Method for utilizing prediction based on measurement result

Abstract

An embodiment of the present disclosure comprises the steps in which: a UE predicts measurement values for a neighboring cell and a serving cell in a PW on the basis of a measurement result; on the basis of the prediction result, a first time point in the PW at which an entry condition for event A3 is satisfied is determined; on the basis of the prediction result, the UE determines that an exit condition for event A3 is not satisfied during a TTT time from the first time point; and the UE transmits a report for event A3 to a network at the first time point on the basis of determining that the exit condition is not satisfied during the TTT time from the first time point.

Description

Methods for utilizing predictions based on measurement results

[0001] This specification relates to mobile communication.

[0002] 3GPP (3rd generation partnership project) LTE (long-term evolution) is a technology designed to enable high-speed packet communication. Many methods have been proposed to achieve LTE goals, such as reducing costs for users and operators, improving service quality, expanding coverage, and increasing system capacity. As high-level requirements, 3GPP LTE demands reduced cost per bit, improved service availability, flexible use of frequency bands, a simple structure, open interfaces, and appropriate power consumption of terminals.

[0003] Work has begun at the ITU (International Telecommunication Union) and 3GPP to develop requirements and specifications for new radio (NR) systems. 3GPP must identify and develop the technical components necessary to successfully standardize NR in a timely manner, satisfying both urgent market demands and the longer-term requirements presented by the ITU-R (ITU Radio Communication Sector) IMT (International Mobile Telecommunications)-2020 process. Furthermore, NR must be able to utilize any spectrum band up to at least 100 GHz so that it can be used for wireless communication even in the distant future.

[0004] NR targets a single technical framework that covers all deployment scenarios, usage scenarios, and requirements, including eMBB (enhanced mobile broadband), mMTC (massive machine type communications), and URLLC (ultra-reliable and low latency communications). NR must inherently be forward compatible.

[0005] Predictions are made based on measurement results, and the prediction results are utilized.

[0006] FIG. 1 shows an example of a communication system to which the implementation of the present specification is applied.

[0007] FIG. 2 shows an example of a wireless device to which the implementation of the present specification applies.

[0008] FIG. 3 shows an example of a UE to which the implementation of the present specification applies.

[0009] Figure 4 shows an example of a communication structure that can be provided in a 6G system.

[0010] Figure 5 shows an example of an electromagnetic spectrum.

[0011] Figure 6 illustrates an example of a subframe type in NR.

[0012] Figure 7 shows an example of SSB in NR.

[0013] Figure 8 shows an example of beam sweeping in NR.

[0014] Figure 9 shows an example of RSRP change between a serving cell and a neighboring cell related to a handover.

[0015] Figure 10 shows the procedure related to event A3.

[0016] FIG. 11 shows an example of a case where PW is longer than TTT according to the disclosure of the present specification.

[0017] FIG. 12 illustrates three cases related to additional measurements according to the disclosure of the present specification.

[0018] FIG. 13 shows an example of a case where PW is shorter than TTT according to the disclosure of the present specification.

[0019] FIG. 14 shows an example of an RLF determination according to the disclosure of the present specification.

[0020] FIG. 15 shows an example of RLF determination through prediction according to the disclosure of the present specification.

[0021] Figure 16 shows an example of sliding L1 / L3 filtering.

[0022] Figure 17 shows an example of non-sliding L1 / L3 filtering.

[0023] Figure 18 shows an example of a measurement model.

[0024] Figure 19 shows an example of case A.

[0025] Figure 20 shows an example of case B.

[0026] FIG. 21 illustrates an example of a procedure for dynamic PW and OW according to the disclosure of the present specification.

[0027] FIG. 22 shows examples of dynamic PW and OW according to the disclosure of the present specification.

[0028] FIG. 23 illustrates another example of a procedure for dynamic PW and OW according to the disclosure of this specification.

[0029] FIG. 24 shows an example of a distribution of predicted values ​​according to the disclosure of the present specification.

[0030] FIG. 25 illustrates the assumption of a reliability definition according to the disclosure of this specification.

[0031] FIG. 26 illustrates a first example of a reliability definition according to the disclosure of the present specification.

[0032] FIG. 27 illustrates a second example of a reliability definition according to the disclosure of the present specification.

[0033] FIG. 28 illustrates a third example of a reliability definition according to the disclosure of the present specification.

[0034] FIG. 29 shows another expression of the first example of a reliability definition according to the disclosure of the present specification.

[0035] FIG. 30 shows another representation of the third example of the reliability definition according to the disclosure of this specification.

[0036] FIG. 31 shows another representation of the third example of the reliability definition according to the disclosure of this specification.

[0037] FIG. 32 illustrates the procedure of the UE for the first disclosure of the present specification.

[0038] FIG. 33 illustrates the procedure of the UE for the second disclosure of the present specification.

[0039] The following techniques, devices, and systems may be applied to various wireless multiple access systems. Examples of multiple access systems include code division multiple access (CDMA) systems, frequency division multiple access (FDMA) systems, time division multiple access (TDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, and multicarrier frequency division multiple access (MC-FDMA) systems. CDMA may be implemented through wireless technologies such as universal terrestrial radio access (UTRA) or CDMA2000. TDMA may be implemented through wireless technologies such as global system for mobile communications (GSM), general packet radio service (GPRS), or enhanced data rates for GSM evolution (EDGE). OFDMA can be implemented through wireless technologies such as IEEE (Institute of Electrical and Electronics Engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or E-UTRA (evolved UTRA). UTRA is part of UMTS (universal mobile telecommunications system). 3GPP (3rd generation partnership project) LTE (long-term evolution) is part of E-UMTS (evolved UMTS) using E-UTRA.3GPP LTE uses OFDMA in the downlink (DL) and SC-FDMA in the uplink (UL). Evolutions of 3GPP LTE include LTE-A (advanced), LTE-A Pro, and / or 5G NR (new radio).

[0040] For convenience of explanation, the implementation of this specification is described primarily in relation to 3GPP-based wireless communication systems. However, the technical characteristics of this specification are not limited thereto. For example, the following detailed description is provided based on a mobile communication system corresponding to a 3GPP-based wireless communication system, but aspects of this specification that are not limited to 3GPP-based wireless communication systems may be applied to other mobile communication systems.

[0041] For terms and technologies used in this specification that are not specifically described, reference may be made to wireless communication standard documents published prior to this specification.

[0042] In this specification, “A or B” may mean “only A,” “only B,” or “both A and B.” Alternatively, in this specification, “A or B” may be interpreted as “A and / or B.” For example, in this specification, “A, B or C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.”

[0043] As used herein, a slash ( / ) or a comma may mean “and / or.” For example, “A / B” may mean “A and / or B.” Accordingly, “A / B” may mean “only A,” “only B,” or “both A and B.” For example, “A, B, C” may mean “A, B, or C.”

[0044] In this specification, “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” Additionally, in this specification, 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.”

[0045] Additionally, in this specification, “at least one of A, B and C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Furthermore, “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.”

[0046] Additionally, parentheses used in this specification may mean “for example.” Specifically, when indicated as “Control Information (PDCCH),” “PDCCH” may be proposed as an example of “Control Information.” In other words, “Control Information” in this specification is not limited to “PDCCH,” and “PDCCH” may be proposed as an example of “Control Information.” Furthermore, even when indicated as “Control Information (i.e., PDCCH),” “PDCCH” may be proposed as an example of “Control Information.”

[0047] Technical features described individually within a single drawing in this specification may be implemented individually or simultaneously.

[0048] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification may be applied to various fields where wireless communication and / or connectivity between devices (e.g., 5G) is required.

[0049] The present specification will be described in more detail below with reference to the drawings. In the following drawings and / or description, the same reference numerals may refer to the same or corresponding hardware blocks, software blocks, and / or function blocks unless otherwise indicated.

[0050] FIG. 1 shows an example of a communication system to which the implementation of the present specification is applied.

[0051] The 5G usage scenario shown in FIG. 1 is merely an example, and the technical features of this specification may be applied to other 5G usage scenarios not shown in FIG. 1.

[0052] The three main requirements categories for 5G are (1) enhanced mobile broadband (eMBB) category, (2) massive machine type communication (mMTC) category, and (3) ultra-reliable and low latency communications (URLLC) category.

[0053] Referring to FIG. 1, the communication system (1) includes wireless devices (100a to 100f), a base station (BS; 200), and a network (300). FIG. 1 illustrates a 5G network as an example of the network of the communication system (1), but the implementation of the present specification is not limited to a 5G system and may be applied to future communication systems beyond a 5G system.

[0054] The base station (200) and the network (300) can be implemented as wireless devices, and a specific wireless device can operate as a base station / network node in relation to another wireless device.

[0055] Wireless devices (100a to 100f) represent devices that perform communication using radio access technology (RAT) (e.g., 5G NR or LTE) and may also be referred to as communication / wireless / 5G devices. Wireless devices (100a to 100f) may include, but are not limited to, robots (100a), vehicles (100b-1 and 100b-2), extended reality (XR) devices (100c), portable devices (100d), home appliances (100e), IoT devices (100f), and artificial intelligence (AI) devices / servers (400). For example, vehicles may include vehicles with wireless communication capabilities, autonomous vehicles, and vehicles capable of performing communication between vehicles. Vehicles may include unmanned aerial vehicles (UAVs) (e.g., drones). XR devices may include AR / VR / mixed reality (MR) devices and may be implemented in the form of head-mounted devices (HMDs) and head-up displays (HUDs) mounted on vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signs, vehicles, robots, etc. Portable devices may include smartphones, smart pads, wearable devices (e.g., smartwatches or smart glasses), and computers (e.g., laptops). Home appliances may include TVs, refrigerators, and washing machines. IoT devices may include sensors and smart meters.

[0056] In this specification, wireless devices (100a to 100f) may be referred to as user equipment (UE). The UE may include, for example, a mobile phone, a smartphone, a laptop computer, a digital broadcasting terminal, a PDA (personal digital assistant), a PMP (portable multimedia player), a navigation system, a slate PC, a tablet PC, an ultrabook, a vehicle, a vehicle with autonomous driving capabilities, a connected car, a UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a fintech device (or financial device), a security device, a weather / environment device, a 5G service-related device, or a device related to the Fourth Industrial Revolution.

[0057] For example, a UAV can be an aircraft that is not on board and is navigated by radio control signals.

[0058] For example, a VR device may include a device for implementing objects or backgrounds in a virtual environment. For example, an AR device may include a device that implements objects or backgrounds in a virtual world by connecting them to objects or backgrounds in a real world. For example, an MR device may include a device that implements objects or backgrounds in a virtual world by merging them with objects or backgrounds in a real world. For example, a holographic device may include a device for implementing a 360-degree stereoscopic image by recording and playing back stereoscopic information using the phenomenon of light interference that occurs when two laser lights called holograms meet.

[0059] For example, a public safety device may include an image relay device or an image device that can be worn on a user's body.

[0060] For example, MTC devices and IoT devices may be devices that do not require direct human intervention or operation. For instance, MTC devices and IoT devices may include smart meters, vending machines, thermometers, smart light bulbs, door locks, or various sensors.

[0061] For example, a medical device may be a device used for the purpose of diagnosing, treating, alleviating, curing, or preventing a disease. For example, a medical device may be a device used to diagnose, treat, alleviate, or correct an injury or damage. For example, a medical device may be a device used for the purpose of examining, replacing, or modifying a structure or function. For example, a medical device may be a device used for the purpose of regulating pregnancy. For example, a medical device may include a therapeutic device, a driving device, a (in vitro) diagnostic device, a hearing aid, or a surgical device.

[0062] For example, a security device may be a device installed to prevent potential risks and maintain safety. For example, a security device may be a camera, closed-circuit TV (CCTV), a recorder, or a black box.

[0063] For example, a fintech device may be a device capable of providing financial services such as mobile payments. For example, a fintech device may include a payment device or a POS system.

[0064] For example, a weather / environment device may include a device for monitoring or predicting the weather / environment.

[0065] Wireless devices (100a to 100f) can be connected to a network (300) through a base station (200). AI technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) through the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, and a network after 5G. The wireless devices (100a to 100f) may communicate with each other through the base station (200) / network (300), but they may also communicate directly (e.g., sidelink communication) without going through the base station (200) / network (300). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle-to-vehicle) / V2X (vehicle-to-everything) communication). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0066] Wireless communication / connections (150a, 150b, 150c) can be established between wireless devices (100a to 100f) and / or between wireless devices (100a to 100f) and base station (200) and / or between base station (200). Here, the wireless communication / connections can be established through various RATs (e.g., 5G NR), such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D (device-to-device) communication), and communication between base stations (150c) (e.g., relay, IAB (integrated access and backhaul)). Through the wireless communication / connections (150a, 150b, 150c), wireless devices (100a to 100f) and base station (200) can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) may transmit / receive signals through various physical channels. To this end, based on various proposals in this specification, 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.), and a resource allocation process.

[0067] AI refers to the field of researching artificial intelligence or the methodologies to create it, while machine learning refers to the field of researching methodologies to define and solve various problems within the realm of artificial intelligence. Machine learning is also defined as an algorithm that improves performance on a task through continuous experience.

[0068] A robot can refer to a machine that automatically processes or operates given tasks based on its own capabilities. In particular, a robot equipped with the ability to perceive its environment, make independent judgments, and perform actions can be called an intelligent robot. Robots can be classified into industrial, medical, domestic, and military types depending on their purpose or field of use. Robots are equipped with drive units, including actuators or motors, to perform various physical movements, such as moving robot joints. Additionally, mobile robots include wheels, brakes, propellers, etc., in their drive units, enabling them to drive on the ground or fly in the air.

[0069] Autonomous driving refers to technology that drives itself, and an autonomous vehicle refers to a vehicle that drives without user intervention or with minimal user intervention. For example, autonomous driving can include technologies such as maintaining the driving lane, automatically adjusting speed like adaptive cruise control, driving automatically along a predetermined route, and automatically setting a route and driving once a destination is set. The term "vehicle" encompasses vehicles equipped solely with internal combustion engines, hybrid vehicles equipped with both internal combustion engines and electric motors, and electric vehicles equipped solely with electric motors; it can include not only automobiles but also trains and motorcycles. An autonomous vehicle can be viewed as a robot equipped with autonomous driving capabilities.

[0070] Augmented Reality is a collective term for VR, AR, and MR. VR technology provides real-world objects or backgrounds solely as CG images, AR technology provides virtual CG images superimposed on images of real objects, and MR technology is a CG technology that mixes and combines virtual objects with the real world. MR technology is similar to AR technology in that it displays real-world and virtual objects together. However, there is a difference in that while virtual objects in AR technology are used to complement real-world objects, virtual and real objects in MR technology are used as equal entities.

[0071] NR supports multiple numerologies or subcarrier spacings (SCS) to support various 5G services. For example, when the SCS is 15 kHz, it supports a wide area in traditional cellular bands; when the SCS is 30 kHz / 60 kHz, it supports dense-urban areas, lower latency, and wider carrier bandwidth; and when the SCS is 60 kHz or higher, it supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0072] The NR frequency band can be defined by two types of frequency ranges (FR1, FR2). The numerical values ​​of the frequency ranges may change. For example, the two types of frequency ranges (FR1, FR2) may be as shown in Table 1 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 may mean the “sub 6GHz range” and FR2 may mean the “above 6GHz range” and may be referred to as millimeter wave (mmW).

[0073] Frequency Range Definition Frequency Range Subcarrier Spacing FR1 450 MHz - 6000 MHz 15, 30, 60 kHz FR2 24 250 MHz - 52600 MHz 60, 120, 240 kHz

[0074] As described above, the numerical values ​​of the frequency range of the NR system may change. For example, FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 2 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, the frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, for example, for communication for vehicles (e.g., autonomous driving).

[0075] Frequency Range Definition Frequency Range Subcarrier Spacing FR1 4 10 MHz - 7 125 MHz 15, 30, 60 kHz FR2 24 250 MHz - 5 2600 MHz 60, 120, 240 kHz

[0076] Here, the wireless communication technology implemented in the wireless device of this specification may include LTE, NR, and 6G, as well as narrowband IoT (NB-IoT) for low-power communication. For example, NB-IoT technology may be an example of low-power wide-area network (LPWAN) 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 wireless device of this specification may perform communication based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as enhanced MTC (eMTC). 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 MTC, and / or 7) LTE M, and is not limited to the names mentioned above. Additionally or generally, wireless communication technology implemented in the wireless device of this specification may include at least one of ZigBee, Bluetooth, and / or LPWAN for low-power communication, and is not limited to the names mentioned above. For 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.

[0077] FIG. 2 shows an example of a wireless device to which the implementation of the present specification applies.

[0078] In FIG. 2, the first wireless device (100) and / or the second wireless device (200) may be implemented in various forms depending on the use example / service. For example, {the first wireless device (100) and the second wireless device (200)} may correspond to at least one of {wireless devices (100a–100f) and base station (200)}, {wireless devices (100a–100f) and wireless devices (100a–100f)} and / or {base station (200) and base station (200)} of FIG. 1. The first wireless device (100) and / or the second wireless device (200) may be composed of various components, devices / parts and / or modules.

[0079] The first wireless device (100) may include at least one transceiver such as a transceiver (106), at least one processing chip such as a processing chip (101), and / or one or more antennas (108).

[0080] The processing chip (101) may include at least one processor, such as a processor (102), and at least one memory, such as a memory (104). Additionally and / or generally, the memory (104) may be placed outside the processing chip (101).

[0081] The processor (102) can control the memory (104) and / or the transceiver (106) and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein. For example, the processor (102) may process information within the memory (104) to generate a first information / signal and transmit a wireless signal containing the first information / signal through the transceiver (106). The processor (102) may receive a wireless signal containing a second information / signal through the transceiver (106) and process the second information / signal to store the obtained information in the memory (104).

[0082] Memory (104) may be connected to the processor (102) so as to be operable. Memory (104) may store various types of information and / or instructions. Memory (104) may store firmware and / or software code (105) that implements code, instructions, and / or a set of instructions that perform the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification when executed by the processor (102). For example, firmware and / or software code (105) may implement instructions that perform the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification when executed by the processor (102). For example, firmware and / or software code (105) may control the processor (102) to perform one or more protocols. For example, firmware and / or software code (105) may control the processor (102) to perform one or more wireless interface protocol layers.

[0083] Here, the processor (102) and memory (104) may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). A transceiver (106) may be connected to the processor (102) and may transmit and / or receive a wireless signal through one or more antennas (108). Each transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be interchangeably used with an RF (radio frequency) unit. In this specification, the first wireless device (100) may represent a communication modem / circuit / chip.

[0084] The second wireless device (200) may include at least one transceiver such as a transceiver (206), at least one processing chip such as a processing chip (201), and / or one or more antennas (208).

[0085] The processing chip (201) may include at least one processor, such as a processor (202), and at least one memory, such as a memory (204). Additionally and / or alternatively, the memory (204) may be placed outside the processing chip (201).

[0086] The processor (202) can control the memory (204) and / or the transceiver (206) and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein. For example, the processor (202) may process information within the memory (204) to generate a third information / signal and transmit a wireless signal containing the third information / signal through the transceiver (206). The processor (202) may receive a wireless signal containing a fourth information / signal through the transceiver (206) and process the fourth information / signal to store the obtained information in the memory (204).

[0087] Memory (204) may be connected to the processor (202) so as to be operable. Memory (204) may store various types of information and / or instructions. Memory (204) may store firmware and / or software code (205) that implements instruction code, instructions, and / or sets of instructions that perform descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification when executed by the processor (202). For example, firmware and / or software code (205) may implement instructions that perform descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification when executed by the processor (202). For example, firmware and / or software code (205) may control the processor (202) to perform one or more protocols. For example, firmware and / or software code (205) may control the processor (202) to perform one or more wireless interface protocol layers.

[0088] Here, the processor (202) and memory (204) may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). A transceiver (206) may be connected to the processor (202) and transmit and / or receive a wireless signal through one or more antennas (208). Each transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeably used with an RF unit. In this specification, the second wireless device (200) may represent a communication modem / circuit / chip.

[0089] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as a PHY (physical) layer, a MAC (media access control) layer, a RLC (radio link control) layer, a PDCP (packet data convergence protocol) layer, a RRC (radio resource control) layer, and an SDAP (service data adaptation protocol) layer). One or more processors (102, 202) may generate one or more PDUs (protocol data units), one or more SDUs (service data units), messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification. One or more processors (102, 202) may generate a signal (e.g., baseband signal) including a PDU, SDU, message, control information, data, or information according to the description, function, procedure, proposal, method, and / or operation flowchart disclosed in this specification and provide it to one or more transceivers (106, 206). One or more processors (102, 202) may receive a signal (e.g., baseband signal) from one or more transceivers (106, 206) and may obtain a PDU, SDU, message, control information, data, or information according to the description, function, procedure, proposal, method, and / or operation flowchart disclosed in this specification.

[0090] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, and / or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, and / or a combination thereof. For example, one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), and / or one or more field programmable gate arrays (FPGAs) may be included in one or more processors (102, 202). For example, one or more processors (102, 202) may be composed of a set of communication control processors, application processors (APs), electronic control units (ECUs), central processing units (CPUs), graphic processing units (GPUs), and memory control processors.

[0091] One or more memories (104, 204) may be connected to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. One or more memories (104, 204) may consist of random access memory (RAM), dynamic RAM (DRAM), read-only memory (ROM), erasable programmable ROM (EPROM), flash memory, volatile memory, non-volatile memory, hard drives, registers, cache memory, computer read storage media, and / or combinations thereof. One or more memories (104, 204) may be located inside and / or outside of one or more processors (102, 202). Additionally, one or more memories (104, 204) may be connected to one or more processors (102, 202) through various technologies such as wired or wireless connections.

[0092] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this specification from one or more other devices. For example, one or more transceivers (106, 206) may be connected to one or more processors (102, 202) and may transmit and receive wireless signals. For example, one or more processors (102, 202) may control one or more transceivers (106, 206) to transmit user data, control information, wireless signals, etc., to one or more other devices. Additionally, one or more processors (102, 202) can control one or more transceivers (106, 206) to receive user data, control information, wireless signals, etc. from one or more other devices.

[0093] One or more transceivers (106, 206) may be connected to one or more antennas (108, 208). Additionally and / or generally, one or more transceivers (106, 206) may include one or more antennas (108, 208). One or more transceivers (106, 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 flowcharts disclosed herein through one or more antennas (108, 208). In this specification, one or more antennas (108, 208) may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).

[0094] One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202). One or more transceivers (106, 206) can convert processed user data, control information, wireless signals / channels, etc. from baseband signals to RF band signals using one or more processors (102, 202). To this end, one or more transceivers (106, 206) may include (analog) oscillators and / or filters. For example, one or more transceivers (106, 206) can up-convert an OFDM baseband signal into an OFDM signal through an (analog) oscillator and / or filter under the control of one or more processors (102, 202) and transmit the up-converted OFDM signal at a carrier frequency. One or more transceivers (106, 206) can receive an OFDM signal at a carrier frequency and down-convert the OFDM signal into an OFDM baseband signal through an (analog) oscillator and / or filter under the control of one or more processors (102, 202).

[0095] Although not illustrated in FIG. 2, the wireless device (100, 200) may include additional components. The additional components (140) may be configured in various ways depending on the type of the wireless device (100, 200). For example, the additional components (140) may include at least one of a power unit / battery, an input / output (I / O) device (e.g., audio I / O port, video I / O port), a driving unit, and a computing unit. The additional components (140) may be connected to one or more processors (102, 202) through various technologies, such as wired or wireless connections.

[0096] In an implementation of this specification, the UE may operate as a transmitting device in the uplink (UL; uplink) and as a receiving device in the downlink (DL; downlink). In an implementation of this specification, the base station may operate as a receiving device in the UL and as a transmitting device in the DL. For technical convenience, it is generally assumed that the first wireless device (100) operates as a UE and the second wireless device (200) operates as a base station. For example, a processor (102) connected to, mounted on, or released to the first wireless device (100) may be configured to perform UE operations according to an implementation of this specification or to control a transceiver (106) to perform UE operations according to an implementation of this specification. A processor (202) connected to, mounted on, or released to the second wireless device (200) may be configured to perform base station operations according to an implementation of this specification or to control a transceiver (206) to perform base station operations according to an implementation of this specification.

[0097] In this specification, the base station may be referred to as Node B, eNode B, or gNB.

[0098] FIG. 3 shows an example of a UE to which the implementation of the present specification applies.

[0099] Referring to FIG. 3, the UE (100) can correspond to the first wireless device (100) of FIG. 2.

[0100] The UE (100) includes a processor (102), memory (104), transceiver (106), one or more antennas (108), a power management module (141), a battery (142), a display (143), a keypad (144), a SIM (Subscriber Identification Module) card (145), a speaker (146), and a microphone (147).

[0101] The processor (102) may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein. The processor (102) may be configured to control one or more other components of the UE (100) to implement the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein. Layers of a wireless interface protocol may be implemented in the processor (102). The processor (102) may include an ASIC, other chipsets, logic circuits, and / or data processing devices. The processor (102) may be an application processor. The processor (102) may include at least one of a DSP, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a modem (modulator and demodulator). An example of the processor (102) is the SNAPDRAGON manufactured by Qualcomm®. TM Series processor, EXYNOS made by Samsung® TM Series processors, A Series processors made by Apple®, HELIO made by MediaTek® TM Series processors, ATOM made by Intel® TM It can be found in series processors or corresponding next-generation processors.

[0102] Memory (104) is coupled to the processor (102) so as to be operable and stores various information for operating the processor (102). Memory (104) may include ROM, RAM, flash memory, memory card, storage medium and / or other storage device. When the implementation is implemented in software, the technology described herein may be implemented using modules (e.g., procedures, functions, etc.) that perform the descriptions, functions, procedures, proposals, methods and / or operation flowcharts disclosed herein. Modules may be stored in memory (104) and executed by the processor (102). Memory (104) may be implemented within the processor (102) or outside the processor (102), in which case it may be communicatively coupled to the processor (102) through various methods known in the technology.

[0103] A transceiver (106) is coupled to operate with a processor (102) and transmits and / or receives a wireless signal. The transceiver (106) includes a transmitter and a receiver. The transceiver (106) may include a baseband circuit for processing a wireless frequency signal. The transceiver (106) controls one or more antennas (108) to transmit and / or receive a wireless signal.

[0104] The power management module (141) manages the power of the processor (102) and / or the transceiver (106). The battery (142) supplies power to the power management module (141).

[0105] The display (143) outputs the result processed by the processor (102). The keypad (144) receives input to be used by the processor (102). The keypad (144) can be displayed on the display (143).

[0106] A SIM card (145) is an integrated circuit for securely storing an International Mobile Subscriber Identity (IMSI) and associated keys, and is used to identify and authenticate a subscriber in a mobile device such as a mobile phone or computer. Additionally, contact information can be stored on many SIM cards.

[0107] The speaker (146) outputs sound-related results processed by the processor (102). The microphone (147) receives sound-related input to be used by the processor (102).

[0108] <6G System General>

[0109] The 6G (wireless communication) system aims for (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) reduced energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be seen in four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements shown in Table 1 below. In other words, Table 1 is a table showing an example of the requirements for a 6G system.

[0110] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0111] 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0112] Figure 4 shows an example of a communication structure that can be provided in a 6G system.

[0113] 6G systems are expected to have 50 times higher simultaneous wireless connectivity than 5G wireless communication systems. URLLC, a key feature of 5G, will become an even more dominant technology in 6G communication by providing end-to-end latency of less than 1ms. Unlike the frequently used area spectrum efficiency, 6G systems will exhibit significantly superior volume spectrum efficiency. 6G systems can provide very long battery life and advanced battery technologies for energy harvesting, meaning mobile devices in 6G systems will not require separate charging. New network characteristics in 6G may include the following.

[0114] - Satellite Integrated Network: 6G is expected to be integrated with satellites to provide a global mobile population. Integrating terrestrial, satellite, and airborne networks into a single wireless communication system is crucial for 6G.

[0115] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and will update wireless evolution from "connected things" to "connected intelligence." AI can be applied at each stage of the communication process (or at each step of the signal processing described below).

[0116] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0117] - Ubiquitous Super 3D Connectivity: Connectivity to the network and core network functions of drones and very low Earth orbit satellites will create Super 3D connectivity in 6G ubiquitous.

[0118] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0119] - Small cell networks: The idea of ​​small cell networks was introduced to improve the quality of received signals in cellular systems as a result of increased throughput, energy efficiency, and spectrum efficiency. Consequently, small cell networks are an essential feature of communication systems for 5G and beyond 5G (5GB). Therefore, 6G communication systems also adopt the characteristics of small cell networks.

[0120] - Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.

[0121] - High-capacity backhaul: Backhaul connections are characterized as high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems can be possible solutions to this problem.

[0122] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communication is one of the functions of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0123] - Softwarization and virtualization: Softwarization and virtualization are two important features that form the basis of the design process in 5GB networks to ensure flexibility, reconfigurability, and programmability. Additionally, billions of devices can be shared across a shared physical infrastructure.

[0124] <Key Implementation Technologies of 6G Systems>

[0125] Artificial Intelligence

[0126] The most critical and newly introduced technology for 6G systems is AI. AI was not involved in 4G systems. 5G systems will support AI partially or to a very limited extent. However, 6G systems will be supported by AI for complete automation. Advancements in machine learning will create more intelligent networks for real-time communication in 6G. Introducing AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency.

[0127] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play a significant role in 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.

[0128] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application and network layers, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward the MAC and physical layers, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of signal processing and communication mechanisms based on AI drivers rather than traditional communication frameworks in terms of fundamental signal processing and communication mechanisms. Examples include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0129] Machine learning can be used for channel estimation and channel tracking, and for power allocation and interference cancellation in the physical layer of the downlink (DL). In addition, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0130] Machine learning refers to a series of operations for training machines to create machines capable of performing tasks that humans can or find difficult to do. Machine learning requires data and learning models. Data learning methods in machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0131] The purpose of neural network training is to minimize output errors. It is a process that repeatedly inputs training data into a neural network, calculates the error between the network's output and the target for the training data, and updates the weights of each node by backpropagating the error from the output layer to the input layer in a direction that reduces the error.

[0132] Supervised learning uses training data with correct answers labeled, whereas unsupervised learning may not have correct answers labeled. That is, for example, in the case of supervised learning regarding data classification, the training data may consist of data where each training data point is labeled with a category. Labeled training data is input into a neural network, and an error can be calculated by comparing the network's output (category) with the labels of the training data. The calculated error is backpropagated within the neural network (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to this backpropagation. The amount of change in the connection weights of each node being updated can be determined by the learning rate. The neural network's calculations on the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, efficiency can be increased by using a high learning rate in the early stages of neural network training to enable the network to quickly achieve a certain level of performance, and accuracy can be improved by using a low learning rate in the later stages of training.

[0133] The learning method may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted from the transmitting end at the receiving end in a communication system, it is desirable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0134] A learning model corresponds to the human brain, and while the most basic linear model can be considered, a machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0135] The neural network cores used for learning methods are broadly classified into deep neural networks (DNN), convolutional deep neural networks (CNN), recurrent Boltzmann machines (RNN), and spiking neural networks (SNN).

[0136] THz Communication (Terahertz Communication)

[0137] 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.

[0138] Figure 5 shows an example of an electromagnetic spectrum.

[0139] 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.

[0140] Large-scale MIMO

[0141] One of the key technologies for improving spectrum efficiency is the application of MIMO technology. As MIMO technology improves, spectrum efficiency also improves. Therefore, large-scale MIMO technology will be important in 6G systems. Since MIMO technology utilizes multiple paths, multiplexing technology and beam generation and operation technology suitable for the THz band must also be given important consideration to enable data signals to be transmitted through one or more paths.

[0142] Hologram Beam Forming (HBF)

[0143] Beamforming is a signal processing procedure that adjusts an antenna array to transmit wireless signals in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, such as a high signal-to-noise ratio, interference prevention and rejection, and high network efficiency. Holographic Beamforming (HBF) is a new beamforming method that differs significantly from MIMO systems because it utilizes software-defined antennas. HBF is expected to be a highly effective approach for the efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

[0144] Optical wireless technology

[0145] Optical wireless communication (OWC) is a form of optical communication that transmits signals using visible light, infrared (IR), or ultraviolet (UV). OWC operating in the visible light band (e.g., 390–750 nm) is generally referred to as Visible Light Communication (VLC). Light-emitting diodes (LEDs) can be utilized for VLC implementation. VLC can be used in various applications, including wireless local area networks, wireless personal communication networks, and vehicle networks.

[0146] VLC offers the following advantages over RF-based technologies. First, the spectrum occupied by VLC is in the free / unlicensed band and can provide extensive bandwidth (THz-level bandwidth). Second, VLC causes minimal interference to other electromagnetic devices. Therefore, VLC can be applied to sensitive electromagnetic interference applications, such as aircraft and hospitals. Third, VLC offers strengths in communication security and privacy protection. The transmission medium of VLC-based networks, namely visible light, cannot penetrate walls or other opaque obstacles. Consequently, the transmission range of VLC can be limited to indoors, thereby protecting users' personal and sensitive information. Fourth, since VLC can utilize lighting sources as base stations, expensive base stations are not required.

[0147] Free-space Optical Communication (FSO) is an optical communication technology that uses light propagating in free space, such as air, outer space, or a vacuum, to wirelessly transmit data for communication or computer networking. FSO can be used as a point-to-point OWC system on the ground. FSO can operate at near-infrared frequencies (750–1600 nm). Laser transmitters can be used for FSO implementation, and FSO can provide high data rates (e.g., 10 Gbit / s), offering a potential solution to backhaul bottlenecks.

[0148] These OWC technologies were planned for 6G communication in addition to RF-based communication for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul network connections. Although OWC technologies have already been in use since 4G communication systems, they will be used more widely to meet the demands of 6G communication systems. OWC technologies such as light fidelity, visible light communication, optical camera communication, and broadband-based FSO communication are already well-known technologies. Communication based on optical radio technology can provide very high data rates, low latency, and secure communication.

[0149] LiDAR (Light Detection And Ranging) can also be utilized for ultra-high resolution 3D mapping in 6G communication based on wide bandwidth. LiDAR refers to a remote sensing method that measures distance by illuminating an object with near-infrared, visible, and ultraviolet light and detecting the reflected light through an optical sensor. LiDAR can be used for fully autonomous driving in automobiles.

[0150] FSO Backhaul Network

[0151] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network. Therefore, data transmission in an FSO system is similar to that of a fiber optic system. Consequently, FSO can be a good technology for providing backhaul connectivity in 6G systems in conjunction with fiber optic networks. Using FSO enables very long-distance communication over distances of more than 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas such as the ocean, space, underwater, and isolated islands. FSO also supports cellular backhaul connectivity.

[0152] Non-Terrestrial Networks (NTN)

[0153] 6G systems integrate terrestrial and air networks to support vertically scalable user communications. 3D BS will be provided via low-orbit satellites and UAVs. By adding new dimensions in terms of altitude and associated degrees of freedom, 3D connectivity differs significantly from existing 2D networks. In NR, the Non-Terrestrial Network (NTN) is considered as one method for this. An NTN refers to a network or network segment that utilizes RF resources mounted on a satellite (or UAS platform). There are two common scenarios for NTNs that provide access to user equipment: transparent payload and regenerative payload. The following are the basic elements of an NTN.

[0154] - One or more sat-gateways connecting NTN to a public data network

[0155] - GEO satellites are supplied by one or more satellite gateways deployed across a satellite target range (e.g., regional or continental range). We assume that the UEs in a cell are serviced by only one satellite gateway.

[0156] - Non-GEO satellites providing continuous service from one or more satellite gateways at a time. The system ensures service and feeder link continuity between continuous service satellite gateways with a time duration sufficient to perform mobility anchoring and handover.

[0157] - Feeder link or wireless link between the satellite gateway and the satellite (or UAS platform)

[0158] - Service link or wireless link between user equipment and satellite (or UAS platform).

[0159] - A satellite (or UAS platform) capable of implementing transparent or regenerative (including onboard processing) payloads. Satellite (or UAS platform) generated beams typically produce multiple beams for a designated service area based on the line of sight. The beam footprint is generally elliptical. The satellite (or UAS platform)'s line of sight depends on the onboard antenna diagram and the minimum elevation angle.

[0160] - Transparent payload: Radio frequency filtering, frequency conversion, and amplification. Therefore, the waveform signal repeated by the payload is not altered.

[0161] - Playback Payload: Radio frequency filtering, frequency conversion and amplification, demodulation / decoding, switching and / or routing, coding / modulation. This is virtually equivalent to equipping a satellite (or UAS platform) with all or part of the base station functions (e.g., gNB).

[0162] - For satellite deployments, Inter-Satellite Links (ISL) are optional. This requires a regenerative payload on the satellite. ISL can operate at RF frequencies or in the broadband.

[0163] - User equipment is serviced by a satellite (or UAS platform) within the target service area.

[0164] Generally, GEO satellites and UAS are used to provide continental, regional, or local services.

[0165] Generally, LEO and MEO constellations are used to provide services in both the Northern and Southern hemispheres. In some cases, constellations may provide global coverage, including the polar regions. For this to work, appropriate orbital inclination, a sufficiently generated beam, and inter-satellite links are required.

[0166] Quantum Communication

[0167] Quantum communication is a next-generation communication technology that applies quantum mechanical properties to the field of information and communications to overcome the limitations of existing technologies, such as security and ultra-high-speed computing. Quantum communication provides a means to generate, transmit, process, and store information that cannot be represented in the form of 0 and 1 based on binary bits used in conventional communication technologies, or that is difficult to represent. While conventional communication technologies utilize wavelength or amplitude for information transmission between a transmitter and a receiver, quantum communication, in contrast, utilizes photons—the smallest unit of light—for this purpose. In particular, since quantum uncertainty and quantum irreversibility can be applied to the polarization or phase difference of photons (light), quantum communication possesses the characteristic of enabling communication with guaranteed perfect security. Furthermore, under specific conditions, quantum communication may enable ultra-high-speed communication by utilizing quantum entanglement.

[0168] Cell-free Communication

[0169] The tight integration of multiple frequencies and heterogeneous communication technologies is crucial in 6G systems. Consequently, users can seamlessly move from one network to another without the need for any manual configuration on their devices. The best network among available communication technologies is automatically selected. This will break the limitations of the cellular concept in wireless communication. Currently, user movement from one cell to another causes excessive handovers in high-density networks, leading to handover failures, delays, data loss, and the "ping-pong" effect. 6G cell-free communication will overcome all of these issues and provide better QoS.

[0170] Cell-free communication is defined as a “system in which multiple geographically distributed access points (APs) cooperatively serve a small number of terminals using the same time and frequency resources with the help of a fronthaul network and a CPU.” A single terminal is served by a set of multiple APs, which is called an AP cluster. There are various ways to form an AP cluster; among them, the method of configuring an AP cluster with APs that can significantly contribute to improving the terminal's reception performance is called terminal-centric clustering. When using this method, the configuration is dynamically updated as the terminal moves. By introducing this terminal-centric AP clustering technique, the terminal is always located at the center of the AP cluster, thereby becoming free from inter-cluster interference that can occur when a terminal is located at the boundary of the AP cluster. This cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as heterogeneous radios used by different devices.

[0171] Integration of Wireless Information and Energy Transfer (WIET)

[0172] WIET uses the same fields and waves as wireless communication systems. In particular, sensors and smartphones will be charged using wireless power transmission during communication. WIET is a promising technology for extending the lifespan of wireless battery charging systems. Therefore, devices without batteries will be supported in 6G communication.

[0173] Integration of Wireless Communication and Sensing

[0174] Autonomous wireless networks are capable of continuously detecting dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communication to support autonomous systems.

[0175] Integrated Access and Backhaul Network

[0176] In 6G, the density of access networks will be enormous. Each access network will be connected via backhaul connections such as fiber optics and FSO networks. To cope with a very large number of access networks, there will be tight integration between access and backhaul networks.

[0177] Big Data Analysis

[0178] Big data analysis is a complex process for analyzing various large-scale data sets or big data. This process ensures perfect data management by uncovering information such as hidden data, unknown correlations, and customer preferences. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process vast amounts of data in 6G systems.

[0179] Reconfigurable Intelligent Surface

[0180] Numerous studies have been conducted that treat the wireless environment, along with transmitters and receivers, as a variable to be optimized. To emphasize the fundamental difference between wireless environments created through this approach and past design and optimization standards, they are referred to as Smart Radio Environments (SRE) or Intelligent Radio Environments (IRE). Regarding reconfigurable intelligent antenna (or intelligent reconfigurable antenna technology) as a technology for realizing SRE, various terms have been proposed, such as Reconfigurable Metasurfaces, Smart Large Intelligent Surfaces (SLIS), Large Intelligent Surfaces (LIS), Reconfigurable Intelligent Surface (RIS), and Intelligent Reflecting Surface (IRS).

[0181] THz band signals exhibit strong directivity, which can lead to numerous dead zones caused by obstacles. Consequently, RIS technology becomes crucial as it allows for the expansion of communication coverage, enhanced communication stability, and the provision of additional value-added services by installing RIS systems near these dead zones. An RIS is an artificial surface made of electromagnetic materials capable of altering the propagation of incoming and outgoing radio waves. While RIS may appear to be an extension of massive MIMO, it differs from massive MIMO in its array structure and operational mechanism. Furthermore, RIS offers the advantage of low power consumption because it operates as a reconfigurable reflector with passive elements—meaning it reflects signals passively without using an active RF chain. Additionally, since each passive reflector in the RIS must independently adjust the phase shift of the incident signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift through the RIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0182] There are also RISs that can control transmission and refraction characteristics as well as reflect wireless signals, and such RISs are mainly used for O2I (Outdoor to Indoor). Recently, STAR-RIS (Simultaneous Transmission and Reflection RIS), which provides transmission and reflection simultaneously, is also being actively researched.

[0183] Metaverse

[0184] Metaverse is a compound word formed from 'Meta,' meaning virtual or transcendent, and 'Universe,' meaning the universe. Generally, the term metaverse is used to mean something like 'a three-dimensional virtual space where social and economic activities similar to those in the real world are prevalent.'

[0185] Extended Reality (XR), a core technology for implementing the metaverse, can expand real-world experiences and provide a unique sense of immersion through the convergence of the virtual and the real. The high bandwidth and low latency of 6G networks enable users to experience Virtual Reality (VR) and Augmented Reality (AR) with enhanced immersion.

[0186] Autonomous Driving (Self-driving)

[0187] For perfect autonomous driving, vehicles must communicate with each other to alert one another to dangerous situations, or communicate with infrastructure such as parking lots and traffic lights to verify information like parking locations and signal change times. V2X (Vehicle-to-Everything), a core element of building autonomous driving infrastructure, is a technology that enables vehicles to communicate and share with various elements on the road to perform autonomous driving, including wireless communication between vehicles (V2V) and between vehicles and infrastructure (V2I).

[0188] Fast transmission speeds and low-latency technologies are essential to maximize the performance of autonomous driving and ensure high safety. Furthermore, as the amount of information to be transmitted and received increases significantly in the future—moving beyond the level of delivering warning or guidance messages to the driver to actively intervene in vehicle operation and directly control the vehicle in dangerous situations—it is expected that 6G will be able to maximize autonomous driving through faster transmission speeds and lower latency than 5G.

[0189] Unmanned Aerial Vehicle (UAV)

[0190] Unmanned Aerial Vehicles (UAVs) or drones will become a critical element in 6G wireless communication. In most cases, high-speed data wireless connectivity is provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs possess specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled degrees of freedom for mobility. During emergencies, such as natural disasters, the deployment of ground communication infrastructure is not economically feasible, and sometimes services cannot be provided in volatile environments. UAVs can easily handle these situations. UAVs will become a new paradigm in the field of wireless communication. This technology facilitates the three fundamental requirements of wireless networks: eMBB, URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most critical technologies for 6G communication.

[0191] Blockchain

[0192] Blockchain will become a critical technology for managing massive amounts of data in future communication systems. As a form of distributed ledger technology, a distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchain is managed via a peer-to-peer (P2P) network and can exist without being managed by a centralized authority or server. Data in a blockchain is collected together and organized into blocks. These blocks are linked together and protected using encryption. Blockchain inherently complements large-scale IoT perfectly through enhanced interoperability, security, privacy, stability, and scalability. Therefore, blockchain technology provides various capabilities such as inter-device interoperability, large-scale data traceability, autonomous interaction with other IoT systems, and the large-scale connectivity stability of 6G communication systems.

[0193] Figure 6 illustrates an example of a subframe type in NR.

[0194] The transmission time interval (TTI) illustrated in Fig. 6 can be referred to as a subframe or slot for NR (or new RAT). The subframe (or slot) of Fig. 6 can be used in the TDD system of NR (or new RAT) to minimize data transmission delay. As illustrated in Fig. 4, the subframe (or slot) contains 14 symbols, similar to the current subframe. The symbols at the beginning of the subframe (or slot) can be used for the DL control channel, and the symbols at the end of the subframe (or slot) can be used for the UL control channel. The remaining symbols can be used for DL ​​data transmission or UL data transmission. According to this subframe (or slot) structure, downlink transmission and uplink transmission can proceed sequentially within a single subframe (or slot). Thus, downlink data can be received within the subframe (or slot), and uplink acknowledgments (ACK / NACK) can be transmitted within that subframe (or slot). The structure of such a subframe (or slot) can be referred to as a self-contained subframe (or slot). Using this subframe (or slot) structure has the advantage of minimizing the final data transmission waiting time by reducing the time required to retransmit data that has received errors. In such a self-contained subframe (or slot) structure, a time gap may be required during the transition process from transmit mode to receive mode or from receive mode to transmit mode. To this end, some OFDM symbols during the transition from DL to UL in the subframe structure may be set as a Guard Period (GP).

[0195] <NR에서 SS 블록>

[0196] In 5G NR, the SS block (SS / PBCH Block: SSB) contains the Physical Broadcast Channel (PBCH) containing the Master Information Block (MIB), which is necessary for the terminal to perform initial access, and the Synchronization Signal (SS) (including PSS and SSS).

[0197] Furthermore, multiple SSBs can be grouped together and defined as an SS burst, and multiple SS bursts can be grouped together and defined as an SS burst set. It is assumed that each SSB is beamformed in a specific direction, and the various SSBs within an SS burst set are designed to support terminals located in different directions.

[0198] Figure 7 shows an example of SSB in NR.

[0199] Referring to Fig. 7, the SS burst is transmitted at predetermined periodicities. Accordingly, the terminal receives the SSB and performs cell detection and measurement.

[0200] Meanwhile, in 5G NR, beam sweeping is performed on the SSB. This will be explained with reference to Fig. 8.

[0201] Figure 8 shows an example of beam sweeping in NR.

[0202] The base station transmits each SSB within the SS burst while beam sweeping over time. At this time, multiple SSBs within the SS burst set are transmitted to support terminals located in different directions.

[0203] The following case relates to AI / ML mobility:

[0204] - RRM measurement prediction

[0205] - Measurement event prediction

[0206] - RLF (Radio Link Failure) / HOF (Hand-Over Failure) prediction (RLF / HOF prediction)

[0207] The goal in this case could be as follows:

[0208] - Reduction of measurement effort in the time domain, space domain, or frequency domain

[0209] - Improvement of handover performance (e.g., ping-pong handover, HOF / RLF, short dwell time, handover suspension)

[0210] I. First Disclosure

[0211] In the first disclosure, a method related to measurement event prediction and / or RLF / HOF prediction may be proposed.

[0212] 1. Predicting Measurement Events

[0213] Regarding the prediction of measurement events, a prediction for event A3 can be performed.

[0214] In this specification, prediction may be a prediction through AI / ML.

[0215] Event A3 may be an event triggered when the metric of a neighboring cell (e.g., RSRP, RSRQ, or RS-SINR (Reference Signal - Signal to Interference and Noise Ratio)) is higher than that of the serving cell by a certain offset.

[0216] Event A3 may be an event required during the handover process.

[0217] Figure 9 shows an example of RSRP change between a serving cell and a neighboring cell related to a handover.

[0218] The event A3 condition may be that the metric of a neighboring cell (e.g., RSRP) is greater than or equal to a threshold than the metric of the serving cell.

[0219] If the conditions for Event A3 are continuously satisfied for a TimeToTriger (TTT) period from the time when the entering condition for Event A3 is satisfied (e.g., if the conditions for Event A3 are continuously satisfied during the TTT period) (e.g., if the RSRP of a neighboring cell is maintained at a level higher than the RSRP of a serving cell by a threshold amount during the TTT period), the terminal may report Event A3 to the network.

[0220] To minimize the ping-pong phenomenon during a handover, even if the event A3 entry condition is satisfied, the terminal can report event A3 to the network by checking whether the event A3 condition is maintained during the TTT time without satisfying the event A3 leaving condition.

[0221] In this specification, satisfying the conditions of event A3 during TTT may mean that the exit condition of event A3 during TTT is not satisfied.

[0222] In this specification, satisfying the conditions of event A3 during TTT may mean that the state in which the conditions of event A3 are satisfied (e.g., when the RSRP of a neighboring cell is higher than the RSRP of a serving cell by more than a threshold value is maintained during the TTT time) is maintained during TTT.

[0223] TTT can be a value set for a terminal via an EventTriggerConfig message in the network. The list of TTT setting values ​​can be as follows:

[0224] - TimeToTrigger ::= ENUMERATED { ms0, ms40, ms64, ms80, ms100, ms128, ms160, ms256, ms320, ms480, ms512, ms640, ms1024, ms1280, ms2560, ms5120}

[0225] A network (e.g., serving cell) that receives event A3 report from a terminal can secure resources for a target cell (=neighboring cell) where the terminal will hand over and send a handover command (a message requesting a handover to the target cell) to the terminal.

[0226] For example, the entry and exit conditions for event A3 could be as follows:

[0227] - Entering condition (A3-1): Mn + Ofn + Ocn - Hys > Mp + Ofp + Ocp + Off

[0228] - Leaving condition (A3-2): Mn + Ofn + Ocn + Hys < Mp + Ofp + Ocp + Off

[0229] Each parameter is described below:

[0230] - Mn: Measurement result of a neighboring cell without considering offset

[0231] - Ofn: Offset per measurement object relative to the neighbor cell's reference signal (e.g., offsetMO defined within measObjectNR corresponding to the neighbor cell's frequency)

[0232] - Ocn: Cell-specific offset of neighboring cells (e.g., cellIndividualOffset defined in measObjectNR or cellIndividualOffset defined in reportConfigNR corresponding to the frequency of neighboring cells). Set to 0 if not set for neighboring cells.

[0233] - Mp: SpCell measurement result without considering offset

[0234] - Ofp: Offset per measurement object for SpCell (e.g., offsetMO defined within the measObjectNR corresponding to SpCell)

[0235] - Ocp: Cell-specific offset for SpCell (e.g., cellIndividualOffset defined within measObjectNR corresponding to SpCell). Set to 0 if not set for SpCell.

[0236] - Hys is the hysteresis parameter for this event (e.g., hysteresis for this event defined in reportConfigNR)

[0237] - Off: Offset parameter for this event (e.g., a3-Offset for this event defined in reportConfigNR).

[0238] - Mn and Mp can be expressed in dBm for RSRP. Mn and Mp can be expressed in dB for RSRQ and RS-SINR.

[0239] - Ofn, Ocn, Ofp, Ocp, Hys, and Off can all be expressed in dB.

[0240] For example, it could be as follows:

[0241] - Entry condition: Neighbor cell measurement result > Serving cell measurement result + Entry threshold

[0242] - Exit condition: Neighbor cell measurement result < Serving cell measurement result + Exit threshold

[0243] - Entry threshold: off+ Hys

[0244] - Deviation threshold: off-Hys

[0245] - off: Ofp + Ocp + Off - Ofn - Ocn

[0246] For example, if the measurement of a neighboring cell exceeds the measurement of the serving cell plus an entry threshold, the entry condition for event A3 may be satisfied.

[0247] For example, if the measurement of a neighboring cell is less than the value obtained by adding the churn threshold to the measurement of the serving cell, the churn condition of event A3 may be satisfied.

[0248] (1) Conventional operation

[0249] Figure 10 shows the procedure related to event A3.

[0250] The entry condition for Event A3 may be satisfied. At that point, if the exit condition for Event A3 is not satisfied during TTT, the terminal may report Event A3 to the network.

[0251] After the terminal reports event A3 to the network, the terminal can receive a handover command from the network.

[0252] (2) Shift in reporting time when PW is longer than TTT (reporting in B1 or B1+M)

[0253] In accordance with the disclosure of this specification, the reporting time may be advanced.

[0254] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0255] FIG. 11 shows an example of a case where PW is longer than TTT according to the disclosure of the present specification.

[0256] Regarding OW (Observation Window) and PW (Prediction Window), the proposed method will be described below.

[0257] The OW (Observation Window) can be the period during which measurements (e.g., RRM measurements) are actually performed. The PW (Prediction Window) can be the period during which future measurements are predicted (e.g., predictions via AI / ML) based on the values ​​measured in the OW.

[0258] For example, based on the value measured in OW, the value measured in PW can be predicted.

[0259] The network can transmit information about OW and PW to the terminal in advance.

[0260] The cases where PW is shorter than TTT and where it is longer will be discussed later.

[0261] The password length can be transmitted to the terminal from the network as a setting value. Alternatively, the terminal may select from a fixed set of password lengths depending on the situation.

[0262] When the terminal selects it itself, the length of the PW may be determined based on the terminal's Doppler frequency and / or Signal-to-Noise Ratio (SNR). For example, if the terminal's Doppler frequency is low and the SNR is high, the terminal may set the length of the PW to a long value. For example, if the terminal's Doppler frequency is high and the SNR is low, the terminal may set the length of the PW to a low value. The high or low values ​​of the Doppler frequency and SNR may be determined based on their respective thresholds.

[0263] Conventionally, since prediction is not utilized, the terminal reports event A3 at D1 and receives a handover command after a certain period of time. According to the disclosure of this specification, the reporting time of event A3 may be advanced.

[0264] At A1, the end point of OW, the terminal can predict the measurement value at PW. Based on this, the terminal can predict / determine the following:

[0265] - The entry condition for Event A3 is met in B1.

[0266] - During the TTT time from B1, the exit condition for event A3 is not met.

[0267] Based on this prediction / decision, the terminal can recognize that reporting of event A3 is possible at A1.

[0268] However, if the terminal reports Event A3 at A1, a problem may occur where the handover proceeds before B1 (before the conditions for entering Event A3 are met). To prevent this, the terminal can report Event A3 to the network at B1. This ensures that the terminal receives the handover command after B1.

[0269] If the terminal reports event A3 to B1, the terminal can receive a handover command from C1. Based on this, the terminal can perform the handover procedure.

[0270] In this way, when prediction is utilized, the terminal can receive the handover command by time D1 faster than the conventional procedure. Thus, according to the disclosure of this specification, the handover can proceed by time D1 faster than the prior art.

[0271] Alternatively, taking into account the error of the prediction, the terminal can report event A3 at a point in time (B1+M) after a certain amount of time has passed from B1.

[0272] Here, M can be greater than or equal to 0 and less than TTT. M can be determined based on the level of prediction error. M can represent a margin of safety. If the prediction error is above a certain level, M can be set to a large value. If the prediction error is lower than a certain level and is sufficiently reliable, M can be set to a small value. As M is set to a smaller value, D1 can increase.

[0273] (3) Whether additional measurement of the terminal occurs during the period from A1 to B1

[0274] If the terminal predicts through prediction at A1 that the condition of event A3 is maintained during TTT, the terminal may determine whether to perform additional measurements (e.g., RSRP measurements) up to B1 (or B1+M) based on at least one of the following:

[0275] - Device battery level

[0276] - Whether power saving mode is on / off

[0277] - Error levels in AI / ML

[0278] For example, if the remaining battery level of the terminal is below a certain level or if power saving mode is in progress, the terminal may not perform RSRP measurements during that period (the period from A1 to B1 (or B1+M)). Alternatively, if the error level of AI / ML is sufficiently reliable, the terminal may not perform RSRP (Reference Signal Received Power) measurements during that period.

[0279] (3)-1. When the terminal performs additional measurements during the period from A1 to B1

[0280] The terminal can perform measurements during the relevant period. Based on the results of the measurements, the terminal can determine whether the entry condition for event A3 is satisfied at B1 (or B1+M).

[0281] If the terminal determines that the entry condition for event A3 is satisfied at B1 (or B1+M), the terminal may report event A3 at B1 (or B1+M).

[0282] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0283] FIG. 12 illustrates three cases related to additional measurements according to the disclosure of the present specification.

[0284] If the terminal determines that the entry condition for event A3 at B1 (or B1+M) is not satisfied (different from the result of the prediction), the terminal may perform the following action at B1:

[0285] - case 1) The terminal does not trust the AI / ML model and can fall back to legacy behavior.

[0286] - Case 2) The terminal can perform a prediction again based on the measurement results up to the present (B1). Based on the prediction results, the terminal can predict / estimate / determine the time point (B1') of the entry condition for Event A3 again. During the period from B1 to B1', the terminal can perform measurements. If the result at B1' satisfies the entry condition for Event A3 as predicted, the terminal can report Event A3 to the network at B1'.

[0287] - case 3) When OW and PW are set periodically, the terminal can perform measurements only in the PW containing B1, and perform existing operations (e.g., measurements only in OW) in subsequent OW and PW.

[0288] (3)-2. If the terminal does not perform additional measurements during the period from A1 to B1

[0289] The terminal can report Event A3 at time B1.

[0290] (4) Message in A1

[0291] In A1, the terminal can send a new message to the network.

[0292] The above new message may include the following information:

[0293] - Information about B1

[0294] - Request to send a handover command from B1

[0295] Based on this, the terminal can receive a handover command from the network at B1. Based on this, the handover procedure can proceed.

[0296] Through the new message above, C1 can be advanced to B1.

[0297] If the time interval between time points A1 and B1 is short, the time to receive the handover command may be later than time point B1.

[0298] Even if the time interval between time points A1 and B1 is sufficiently long, the network may send a handover command to the terminal at time B1+M (M>= 0) in consideration of the prediction error.

[0299] (5) When PW is shorter than TTT

[0300] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0301] FIG. 13 shows an example of a case where PW is shorter than TTT according to the disclosure of the present specification.

[0302] Since prediction is not utilized in the conventional method, the terminal reports Event A3 after TTT has passed from the time when the Event A3 entry condition is satisfied, and receives a handover command after a certain period of time.

[0303] The terminal can recognize / determine through measurement that the conditions for entering event A3 before A2 have been met.

[0304] At A2, the end point of OW, the terminal can predict the measurement value at PW. Based on this, the terminal can predict / determine the following:

[0305] - During PW, the exit condition for Event A3 is not met

[0306] Based on this prediction / decision, the terminal can recognize that reporting of event A3 is possible at A2.

[0307] Based on this, the terminal can report event A3 from A2 to the network. Subsequently, the terminal can receive a handover command from B2. Based on this, the terminal can perform the handover procedure.

[0308] Thus, when prediction is utilized, the terminal can receive the handover command D2 times faster than the conventional procedure. As such, according to the disclosure of this specification, the handover can proceed D2 times faster than the prior art.

[0309] According to the disclosure of the present specification, the terminal can know in advance that the event A3 entry condition is satisfied and the event A3 exit condition is not satisfied during TTT. Through this, the handover time can be advanced and the mobility delay of the terminal can be reduced.

[0310] However, if the handover proceeds too early before the entry conditions for Event A3 are satisfied, performance degradation may occur during the initial handover. To prevent this, the handover may proceed after the entry conditions for Event A3 are satisfied. Through the disclosure of this specification, faster handovers than legacy methods can be supported by predicting measurement events using AI / ML. Additionally, performance degradation caused by an excessively early handover can be prevented.

[0311] 2. RLF Prediction

[0312] According to the disclosure of this specification, rapid radio link recovery can be performed through RLF prediction using AI / ML.

[0313] In relation to RLF operation, the terminal can determine whether it is in an Out-of-Sync state or an In-Sync state through Qout (Out-of-Sync Threshold) and Qin (In-Sync Threshold). The metric used at this time may be based on the reception performance of the SSB (synchronization signal block) / CSI-RS (channel state information reference signal) or the BLER (Block Error Rate) performance of the PDCCH (Physical Downlink Control Channel).

[0314] For example, the criteria for Out-of-Sync and In-Sync based on BLER standards can be defined as follows:

[0315] - BLER out : 10% (Based on Out-of-Sync BLER)

[0316] - BLER in : 2% (Based on In-Sync BLER)

[0317] The network can configure the following parameters for radio link monitoring:

[0318] - N310: Number of consecutive 'Out-of-Sync' indications required to start Timer T310

[0319] - T310: The timer expires after a certain amount of time, and upon expiration, wireless link error detection is triggered.

[0320] - N311: Number of consecutive 'In-Sync' indications required to stop and reset the T310 timer

[0321] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0322] FIG. 14 shows an example of an RLF determination according to the disclosure of the present specification.

[0323] If Out-of-Sync occurs consecutively for the value set by N310, T310 is triggered, and if In-Sync does not occur consecutively for N311 until T310 expires, it can be determined that a Radio Link Failure (RLF) has occurred.

[0324] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0325] FIG. 15 shows an example of RLF determination through prediction according to the disclosure of the present specification.

[0326] The terminal can predict that T310 will expire through prediction (e.g., RLF prediction via AI / ML). For instance, it can be predicted that N311 consecutive In-Syncs will not occur until T310 expires.

[0327] In this case, the terminal can quickly declare an RLF. For example, the terminal can declare an RLF when the OW expires. Based on this, subsequent procedures (e.g., searching for a new cell) can be performed faster than before.

[0328] In this case, regardless of whether Out-of-sync or In-sync occurs, it may be sufficient for AI / ML prediction to predict only whether T310 expires.

[0329] The RLF declaration may occur after the 'N310 consecutive Out-of-Sync indication,' or before Out-of-Sync if the PW is longer than T310. In this case, the RLF declaration is executed earlier than the legacy operation, allowing for faster Radio Link Recovery, which results in the maintenance of more stable link quality.

[0330] The first disclosure described above may be performed in combination with the second disclosure described below.

[0331] For example, regarding the aforementioned prediction, the terminal can determine the reliability. If the reliability is lower than a threshold, the terminal can extend the OW to perform additional measurements. This operation can be repeated until the reliability becomes greater than or equal to the threshold.

[0332] II. Second Commencement

[0333] In the second disclosure, I would like to summarize the RRM measurement prediction.

[0334] In the case of RRM measurement prediction, it can be divided into a total of six cases: three cell-level RRM measurement predictions and three beam-level RRM measurement predictions. The total six cases are as follows:

[0335] - 1) Predict L1 beam level measurement results based on actual L1 beam level measurement results, and generate L3 cell level measurement results based on those results.

[0336] - 2) Predict L3 cell level measurement results based on actual L3 cell level measurement results

[0337] - 3) Predict L3 cell level measurement results based on actual L1 beam level measurement results

[0338] - 4) Predict the L1 filtered beam level measurement result based on the actual L1 beam level measurement result, and generate the L3 beam level measurement result based on that result.

[0339] - 5) Predict L3 beam level measurement results based on actual L3 beam level measurement results

[0340] - 6) Predict L3 beam level measurement results based on actual L1 beam level measurement results

[0341] For performance evaluation, two options can be considered: sliding L1 / L3 filtering and non-sliding L1 / L3 filtering.

[0342] Figure 16 shows an example of sliding L1 / L3 filtering.

[0343] Figure 17 shows an example of non-sliding L1 / L3 filtering.

[0344] Figure 18 shows an example of a measurement model.

[0345] The L1 filtered RSRP can be the value measured at Point A1. The L3 cell level filtered RSRP can be the value measured at Point C, and the L3 beam level filtered RSRP can be the value measured at Point E.

[0346] MRRT and MRRS are defined as metrics to measure the extent to which measurement effort is reduced through AI / ML prediction.

[0347] The measurement reduction rate for intra-frequency scenarios is defined in the time domain (MRRT) and also in the spatial domain (MRRS), assuming that the lengths of the measurement time instances are the same.

[0348] - MRRT = skipped measurement time instance / total measurement instances

[0349] - MRRS = skipped beams to be measured / total beams to be measured

[0350] Examples of measurement and prediction methods in the intra-frequency time domain are described below for Case A and Case B, respectively.

[0351] Figure 19 shows an example of case A.

[0352] In Case A, continuous measurement results in the continuous measurement PW (prediction window) are predicted by continuous past measurement results in the OW (observation window). Then, OW and PW slide forward according to the sampling period (when the sliding L1 / L3 filtering option is applied) or measurement period (when the non-sliding L1 / L3 filtering option is applied), where the measurement results are the values ​​actually measured before the slide. The predictions for Case A in the intra-frequency and intra-cell time domains are evaluated for the second study goal for both FR1 and FR2 scenarios.

[0353] Figure 20 shows an example of case B.

[0354] In case B, the measurement results in the measurement result skip PW (prediction window) are predicted by past measurement results in the OW (observation window). Then, the OW and PW are moved forward according to the sampling period (when the sliding L1 / L3 filtering option is applied) or the measurement period (when the non-sliding L1 / L3 filtering option is applied), and the measurement results included in the previous PW are skipped during the window movement. The predictions for case B in the intra-frequency and intra-cell time domains are evaluated for the first study goal by predicting a subset of measurement instances within the same cell's time domain for both FR1 and FR2 scenarios.

[0355] The priority among evaluation scenarios may be as shown in Table 4.

[0356] scenario numberPriorityEvaluation scenarioTarget study goalMethodology1LowFR1 to FR1 intra-frequency temporal domain case A2 nd goalTBD2HighFR1 to FR1 intra-frequency temporal domain case B1 st goalIntra-cell3HighFR1 to FR1 inter-frequency (frequency domain)1 st goalInter-cell4HighFR2 to FR2 intra-frequency temporal domain case A2 nd goalIntra-cell5LowFR2 to FR2 intra-frequency temporal domain case B1 st goalTBD6MiddleFR2 to FR2 intra-frequency spatial domain1 st goalIntra-cell

[0357] Regarding the method of setting OW (observation window) and PW (prediction window), a method to increase the benefit of the AI / ML model can be proposed.

[0358] First, define a metric that indicates the reliability of the results of the RRM measurement prediction.

[0359] In the case of AI / ML models, the reliability of specific inference results can also be measured.

[0360] In the RRM measurement prediction as well, a metric regarding the reliability of the RRM measurement value estimated by the AI / ML model is calculated together, and for convenience, this metric is defined as M-reliable.

[0361] Discussions are underway regarding determining OW and PW based on the channel environment or the state of the UE. However, in such cases, there is little difference from existing rule-based methods, and the result is that the advantages of AI / ML are not fully utilized.

[0362] The method of utilizing M-reliable will be described later.

[0363] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0364] FIG. 21 illustrates an example of a procedure for dynamic PW and OW according to the disclosure of the present specification.

[0365] FIG. 22 shows examples of dynamic PW and OW according to the disclosure of the present specification.

[0366] The terminal can perform a prediction after a minimum OW interval (Tmin).

[0367] If the M-reliable value of the prediction result exceeds a certain threshold (THrel), the terminal can utilize the prediction result.

[0368] If the M-reliable value of the prediction result does not exceed a certain threshold (THrel), the terminal can continue to perform measurements without utilizing the prediction result.

[0369] OW and PW can be varied depending on the M-reliable value. For example, if the M-reliable value is smaller than the threshold, OW can be extended so that measurements can continue.

[0370] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0371] FIG. 23 illustrates another example of a procedure for dynamic PW and OW according to the disclosure of this specification.

[0372] You can also set a Tmax value for Tcount. In this case, if Tcount does not continue to increase and M-reliable remains lower than THrel until Tmax is reached, the terminal can fall back to legacy behavior or perform an action to switch to another AI / ML model.

[0373] M-reliable will be discussed later.

[0374] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0375] FIG. 24 shows an example of a distribution of predicted values ​​according to the disclosure of the present specification.

[0376] In the probability density function (PDF) for candidate values ​​for RRM prediction, the candidate values ​​can be on the x-axis.

[0377] The smaller the variance value in the PDF, the higher the confidence level can be set.

[0378] When observing variation with the predicted RRM measurement as the center, the smaller the variation, the higher the reliability can be considered.

[0379] Reliability can be defined by a formula.

[0380] f(x) represents the PDF, and x represents the candidate value.

[0381] For example, if the range of candidate values ​​in RSRP prediction is from -110dBm to -20dBm, the range of the x-axis is from -110dBm to -20dBm, and the x corresponding to the maximum value in the PDF can be considered as the Predicted RSRP.

[0382] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0383] FIG. 25 illustrates the assumption of a reliability definition according to the disclosure of this specification.

[0384] The method for defining reliability will be described later.

[0385] FIG. 26 illustrates a first example of a reliability definition according to the disclosure of the present specification.

[0386] FIG. 27 illustrates a second example of a reliability definition according to the disclosure of the present specification.

[0387] FIG. 28 illustrates a third example of a reliability definition according to the disclosure of the present specification.

[0388] Here, the scale can be predetermined as a value for converting decimal values ​​into integers. It can also be quantized.

[0389] Alternatively, M-reliable values ​​may not be defined in the standard and may be left to the UE implementation.

[0390] When calculating M-reliable according to each definition, since the x-axis values ​​are not continuous in most cases, in actual implementation, a sum formula rather than an integral formula can be used as shown in Fig. 29 or Fig. 31.

[0391] FIG. 29 shows another expression of the first example of a reliability definition according to the disclosure of the present specification.

[0392] FIG. 30 shows another representation of the third example of the reliability definition according to the disclosure of this specification.

[0393] The capability of the UE to measure the reliability of RRM predictions can be defined.

[0394] Depending on the UE capability, there may be UEs that follow this algorithm and those that follow a rule-based approach.

[0395] When RRM measurement is performed, the terminal can report the result. At this time, it may also transmit information regarding whether the reported value was obtained through measurement or prediction. If the terminal reports a value obtained through prediction, it may also transmit a reliability metric value, depending on the terminal's capabilities.

[0396] If a reliability metric cannot be utilized, a rule-based method may be applied to OW and PW. In this case, the metric that can serve as a reference may be the terminal's Doppler frequency or the received SNR. Alternatively, OW and PW may be pre-set according to other metrics that affect OW and PW. For example, OW may be determined according to the Doppler frequency and SNR as shown in FIG. 31.

[0397] FIG. 31 shows another representation of the third example of the reliability definition according to the disclosure of this specification.

[0398] The darker the color, the longer the OW.

[0399] The lower the SNR, the longer the OW should be, and the higher the Doppler frequency, the shorter the OW should be.

[0400] When the SNR is lower than TH_SNRLow and the Doppler frequency is smaller than TH_DFLow, OW can be set to the longest.

[0401] When the SNR is higher than THSNRHigh and the Doppler frequency is greater than THDFHigh, OW can be set to the shortest possible length.

[0402] The aforementioned setting may be an example.

[0403] Each threshold value may be determined later, and the OW value in each area may also be determined later.

[0404] The terminal must be able to measure the Doppler frequency and SNR.

[0405] Alternatively, a metric with a similar function may be used. For example, the RSRQ metric may be used instead of SNR. The rate of change of RSRP may be used instead of the Doppler frequency.

[0406] PW may be set to a fixed length in all areas. Alternatively, PW may be determined proportionally to OW. For example, if OW is 64 samples, PW may be determined to be 4 samples, and if OW is 128 samples, PW may be determined to be 8 samples. The exact proportional value may be determined later.

[0407] In addition to RSRP and RSRQ, other metrics (such as SNR or Doppler frequency, which aid in RRM measurement prediction and confidence calculation) can be added as input data for AI / ML models.

[0408] Input data in AI / ML model training / testing / inference operations may include not only RRM measurements but also metrics (e.g., SNR Doppler frequency) that can influence RRM prediction and reliability.

[0409] In terms of the accuracy of RSRP prediction using AI / ML, the criteria at high SNR, medium SNR, and low SNR may differ.

[0410] If the confidence in the prediction result is low, a measurement may be performed. In this case, since the error in the measurement result can increase even at low SNR, the confidence threshold for the prediction result at low SNR can be set lower than at high SNR. Therefore, the confidence threshold (e.g., a two-step threshold for high / low, or a three-step threshold for high / mid / low) can be determined based on the SNR level or the RSRP value. Through this, dynamic OW and PW operations can also be supported.

[0411] That is, the THrel values ​​in Figs. 21 and 23 can be applied based on the SNR level or RSRP value.

[0412] In this specification, the first disclosure and the second disclosure may be performed independently or in combination.

[0413] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0414] FIG. 32 illustrates the procedure of the UE for the first disclosure of the present specification.

[0415] 1. The UE (User Equipment) can perform measurements in the OW (Observation Window).

[0416] 2. Based on the results of the above measurements, the UE can predict a first measurement value for a neighboring cell and a second measurement value for a serving cell in the PW (prediction window).

[0417] 3. Based on the results of the above prediction, the UE can determine a first point in time when the entry condition of event A3 is satisfied in the PW.

[0418] 4. Based on the results of the above prediction, the UE may determine that the exit condition of event A3 is not satisfied during the TTT (time to trigger) time from the first time point.

[0419] 5. Based on the determination that the above UE does not satisfy the break-in condition during the TTT time from the above first time point, the above UE may transmit a report on event A3 to the network at the above first time point.

[0420] The above entry condition may be that the first measurement value exceeds the value obtained by adding an entry threshold to the second measurement value.

[0421] The above deviation condition may be that the first measurement value is less than the value obtained by adding a deviation threshold to the second measurement value.

[0422] Based on the above report, the UE can receive a handover command from the network.

[0423] Before performing the above measurement, the UE may receive information related to the OW and information related to the PW from the network.

[0424] The above prediction can be performed based on AI / ML (Artificial intelligence and machine learning).

[0425] The above UE can perform auxiliary measurements for the neighbor cell and the serving cell at the above first time point.

[0426] Based on the results of the above auxiliary measurement, the UE can determine that the entry condition is not satisfied at the first time point.

[0427] Based on the determination that the above UE does not satisfy the entry condition at the above first time, the above UE may skip transmitting the report to the network at the above first time.

[0428] Based on the results of the above measurement, the UE can determine a second point in time when the entry condition is satisfied.

[0429] The above PW can start after the above OW ends.

[0430] The above second point in time may be included in the above OW.

[0431] Based on the results of the above prediction, the UE can determine that the exit condition of event A3 is not satisfied during the TTT time from the second time point.

[0432] Based on the determination that the UE determines that the exit condition of event A3 is not satisfied during the TTT time from the second time point, the UE may transmit a report regarding event A3 to the network at the second time point.

[0433] The above UE can perform auxiliary measurements on the neighboring cell and the serving cell at the above second time point.

[0434] Based on the results of the above auxiliary measurement, the UE can determine that the entry condition is not satisfied at the second time point.

[0435] Based on the determination that the above UE determines that the entry condition is not satisfied at the above second time, the above UE may skip sending the report to the network at the above second time.

[0436] The above UE can determine the reliability of the above prediction.

[0437] Based on the confidence of the above prediction being less than the confidence threshold, the UE can extend the OW.

[0438] The above UE can perform additional measurements in an extended OW.

[0439] Based on the results of the above measurement and the results of the above additional measurement, the UE can additionally predict the measurement value for the neighbor cell in the PW and the measurement value for the serving cell.

[0440] The above UE can determine the reliability of the above additional prediction.

[0441] Based on the confidence of the additional prediction being less than the confidence threshold, the UE may iteratively perform the extension of the OW, the additional measurement, and the additional prediction.

[0442] Based on the fact that the number of iterations of the additional prediction mentioned above is greater than the iteration threshold, the UE may perform a fallback to the legacy or change the AI / ML model.

[0443] The following drawings are prepared to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0444] FIG. 33 illustrates the procedure of the UE for the second disclosure of the present specification.

[0445] 1. The UE can perform measurements in OW.

[0446] 2. Based on the results of the above measurements, the UE can predict the measured value in PW.

[0447] The above PW can start after the above OW ends.

[0448] 3. Based on the results of the above measurements, the UE can determine the reliability of the above prediction.

[0449] 4. Based on the confidence of the above prediction being less than the confidence threshold, the UE can extend the OW.

[0450] 5. The above UE can perform additional measurements in an extended OW.

[0451] Based on the results of the above measurement and the results of the above additional measurement, the UE can additionally predict the measurement value at the PW.

[0452] The above UE can determine the reliability of the above additional prediction.

[0453] Based on the confidence of the additional prediction being less than the confidence threshold, the UE may iteratively perform the extension of the OW, the additional measurement, and the additional prediction.

[0454] Based on the fact that the number of iterations of the additional prediction mentioned above is greater than the iteration threshold, the UE may perform a fallback to the legacy or change the AI / ML model.

[0455] The above prediction can be performed based on AI / ML (Artificial intelligence and machine learning).

[0456] Hereinafter, a device for performing communication according to some embodiments of the present specification will be described.

[0457] For example, the device may include a processor, a transceiver, and memory.

[0458] For example, the processor can be configured to be operablely coupled with memory and the processor.

[0459] The operation performed by the processor comprises: a step in which a UE (User Equipment) performs a measurement in an OW (Observation Window); a step in which, based on the result of the measurement, the UE predicts a first measurement value for a neighboring cell and a second measurement value for a serving cell in a PW (prediction window); a step in which, based on the result of the prediction, the UE determines a first time point in which the entry condition of event A3 in the PW is satisfied; a step in which, based on the result of the prediction, the UE determines that the exit condition of event A3 is not satisfied for a TTT (time to trigger) time from the first time point; and a step in which, based on the determination that the UE determines that the exit condition is not satisfied for a TTT time from the first time point, the UE transmits a report for event A3 to a network at the first time point, wherein the entry condition is that the first measurement value exceeds the value obtained by adding an entry threshold to the second measurement value, and the exit condition is that the first measurement value is less than the value obtained by adding an exit threshold to the second measurement value.

[0460] Hereinafter, a processor of a device for providing communication according to some embodiments of the present specification will be described.

[0461] The operation performed by the processor comprises: a step in which a UE (User Equipment) performs a measurement in an OW (Observation Window); a step in which, based on the result of the measurement, the UE predicts a first measurement value for a neighboring cell and a second measurement value for a serving cell in a PW (prediction window); a step in which, based on the result of the prediction, the UE determines a first time point in which the entry condition of event A3 in the PW is satisfied; a step in which, based on the result of the prediction, the UE determines that the exit condition of event A3 is not satisfied for a TTT (time to trigger) time from the first time point; and a step in which, based on the determination that the UE determines that the exit condition is not satisfied for a TTT time from the first time point, the UE transmits a report for event A3 to a network at the first time point, wherein the entry condition is that the first measurement value exceeds the value obtained by adding an entry threshold to the second measurement value, and the exit condition is that the first measurement value is less than the value obtained by adding an exit threshold to the second measurement value.

[0462] Hereinafter, a non-volatile computer-readable medium storing one or more instructions for providing mobile communication according to some embodiments of the present specification will be described.

[0463] According to some embodiments of the present disclosure, the technical features of the present disclosure may be directly implemented in hardware, software executed by a processor, or a combination of both. For example, a method performed by a wireless device in wireless communication may be implemented in hardware, software, firmware, or any combination thereof. For example, software may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or other storage media.

[0464] In some examples, storage media are coupled to the processor so that the processor can read information from the storage media. Alternatively, the storage media can be integrated into the processor. The processor and storage media can reside in an ASIC. In other examples, the processor and storage media can reside as separate components.

[0465] Computer-readable media may include tangible and non-volatile computer-readable storage media.

[0466] For example, non-volatile computer-readable media may include RAM (Random Access Memory) such as SDRAM (Synchronization Dynamic Random Access Memory), ROM (Read-Only Memory), and NVRAM (Non-Volatile Random Access Memory); read-only memory (EEPROM); flash memory; magnetic or optical data storage media; or other media that can be used to store instructions or data structures. Non-volatile computer-readable media may also include combinations of the above.

[0467] Additionally, the method described herein may be realized at least partially by a computer-readable communication medium that transmits or transmits code in the form of instructions or data structures and can be accessed, read, and / or executed by a computer.

[0468] According to some embodiments of the present disclosure, a non-transient computer-readable medium stores one or more instructions thereon. The stored one or more instructions can be executed by a processor of a base station.

[0469] One or more stored commands include the steps of: a UE (User Equipment) performing a measurement in an OW (Observation Window); based on the result of the measurement, the UE predicting a first measurement value for a neighboring cell and a second measurement value for a serving cell in a PW (prediction window); based on the result of the prediction, the UE determining a first time point in which the entry condition of event A3 in the PW is satisfied; based on the result of the prediction, the UE determining that the exit condition of event A3 is not satisfied for a TTT (time to trigger) time from the first time point; and based on the UE determining that the exit condition is not satisfied for a TTT time from the first time point, the UE transmitting a report for event A3 to a network at the first time point, wherein the entry condition is that the first measurement value exceeds the value obtained by adding an entry threshold to the second measurement value, and the exit condition is that the first measurement value is less than the value obtained by adding an exit threshold to the second measurement value.

[0470] Specifications can have various effects.

[0471] For example, AI / ML prediction can be utilized efficiently.

[0472] The effects obtainable through the specific examples of this specification are not limited to those listed above. For example, there may be various technical effects that a person with ordinary skill in the related art can understand or derive from this specification. Accordingly, the specific effects of this specification are not limited to those explicitly described herein, but may include various effects that can be understood or derived from the technical features of this specification.

[0473] The claims described in this specification may be combined in various ways. For example, the technical features of the method claims in this specification may be combined to be implemented as a device, and the technical features of the device claims in this specification may be combined to be implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims in this specification may be combined to be implemented as a device, and the technical features of the method claims and the technical features of the device claims in this specification may be combined to be implemented as a method. Other implementations are within the scope of the following claims.

Claims

1. As a method, A step in which the UE (User Equipment) performs a measurement in the OW (Observation Window); Based on the results of the above measurements, the UE predicts a first measurement value for a neighboring cell and a second measurement value for a serving cell in a PW (prediction window); Based on the result of the above prediction, the UE determines a first point in time when the entry condition of event A3 is satisfied in the PW; Based on the result of the above prediction, the step of determining that the UE does not satisfy the exit condition of event A3 during the TTT (time to trigger) time from the first time point; and Based on the determination that the UE does not satisfy the exit condition during the TTT time from the first time point, the UE includes the step of transmitting a report on event A3 to the network at the first time point. The above entry condition is that the first measurement value exceeds the value obtained by adding an entry threshold to the second measurement value, and The above deviation condition is a method in which the first measurement value is less than the value obtained by adding a deviation threshold to the second measurement value.

2. In Paragraph 1, A method further comprising the step of the UE receiving a handover command from the network based on the above report.

3. In Paragraph 1 or 2, A method further comprising the step of the UE receiving information related to the OW and information related to the PW from the network before performing the above measurement.

4. In any one of paragraphs 1 through 3, The above prediction is a method performed based on AI / ML (Artificial intelligence and machine learning).

5. In any one of paragraphs 1 through 4, The step of the UE performing auxiliary measurements for the neighbor cell and the serving cell at the first time point; and Based on the result of the above auxiliary measurement, the step of determining that the UE determines that the entry condition is not satisfied at the first time point is further included. A method in which the UE skips transmitting the report to the network at the first time based on the determination that the entry condition is not satisfied at the first time.

6. In Paragraph 1, A step in which the UE determines a second point in time when the entry condition is satisfied, based on the result of the above measurement; After the termination of the above OW, the above PW starts, and The above second point in time is included in the above OW, and Based on the result of the above prediction, the step of determining that the UE does not satisfy the exit condition of event A3 during the TTT time from the second time point; and A method further comprising the step of the UE transmitting a report on event A3 to the network at the second time point, based on the UE determining that the exit condition of event A3 is not satisfied during the TTT time from the second time point.

7. In Paragraph 6, The step of the above UE performing auxiliary measurements for the neighbor cell and the serving cell at the second time point; and Based on the result of the above auxiliary measurement, the step of determining that the UE determines that the entry condition is not satisfied at the second time point is further included. A method in which the UE skips transmitting the report to the network at the second time point based on the UE determining that the entry condition is not satisfied at the second time point.

8. In any one of paragraphs 1 through 7, A step in which the above UE determines the reliability of the above prediction; A step in which the UE extends the OW based on the confidence level of the above prediction being smaller than the confidence threshold; The step of the above UE performing additional measurements in the extended OW; and A method further comprising the step of the UE additionally predicting the measurement value for the neighbor cell and the measurement value for the serving cell in the PW based on the result of the above measurement and the result of the above additional measurement.

9. In Paragraph 8, The step of the above UE determining the reliability of the above additional prediction; Based on the confidence of the additional prediction being less than the confidence threshold, the UE iteratively performs the extension of the OW, the additional measurement, and the additional prediction; and A method further comprising the step of the UE performing a fallback to legacy or a change to the AI / ML model based on the fact that the number of iterations of the additional prediction is greater than the iteration threshold.

10. As a method Step where the UE performs a measurement in the OW; Based on the results of the above measurement, the UE predicts the measurement value in the PW; After the termination of the above OW, the above PW starts, and A step in which the UE determines the reliability of the prediction based on the results of the above measurement; A step in which the UE extends the OW based on the confidence level of the above prediction being smaller than a confidence threshold; and A method comprising the step of the above UE performing additional measurements in the above extended OW.

11. In Paragraph 10, A step in which the UE additionally predicts a measurement value in the PW based on the result of the above measurement and the result of the above additional measurement; The step of the above UE determining the reliability of the above additional prediction; and A method further comprising the step of the UE iteratively performing the extension of the OW, the additional measurement, and the additional prediction based on the confidence of the additional prediction being less than the confidence threshold.

12. In Paragraph 10, A method further comprising the step of the UE performing a fallback to legacy or a change to the AI / ML model based on the fact that the number of iterations of the additional prediction is greater than the iteration threshold.

13. In any one of paragraphs 10 through 12, The above prediction is a method performed based on AI / ML (Artificial intelligence and machine learning). As 14.UE, At least one memory; and At least one processor operablely connectable to the above at least one memory, The above at least one memory is a UE in which the operation performed by the at least one processor based on execution by the at least one processor is a method according to any one of claims 1 to 9.

15. As an apparatus in mobile communication, At least one processor; and It includes at least one memory that stores instructions and is operablely electrically connected to at least one processor, and A device in which the operation performed based on the execution of the above instruction by the at least one processor is a method according to any one of claims 1 to 9.

16. A non-volatile computer-readable storage medium that records instructions, A non-volatile computer-readable storage medium in which, when the above instructions are executed by one or more processors, the operation that causes the one or more processors to perform is a method according to any one of claims 1 to 9.

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