inside the vehicle through the input / output unit (240a). Additionally, the control unit (220) can broadcast a warning message regarding a driving abnormality to surrounding vehicles through the communication unit (210). Depending on the situation, the control unit (220) can transmit the vehicle's location information and information regarding the driving / vehicle abnormality to relevant authorities through the communication unit (210).

[0292] FIG. 30 illustrates an example of an XR device applicable to the present disclosure. The XR device may be implemented as an HMD, a Head-Up Display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc.

[0293] Referring to FIG. 30, the XR device (200a) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), a sensor unit (240b), and a power supply unit (240c). Here, blocks 210 to 230 / 240a to 240c of FIG. 30 correspond to blocks 210 to 230 / 240 of FIG. 26, respectively.

[0294] The communication unit (210) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, mobile devices, or media servers. The media data may include video, images, sound, etc. The control unit (220) can control the components of the XR device (200a) to perform various operations. For example, the control unit (220) may be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation, and processing. The memory unit (230) may store data / parameters / programs / code / commands required for driving the XR device (200a) or creating an XR object. The input / output unit (240a) acquires control information, data, etc. from the outside and can output the created XR object. The input / output unit (240a) may include a camera, microphone, user input unit, display unit, speaker and / or haptic module, etc. The sensor unit (240b) can obtain XR device status, surrounding environment information, user information, etc. The sensor unit (240b) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc. The power supply unit (240c) supplies power to the XR device (200a) and may include a wired / wireless charging circuit, a battery, etc.

[0295] For example, the memory unit (230) of the XR device (200a) may contain information (e.g., data, etc.) necessary for creating an XR object (e.g., AR / VR / MR object). The input / output unit (240a) may receive a command to operate the XR device (200a) from the user, and the control unit (220) may operate the XR device (200a) according to the user's operation command. For example, if the user intends to watch movies, news, etc. through the XR device (200a), the control unit (220) may transmit content request information to another device (e.g., mobile device (200b)) or a media server through the communication unit (230). The communication unit (230) may download / stream content such as movies, news, etc. from another device (e.g., mobile device (200b)) or a media server to the memory unit (230). The control unit (220) controls and / or performs procedures such as video / image acquisition, (video / image) encoding, and metadata generation / processing for the content, and can generate / output an XR object based on information about the surrounding space or real object acquired through the input / output unit (240a) / sensor unit (240b).

[0296] Additionally, the XR device (200a) is wirelessly connected to the mobile device (200b) through the communication unit (210), and the operation of the XR device (200a) can be controlled by the mobile device (200b). For example, the mobile device (200b) can act as a controller for the XR device (200a). To this end, the XR device (200a) can acquire three-dimensional position information of the mobile device (200b), and then generate and output an XR object corresponding to the mobile device (200b).

[0297] FIG. 31 illustrates an example of a robot applicable to the present disclosure. Robots may be classified into industrial, medical, domestic, military, etc., depending on the purpose or field of use.

[0298] Referring to FIG. 31, the robot (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), a sensor unit (240b), and a driving unit (240c). Here, blocks 210 to 230 / 240a to 240c of FIG. 31 correspond to blocks 210 to 230 / 240 of FIG. 26, respectively.

[0299] The communication unit (210) can transmit and receive signals (e.g., driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The control unit (220) can control the components of the robot (200) to perform various operations. The memory unit (230) can store data / parameters / programs / codes / commands that support various functions of the robot (200). The input / output unit (240a) can acquire information from outside the robot (200) and output information to outside the robot (200). The input / output unit (240a) may include a camera, microphone, user input unit, display unit, speaker and / or haptic module, etc. The sensor unit (240b) can obtain internal information of the robot (200), surrounding environment information, user information, etc. The sensor unit (240b) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, a radar, etc. The driving unit (240c) may perform various physical movements, such as moving robot joints. Additionally, the driving unit (240c) may enable the robot (200) to travel on the ground or fly in the air. The driving unit (240c) may include an actuator, a motor, a wheel, a brake, a propeller, etc.

[0300] FIG. 32 illustrates an example of an AI device applicable to the present disclosure.

[0301] AI devices can be implemented as stationary devices or mobile devices, such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc.

[0302] Referring to FIG. 32, the AI ​​device (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a / 240b), a learning processor unit (240c), and a sensor unit (240d). Blocks 210 to 230 / 240a to 240d of FIG. 32 correspond to blocks 210 to 230 / 140 of FIG. 26, respectively.

[0303] The communication unit (210) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning model, control signal, etc.) with external devices such as other AI devices (e.g., 100a to 100f, 120 in FIG. 1) or AI servers (e.g., 100g in FIG. 1) using wired and wireless communication technology. To do this, the communication unit (210) can transmit information within the memory unit (230) to an external device or transmit signals received from an external device to the memory unit (230).

[0304] The control unit (220) can determine at least one executable operation of the AI ​​device (200) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. The control unit (220) can perform the determined operation by controlling the components of the AI ​​device (200). For example, the control unit (220) can request, search, receive, or utilize data from the learning processor unit (240c) or the memory unit (230), and can control the components of the AI ​​device (200) to execute a predicted operation or an operation determined to be desirable among at least one executable operation. Additionally, the control unit (220) can collect historical information, including the operation content of the AI ​​device (200) or user feedback regarding the operation, and store it in the memory unit (230) or the learning processor unit (240c), or transmit it to an external device such as an AI server (Fig. 1, 100g). The collected historical information can be used to update the learning model.

[0305] The memory unit (230) can store data that supports various functions of the AI ​​device (200). For example, the memory unit (230) can store data obtained from the input unit (240a), data obtained from the communication unit (210), output data from the learning processor unit (240c), and data obtained from the sensing unit (140). Additionally, the memory unit (230) can store control information and / or software code required for the operation / execution of the control unit (220).

[0306] The input unit (240a) can acquire various types of data from outside the AI ​​device (200). For example, the input unit (220) can acquire training data for model training and input data to which the training model is applied. The input unit (240a) may include a camera, a microphone and / or a user input unit, etc. The output unit (240b) can generate output related to visual, auditory, or tactile senses, etc. The output unit (240b) may include a display unit, a speaker and / or a haptic module, etc. The sensing unit (140d) can obtain at least one of internal information of the AI ​​device (200), surrounding environment information of the AI ​​device (200), and user information using various sensors. The sensing unit (140d) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc.

[0307] The learning processor unit (240c) can train a model composed of an artificial neural network using training data. The learning processor unit (240c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 1, 100g). The learning processor unit (240c) can process information received from an external device through the communication unit (210) and / or information stored in the memory unit (230). Additionally, the output value of the learning processor unit (240c) can be transmitted to an external device through the communication unit (210) and / or stored in the memory unit (230).

[0308]

[0309] The proposed methods described above may be implemented independently, but they may also be implemented in the form of a combination (or merger) of some of the proposed methods. Rules may be defined so that the base station informs the terminal of the application status of the proposed methods (or information regarding the rules of the proposed methods) through a predefined signal (e.g., a physical layer signal or an upper layer signal).

[0310] The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential features described herein. Accordingly, the above detailed description should not be interpreted restrictively in all respects and should be considered illustrative. The scope of the present disclosure shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are included within the scope of the present disclosure. Furthermore, embodiments may be constructed by combining claims that are not explicitly related in the claims, or new claims may be included by amendments made after filing.

[0311] One embodiment can be applied to various wireless access systems. Examples of various wireless access systems include 3GPP (3rd Generation Partnership Standard Signal Chip Project) or 3GPP2 systems.

[0312] One embodiment can be applied not only to the various wireless access systems mentioned above, but also to all technical fields utilizing the various wireless access systems. Furthermore, the proposed method can be applied to mmWave and THz communication systems utilizing the ultra-high frequency band.

[0313] Additionally, some embodiments may be applied to various applications such as autonomous vehicles and drones.

Claims

1. Regarding the method, A step of performing an initial connection procedure by the first device; Step of transmitting capability information; A step of performing signaling for communication-related settings; A step of transmitting a reference signal to a second device; A step of generating a semantic representation based on a reference signal; and A method comprising the step of matching background knowledge of the first device with background knowledge of the second device based on the above semantic expression.

2. In Paragraph 1, The above semantic representation is generated on the representation space of the first device, and The above representation space comprises a codebook sequence in which the codebook vectors of the first device and the codebook vectors of the first device are connected by at least one line.

3. In Paragraph 2, The above matching step is, In the representation space of the first device, the step of selecting the line closest to the semantic representation among the at least one line; and A step of setting the codebook vector closest to the semantic expression among the codebook vectors located on the above line as an initial index; and A method comprising the step of matching background knowledge based on the above initial index.

4. In Paragraph 3, The step of matching background knowledge based on the above initial index is, A step of sorting the codebook sequence based on the initial index of the first device and the initial index of the second device; and A method comprising the step of matching the codebook vectors of the first device and the codebook vectors of the second device based on the alignment of the codebook sequence.

5. In Paragraph 4, The step of matching background knowledge based on the above initial index is, A step of comparing the number of codebook vectors included in the representation space of the first device and the representation space of the second device; The method further includes the step of grouping at least some of the codebook vectors included in the representation space of the first device based on the comparison result. A method in which the representation space of the first device and the representation space of the second device are matched based on a group including at least a portion.

6. In Paragraph 5, The above grouping is a method performed only when the number of codebook vectors of the first device is greater.

7. In Paragraph 6, The above grouping is a method of generating a single group of codebook vectors that are closest to each other.

8. In Paragraph 6, The above grouping is a method of grouping codebook vectors closest to the center of the representation space.

9. In Paragraph 1, The above reference signal is a method comprising a number of reference signals defined by the first device and the second device.

10. In Paragraph 9, The number defined above includes the number of representation spaces of the first device and the second device.

11. Regarding the method, A step of performing an initial connection procedure by a second device; Step of transmitting capability information; A step of performing signaling for communication-related settings; A step of generating a semantic representation based on a reference signal; and Based on the above semantic representation, the method includes the step of matching the codebook vectors of the second device and the codebook vectors of the first device, wherein The above reference signal is a method of receiving from the first device.

12. In Paragraph 11, The above semantic representation is generated on the representation space of the second device, and The above representation space comprises a codebook vector of the second device and a codebook sequence connecting the codebook vectors of the second device with at least one line.

13. In Paragraph 12, The above matching step is, In the representation space of the second device, the step of selecting the line closest to the semantic representation among the at least one line; and A step of setting the codebook vector closest to the semantic expression among the codebook vectors located on the above line as an initial index; and A method comprising the step of matching background knowledge based on the above initial index.

14. In Paragraph 13, The step of matching background knowledge based on the above initial index is, A step of sorting the codebook sequence based on the initial index of the first device and the initial index of the second device; and A method comprising the step of matching the codebook vectors of the first device and the codebook vectors of the second device based on the alignment of the codebook sequence.

15. In Paragraph 14, The step of matching background knowledge based on the above initial index is, A step of comparing the number of codebook vectors included in the representation space of the first device and the representation space of the second device; The method further includes the step of grouping at least some of the codebook vectors included in the representation space of the first device based on the comparison result. A method in which the representation space of the first device and the representation space of the second device are matched based on a group including at least a portion.

16. In the first device, Transmitter / receiver; and It includes a processor coupled to the above-mentioned transmitter and receiver, The above processor is, Perform the initial connection procedure, Transmit capability information, and Performs signaling for communication-related settings, Transmitting a reference signal to a second device, Generate a semantic representation based on the above reference signal, and A first device configured to match the background knowledge of the first device with the background knowledge of the second device based on the above semantic expression.

17. In the second device, Transmitter / receiver; and It includes a processor connected to the above-mentioned transmitter and receiver, The above processor is, Perform the initial connection procedure, Transmit capability information, and Performs signaling for communication-related settings, Generates a semantic representation based on a reference signal, and Based on the above semantic expression, it is configured to match the codebook vectors of the second device and the codebook vectors of the first device, The above reference signal is a second device received from the first device.

18. In a communication device, At least one processor; It includes at least one computer memory connected to the at least one processor and storing instructions that direct operations as they are executed by the at least one processor, The above operations are, Step of performing the initial connection procedure; Step of transmitting capability information; A step of performing signaling for communication-related settings; A step of transmitting a reference signal to a second device; A step of generating a semantic expression based on the above reference signal; and A communication device comprising the step of matching background knowledge of the first device with background knowledge of the second device based on the above semantic expression.

19. In a non-transitory computer-readable medium storing at least one program instruction, The above at least one program instruction causes the terminal to perform operations as it is executed by at least one processor, and The above operations are, Step of performing the initial connection procedure; Step of transmitting capability information; A step of performing signaling for communication-related settings; A step of transmitting a reference signal to a second device; A step of generating a semantic expression based on the above reference signal; and A computer-readable medium comprising the step of matching background knowledge of the first device with background knowledge of the second device based on the above semantic representation.

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