Virtualization method and apparatus for integration of communication and sensing

Abstract

A virtualization method and apparatus for integration of communication and sensing are disclosed. A method performed by a terminal according to one embodiment of the present disclosure may comprise the steps of: receiving, from a base station, configuration information about a virtual communication instance and a virtual sensing instance; activating the virtual communication instance and the virtual sensing instance on the basis of at least one of the configuration information or an indication by the base station; performing data transmission and reception on the basis of the virtual communication instance; and acquiring or processing sensing data on the basis of the sensing instance.

Description

Virtualization method and device for the integration of communication and sensing

[0001] The present disclosure relates to a method for performing communication and sensing in an integrated manner in a wireless communication system, and to a method and apparatus for creating, managing, and utilizing a virtual instance for communication and a virtual instance for sensing.

[0002] The 5th generation (5G) wireless communication system is a successor technology to 4G LTE (long term evolution) and is a new clean-slate type mobile communication system with characteristics such as high performance, low latency, and high availability. In the case of 5G NR (New Radio), all available spectrum resources can be utilized, ranging from low-frequency bands below 1 GHz to intermediate frequency bands between 1 GHz and 10 GHz, and high-frequency (or millimeter wave) bands above 24 GHz. Based on the foundational technology of 5G wireless communication, 6G wireless communication systems are being developed.

[0003] 6G wireless communication systems are being developed with the goal of (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 (internet of things) devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of 6G systems can be seen in four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity. Various technologies are being researched in consideration of the requirements for 6G systems, such as a peak data rate of 1 Tbps per device, an end-to-end (E2E) latency of 1ms, a maximum spectrum efficiency of 100 bps / Hz, support for mobility of 1000 km / h, satellite integration, artificial intelligence (AI), autonomous vehicles, extended reality (XR), and haptic communication.

[0004] The technical problem of the present disclosure is to provide a method and apparatus for creating, managing, and utilizing a virtual instance for communication and a virtual instance for sensing, in relation to a method for performing communication and sensing in an integrated manner in a wireless communication system.

[0005] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs from the description below.

[0006] A method according to one aspect of the present disclosure may include: receiving configuration information for a virtual communication instance and a virtual sensing instance from a base station by a terminal; activating the virtual communication instance and the virtual sensing instance by the terminal based on at least one of the configuration information or instructions by the base station; and performing data transmission and reception based on the virtual communication instance by the terminal and acquiring or processing sensing data based on the sensing instance.

[0007] A method according to a further aspect of the present disclosure may include: receiving a message from a terminal requesting the configuration of a virtual instance by a base station; transmitting configuration information for a virtual instance created by abstracting physical resources to the terminal by the base station; transmitting instruction information to the terminal for activating the virtual instance by the base station; and performing data transmission and reception with the terminal and providing sensing data to the terminal by the base station based on the virtual instance. Herein, the virtual instance may be registered in a virtualization layer.

[0008] According to the present disclosure, a method for performing communication and sensing in an integrated manner in a wireless communication system may be provided, and a method and apparatus for creating, managing, and utilizing a virtual instance for communication and a virtual instance for sensing may be provided.

[0009] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0010] The accompanying drawings, which are included as part of the detailed description to aid in understanding the present disclosure, provide embodiments of the present disclosure and explain the technical features of the present disclosure together with the detailed description.

[0011] FIG. 1 illustrates an exemplary flexible network topology to which some examples of the present disclosure may be applied.

[0012] FIG. 2 illustrates an exemplary communication system to which some examples of the present disclosure may be applied.

[0013] FIG. 3 illustrates an exemplary wireless device to which some examples of the present disclosure may be applied.

[0014] FIG. 4 illustrates an exemplary communication procedure between a first node and a second node to which some examples of the present disclosure may be applied.

[0015] FIG. 5 illustrates an exemplary functional framework for AI operations to which some examples of the present disclosure may be applied.

[0016] FIG. 6 illustrates an example of operations related to AI model training and AI model inference to which some examples of the present disclosure may be applied.

[0017] FIG. 7 illustrates another example of operations related to AI model training and AI model inference to which some examples of the present disclosure may be applied.

[0018] FIG. 8 illustrates another example of operations related to AI model training and AI model inference to which some examples of the present disclosure may be applied.

[0019] FIG. 9 shows an electromagnetic spectrum to which some examples of the present disclosure may be applied.

[0020] FIG. 10 illustrates an exemplary system information transmission / reception procedure to which some examples of the present disclosure may be applied.

[0021] FIG. 11 illustrates an exemplary beam management procedure to which some examples of the present disclosure may be applied.

[0022] FIGS. 12 and FIGS. 13 show examples of NTN scenarios to which some examples of the present disclosure may be applied.

[0023] FIG. 14 shows examples of sensing operations to which some examples of the present disclosure may be applied.

[0024] FIG. 15 shows an example of a virtualization of integrated sensing and communication and an operation flowchart based thereon according to an embodiment of the present disclosure.

[0025] FIG. 16 shows an additional example of a virtualization of integrated sensing and communication and an operation flowchart based thereon according to an embodiment of the present disclosure.

[0026] FIG. 17 shows another additional example of a virtualization of integrated sensing and communication and an operation flowchart based thereon according to an embodiment of the present disclosure.

[0027] FIG. 18 illustrates a flowchart of the operation of a terminal for virtualized integrated sensing and communication operations according to an embodiment of the present disclosure.

[0028] FIG. 19 illustrates a sequence of operations of a base station for virtualized integrated sensing and communication operations according to an embodiment of the present disclosure.

[0029] 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 present disclosure may be practiced. The following detailed description includes specific details to provide a complete understanding of the present disclosure. However, those skilled in the art will know that the present disclosure may be practiced without such specific details.

[0030] In some cases, to avoid obscuring the concept of the present disclosure, known structures and devices may be omitted or illustrated in the form of a block diagram focusing on the core functions of each structure and device.

[0031] In the present disclosure, when a component is described as being “connected,” “combined,” or “joined” with another component, this may include not only a direct connection but also an indirect connection in which another component exists between them. Furthermore, in the present disclosure, the terms “comprising” or “having” specify the presence of the mentioned features, steps, actions, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, actions, elements, components, and / or groups thereof.

[0032] In the present disclosure, terms such as "first," "second," etc. are used solely for the purpose of distinguishing one component from another and are not used to limit the components, nor do they limit the order or importance of the components unless specifically stated otherwise. Accordingly, within the scope of the present disclosure, a first component in one embodiment may be referred to as a second component in another embodiment, and likewise, a second component in one embodiment may be referred to as a first component in another embodiment.

[0033] The terms used in this disclosure are for the description of specific embodiments and are not intended to limit the claims. As used in the description of embodiments and the appended claims, the singular form is intended to include the plural form unless the context clearly indicates otherwise.

[0034] In the present disclosure, "A or B" may mean "only A," "only B," or "both A and B." Alternatively, in the present disclosure, "A or B" may be interpreted as "A and / or B." For example, in the present disclosure, "A, B or C" may mean "only A," "only B," "only C," or "any combination of A, B and C."

[0035] A slash ( / ) or a comma used in the present disclosure may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B or C."

[0036] In the present disclosure, "at least one of A and B" may mean "only A," "only B," or "both A and B." Additionally, in the present disclosure, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted as synonymous with "at least one of A and B."

[0037] Additionally, in the present disclosure, "at least one of A, B and C" may mean "only A," "only B," "only C," or "any combination of A, B and C." Additionally, "at least one of A, B or C" or "at least one of A, B and / or C" may mean "at least one of A, B and C."

[0038] Additionally, parentheses used in the present disclosure may mean "for example." Specifically, when indicated as "control information (PDCCH)," "PDCCH" may be described as an example of "control information." In other words, the "control information" of the present disclosure is not limited to "PDCCH," and "PDCCH" may be described as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)," "PDCCH" may be described as an example of "control information."

[0039] In the following explanation, '...when, if, in case of' can be replaced with '...based on'.

[0040] Technical features described individually within one drawing in this disclosure may be implemented individually or simultaneously.

[0041] In the present disclosure, a terminal or user equipment (UE) may be a portable device and may be a first node that receives a signal from a base station / second node / integrated access backhaul (IAB) node.

[0042] In the present disclosure, the base station (BS, Base Station) may be a second node / IAB node / Transmission-Reception Point (TRP).

[0043] In the present disclosure, a higher layer parameter may be a parameter configured, pre-configured, or pre-defined for a terminal. For example, a base station or network may transmit the higher layer parameter to the terminal. For example, the higher layer parameter may be transmitted via radio resource control (RRC) signaling or medium access control (MAC) signaling.

[0044] In the present disclosure, "set or defined" may be interpreted as being set to a device through predefined signaling (e.g., System Information Block (SIB), MAC, RRC) from a base station or network. In the present disclosure, "set or defined" may be interpreted as being set to a device through separate signaling or being predefined without separate signaling.

[0045] In the present disclosure, transmitting or receiving a channel includes the meaning of transmitting or receiving information or a signal through said channel. For example, transmitting a control channel means transmitting control information or a signal through the control channel. Similarly, transmitting a data channel means transmitting data information or a signal through the data channel.

[0046] The technology described in this disclosure can be used in various wireless communication 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). CDMA can be implemented with wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented with wireless technologies such as GSM (global system for mobile communications), GPRS (general packet radio service), and EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented with wireless technologies such as IEEE (institute of electrical and electronics engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (evolved UTRA), LTE (long term evolution), and 5G NR.

[0047] The technology described in this disclosure can be implemented as 6G wireless technology and applied to various 6G systems. For example, 6G systems may have key factors such as eMBB (enhanced mobile broadband), URLLC (ultra-reliable low latency communications), mMTC (massive machine-type communication), AI (artificial intelligence) integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0048] Network structure

[0049] FIG. 1 illustrates an exemplary flexible network topology to which some examples of the present disclosure may be applied.

[0050] To compensate for incomplete areas of network coverage, a network topology in which the split radio access network (RAN) is configured more flexibly and resiliently may be considered. To this end, various nodes such as integrated access backhaul (IAB) nodes, relays, and radio frequency (RF) repeaters, as exemplified in Fig. 1, may be applied, and a non-terrestrial network (NTN) may be integrated. For example, an IAB node may correspond to a node that provides wireless backhaul. For example, a relay may refer to any intermediate point, and in the case of a sidelink relay where a terminal functions as a relay, it may collectively refer to a terminal-to-network (U2N) relay and a terminal-to-terminal (U2U) relay. For example, an RF repeater may correspond to a node that performs simple signal amplification and forwarding functions, and in the case of a network-controlled repeater, it may adjust transmit / receive settings based on information provided by the network as well as signal amplification and forwarding. For example, NTN nodes can correspond to satellites or aircraft that provide NTN coverage that is difficult for terrestrial networks to provide. In addition to these examples, various intermediate points can be introduced to improve the network topology.

[0051] Referring to FIG. 1, a split RAN can support the division of a base station into one centralized unit (CU) and one or more distributed units (DU). The CU and DU may correspond to logical units. The CU may be further divided into a control plane (CP) portion and one or more user plane (UP) portions. Since a failure in the CU-CP affects not only the CU-UP but also the DU, various intermediate points may be introduced to compensate for this.

[0052] An intermediate point may correspond to a terminal or a base station depending on its relative relationship with other nodes. For example, an IAB node may include a mobile-termination (MT) portion and a DU. The MT may connect the IAB node to a donor node. The DU of the IAB node may serve other terminals or connect to other IAB nodes to provide multi-hop wireless backhaul to terminals. For example, an IAB node may correspond to a base station in its relative relationship with a user-side node and to a terminal in its relative relationship with a network-side node.

[0053] In some examples of the present disclosure, the description of a terminal may apply equally to an intermediate point corresponding to a terminal in relation to a network-side endpoint as well as to a user-side endpoint. Similarly, in some examples of the present disclosure, the description of a base station may apply equally to an intermediate point corresponding to a base station in relation to a user-side endpoint as well as to a network-side endpoint. In most cases where there is no additional description of the operation of three or more subjects, the communication subjects in the present disclosure are briefly described by the term terminal and / or base station (or first node and / or second node), wherein the term terminal and / or base station (or first node and / or second node) is interpreted to include or replace any endpoint or any intermediate point in relation to other nodes.

[0054] As such, in some examples of the present disclosure, for the sake of brevity of description, the subject of the operation may be referred to as a terminal and / or base station (or a first node and / or a second node). Additionally, the term terminal and / or base station (or a first node and / or a second node) may be interpreted or substituted as in the following examples: for example, the terminal (or first node) and the base station (or second node) may correspond to a first endpoint and a second endpoint, respectively; may correspond to an endpoint and an intermediate point, respectively; may correspond to an intermediate point and an endpoint, respectively; or may correspond to a first intermediate point and a second intermediate point, respectively.

[0055] In the present disclosure, there may be no intermediate points between the base station and the terminal, or there may be one or more. If intermediate points exist, the intermediate points may correspond to IAB nodes, relays, RF repeaters, NTN nodes, or nodes supporting other functions. The intermediate points may be nodes with a fixed location or nodes with an indefinite location.

[0056] Systems applicable to the present disclosure

[0057] FIG. 2 illustrates an exemplary communication system to which some examples of the present disclosure may be applied.

[0058] The communication system (100) to which the present disclosure applies includes a wireless device (110), a network device (120), and a network (130). Here, the wireless device (110) 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 / 6G device. Although not limited thereto, the wireless device (110) may include a robot (110a), a vehicle (110b-1, 110b-2), an XR (extended reality) device (110c), a hand-held device (110d), a home appliance (110e), an IoT (Internet of Thing) device (110f), and an AI (artificial intelligence) device / server (110g). 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 (110b-1, 110b-2) may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device (110c) 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 (110d) may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.). The home appliance (110e) may include a TV, a refrigerator, a washing machine, etc. The IoT device (110f) may include a sensor, a smart meter, etc. The wireless device (110) may correspond to a terminal (or first node) or an intermediate point.The network device (120) may correspond to a base station (or a second node) or another intermediate point. For example, the network device (120) may also be implemented as a wireless device (110), and a specific wireless device (120a) may operate as a network device (120) to another wireless device (110).

[0059] Wireless devices (110a to 110f) can be connected to a network (130) through a network device (120). AI technology may be applied to the wireless devices (110a to 110f), and the wireless devices (110a to 110f) can be connected to an AI server (110g) 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 (110a to 110f) may communicate with each other through the network device (120) / network (130), but may also communicate directly (e.g., sidelink communication) without going through the network device (120) / network (130). For example, vehicles (110b-1, 110b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). Also, an IoT device (110f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or other wireless devices (110a to 110f).

[0060] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (110a to 110f) / network devices (120) and between network devices (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 network devices (150c) (e.g., relay, IAB (integrated access backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and network devices / wireless devices, and network devices and network devices 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 descriptions 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.

[0061] Devices applicable to the present disclosure

[0062] FIG. 3 illustrates an exemplary wireless device to which some examples of the present disclosure may be applied.

[0063] Referring to FIG. 3, 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).

[0064] 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.

[0065] 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., a 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., a 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.

[0066] 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.

[0067] 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.

[0068] 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) can convert user data, control information, wireless signals / channels, etc. processed using at least one processor (202) from a baseband signal to an RF band signal. To this end, at least one transceiver (206) may include an (analog) oscillator and / or filter.

[0069] The components of the wireless device described with reference to FIG. 3 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).

[0070] The structure of the wireless device described with reference to FIG. 3 can be understood as the structure of at least part of various devices. For example, the structure of the wireless device illustrated in FIG. 3 may be at least part of the various devices described with reference to FIG. 2 (e.g., robot (110a), vehicle (110b-1, 110b-2), XR device (110c), portable device (110d), home appliance (110e), IoT device (110f), AI device / server (110g)). Furthermore, according to various embodiments, the device may include other components in addition to the components illustrated in FIG. 3.

[0071] 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., an audio input / output port, a 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] The structure of the wireless device illustrated in FIG. 3 may be understood as part of a terminal (or first node), or part of an intermediate point, or part of a base station (or second node). If the device illustrated in FIG. 3 is a base station (or second node), the device may further include a wired transceiver for front haul and / or back haul communication. If the front haul and / or back haul communication is based on wireless communication, at least one transceiver (206) illustrated in FIG. 3 is used for front haul and / or back haul communication, and a wired transceiver may not be included.

[0077] Communication procedures

[0078] FIG. 4 illustrates an exemplary communication procedure between a first node and a second node to which some examples of the present disclosure may be applied.

[0079] FIG. 4 illustrates the operation of a first node (110) (e.g., a terminal) and a second node (120) (e.g., a base station) transmitting and / or receiving data, and the operation performed prior to this.

[0080] In step S101, the first node (110) and the second node (120) can perform synchronization. For example, the terminal (110) performs an initial cell search operation. Specifically, the terminal (110) can detect at least one synchronization signal transmitted from the base station (120) according to a predefined rule. Here, the synchronization signal may include a plurality of synchronization signals (e.g., a primary synchronization signal, a secondary synchronization signal) classified according to structure or use. Through this, the terminal (110) can identify the boundaries of the frame, subframe, slot, and / or symbol of the base station (120) and obtain information about the base station (120) (e.g., a cell identifier).

[0081] In step S103, the first node (110) can obtain system information transmitted from the second node (120). For example, the system information is information related to the attributes, characteristics, and / or capabilities of the base station (120) required to connect to the base station (120) and use the service, and can be classified according to content (e.g., whether it is essential for connection), transmission structure (e.g., the 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 (110) may transmit a signal requesting the system information prior to receiving the system information. Such request and provision of system information may be performed after a random access procedure described later.

[0082] In step S105, the first node (110) and the second node (120) can perform a random access procedure. For example, the terminal (110) can transmit and / or receive at least one message for a random access procedure (e.g., a random access preamble, a RAR (random access response) message, etc.) based on information related to the random access channel of the base station (120) obtained through system information (e.g., channel location, channel structure, structure of a supported preamble, etc.). For example, the terminal (110) may transmit a preamble (e.g., message 1 (MSG1)) through a random access channel, receive a random access response (RAR) message (e.g., message 2 (MSG2)), transmit a message (e.g., message 3 (MSG3)) containing information related to the terminal (110) (e.g., identification information) to the base station (120) using scheduling information included in the RAR message, and receive a message (e.g., message 4 (MSG4)) for contention resolution and / or connection establishment. As another example, MSG1 and MSG3 may be transmitted and received as a single message (e.g., message A (MSG A), or MSG2 and MSG4 may be transmitted and received as a single message (e.g., message B (MSG B).

[0083] In step S107, the first node (110) and the second node (120) can perform signaling of control information. For example, 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 (110) and the base station (120) may perform at least one of signaling to establish a connection, signaling to determine settings related to communication, and signaling to indicate allocated resources.

[0084] In step S109, the first node (110) and the second node (120) can transmit and / or receive data. For example, the terminal (110) and the base station (120) can process data based on the signaling of control information and transmit and / or receive data. For example, when transmitting data, the terminal (110) or the base station (120) can perform at least one of channel encoding, rate matching, scrambling, constellation mapping, layer mapping, waveform modulation, antenna mapping, and resource mapping on the information bits. For example, when receiving data, the terminal (110) or the base station (120) can perform at least one of extracting a signal from a resource, antenna-specific waveform demodulation, signal placement considering layer mapping, constellation demapping, descrambling, and channel decoding.

[0085] 6G System Core Technology

[0086] As core implementation technologies for 6G systems, technologies such as artificial intelligence (AI), THz (Terahertz) communication, optical wireless technology, free space optics (FSO) backhaul network, multiple input multiple output (MIMO) technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS) can be adopted.

[0087] artificial intelligence

[0088] The introduction of AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. AI can increase efficiency and reduce processing latency. Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a significant role in machine-to-machine (M2M), machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0089] FIG. 5 illustrates an exemplary functional framework for AI operations to which some examples of the present disclosure may be applied.

[0090] Below, to provide a more specific explanation of AI (or AI / ML (machine learning)), terms can be defined as follows.

[0091] - Data collection: Data collected from network nodes, management entities, or terminals, serving as a basis for AI model training, data analysis, and inference.

[0092] - AI model: A data-driven algorithm that applies AI technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0093] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent data and acquire an AI / ML model trained for inference.

[0094] - AI / ML Inference: A process of making predictions or deriving decisions based on collected data and an AI model using a trained AI model.

[0095] Referring to FIG. 5, the data collection function (10) is a function that collects input data and provides processed input data to the model training function (20) and the model inference function (30).

[0096] Examples of input data may include measurements from terminals or other network entities, feedback from actors, and outputs from AI models.

[0097] The data collection function (10) performs data preparation based on input data and provides the input data processed through data preparation. Here, the data collection function (10) does not perform specific data preparation (e.g., data pre-processing and cleaning, forming and transformation) for each AI algorithm, and can perform data preparation common to AI algorithms.

[0098] After the data preparation process is performed, the data collection function (10) can provide training data (11) to the model training function (20) and provide inference data (12) to the model inference function (30). Here, the training data (11) corresponds to data required as input for the AI ​​model training function (20), and the inference data (12) corresponds to data required as input for the AI ​​model inference function (30).

[0099] The data collection function (10) may be performed by a single entity (e.g., terminal, RAN node, network node, etc.) but may also be performed by multiple entities. In this case, training data (11) and inference data (12) from multiple entities may be provided to the model training function (20) and the model inference function (30), respectively.

[0100] The model training function (20) may correspond to a function that performs AI model training, validation, and testing, which can generate model performance metrics as part of the AI ​​model testing procedure. If necessary, the model training function (20) may also be responsible for data preparation (e.g., data pre-processing and cleaning, formatting and transformation, etc.) based on training data (11) provided by the data collection function (10).

[0101] Here, model deployment / update (13) can be used to initially deploy a trained, validated, and tested AI model to the model inference function (30) or to provide an updated model to the model inference function (30).

[0102] The model inference function (30) may correspond to a function that provides an AI model inference output (16) (e.g., a prediction or a decision). The model inference function (30) may provide model performance feedback (14) to the model training function (20) where applicable. Additionally, the model inference function (30) may be responsible for data preparation (e.g., data pre-processing and cleaning, formatting and transformation, etc.) based on the inference data (12) provided by the data collection function (10) if necessary.

[0103] Here, output (16) refers to the inference output of an AI model generated by the model inference function (30), and the details of the inference output may vary depending on the use case.

[0104] Model performance feedback (14) can be used to monitor the performance of the AI ​​model if available, and this feedback may be omitted.

[0105] The actor function (40) is a function that receives an output (16) from the model inference function (30) and triggers or performs a corresponding operation / action. The actor function (40) can trigger an operation / action on another entity (e.g., one or more terminals, one or more RAN nodes, one or more network nodes, etc.) or on itself.

[0106] Feedback (15) can be used to derive training data (11) and inference data (12), or to monitor the performance of the AI ​​model, the impact on the network, etc.

[0107] Meanwhile, the definitions of training, validation, and testing in data sets used in AI / ML can be distinguished as follows.

[0108] - Training data: Refers to the dataset used to train a model.

[0109] - Validation data: This refers to a dataset used to validate a model that has already been trained. Validation data typically refers to a dataset used to prevent overfitting of the training dataset. Additionally, validation data can refer to a dataset used to select the best model among the various models trained during the learning process. Therefore, validation can be viewed as a type of training.

[0110] - Test data: Refers to the dataset for final evaluation. This data is unrelated to training.

[0111] For example, within the entire dataset, training data and validation data can be divided in a ratio of approximately 8:2 or 7:3. Alternatively, within the entire dataset, training data:validation data:test data can be divided in a ratio of 6:2:2.

[0112] Depending on whether the base station and the terminal possess the capability for AI / ML functions, the cooperation level can be defined as follows, and variations resulting from the combination of multiple levels below or the separation of any one level are also possible.

[0113] Category 0a: This corresponds to a no collaboration framework. In this case, the AI / ML algorithm is based on pure implementation and may not require changes to the wireless interface.

[0114] Category 0b: Corresponds to a framework that involves a wireless interface modified to fit efficient implementation-based AI / ML algorithms but lacks cooperation.

[0115] Category 1: This applies to cases involving inter-node support to improve the AI / ML algorithms of each node. For example, it applies when a terminal receives support from a base station (for training, adaptation, etc.), and vice versa. At this level, model exchange between network nodes is not required.

[0116] Category 2: This applies to cases where joint ML operations between a terminal and a base station can be performed. This level requires AI / ML model commands or exchanges between network nodes.

[0117] The functions exemplified in Figure 5 above may be implemented at RAN nodes (e.g., base station, TRP, base station CU, etc.), network nodes, network operator's OAM (operation administration maintenance), or terminals.

[0118] Alternatively, two or more entities among a RAN, a network node, a network operator's OAM, or a terminal may cooperate to implement the functions exemplified in FIG. 5. For example, one entity may perform some of the functions of FIG. 5, and another entity may perform the remaining functions. As such, some of the functions exemplified in FIG. 5 are performed by a single entity (e.g., a terminal, a RAN node, a network node, etc.), the transmission / provision of data / information between each function may be omitted. For example, if the model training function (20) and the model inference function (30) are performed by the same entity, the transmission / provision of model distribution / update (13) and model performance feedback (14) may be omitted.

[0119] Alternatively, any one of the functions exemplified in FIG. 5 may be performed by two or more entities among the RAN, network node, network operator's OAM, or terminal in collaboration. This may be referred to as a split AI operation.

[0120] FIG. 6 illustrates an example of operations related to AI model training and AI model inference to which some examples of the present disclosure may be applied.

[0121] For example, the AI ​​model training function can be performed by network nodes (e.g., core network nodes, network operator's OAM, etc.), and the AI ​​model inference function can be performed by RAN nodes (e.g., base station, TRP, base station's CU, etc.).

[0122] Step 1: RAN Node 1 and RAN Node 2 can transmit input data (e.g., training data) for training an AI model to a network node. Here, RAN Node 1 and RAN Node 2 can also transmit data collected from terminals to the network node (e.g., terminal measurements related to RSRP (reference signal received power), RSRQ (reference signal received quality), and SINR (signal to interference-plus-noise ratio) of the serving cell and neighboring cells, terminal location, speed, etc.).

[0123] Step 2: Network nodes can train AI models using the received training data.

[0124] Step 3: The network node can distribute / update the AI ​​model to RAN Node 1 and / or RAN Node 2. RAN Node 1 (and / or RAN Node 2) may also continue model training based on the received AI model.

[0125] For the sake of convenience of explanation, it is assumed that the AI ​​model was deployed / updated only to RAN Node 1.

[0126] Step 4: RAN Node 1 can receive input data (e.g., inference data) for AI model inference from the terminal and RAN Node 2.

[0127] Step 5: RAN Node 1 can perform AI model inference using the received inference data to generate output data (e.g., prediction or decision).

[0128] Step 6: If applicable, RAN node 1 can send model performance feedback to network nodes.

[0129] Step 7: RAN Node 1, RAN Node 2, and the terminal (or 'RAN Node 1 and the terminal', or 'RAN Node 1 and RAN Node 2') can perform an action based on the output data. For example, in the case of a load balancing action, the terminal may move from RAN Node 1 to RAN Node 2.

[0130] Step 8: RAN Node 1 and RAN Node 2 can transmit feedback information to network nodes.

[0131] FIG. 7 illustrates another example of operations related to AI model training and AI model inference to which some examples of the present disclosure may be applied.

[0132] For example, both AI model training and AI model inference functions can be performed by RAN nodes (e.g., base station, TRP, base station's CU, etc.).

[0133] Step 1: The terminal and RAN node 2 can transmit input data (e.g., training data) for training an AI model to RAN node 1.

[0134] Step 2: RAN Node 1 can train an AI model using the received training data.

[0135] Step 3: RAN Node 1 can receive input data (e.g., inference data) for AI model inference from the terminal and RAN Node 2.

[0136] Step 4: RAN Node 1 can perform AI model inference using the received inference data to generate output data (e.g., prediction or decision).

[0137] Step 5: RAN Node 1, RAN Node 2, and the terminal (or 'RAN Node 1 and the terminal', or 'RAN Node 1 and RAN Node 2') can perform an action based on the output data. For example, in the case of a load balancing action, the terminal may move from RAN Node 1 to RAN Node 2.

[0138] Step 6: RAN Node 2 can send feedback information to RAN Node 1.

[0139] FIG. 8 illustrates another example of operations related to AI model training and AI model inference to which some examples of the present disclosure may be applied.

[0140] For example, the AI ​​model training function may be performed by a RAN node (e.g., base station, TRP, base station CU, etc.), and the AI ​​model inference function may be performed by a terminal.

[0141] Step 1: A terminal can transmit input data (e.g., training data) for training an AI model to a RAN node. Here, the RAN node can collect data (e.g., terminal measurements related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, terminal location, velocity, etc.) from various terminals and / or other RAN nodes.

[0142] Step 2: The RAN node can train an AI model using the received training data.

[0143] Step 3: The RAN node can distribute / update the AI ​​model to the terminal. The terminal may also continue model training based on the received AI model.

[0144] Step 4: Input data (e.g., inference data) for AI model inference can be received from terminals and RAN nodes (and / or other terminals).

[0145] Step 5: The terminal can perform AI model inference using the received inference data to generate output data (e.g., prediction or decision).

[0146] Step 6: If applicable, the terminal can transmit model performance feedback to the RAN node.

[0147] Step 7: The terminal and the RAN node can perform actions based on the output data.

[0148] Step 8: The terminal can transmit feedback information to the RAN node.

[0149] THz communication

[0150] Data transmission rates can be increased by expanding bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, generally refer to a frequency band between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz–300 GHz band range (sub-THz band) is considered the primary portion of the THz band for cellular communication. Adding the sub-THz band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, the 300 GHz–3 THz band is located in the far-infrared (IR) frequency band. Although the 300 GHz–3 THz band is part of the broadband, it lies at the boundary of the broadband and immediately following the RF band. Therefore, this 300 GHz–3 THz band exhibits similarities to RF.

[0151] FIG. 9 shows an electromagnetic spectrum to which some examples of the present disclosure may be applied.

[0152] Key characteristics of THz communication include (i) widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array technologies that can overcome range limitations.

[0153] When transmitting system information (e.g., MIB) of a cell in the THz frequency band, it can be inefficient because, in the case of high frequency bands, beam sweeping must be performed more frequently to cover the entire area of ​​the cell as the beam width becomes narrower. In particular, transmitting system information using this method is even more inefficient when there are not many users in the cell.

[0154] FIG. 10 illustrates an exemplary system information transmission / reception procedure to which some examples of the present disclosure may be applied.

[0155] The example of FIG. 10 is applicable not only to THz communication environments but also to 6G communication environments where THz communication is not applied. In addition, the procedure exemplified in FIG. 10 can be combined with various embodiments of the present disclosure described below. For example, embodiments described below can be performed based on system information obtained by the procedure exemplified in FIG. 10.

[0156] In step S1010, the second node (120) (e.g., a base station) can transmit system information of cell #1 through cell #2. For example, the base station provides at least two cells, cell #1 uses a THz frequency band, and cell #2 uses a frequency band other than the THz frequency band. Here, the system information may include at least one of a system frame number (SFN) generated at a higher layer, a PDCCH configuration for SIB1, cell barring, cell re-selection, and subcarrier spacing, and may include at least one of a synchronization signal / PBCH (physical broadcast channel) block index generated at a physical layer. To this end, as an example, cell #1 and cell #2 may have a secondary cell and primary cell relationship.

[0157] In step S1030, the first node (110) (e.g., a terminal) can acquire synchronization for cell #1. Synchronization can be acquired by detecting a synchronization signal. Generally, synchronization is acquired prior to receiving system information, but since the system information of cell #1 is received in cell #2, the acquisition of synchronization for cell #1 can be performed after receiving system information. For example, the terminal can acquire synchronization based on system information. Alternatively, the acquisition of synchronization may be performed prior to step S1010.

[0158] In step S1050, the first node (110) may transmit a signal to connect to cell #1. For example, the signal may include a random access preamble. The structure of such a signal and the resource for transmitting the signal (e.g., a channel) may be identified through system information. Subsequently, in step S1070, the first node (110) and the second node (120) may perform a connection procedure to cell #1 and perform communication.

[0159] The procedure described with reference to FIG. 10 may be performed when the first node (110) first connects to cell #1 of the second node (120). Alternatively, a similar procedure may be performed when the first node (110) handovers to cell #1 of the second node (120). However, in the case of a handover, the system information of cell #1 may be received from a cell of a different base station other than cell #2 of the second node (120).

[0160] Communication in the THz band is expected to experience severe path loss, and to overcome this, terminals and base stations may be required to use very sharp beams. The use of sharp beams implies that terminals and base stations must perform beam control in addition to beamforming, meaning that a very large number of beams are utilized. Consequently, aligning the transmit and receive beams between the base station and the terminal takes a very long time. Furthermore, if the beam alignment between the base station and the terminal is disrupted due to the movement of the terminal, time is frequently required to realign the beams, which may lead to link instability.

[0161] FIG. 11 illustrates an exemplary beam management procedure to which some examples of the present disclosure may be applied.

[0162] Figure 11 illustrates an example of a procedure for searching and / or selecting beams for THz communication, but this procedure is not limited to a THz environment and can also be applied in a 6G communication environment where THz communication is not applied.

[0163] Here, "beam" can be interpreted as other terms having equivalent technical meanings capable of distinguishing beams, such as "spatial domain filter," "spatial domain transmit filter," "spatial domain receive filter," reference signal (RS) resources for distinguishing beams, and SSB index.

[0164] In step S1110, the second node (120) (e.g., base station) may set resources for beam management to the first node (110) (e.g., terminal). Here, the resources may include at least one of time-frequency resources, channels, and spatial resources (e.g., antenna ports). For example, the base station may utilize a beam search signal (BSS) that is transmitted spatially separated from the existing downlink signal / channel for beam search. Here, the BSS may be transmitted based on a dedicated port for beam search. The dedicated port may be a port different from the port used for transmitting the existing downlink signal / channel (e.g., SSB, PDSCH (physical downlink shared channel), etc.). BSS is a term defined for convenience of explanation, and the technical concept according to the present embodiment is not limited to the term BSS itself. For example, a signal transmitted based on a dedicated port defined / set for beam search may be included in the technical concept according to the present embodiment.

[0165] In step S1130, the second node (120) (e.g., a base station) transmits measurement signals using multiple transmission beams. For example, the measurement signals may include at least one of a reference signal and a synchronization signal. At this time, the measurement signals may be transmitted as many times as the number of beams requiring measurement, and may be transmitted using a multi-beam transmission method that forms multiple beams simultaneously to reduce sweeping time. Here, multi-beam transmission may be performed based on at least one of a multi-panel, a sub-array, or a true time delay (TTD).

[0166] In step S1050, the first node (110) (e.g., a terminal) may transmit a feedback signal to the second node (120) (e.g., a base station). The feedback signal may indicate at least one beam selected by the terminal. The terminal may select at least one preferred beam based on the measurement signals received in step S1030.

[0167] In step S1070, the first node (110) and the second node (120) can perform communication. For example, the second node (120) can perform transmission to the first node (110) using the receiving beam of the first node (110) selected in step S1050. If channel reciprocity is established, the transmission beam of the first node (110) can also be determined through steps S1030 and S1050, so the transmission operation from the first node (110) can also be performed using a beam that has a reciprocity relationship with the beam selected in step S1050. If channel reciprocity is not established, a procedure including the transmission of measurement signal(s) by the first node (110) and the transmission of feedback signal(s) by the second node (120) may be performed first to determine the transmission beam of the first node (110).

[0168] Non-terrestrial networks (NTN)

[0169] FIGS. 12 and FIGS. 13 show examples of NTN scenarios to which some examples of the present disclosure may be applied.

[0170] NTN can represent a network or network segment that uses RF (radio frequency) resources mounted on a satellite (or UAS (unmanned aerial system) platform).

[0171] Figure 12 shows an example of a typical scenario of an NTN based on a transparent payload, and Figure 13 shows an example of a typical scenario of an NTN based on a regenerative payload.

[0172] Referring to FIG. 12, the satellite (or UAS platform) can establish a service link with a terminal. The satellite (or UAS platform) can be connected to a gateway via a feeder link. The satellite can be connected to a data network via the gateway. A beam footprint may refer to an area where signals transmitted by the satellite can be received.

[0173] Referring to FIG. 13, a satellite (or UAS platform) can establish a service link with a terminal. The satellite (or UAS platform) connected to the terminal can be connected to another satellite (or UAS platform) via inter-satellite links (ISL). Another satellite (or UAS platform) can be connected to a gateway via a feeder link. Based on a regenerated payload, the satellite can be connected to a data network via another satellite and a gateway. If no ISL exists between the satellite and another satellite, a feeder link between the satellite and the gateway may be required.

[0174] FIGS. 12 and 13 are merely examples of NTN scenarios, and NTN can be implemented based on various scenarios. For example, a satellite (or UAS platform) can implement a transparent or regenerative (with on-board processing) payload. For example, a satellite (or UAS platform) can generate multiple beams across a designated service area depending on the field of view of the satellite (or UAS platform). For example, the field of view of the satellite (or UAS platform) may vary depending on the on-board antenna diagram and the minimum elevation angle.

[0175] For example, the transparent payload may include radio frequency filtering, frequency conversion, and amplification. Therefore, the waveform signal repeated by the payload may not be altered.

[0176] For example, the regeneration payload may include radio frequency filtering, frequency conversion and amplification, demodulation / decoding, switching and / or routing, and coding / modulation. For example, the regeneration payload may be substantially the same as carrying all or part of the base station functions on a satellite (or UAS platform).

[0177] Integrated Sensing and Communication (ISAC)

[0178] Wireless sensing is a technology that utilizes radio frequencies to determine the instantaneous linear velocity, angle, distance (or range) of an object, thereby obtaining information about the characteristics of the environment and / or objects within that environment. Since radio frequency sensing capabilities do not require connecting to objects via devices within a network, they can provide services for determining object locations without the need for devices. The ability to obtain range, velocity, and angle information from radio frequency signals can provide a wide range of new functions, such as various object detection and recognition (e.g., vehicles, humans, animals, UAVs), as well as high-precision localization, tracking, and activity recognition. Wireless sensing services can provide information to various industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.) that enable applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, wireless sensing may utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a wireless sensing service, such as sensing operations, may depend on the transmission, reflection, and scattering processing of wireless sensing signals. Therefore, wireless sensing can provide an opportunity to enhance existing communication systems from communication networks to wireless communication and sensing networks.

[0179] FIG. 14 shows examples of sensing operations to which some examples of the present disclosure may be applied.

[0180] Specifically, FIG. 14(a) illustrates an example of monostatic sensing operation using a sensing receiver and a sensing transmitter located at the same position. FIG. 14(b) illustrates an example of bistatic sensing operation using a sensing receiver and a sensing transmitter located at separate positions. A sensing receiver receives a signal that is reflected or scattered by a sensing object from a sensing signal transmitted from a sensing transmitter, and can extract or acquire sensing data based on the received signal. A sensing result can be generated or determined through appropriate processing of this sensing data. The sensing result can be provided to a trusted third-party entity or service outside the 3GPP system via an entity or service within the 3GPP system.

[0181] Virtualization-based Integrated Sensing and Communication (ISAC)

[0182] With the recent proliferation of intelligent service environments, the importance of technologies that integrate communication and sensing functions is increasing. Traditional Radio Access Networks (RANs) have been designed to handle either communication or sensing functions independently. For example, 3G / 4G base stations (e.g., NodeB, eNodeB) or 5G gNBs (gNodeBs) are dependent on specific hardware and perform only wireless transmission, reception, and basic network functions, while sensing functions, such as environmental monitoring or security surveillance, are supported by separate systems. In such structures, the physical separation of communication and sensing functions can lead to inefficient resource utilization and make integrated management difficult.

[0183] In addition, with the introduction of 5G NR and the application of network function virtualization (NFV) and software-defined networking (SDN), an environment can be considered where network functions are separated from dedicated hardware and run on general-purpose servers or cloud-based infrastructure. Such a virtualization-based architecture can provide advantages that significantly improve resource scalability, operational flexibility, and cost efficiency.

[0184] In cutting-edge application fields such as smart cities, autonomous driving, industrial automation, and augmented reality, real-time sensor data and high-reliability, low-latency communication capabilities are required simultaneously. Consequently, there is an increasing demand to integrate and operate communication and sensing functions on the same platform. However, in existing methods, communication and sensing equipment are built and managed separately, leading to problems such as i) resource redundancy and low efficiency, ii) complexity of system integration, and iii) rigidity that makes it difficult to respond quickly to changes in demand.

[0185] To address this problem, a virtualization-based RAN (VRAN) can be considered. vRAN virtualizes communication functions to enable centralized management and dynamic resource allocation. When integrated with sensing functions, it can achieve technical benefits such as flexible expansion and dynamic provisioning of communication and sensing resources, as well as integrated resource management and improved operational efficiency.

[0186] In particular, by applying NFV, SDN, and Edge Computing together in a 5G NR environment, it is possible to support real-time integrated processing of communication and sensing functions, low-latency response, and intelligent decision-making based on distributed infrastructure. This can serve as a foundational technology essential for various future services, such as IoT expansion, smart infrastructure management, and the provision of customized services based on network slicing.

[0187] Therefore, there is a need for technology capable of integrally managing and operating communication and sensing functions on a single virtualized platform, and the present disclosure proposes a system and method for jointly (e.g., integrally) providing communication and sensing functions in a smart environment within a virtualized environment.

[0188] The system proposed in the present disclosure may include a virtualization layer, a communication module, a sensing module, and an orchestration module.

[0189] The virtualization layer abstracts underlying hardware resources to create virtual instances for communication and sensing tasks. Through this, various communication and sensing functions can be provided in a virtualized form, enabling the system to secure scalability and flexibility.

[0190] The communication module manages virtualized communication resources and can control communication-related resources, including network functions and protocols, to ensure smooth data transmission. Additionally, the sensing module manages virtualized sensing devices and data collection processes to control the efficient collection of data from sensors deployed within the environment.

[0191] The orchestration module coordinates the operation of communication modules and sensing modules and can control the dynamic allocation of resources according to application requirements and environmental conditions. Through this, the reliability and performance of the system can be improved by performing functions such as adaptive resource provisioning, load balancing, and fault tolerance.

[0192] The method and system proposed in this disclosure integrate communication and sensing functions to enable various application services. For example, in a smart city environment, it can be utilized for real-time traffic management, air quality monitoring, and public safety services, while in an industrial IoT environment, it enables predictive maintenance, asset tracking, and process optimization. Furthermore, in a healthcare environment, it can support remote patient monitoring, medical device integration, and the provision of personalized healthcare services.

[0193] According to the present disclosure, the flexibility, scalability, and resource efficiency of a system are increased through the virtualization integration of communication and sensing functions, and benefits such as optimized resource utilization, reduced operating costs, and improved user experience can be obtained. Furthermore, the seamless combination of communication and sensing functions within a virtualization-based infrastructure provides significant advantages compared to existing systems.

[0194] Hereinafter, each component and the overall operation of the proposed method and system of the present disclosure will be described in detail.

[0195] As described above, the system according to the embodiment of the present disclosure may be configured to include a virtualization layer and an orchestration module.

[0196] First, I will explain the virtualization layer.

[0197] A virtualization layer according to one embodiment of the present disclosure exists on physical hardware comprising an RF frontend, an antenna, a sensor, a digital signal processor (DSP), a MEC server, a physical network interface, etc., and may include various components to support the virtualization of communication functions and sensing functions.

[0198] For example, the Hardware Abstraction Component (HAC) can abstract physical RF resources, physical network resources, and sensor resources and integrate them into virtual resources available at the upper layer. The HAC can organize physically distributed wireless / network / sensor resources into a single virtual pool, providing a foundation for various virtual instances to independently request and use resources.

[0199] In addition, the Virtual Resource Pool (VRP) can be configured into a single virtualized resource pool by integrating time-frequency resources, spatial resources, and signal processing resources. Through this, communication functions (e.g., PDSCH, PUSCH, CSI-RS, etc.) and sensing functions (e.g., SRS, PR, echo profile, etc.) can be flexibly processed on the same virtualization infrastructure.

[0200] Additionally, the Virtual Instance Generator (VIG) can be configured to create different types of virtual instances based on the VRP. The virtual instances created may include a Virtual Communication Instance (VCI) for performing communication functions and a Virtual Sensing Instance (VSI) for performing sensing functions.

[0201] As a specific example, VCI can be defined as a logical unit for performing wireless communication functions such as PDSCH, PUSCH, and CSI-RS, and VSI can be defined as a logical unit for performing sensing functions such as SRS, PR (probing reference), and echo profile generation.

[0202] Each virtual instance is identified by a unique identifier (e.g., VirtualInstanceID) and can be configured to correspond directly to scheduling elements of the RRC / MAC / PHY layer. Therefore, virtual instances can be utilized for communication and sensing tasks in a manner linked to physical resource scheduling.

[0203] Additionally, the Resource Map Builder (RMB) can create and manage virtual-to-physical mapping tables (e.g., VS-ResourceMap) that define the mapping between virtual and physical resources. By appropriately allocating time-frequency resources, antenna resources, signal processing resources, etc., required by virtual instances to physical resources, the RMB can control virtualized communication and sensing functions to be performed efficiently on physical hardware.

[0204] A virtualization layer including the aforementioned configurations can provide the effect of improving the scalability, flexibility, and efficiency of the system by abstracting the constraints of the physical infrastructure and managing communication / sensing resources in an integrated manner.

[0205] Next, the orchestration module will be explained.

[0206] An orchestration module according to one embodiment of the present disclosure is configured to integrally control the operation of virtualized communication instances and sensing instances and may include the following components.

[0207] For example, the Joint Resource Scheduler (JRS) can perform the function of scheduling to dynamically share or separate communication and sensing resources, and to scale resources as needed. Additionally, the Scaling Manager (SM) can control the horizontal or vertical scaling of virtual instances based on traffic or environmental changes. Furthermore, the Load Balancer (LB) can perform the function of balancing the load by coordinating instance placement between the edge, DU, and RU. Moreover, the Fault Tolerance Controller (FTC) can control rapid failover in the event of an instance failure.

[0208] The orchestration module can support the efficient operation of the system by unifying resource management and operation control of communication and sensing instances.

[0209] The joint optimization function performed by the corresponding orchestration module may be based on an optimization model such as Equation 1 to comprehensively consider communication performance, sensing accuracy, and the availability of virtual instances.

[0210]

[0211] In Equation 1, SE (Spectral Efficiency) represents the spectral efficiency of the communication function and can refer to the data transmission efficiency achieved within a given time-frequency resource. Additionally, CRB (Cramir-Rao Bound for Sensing Delay) represents the Cramir-Rao bound for the estimation delay when performing the sensing function, and a smaller value indicates higher sensing accuracy. Therefore, the (1 - CRB) term can serve as a factor for improving sensing performance. Furthermore, Availability is an indicator representing the survivability of a virtual instance, which can evaluate the extent to which a virtualized communication / sensing instance can continue to provide service even in the event of a failure.

[0212] also, , , These are weights for communication performance (SE), sensing accuracy (CRB), and virtual instance availability, respectively, and can be set according to service requirements and system policies.

[0213] According to the mathematical formula 1, the proposed system of the present disclosure can jointly optimize resources to maximize communication efficiency, secure the stability of sensing performance, and maintain high availability of virtual instances.

[0214] The virtualization-based communication and sensing integration method proposed in the present disclosure may be based on the following operations.

[0215] The operations described below are separated solely for the sake of convenience of explanation; depending on certain settings / environments, the operations described below may be performed independently / in parallel or integrated.

[0216] For example, the proposed method and system of the present disclosure relate to a technology for integrating and virtualizing communication and sensing functions, and to a technology for logically abstracting physical resources and configuring and managing virtual communication resources (e.g., VCI) and virtual sensor resources (e.g., VSI) based thereon. Through this, the flexibility, scalability, and resource utilization efficiency of the system can be improved.

[0217] The present disclosure may be implemented / configured to include the following operations.

[0218] 1) Hardware resource abstraction operation

[0219] Physical hardware, such as network interfaces, sensors, and data acquisition devices required for communication and sensing functions, can be identified, and various virtualization technologies, such as virtual machines (VMs) and containers, can be applied to abstract them into logical resources. This operation provides a foundation that enables multiple virtual resources to run concurrently and independently on a single physical device.

[0220] 2) Communication function virtualization operation

[0221] By utilizing abstracted resources, virtual network interfaces, virtual routers, virtual switches, and virtual protocols can be configured, and operations to form an independent virtual network separated from the physical network through network virtualization technology can be performed. Furthermore, by applying the concept of Software Defined Networking (SDN) to programmatically control and manage virtual communication resources, dynamic configuration changes, path optimization, and operational automation can be realized.

[0222] 3) Sensing function virtualization operation

[0223] Physical sensors and data acquisition devices can be abstracted into the form of virtual sensors, and operations can be performed to generate synthetic sensor data by implementing sensor models in a virtual environment or utilizing simulation / emulation. In this case, the generated virtual sensor can be configured to provide the same interface as a real sensor, and a virtual sensor framework can be applied to experiment with and / or verify various sensing scenarios.

[0224] 4) Virtualization Layer Integration Operation

[0225] Interoperability with virtualization infrastructure (e.g., cloud orchestration platform, virtual network management layer, etc.) can be ensured based on the operation of linking the communication virtualization module and the sensing virtualization module with the system's virtualization layer. Additionally, APIs, interfaces, or message path structures for linking and / or controlling modules may be provided, and consistent operation within the entire virtualization environment can be guaranteed.

[0226] 5) Resource Management and Orchestration Operations

[0227] This operation may involve performing dynamic allocation, reallocation, scaling up, and scaling down of virtual resources, as well as determining and applying resource optimization policies that consider application requirements, network status, sensing load, etc. The entire system is monitored in real time using resource monitoring tools and a virtualization management platform, and automatic adjustments can be performed when necessary.

[0228] 6) Test and verification operations

[0229] Performance, scalability, and interoperability tests can be performed on virtualized communication and sensing functions, and the operational stability of the module can be verified by reproducing various real-world conditions through a simulation environment. Through this, the reliability of the entire system can be ensured by checking configuration errors, latency characteristics, and load balancing efficiency of the virtual module.

[0230] By virtualizing communication and sensing functions according to the aforementioned operations, effects such as improved flexibility of system configuration, ease of service expansion, possibility of experimenting with various scenarios, and increased resource efficiency can be provided.

[0231] In addition, in relation to the aforementioned operations, a configuration / integration procedure according to the RRC layer may be performed to configure a virtual instance created in the virtualization layer to the terminal. An extended RRC message / container structure may be used for the said configuration / integration procedure.

[0232] For example, the RRC message / container may include message / IE (information element) for communication / sensing instance configuration information (e.g., VitrualCommSensingConfig), message / IE for instance unique identifier (e.g., VitrualInstanceID), message / IE for common resource mapping identifier (e.g., VS-ResourceMapID), message / IE for time / frequency / joint sensing / communication relationships (e.g., VS-TimingRelation), message / IE for joint key performance indicator (e.g., VCS-KPI-Report), etc.

[0233] In this regard, a base station (e.g., gNB-CU) may transmit communication / sensing instance configuration information (e.g., VirtualCommSensingConfig) to a terminal via an RRC Reconfiguration message. After configuring a virtual instance (e.g., VCI, VSI), the terminal may notify that the configuration is complete via an RRC Reconfiguration Complete message (e.g., VS-ConfigStatus). Additionally, if the environment changes, the base station may transmit configuration information (e.g., VS-AdaptationConfig) to the terminal for adjusting the virtual instance.

[0234] Unlike conventional SNF methods, the present disclosure provides a structure that integrates and configures communication / sensing configuration information in an RRC procedure.

[0235] In addition, in relation to the aforementioned operations, a configuration / interaction procedure according to the MAC / PHY layer may be performed to execute scheduling based on virtual instances created in the virtualization layer. For said configuration / interaction procedure, extended MAC CE and / or DCI extension fields may be defined.

[0236] For example, virtualization activation MAC CE messages to control instance / mode activation may be defined, and these messages may include an instance identifier (e.g., CS-InstanceID) and an activation pattern (e.g., VS-ActivationPattern). Additionally, extended fields within the DCI (e.g., VS-ResourceIndicator, VS-InstanceID) to specify resource-level scheduling may be defined. Furthermore, PUCCH reporting messages and new fields (e.g., VS-SensingKPI) to report sensing results may be defined, and new fields to distinguish between communication and sensing payloads may be defined.

[0237] The present disclosure provides a structure that directly maps virtual instance IDs and physical resources at the scheduling unit, in contrast to existing vRAN-based methods.

[0238] FIG. 15 shows an example of a virtualization of integrated sensing and communication and an operation flowchart based thereon according to an embodiment of the present disclosure.

[0239] Referring to FIG. 15, a virtualization resource initialization operation (S1510), a virtual communication channel setting operation (S1520), a virtual sensing resource setting operation (S1530), and a virtualization layer integration processing and resource management operation (S1540) may be performed.

[0240] Although the performance of the corresponding operations is described as a representative example in FIG. 15, this does not limit the scope of the present disclosure. For example, depending on the state / situation / environment of the base station and terminal, some operations shown in FIG. 15 may be omitted or the order of operations may be changed / adjusted.

[0241] First, the virtualization resource initialization operation (S1510) may be for instance creation and RRC configuration. That is, through this operation, the (initial) creation and delivery of the ID, resource structure, etc., for the virtual instance may be performed.

[0242] For example, a terminal may send a request to a base station to create a virtual instance for communication and sensing functions (e.g., a terminal initialization request message). Based on this, the base station may internally abstract physical resources to perform virtual resource allocation, including an ID for the virtual instance (e.g., VirtualInstanceID) and an ID for the resource mapping (e.g., VS-ResourceMapID). Subsequently, the base station may send configuration information for the virtual instance to the terminal via an RRC message (e.g., VirtualCommSensingConfig within an RRC reconfiguration message). Upon receiving this, the terminal internally sets, configures, and applies the virtual instance, and may notify the base station that the configuration is complete (e.g., an RRC reconfiguration completion message).

[0243] Next, the virtual communication channel setup operation (S1520) may correspond to a step of switching the virtual communication instance (VCI) to a state capable of transmitting actual communication data, and a resource activation step. Such operations may be based on instruction / activation via MAC CE and / or DCI.

[0244] For example, a terminal may request the base station to establish / set up a virtual communication instance (e.g., a virtual communication channel) that utilizes abstracted network resources. Upon receiving such a request, the base station may establish the virtual communication instance by utilizing network virtualization technology and SDN techniques. Subsequently, the base station may instruct the terminal to activate the virtual communication instance through MAC CE and / or extended DCI fields, etc.

[0245] Additionally, the virtual sensing resource setting operation (S1530) corresponds to a step of converting sensing data into a state where it can be collected, and may be a step of activating a dedicated sensing resource. Specifically, as it is required that the sensing function be configured independently of the communication function and be set separately from the operation of the communication channel, a step of determining / setting / confirming the priority and sharing strategy between the communication resource and the sensing resource may be necessary.

[0246] For example, a terminal may request access to / activation of a virtual sensing instance (VSI) from a base station (e.g., a terminal sensing request message), and the base station may provide / transmit sensing information (e.g., a virtual sensor representation, a synthetic data stream through emulation, etc.) to the terminal in response to the request.

[0247] Next, the virtualization layer integration processing operation (S1540) may correspond to a step of linking / binding a virtual instance to an actual physical resource to transition it into an executable state. That is, the operation may be related to the method of registering and actually utilizing the virtual instance in the virtualization layer.

[0248] In this regard, the base station can register virtual instances (e.g., VCI, VSI) with the virtualization layer and perform mapping between virtual resources and physical resources based on the ID of the virtual instance and the ID associated with resource mapping. Subsequently, the terminal and the base station can perform actual communication based on the virtual communication instance (VCI) and utilize sensing data based on the virtual sensing instance (VSI).

[0249] For example, a base station may register virtual communication instances and virtual sensing instances with the virtualization layer so that they can be fully integrated with the system's virtualization layer, and may provide APIs and interfaces for interaction between modules. Accordingly, virtual communication modules and virtual sensing modules operate in conjunction with resource management functions provided by the base station's virtualization infrastructure, and data exchange between individual instances or control signal processing can be consistently performed through the virtualization layer. During this integration process, the base station may check the status of virtual instances and perform initial configuration to ensure compatibility between modules within the virtualization layer.

[0250] Furthermore, the terminal can initiate data transmission using an initialized virtual communication instance and perform application services by utilizing a sensing data stream provided through a virtual sensing instance. In response, the base station can monitor the resource status of the entire virtualization environment in real time and optimize the performance of the entire system by dynamically allocating additional computational resources, bandwidth, or scheduling resources as needed. This resource orchestration process can be performed based on policies set by the base station, and resource allocation can be adjusted (automatically) in response to instantaneous traffic changes, increased sensing load, or fluctuations in user requirements. Through such an operational method, virtualized communication and sensing functions can be maintained stably under various environmental conditions and provided at a level that satisfies Quality of Service (QoS).

[0251] The operations in FIGS. 16 and 17 described below are intended to optimize virtualization-based integrated sensing and communication operations and can be performed as necessary.

[0252] FIG. 16 shows an additional example of a virtualization of integrated sensing and communication and an operation flowchart based thereon according to an embodiment of the present disclosure.

[0253] The operation illustrated in FIG. 16 is related to performance verification and may correspond to an optimization loop process that performs adaptation based on the operational results of virtualization-based integrated sensing and communication.

[0254] For example, a terminal may request performance verification from a base station. This request may be intended to seek feedback on the system performance and verification of communication and sensing operations in a virtualized environment. Upon receiving this request, the base station may evaluate the operational status of the virtual instance. Additionally, the terminal may transmit reports to the base station regarding performance metrics (e.g., SINR, BLER, Delay CRB, detection probability, etc.) for the virtualized communication and sensing operations. Based on this, the base station may readjust resource ratios and priorities as necessary. In this case, the base station may utilize settings based on RRC messages (e.g., VS-AdaptationConfig, etc.).

[0255] FIG. 17 shows another additional example of a virtualization of integrated sensing and communication and an operation flowchart based thereon according to an embodiment of the present disclosure.

[0256] The operation illustrated in FIG. 17 is for the termination and / or adjustment of a virtual instance and may correspond to an operational step for handling changes to the instance (e.g., expansion, reduction, movement, etc.).

[0257] For example, a terminal may request resource scaling or virtual instance adjustment (e.g., UE Resource Adjustment Request). Based on such request, the base station may dynamically allocate necessary resources, reallocate resources, or reconfigure virtual instances (e.g., VCI, VSI) for optimal performance. The base station may provide or transmit information regarding the allocated, reallocated, or reconfigured resources and / or virtual instances to the terminal.

[0258] Hereinafter, the operation of a terminal and a base station based on the various methods and examples of the present disclosure described above is explained through FIGS. 18 and 19. The examples of FIGS. 18 and 19 may correspond to some of the various examples of the present disclosure.

[0259] FIG. 18 illustrates a flowchart of the operation of a terminal for virtualized integrated sensing and communication operations according to an embodiment of the present disclosure.

[0260] The terminal can receive configuration information for a virtual communication instance and a virtual sensing instance from the base station (S1810).

[0261] For example, configuration information may be received in response to a message requesting virtual resource initialization transmitted by a terminal, and said configuration information may include identification information for a virtual instance and identification information related to resource mapping.

[0262] For example, the configuration information can be transmitted via an RRC (Radio Resource Control) reconfiguration message.

[0263] The terminal can activate a virtual communication instance and a virtual sensing instance based on at least one of configuration information or instructions by a base station (S1820).

[0264] For example, an instruction by a base station may correspond to a response to one or more of a configuration request for a virtual communication instance or an access request for a virtual sensing instance transmitted by a terminal. Such instruction by the base station may be transmitted via a MAC-CE (medium access control-control element) or DCI (downlink control information).

[0265] As a specific example, if an instruction by a base station relates to a request for access to a virtual sensing instance, the terminal may additionally receive information from the base station regarding the pattern or mode of the virtual sensing instance.

[0266] The terminal can perform data transmission and reception based on a virtual communication instance and can acquire or process sensing data based on a sensing instance (S1830).

[0267] In relation to the operations described above, according to the present disclosure, a virtual communication instance and a sensing instance are registered in a virtualization layer and can be mapped to one or more physical resources.

[0268] Additionally, according to the present disclosure, a terminal may transmit report information for performance verification to a base station. Herein, the report information may include information on at least one of a signal-to-interference noise ratio (SINR), a block error rate (BLER), a delay time, or a detected error. In this case, if necessary, the terminal may receive information on at least one of an adjusted resource ratio or an adjusted priority from the base station based on the report information.

[0269] Additionally, according to the present disclosure, the terminal may transmit a message to the base station requesting a change to a virtual instance based on performance degradation or a change in service requirements. For example, the change to the virtual instance may include sacling or migration to another resource of at least one of a virtual communication instance or a virtual sensing instance.

[0270] The method described in the example of FIG. 18 may be performed by the wireless device (200) of FIG. 3 corresponding to the second node (120) of FIG. 2 described above. For example, one or more processors (202) of the wireless device (200) of FIG. 3 may be configured to receive configuration information for a virtual communication instance and a virtual sensing instance from a base station; activate the virtual communication instance and the virtual sensing instance by the terminal based on at least one of the configuration information or instructions by the base station; perform data transmission and reception based on the virtual communication instance by the terminal and acquire or process sensing data based on the sensing instance.

[0271] Furthermore, one or more memories (204) of the wireless device (200) may store instructions for performing the method described in the example of FIG. 18 or the examples described above when executed by one or more processors (202).

[0272] FIG. 19 illustrates a sequence of operations of a base station for virtualized integrated sensing and communication operations according to an embodiment of the present disclosure.

[0273] The base station can receive a message from the terminal requesting the configuration of a virtual instance (S1910).

[0274] The base station can transmit configuration information for a virtual instance created by abstracting physical resources to the terminal (S1920). In addition, the base station can transmit instruction information to activate the virtual instance to the terminal (S1930).

[0275] Afterwards, based on the virtual instance, the base station can perform data transmission and reception with the terminal and provide sensing data to the terminal (S1940).

[0276] In this regard, the aforementioned virtual instance can be registered and managed in the virtualization layer by a base station, etc.

[0277] The specific features regarding configuration information for a virtual instance, instructions by a base station (e.g., instructions related to activation), performance verification and adjustment actions based thereon, and requests for changes to a virtual instance and actions based thereon are identical to the description with reference to the example in FIG. 18, so redundant descriptions are omitted.

[0278] The method described in the example of FIG. 19 can be performed by the wireless device (200) of FIG. 3 corresponding to the second node (120) of FIG. 2 described above. For example, one or more processors (202) of the wireless device (200) of FIG. 3 may be configured to receive a message requesting the configuration of a virtual instance from a terminal by a base station; transmit configuration information for a virtual instance created by abstracting physical resources to the terminal by the base station; transmit instruction information to activate the virtual instance to the terminal by the base station; and perform data transmission and reception with the terminal and provide sensing data to the terminal based on the virtual instance by the base station.

[0279] Furthermore, one or more memories (204) of the wireless device (200) may store instructions for performing the method described in the example of FIG. 19 or the examples described above when executed by one or more processors (202).

[0280] According to the present disclosure, communication and sensing functions can be managed integrally in a virtualized environment, thereby effectively resolving various inefficiencies found in conventional technologies. That is, unlike existing methods that relied on dedicated hardware, dynamically allocating resources based on virtual instances mitigates hardware dependency and resource underutilization issues, and significantly reduces infrastructure construction and operation costs for communication and sensing functions.

[0281] In addition, conventionally, communication systems and sensing systems were operated separately, resulting in complex interoperability tasks and data silos; however, through the integrated virtualization layer according to the present disclosure, compatibility issues between systems can be minimized, and consistency and efficiency in data processing can be ensured.

[0282] In addition, the present disclosure enables flexible resource expansion and configuration changes based on virtualization, thereby overcoming the rigidity of existing RAN infrastructure and the limitations of static resource allocation. Since resource adjustment is possible in response to real-time changes in demand, various application service requirements can be satisfied without degrading service quality.

[0283] In addition, latency and bottlenecks caused by the distributed operation of communication and sensing functions can be reduced, and operational procedures can be simplified and integrated monitoring and control of network status can be supported through integrated management functions at the virtualization layer.

[0284] The proposed method and system of the present disclosure have the technical effect of significantly improving the overall performance and operational efficiency of the network by resolving structural limitations of existing systems, such as complexity, cost burden, lack of scalability, and resource inefficiency, and by enabling integrated and intelligent operation of communication and sensing functions.

[0285] The embodiments described above are combinations of the components and features of the present disclosure in a specific form. Each component or feature should 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, it is possible to construct embodiments of the present disclosure by combining some components and / or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment. It is obvious that embodiments may be constructed by combining claims that are not explicitly related in the claims, or that they may be included as new claims by amendment after filing.

[0286] It is obvious to those skilled in the art that the present disclosure may be embodied in other specific forms without departing from the essential features of the present disclosure. Accordingly, the detailed description set forth above should not be interpreted restrictively in all respects and should be considered exemplary. 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.

[0287] The scope of the present disclosure includes software or machine-executable instructions (e.g., operating systems, applications, firmware, programs, etc.) that enable operations according to the methods of various embodiments to be executed on a device or computer, and a non-transitory computer-readable medium on which such software or instructions, etc. are stored and executable on a device or computer. Instructions that may be used to program a processing system to perform the features described in the present disclosure may be stored on or within a storage medium or a computer-readable storage medium, and the features described in the present disclosure may be implemented using a computer program product comprising such a storage medium. The storage medium may include, but is not limited to, high-speed random access memory such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices, and may include non-volatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory may optionally include one or more storage devices located remotely from the processor(s). Memory or alternatively, non-volatile memory device(s) within memory comprises a non-transient computer-readable storage medium. The features described in this disclosure may be stored in any one of the machine-readable media and integrated into software and / or firmware that can control the hardware of a processing system and allow the processing system to interact with other mechanisms utilizing results according to the embodiments of this disclosure. Such software or firmware may include, but is not limited to, application code, device drivers, operating systems, and execution environments / containers.

[0288] Here, the wireless communication technology implemented in the device of the present disclosure may include LTE, NR, and 6G, as well as Narrowband Internet of Things for low-power communication. In this case, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented according to standards such as LTE Cat NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above. Additionally, or generally, the wireless communication technology implemented in the device of the present disclosure may perform communication based on LTE-M technology. In this case, for example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented in at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names mentioned above. Additionally or generally, wireless communication technology implemented in the device (100, 200) of the present disclosure may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) for low-power communication, and is not limited to the names mentioned above. As an example, ZigBee technology may create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and may be referred to by various names.

[0289] Although the method proposed in this disclosure has been described with an example applied to 3GPP LTE / LTE-A, 5G, and 6G systems, it is possible to apply it to various wireless communication systems in addition to 3GPP LTE / LTE-A, 5G, and 6G systems.

Claims

1. Regarding the method, A step of receiving configuration information for a virtual communication instance and a virtual sensing instance from a base station by a terminal; A step of activating the virtual communication instance and the virtual sensing instance by the terminal based on at least one of the above configuration information or instructions by the base station; and A method comprising the steps of performing data transmission and reception based on the virtual communication instance by the terminal, and acquiring or processing sensing data based on the sensing instance.

2. In Paragraph 1, The above configuration information is received as a response to a message requesting virtual resource initialization transmitted by the terminal, and A method in which the above configuration information includes identification information for a virtual instance and identification information related to resource mapping.

3. In Paragraph 2, A method in which the above configuration information is transmitted via an RRC (Radio Resource Control) reconfiguration message.

4. In Paragraph 1, The instruction by the base station above corresponds to a response to one or more of a configuration request for a virtual communication instance or an access request for a virtual sensing instance transmitted by the terminal, and A method in which instructions by the above-mentioned base station are transmitted via MAC-CE (medium access control-control element) or DCI (downlink control information).

5. In Paragraph 4, A method further comprising the step of receiving information about a pattern or mode for a virtual sensing instance from a base station by the terminal, based on the fact that an instruction by the base station is related to an access request for the virtual sensing instance.

6. In Paragraph 1, A method in which the above virtual communication instance and the above sensing instance are registered in a virtualization layer and mapped to one or more physical resources.

7. In Paragraph 1, The above terminal further includes the step of transmitting report information for performance verification to the base station. A method comprising information on at least one of SINR (signal-to-interference noise ratio), BLER (block error rate), delay time, or detection error, based on the above-mentioned reporting information.

8. In Paragraph 7, A method further comprising the step of receiving from the base station information regarding at least one of a resource ratio adjusted based on the report information by the terminal.

9. In Paragraph 1, A method further comprising the step of transmitting a message to the base station requesting a change to a virtual instance based on performance degradation or a change in service requirements by the terminal.

10. In Paragraph 9, A method in which changes to the virtual instance include sacling or migration to another resource for at least one of the virtual communication instance or the virtual sensing instance.

11. One or more transceivers; and It includes one or more processors connected to the above one or more transmitters and receivers, and The above one or more processors are: The terminal receives configuration information for a virtual communication instance and a virtual sensing instance from the base station; Based on at least one of the above configuration information or instructions by the base station, the terminal activates the virtual communication instance and the virtual sensing instance; A device configured to perform data transmission and reception with the base station based on the virtual communication instance by the above terminal, and to acquire or process sensing data based on the sensing instance.

12. One or more processors; and A processing device comprising one or more computer memories that are operably connected to one or more processors and store instructions for performing a method according to any one of claims 1 to 10 based on execution by one or more processors.

13. One or more non-transitory computer-readable media storing one or more instructions that are executed by one or more processors to control the execution of a method according to any one of claims 1 through 10.

14. Regarding the method, A step of receiving a message from a terminal requesting the configuration of a virtual instance by a base station; A step of transmitting configuration information for a virtual instance created by abstracting physical resources by the base station to the terminal; The step of transmitting instruction information to the terminal to activate the virtual instance by the base station; and Based on the above virtual instance, the method includes the step of performing data transmission and reception with the terminal by the base station and the step of providing sensing data to the terminal. A method in which the above virtual instance is registered in the virtualization layer.

15. One or more transceivers; and It includes one or more processors connected to the above one or more transmitters and receivers, and The above one or more processors are: A base station receives a message from a terminal requesting the configuration of a virtual instance; The above base station transmits configuration information for a virtual instance created by abstracting physical resources to the terminal; The base station transmits instruction information to the terminal to activate the virtual instance; Based on the above virtual instance, the base station is configured to perform data transmission and reception with the terminal and to provide sensing data to the terminal, and The above virtual instance is a device registered in the virtualization layer.

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